RESEARCH CENTRE

Inria Saclay Centre

2023
ACTIVITY REPORT

Project-Team

SIMBIOTX

SImulations in Medicine, BIOtechnology
and ToXicology of multicellular systems

DOMAIN

Digital Health, Biology and Earth

THEME

Modeling and Control for Life Sciences

Contents

Project-Team SIMBIOTX

1 Team members, visitors, external collaborators

2 Overall objectives

3 Research program

3.1 Methodology 1: Agent-based models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1.1 Cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1.2 Other structures: networks of ellongated components . . . . . . . . . . . . . . . . . .
3.2 Methodology 2: Flow models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3 Methodology 3: Transport and intra-cellular models . . . . . . . . . . . . . . . . . . . . . . .
3.3.1 Transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3.2 Reactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.4 Complementing methodologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.4.1 Image analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.4.2 Integrative, Multiscale, multilevel and multicomponent models . . . . . . . . . . . .

4 Application domains

4.1 Systems Medicine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.1.1 Liver
4.1.2 Congenital heart disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.1.3 In vitro cell populations, tumors and cancer . . . . . . . . . . . . . . . . . . . . . . . .
4.2 Systems Biotechnology and Systems Toxicology . . . . . . . . . . . . . . . . . . . . . . . . . .

5 Social and environmental responsibility

5.1 Impact of research results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

6 Highlights of the year

6.1 Awards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.2 Patent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1

2

3

3
4
4
4
4
5
5
5
5
5
6

6
6
6
6
7
7

7
7

8
8
8

7 New software, platforms, open data

8
8
7.1 New software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8
7.1.1 LumpedFlow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9
7.1.2 TiSim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.1.3 TiQuant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9
7.1.4 CompuTiX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
7.2 Open data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

8 New results

10
8.1 Liver: micro-architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
8.1.1 Modeling of liver disease progression from liver steatosis to cirrhosis and HCC: digital

twins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
8.1.2 Multilevel modeling of flow and transport in liver lobules in health and disease . . . 11
8.1.3 Temporal diffusion spectroscopy for the characterization of NASH . . . . . . . . . . . 11
8.1.4 Microvascular modeling for medical imaging and toxicity assessment . . . . . . . . . 12
8.1.5 Physics informed tissue architecture reconstruction . . . . . . . . . . . . . . . . . . . 12
8.1.6 A digital liver twin to demonstrate the significance of disease-related remodeling of

liver architecture in ammonia detoxification . . . . . . . . . . . . . . . . . . . . . . . . 13
8.2 Liver: From the micro- to the mesoscale 3D . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
8.2.1 3D digital histopathology: a new methodology for morphological characterization of

the human liver . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
8.3 Liver: Organ-scale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

8.3.1 Automatic Generation of Liver Digital Twins with Artificial Intelligence: Application

to Liver Resection Complexity Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . 14

8.3.2 Geometric and Hemodynamic Study of the Portal Vein: Identification of Predictive
Factors for Portal Thrombosis through 3D Numerical Simulation after Extended Liver
Resection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
8.3.3 Hemodynamics modeling for liver surgery: digital twins . . . . . . . . . . . . . . . . . 15
8.3.4 Numerical investigation of particle aggregate steering with magnetic resonance

navigation for targeted embolization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
8.4 Digital twins for a better understanding and leveraging of medical dynamic imaging data . 16
8.4.1 A proof-of-concept pipeline to guide evaluation of tumor tissue perfusion by dy-

namic contrast-agent imaging: Direct simulation and inverse tracer-kinetic procedures 16
8.4.2 Whole-body vascular transport and pharmacokinetics models: application to imaging 16
8.5 Digital twins of blood flow for disease or treatment assessment . . . . . . . . . . . . . . . . . 17
8.5.1 Myocardial perfusion simulation for coronary artery disease: combination of Ma-

chine Learning and physical simulation . . . . . . . . . . . . . . . . . . . . . . . . . . 17

8.5.2 Potts Shunt as a palliative treatment option for suprasystemic idiopathic Pulmonary

Arterial Hypertension: an in-silico modelling study . . . . . . . . . . . . . . . . . . . . 18
8.5.3 In-silico modeling in deciphering stroke risk factors in children with sickle cell disease. 18
8.5.4 Multiscale modeling of feto-placental hemodynamics . . . . . . . . . . . . . . . . . . 19
8.5.5 Methodology on blood flow model parameter estimation . . . . . . . . . . . . . . . . 19

9 Bilateral contracts and grants with industry

19
9.1 Bilateral contracts with industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
9.1.1 Guerbet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
9.1.2 Heartflow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

10 Partnerships and cooperations

10.1 International research visitors

20
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
10.1.1 Visits of international scientists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
10.1.2 Visits to international teams
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
10.2.1 H2020 projects
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
10.2.2 Other european programs/initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
10.3 National initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

10.2 European initiatives

11 Dissemination

21
11.1 Promoting scientific activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
11.1.1 Scientific events: organisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
11.1.2 Scientific events: selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
11.1.3 Journal
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
11.1.4 Conferences and Talks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
11.1.5 Leadership within the scientific community . . . . . . . . . . . . . . . . . . . . . . . . 25
11.1.6 Scientific expertise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
11.1.7 Research administration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
11.2 Teaching - Supervision - Juries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
11.2.1 Teaching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
11.2.2 Supervision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
11.2.3 Juries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
11.3 Popularization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
11.3.1 Internal or external Inria responsibilities . . . . . . . . . . . . . . . . . . . . . . . . . . 27
11.3.2 Articles and contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
11.3.3 Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
11.3.4 Interventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

12 Scientific production

28
12.1 Major publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
12.2 Publications of the year
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
12.3 Cited publications

Project SIMBIOTX

1

Project-Team SIMBIOTX

Creation of the Project-Team: 2021 March 01

Keywords

Computer sciences and digital sciences

A3.4.1. – Supervised learning

A3.4.8. – Deep learning

A6.1.1. – Continuous Modeling (PDE, ODE)

A6.1.2. – Stochastic Modeling

A6.1.3. – Discrete Modeling (multi-agent, people centered)

A6.1.4. – Multiscale modeling

A6.3.2. – Data assimilation

A6.3.5. – Uncertainty Quantification

A6.5.1. – Solid mechanics

A6.5.2. – Fluid mechanics

A6.5.3. – Transport

Other research topics and application domains

B1.1.7. – Bioinformatics

B1.1.9. – Biomechanics and anatomy

B1.1.10. – Systems and synthetic biology

B2.2. – Physiology and diseases

B2.4.1. – Pharmaco kinetics and dynamics

B2.4.3. – Surgery

B2.6.3. – Biological Imaging

B5.10. – Biotechnology

2

Inria Annual Report 2023

1 Team members, visitors, external collaborators

Research Scientists

• Dirk Drasdo [Team leader, INRIA, Senior Researcher, HDR]

• Irene Vignon Clementel [INRIA, Senior Researcher, HDR]

Post-Doctoral Fellows

• Roel Meiburg [INRIA, from Mar 2023]

• Jieling Zhao [IFADO, Post-Doctoral Fellow]

PhD Students

• Omar Ali [INSERM, until Jun 2023]

• Peter Kottman [INRIA, from Sep 2023]

• Jerome Kowalski [INRIA]

• Mahdi Rezaei Adariani [UNIV MONTREAL]

• Raoul Salle De Chou [INRIA]

• Pavlos Varsos [INRIA, from Jul 2023]

Technical Staff

• Ramdane Bessaid [INRIA, from Oct 2023]

• Charles Boulitrop [INRIA, Engineer, from Jun 2023]

• Mathieu De Langlard [INRIA, Engineer, until Mar 2023]

• Jules Dichamp [INRIA, Engineer]

• Weiqiang Liu [INRIA, until Sep 2023]

• Jiri Pesek [INRIA, Engineer]

Interns and Apprentices

• Amaury Facque [CHU Tours, until Oct 2023]

• Clemence Finotto [CentraleSupelec, Intern, from Nov 2023]

• Ihsane Hadeg [INRIA, Intern, from Jun 2023 until Oct 2023]

• Kevin Hakkakian [AP-HP, Intern, from Nov 2023]

• Peter Kottman [UNIV CHARLES - PRAGUE, until May 2023]

• Yassine Machta [INRIA, Intern, from May 2023 until Nov 2023]

• Mohamed Ali Srir [INRIA, Intern, from Jul 2023]

• Ana Vlasceanu [CHU Tours, Intern, from Nov 2023]

Administrative Assistant

• Anna Dib [INRIA]

3

Project SIMBIOTX

Visiting Scientist

• Pascalle Wijntjes [UNIV EINDHOVEN, until Jul 2023]

External Collaborators

• Omar Ali [Guerbet, from Jul 2023]

• Darius Dabert [ECOLE POLY PALAISEAU, until May 2023, Intern]

• Axel De Sinzogan [ECOLE POLY PALAISEAU, until May 2023, Intern]

• Nicolas Golse [AP-HP]

• Richard Hruby [ECOLE POLY PALAISEAU, until May 2023, Intern]

• Lorenzo Sala [INRAE]

2 Overall objectives

The overall objective of SIMBIOTX is the implementation of computational models and tools in systems
medicine, systems toxicology and systems biotechnology to guide clinical and experimental designs and
decisions. As many of the models are very close to their "real" counterpart ("Twins"), and so complex that
they need to be implemented on the computer to be studied (translating the models into "digits" on the
computer), they are, once they sufficiently display the systems behavior of their real counterpart, very
well characterized by "Digital Twin Models" (DTMs). One important challenge is the systems behavior
at the microscale and at the macroscale scale. In medicine, clinical decisions are still largely guided by
clinical experience, as completely standardized workflows and protocols are hampered the complexity
of the human body and the variety of patient responses on therapeutic approaches. Medicine permits
acquisition of an increasing amount of data on the individual patient at all levels, which requires correct
interpretation and processing to ensure the optimal decisions for each patient are taken. SIMBIOTX aims
at better understanding by in-silico modeling how non-invasive imaging reflects the underlying organ
architecture, perfusion and function. In synergy with the first aim, SIMBIOTX aims at guiding clinical
decisions by mathematical models integrating data from multiple sources to inform clinicians and build
predictions of possible therapy consequences. Both, models and software generated in that process will
pertain to standardization. Systems toxicology aims at grasping the complexity of a substance-system
interaction, ideally by direct extrapolation from in vitro toxicological experiments to human toxicity,
thereby reducing animal experiments. SIMBIOTX aims at building models explaining the outcome of in
vitro experiments and guiding in vivo toxicity predictions from in vitro toxicity data, eventually by building
spatial-temporal in silico abstractions of the in vitro and in vivo systems. Biotechnology increasingly
develops sophisticated experimental set ups that more and more resemble in vivo systems to permit
realistic experiments with human material in vitro that otherwise cannot be performed, and to generate
replacement tissue to long-term replace donor organs in transplantations. SIMBIOTX aims at calibrating
models with this data, to explain the underlying processes, which may contribute to a better control
of experiments, and to guide their designs by mimicking bioengineering process scenarios. Particular
emphasis of SIMBIOTX is on liver and liver cells.

3 Research program

SIMBIOTX’s research addresses research topics in three main related subject areas, on systems medicine,
systems toxicology and systems biotechnology, and in addition a complementary subject image analysis
as one major interface between modeling and data. The choice and the development of a method or
model (the "theoretical technology" or "methodology") are in most cases driven by a specific application.
Most of the methods and models address within a specific application specific sub-components of the
system (e.g. cells, flow, transport), that may occur also in other applications. Accordingly, the development

4

Inria Annual Report 2023

of methods and models that was originally driven by one guiding application can later often be adapted
to another application.

Based on this line of argument, we present our research lines within the prescribed scheme by
describing the methodology with illustrating examples under the rubric "research program" and specify
the examples as applications under the rubric "Application domains".

3.1 Methodology 1: Agent-based models

Agent-based models in which each basic modeling units are represented as individual agent are mainly
used to simulate the spatial-temporal dynamics of biological cells when the cell population sizes are
moderate and/or the spatial architecture of the system of interest does not favor averaging. In addition,
they are applied to mimic networks of filaments, whereby filaments can for example be blood vessels,
long molecules (e.g. collagen) or molecule bundles (e.g. bundles of collagen fibres).

3.1.1 Cells

Several of our applications in systems medicine and systems biotechnology address questions at the
tissue micro-architecture at cell-and sub-cellular spatial scale. In these applications we present each cell
as individual unit ("agents") in continuum space using mainly two modeling technologies, which we
have co-developed: center-based models (CBMs) and deformable cell models (DCMs) [8], [33], [34].

In CBMs, cells are parameterized by a few geometric parameters such as the cell radius, and axis
length (e.g. to mimic cell elongation prior to undergoing mitosis), material parameters and cell-kinetic
parameters, and forces between cells are approximated as forces between cell centers. CBMs have no
explicit notion of shape, the volume occupied by a cell is approximated by a geometric body (usually a
sphere or dumb-bell) that specifies the approximate position and shape. Hence despite its geometric
representation may indicate a rigid cell body, the cells are usually not rigid, represented by that their
geometric representations can overlap depending on the forces between them.

In DCMs, cells are mimicked as deformable objects with an explicit representation of cell surface
on a mesoscopic level, usually by triangulation of the cell surfaces. The DCM can further represent
cell organelles. As in the CBM, the presented structures are parameterized by material parameters that
are either directly represented or be inferred from the cells’ response on experimental perturbations.
Both CBM-and DCM-cells move according to force-balance equations that account for all passive forces
on the cell plus active forces mimicking the cell movement. For CBM this is usually one equation for
a translatory cell movement, while for DCM, it is one equation for each node of its triangulation. For
different applications, the CBM/DCM-models have to be adapted, which in particular includes the force
terms in the force balance equation(s) (the "equation of motion"). Each time, the model parameters have
to be identified.

3.1.2 Other structures: networks of ellongated components

In certain diseases collagen networks form representing architectural and functional obstacles. Collagen
bundles or fibers are mimicked as semiflexible chains with each node on the chain being mimicked by a
force balance equation as for CBMs. The same approach is partially used to represent capillary networks
as this permits to approximate network distortions upon physical forces on the capillaries in a simple
and computationally efficient way. Alternatively, vessels may be triangulated as cells in the DCM.

3.2 Methodology 2: Flow models

Flow of mainly blood and bile is an important component to model for applications in systems medicine,
toxicology or biotechnology. If the flow structure is intrinsically 3D, then the fluid is modelled by the
incompressible 3D Navier-Stokes equations in multi-branched networks, which blood or bile conduit
geometry comes from imaging data.

At the macroscale, for hemodynamics in the larger vessels, this typically entails coupling with the rest
of the circulation, which is lumped into a 0D model (no dimension in space). Such ODE-based electric
analog is constructed to represent as necessary for the application the downstream vascular bed, other

Project SIMBIOTX

5

organs, the heart, etc. Part of the research consists in adapting its parameters based on subject-specific
data (e.g. [6]).

An in-between model, typically to take into account the effect of a varying vessel cross-sectional
area in space and time, is the 1D (Euler) equations of flow. It is solved here in small networks of ves-
sels [21]. For networks of thousands of small conduits, resistance (0D) models are typically solved,
where a finer rheology can be incorporated [23]. Geometry comes either from synthetically generated
branching trees (mesocirculation) and networks representative of the organ functional unit architecture
(microcirculation), or if available directly from imaging data of the blood or bile system.

3.3 Methodology 3: Transport and intra-cellular models

Multilevel and multi-scale models of biological tissues often include the transport of molecular species
and chemical reactions at many different scales, sometimes up to the entire body.

3.3.1 Transport

Major fluxes considered are those inside the blood vessels and bile conduits, and between blood vessels
or bile conduits and their adjacent structures (cells, extracellular, extravascular space).

Currently two major model types are used to mimic transport phenomena. The first one are compart-
ment models where concentrations are assumed to be homogeneous in a certain spatial compartment
and change upon transport into or from another compartment [32] [22]. In such models, we usually
apply ordinary differential equations (ODEs) for the compartment concentration as a function of time.
The second type emerges if concentrations can vary in space (e.g. along a blood vessel) in which case
usually partial differential equations (PDEs) for the local concentrations depending on space and time
are considered [23], [9]. In both cases, the equations can be derived from mass balance. The equations
require the knowledge of the flow rate (ODs) or local flow velocity (PDE models), which emerge from the
flow models (section 3.2).

3.3.2 Reactions

Besides fluxes, the mass balance can be modified as a consequence of chemical reactions. In our
applications modifications by chemical reactions mostly occur inside cells, which we mostly mimic by
ODE equations assuming the number of molecules inside the cell is sufficiently large to neglect stochastic
fluctuations (e.g. [2]). If the latter is not the case, we develop master equation approaches to cope for
fluctuations. In such an approach, the multivariate probability of a certain chemical species composition
is tracked in time, and, if necessary, in space by subdividing the space into small reaction volumes
(compartments) much smaller than the cell or other local volumes. The main work is the simulation of
different reaction networks that are believed to represent alternative hypotheses on the reaction dynamics.
The simulation results are usually compared to experimental readout observables [3].

3.4 Complementing methodologies

3.4.1 Image analysis

Many parameters used to calibrate the models have to be inferred from images [27]. For this purpose, the
team has been repeatedly performing image analysis. As free tools are usually not suited for the images
used, tools to analyze images of multiple modalities (e.g. light sheet microscopy, confocal laser scanning
microscopy, MRI) to extract information from images are developed. This partially includes new and
refined algorithms to better bridge the gap between experimental images and computational models (e.g.
[26, 25]).

For patients, model parameterization needs to occur from non-invasive or moderately invasive
modalities, e.g. from biomarkers or non-invasive imaging. While non-invasive functional imaging has
been a very active field of research, its translation to the clinics is impeded by a good understanding of
how the extracted parameters relate to the underlying tissue characteristics. A first approach consists in
constructing in-silico models of such tissue images and study how model parameter changes relate to
these in-silico images [10]. A second approach is to perform quantitative image analysis and correlation of

6

Inria Annual Report 2023

different image modalities [35]. One can then study how non-invasive imaging, a macroscale information,
relates to organ microscale architecture, perfusion or function.

3.4.2 Integrative, Multiscale, multilevel and multicomponent models

In a number of models the three methodology axes are combined to a multi-level multi-scale model (for
example, those aiming at a virtual liver at microscale), which raises the challenge how to choose each of
the model components and parametrise them (e.g. [1]).

So far the mostly chosen method is a systematic simulated parameter sensitivity analysis by variation
of each model parameter within its physiological range and studying how this modifies the agreement
between model simulation result and data from experiments for patients. A sensitivity analysis performed
on such models would be crucial in order to (i) identify the most significant parameters to influence
the desired output, (ii) test the robustness of a model in the presence of uncertainties, (iii) determine
the interactions among parameters, and (iv) unveil the optimal regions within the parameters space
for optimization studies. An example is the Saltelli algorithm to compute the Sobol’ indices, a variance-
based sensitivity analysis that exploits the variance decomposition (ANOVA) also in non-linear and
non-monotonic cases. An example of such sensitivity analysis applied to our virtual human twins is [13].

Biophysical models have also been complemented by machine-learning approaches [5, 31].

4 Application domains

4.1 Systems Medicine

4.1.1 Liver

The objective is to establish models at multiple scales and multi-scale models (i.e. linking intracellular
functional units up to the whole organ scale) of the different liver subsystems, aiming finally at a digital
liver model (e.g. [24]). Prospectively the models should be implemented within a single or within linked
software tools permitting systematic hypothesis testing with small extra effort. Applications in liver
concern liver tissue architecture and function in the healthy liver serving as a reference state, as well
as acute liver damage, disease development and its functional consequences, as well as treatment of
aberrant states, for which a prominent example is liver surgery. The computational models integrate
information from in vitro experiments, animal models and human data. At the methodology level, liver
modeling requires all elements introduced in the previous section, integrating agent-based modeling
approaches for cells and molecules, ODE/PDE models of molecular transport, flow, as well as inter-
cellular and intra-cellular reactions (which can for example be signaling cascades, metabolic reaction
networks or detoxification reactions) by ODE models or, if required, stochastic modeling methods.

The first step is to provide biologists, pharmacologists, toxicologists, and clinicians with a better
understanding of the interplay of the many components pertaining to liver function, injury, and the
disease progression in a systems approach. In a further step, modeling is increasingly used to guide the
design of experiments and data acquisition. While a number of aims and concepts can be developed based
on animal models, where mechanisms may be validated, a key challenge will be to develop strategies and
concepts for model and parameter identification in human. The long-term aim is to support clinicians in
diagnosis by informing about disease progression, possible disease origin, disease reversal, and predict
the possible consequences of therapy options. An important example for a therapy studied in SIMBIOTX
is liver surgery [4].

Liver disease partially impacts on other organs such as heart, kidney and lung, which might therefore

be addressed if required by the clinical questions.

4.1.2 Congenital heart disease

Congenital Heart Disease (CHD) consists in diseases that affect children born with heart or connecting
large vessel abnormalities. Pulmonary hypertension is a disease that has several etiologies, one of which
is CHD.

Project SIMBIOTX

7

While great advances have been made in the last decades in their clinical treatment (mainly through
surgery), these patients still suffer from significant mortality and morbidity, due in part to interactions
between heart, systemic circulation, pulmonary circulation and other components such as implanted
graft or devices. The goal here is to perform patient-specific modeling to better understand such interac-
tions (e.g. [29]). Choosing the treatment option (surgical, interventional, drug) and optimizing it based
on modeling opens up several research directions.

4.1.3 In vitro cell populations, tumors and cancer

A tumor can be malign (cancerous) or benign. A malign tumor can grow and spread to other parts of
the body. A benign tumor can grow but will not spread. Benign tumors sometimes degenerate into
malign tumors (cancer). Cancer is a leading cause of death worldwide, accounting for nearly 10 million
deaths in 2020 (WHO) and therefore a major subject of research worldwide. Both, benign and malign
tumors are characterized by being largely unstructured compared to highly structured organs like liver,
lung or kidney, which simplifies the modelling and model implementation effort at the histological scale
compared to highly structured tissues. In vitro growing cell populations are often derived from tumor cell
lines, and are due to the population sizes usually well amenable to agent-based models with each agent
being a single-cell. Modeling of growing cell populations, early tumor growth, different phases in tumor
development (e.g. invasion and intravasation), have hence been a regular work subject of SIMBIOTX
members as it does not only provide interesting insight into the biological processes underlying cancer
development, but also permits to study and develop the modeling concepts and methodology. Many
cell-mechanisms are first studied in-depth in in vitro cell populations such as the effect of mechanical
stress on cell growth and proliferation [7], which makes them prone to be implemented first in models of
the in vitro setting before integrating them into in vivo tumor growth models.

4.2 Systems Biotechnology and Systems Toxicology

In vitro systems are increasingly developed to more closely resemble their in vivo counterparts. This
prospectively permits creation of bio-engineered tissues as replacement of cancerous or non-functional
tissues as well as of in vitro test systems for realistic in vitro - in vivo extrapolation of drug effects, in
particular adverse effects. SIMBIOTX develops computational (digital) twin models of in vitro systems
for growth and toxicology. An example is paracetamol (acetaminophen, APAP) - overdosing - induced
hepatotoxicity that is the major cause for acute liver failure in many countries.

Part of our activity is to establish computational models for simulating detoxification processes in
in vitro and in vivo situations and implementing them in software. These models shall mimic both,
processes in digital in vitro experiments and drug effects in digital organs, eventually in time and space.
An important example is drug action of paracetamol ([28], sect. 4.1.1, Dichamp et al., 2023).

The simulation methods at all scales put us in an good position to develop models to guide experi-
mental designs (which experiment to perform, and how to perform it), and assist in design devices in
biotechnology. The developed models furthermore contain significant information on cell and mul-
ticellular properties and behavior, that often can be used to parameterize models mimicking in vivo
disease or repair processes hence importantly pertain to the systems biology projects in liver (sect. 4.1.1).
The most frequent current culturing methods are monolayers and spheroids and have been studied by
computational agent-based models of different types.

5 Social and environmental responsibility

5.1 Impact of research results

The virtual human twins developed in SIMBIOTX aim on the long run at improving health of patients.
For this reason, we work hand in hand with biologists and clinicians. (E.g. LeMonde Informatique)

8

Inria Annual Report 2023

6 Highlights of the year

6.1 Awards

• Trophée de l’Innovation et de la Transformation Numérique 2023 ( )

• Our group have successfully applied to an EU-call on digital patient twins by the project Artemis,

that focusses on the liver-heart axis.

• Roel Meiburg, postdoc in the team was awarded 3rd place for the Young Investigator’s Award at the

European Heart Rhythm Association conference in Barcelona, Spain [April 2023].

• Weiqiang Liu, research assistant in the team was awarded the McGill Engineering Doctoral Award

and J.W. McConnell Memorial Fellowship in Montreal, Canada [March 2023].

• Weiqiang Liu, research assistant in the team was awarded IP Paris BME Conference fellowship in

Engineering for Health Annual Forum, France [July 2023].

• Mahdi Rezaei Adariani, PhD student in the team, received the Travel Award at the 28th Congress of
the European Society of Biomechanics conference in Maastricht, The Netherlands [July 2023].

• Mahdi Rezaei Adariani, PhD student in the team, received the best poster prize at the Engineering

for Health (E4H) conference in Paris, France [June 2023].

6.2 Patent

Tumour imaging device Authrs: I Vignon-clementel, D Drasdo, Y Yin US Patent App. 18/003,171

An imaging device includes: a memory, an upsampler, a slicer a selector and a guide. The memory is
arranged to receive imaging data, biopsy data including needle data defining dimensions of a puncture
carried out by means of a biopsy needle, and procedure data. The upsampler is arranged to upsample
a raster image into a processing image. The slicer is arranged to partition the processing image from
the upsampler into a set of regions. The selector is arranged to determine within the set of diffusion
parameters a subset of diffusion parameters, and to return a subset of regions derived from the set of
regions. The guide is arranged to determine, from the subset of regions, puncture parameter set data a
puncture orientation and a puncture entry point and defining with the needle data a puncture zone.

7 New software, platforms, open data

A number of smaller codes than the softwares listed below have been developed in the team. For example:
dynamic contrast-enhanced (DCE) perfusion imaging has shown great potential to non-invasively assess
cancer development and its treatment by their characteristic tissue signatures. In [14], we proposed
an in-silico approach in order to better interpret the interplay of DCE-image-(function) and histology
(structure) data. We constructed a pipeline that consists in a vascular architecture with hierarchical
vessel networks, the flow/transport modeling of the contrast agent in and out of the vessels, generation of
dynamic in-silico images and parameter inference from tracer kinetic models. It is applied in the context
of a tissue-embedded tumor. To our knowledge such pipeline, based on a spatial in-silico model, is new.
We made the code opensource on the Inria gitlab . More project details can be found below.

7.1 New software

7.1.1 LumpedFlow

Functional Description: Forward and inverse mathematical models (ODEs) for biomedical applications

(lumped parameter models of the entire blood circulation and pharmacokinetic models)

Publications: hal-01093879v1, hal-01404771v1, hal-01696064v1, hal-01954783v1

Project SIMBIOTX

9

Authors: Irene Vignon Clementel, Sanjay Pant, Chloé Audebert, Jean-Frederic Gerbeau, Quentin Nicolas,

Florian Joly

Contact: Irene Vignon Clementel

7.1.2 TiSim

Name: Tissue Simulator

Scientific Description: TiSim (Tissue Simulator) is a versatile and efficient simulation environment for
tissue models. TiSim is a software for agent-based models of multicellular systems. It permits
model development with center-based models and deformable cell models, it contains modules
for monolayer and multicellular spheroid simulations as well as for simulations of liver lobules.
Besides agent-based simulations, the flow of blood and the transport of molecules can be modelled
in the extracellular space, intracellular processes such as signal transduction and metabolism
can be simulated, for example over an interface permitting integration of SBML-formulated ODE
models. TiSim is written in modern C++ , keeping central model constituents in modules to be able
to reuse them as building blocks for new models. For user interaction, the GUI Framework Qt is
used in combination with OpenGL for visualisation. The simulation code is in the process of being
published. The modeling strategy and approaches slowly reach systems medicine and toxicology.
The diffusion of software is a fundamental component as it provides the models that are complex
and difficult to implement (implementing a liver lobule model from scratch takes about 2-2.5yrs)
in form of a software to the developer and users who like to build upon them. This increases
significantly the speed of implementing new models. Moreover, standardization is indispensible as
it permits coupling different software tools that may have implemented models at different scales /
levels.

Functional Description: TiSim is a software that permits agent-based simulations of multicellular sys-
tems. - center-based lattice-free agent-based model - modular - C++, Qt, OpenGL, GUI, batch mode
- permits multiscale simulations by integration of molecular pathways (for signaling, metabolisms,
drug) into each individual cell - applications so far: monolayer growth, multicellular spheroids -
Boolean networks (development time = coding time (˜60 MMs) + model development time (˜264
MMs)) - in follow-up version 1: - liver lobule regeneration - SBML interface - in follow-up version
2: - deformable cell model (by triangulation of cell surface) - deformable rod models - extracellu-
lar matrix - vascular flow and transport TiSim can be directly fed by processed image data from
TiQuant.

Contact: Dirk Drasdo

Participants: Andreas Buttenschoen, Dirk Drasdo, Eugenio Lella, Géraldine Cellière, Johannes Neitsch,
Margaretha Palm, Nick Jagiella, Noémie Boissier, Paul Van Liedekerke, Stefan Hoehme, Tim Johann

7.1.3 TiQuant

Name: Tissue Quantifier

Keywords: Systems Biology, Bioinformatics, Biology, Physiology

Functional Description: Systems biology and medicine on histological scales require quantification of
images from histological image modalities such as confocal laser scanning or bright field micro-
scopy. The latter can be used to calibrate the initial state of a mathematical model, and to evaluate
its explanatory value, which hitherto has been little recognized. We generated a software for image
analysis of histological material and demonstrated its use in analysing liver confocal micrografts,
called TiQuant (Tissue Quantifier). The software is part of an analysis chain detailing protocols
of imaging, image processing and analysis in liver tissue, permitting 3D reconstructions of liver
lobules down to a resolution of less than a micrometer.

Authors: Dirk Drasdo, Stefan Hoehme, Adrian Friebel

Contact: Dirk Drasdo

10

Inria Annual Report 2023

7.1.4 CompuTiX

Name: Computational Tissue

Keywords: Single cell, Multi-agent, Modularity, Biophysics, Biological tissue, Cell cultures, Physical

simulation, Digital twin

Scientific Description: Establishment of a code architecture to integrate functional modules that rep-
resent computational models, or their components, for digital twin simulations of cell culture
experiments (monolayer, organoids, bioreactors, ...), and of processes in biological tissues (liver,
...).

Functional Description: CompuTiX is a simulational framework for performing and designing physical
simulations of biological cells, organoids, liver and other tissues and for investigation of biological
processes. It is designed as user-extensible and flexible software package which also permits
integration to other software packages.

Release Contributions: It is the first version published on BIL.

Authors: Jiri Pesek, Jules Dichamp, Dirk Drasdo, Charles Boulitrop

Contact: Dirk Drasdo

7.2 Open data

In [13], we developed an innovative approach to reduce the computational cost of performing sensitivity
analysis of models, constrained by physiological bounds. The computed results give new insights on how
to improve the calibration of some model parameters from patient data. Moreover the final parameter
distributions enable the creation of a virtual population available opensource for future works (openAire,
).

8 New results

The results are organized by application theme but many of the concepts range beyond the specific
application. For example, the methodology demonstrated in subsection can be applied to any histology.
The results section is subdivided in a liver section composed of different subsections reflecting that
this is the backbone of the SIMBIOTX team, and some other sections. The subsections on liver are ordered
in a way that they represent a zooming out from smaller to larger lengths scales, while the other subjects
that follow are rather on larger scales.

The paragraphs summarize results from 2023, some of which are accessible at least as preprints,
which are described in more detail, some which are part of projects with sufficient maturity but yet with
no preprint, which are hence only briefly summarized.

8.1 Liver: micro-architecture

At the microarchitectural level important bricks towards a digital liver twin model prototype for the entire
drug induced liver injury (DILI) circle that includes the drug action on the liver, the regeneration process,
and the effect of DILI on the liver metabolic function have been significantly advanced in 2022.

8.1.1 Modeling of liver disease progression from liver steatosis to cirrhosis and HCC: digital twins

Participants:

Jieling Zhao, Dirk Drasdo.

Project SIMBIOTX

11

Hepatocellular carcinoma (HCC) is the most common liver cancer in adults and ranked 3rd in cancer-
caused death. To understand the disease mechanism behind HCC is important to identify patents at risk
and develop possible therapeutical strategies. However, due to the complexity of the disease progression
process, the limited number of quantities that can be simultaneously measured in animal models, and
the even more limited access to patient data, a holistic view on the disease progression mechanisms is
missing. As a consequence classification of patients with regards to disease progression prognosis, which
is important to design a patient-specific surveilance strategy is mainly based on experience and crude
indicators, many of them varying from clinics to clinics. The mechanistic relationship between these
indicators is largely unknown and largely emphirical, while a holistic mechanistic view could provide
a much more systematic approach to disease progression prognosis, and moreover help to identify
patient-specific therapeutic strategies and targets. We extended our computational virtual twin model of
liver regeneration and fibrosis towards a chronic liver disease (CLD) progression model that permits to
mimic disease progression from healthy liver to HCC. This model currently includes many cell types, the
communication among them, and a wide range of signaling molecules, some that may be detected in the
blood serum and hence qualify as signatures of the stage of the disease. The focus is currently on the
transitions to cirrhosis and HCC, and our preliminary results permitted to identify possible interaction
networks that do recapitulate observed or hypothesized disease progression scenarios.

Main collaborators currently: S. Hammad, S. Dooley (Heidelberg University), S. Wolf, J. Bode, A
(Dusseldorf University Hospital), Friebel, S. Hoehme (Leipzig University), Bantel, H (Univ. Clinics of
Hannover)

8.1.2 Multilevel modeling of flow and transport in liver lobules in health and disease

Participants:

Peter Kottman, Dirk Drasdo, Irene Vignon-Clementel.

Chronic liver disease (CLD) displays an increasing incidence. While much focus is on CLDs with
Hepatocellular carcinoma as a possible endpoint (see above), cholestatic diseases, where the flow of bile
from the liver to the duodenum is impaired, can be equally devastating: e.g. primary biliary cholangitis
or primary sclerosing cholangitis are both irreversibly progressing diseases with cholagiocarcinoma,
the 2nd type of primary liver cancer, as possible endpoint. Effective treatment of CLD requires a deep
understanding of liver function in healthy liver, as well as its changes during disease progression and in
response to therapeutic interventions. The high complexity of the underlying disease processes, over
many time and length scales favors a systems medicine approach.

The liver guarantees its function as main detoxifying organ by a complex microarchitecture that
permits a perfect exchange of metabolites between blood and hepatocytes, whereby some molecular
species are secreted into the biliary system. While blood microcirculation became the subject of intensive
research in past years and has been addressed by experimental and modeling methodologies, the biliary
system has yet to receive the same attention. For example, the driving mechanisms of bile salt transport
have recently moved back into the research focus, partially giving rise to controversial discussions on
whether bile salt transport inside liver microarchitecture is dominated by flow or diffusion[9].

We implemented as a first approach a 1D model of bile salt transport towards, inside, and out of
the liver lobule. This captures the transport of bile salts with the blood into the liver lobule over the
portal vein and hepatic artery, inside the basic functional liver units (lobules) through the sinusoids, the
capillaries of the liver tissue, and from the sinusoids into the hepatocyte as well as from the hepatocytes
into the biliary canaliculi system, from which the bile salts are carried to the gallbladder. We compare
different kinds of transport on this path ([KottmanMasterThesis]).

Collaborators: E. Rohan, UWB Pilsen, Czech Republic

8.1.3 Temporal diffusion spectroscopy for the characterization of NASH

Participants: Charles Boulitrop, Jiˇrí Pešek, Dirk Drasdo, Irene Vignon-Clementel.

12

Inria Annual Report 2023

The gold standard in the staging of many liver diseaes is the evaluation of a histological sample of
the patient. Optaining tissue material for histolopathological evaluation requires invasive interventions
such as taking biopsies, which underlies strict criteria due to the possible adverse effects. The objective is
hence to gather knowledge on histopathological characteristics non-invasively, as this not only minimizes
size effects but also permits longitudinal follow-ups. A typical usecase is the classification of nonalcoholic
steatohepatitis (NASH), which includes hepatocyte ballooning. Ballooning is characterized by accumula-
tion of lysis vesicles in the cytoplasmic space, hepatocyte swelling and alterations of the intermediate
filament cytoskeleton. Temporal diffusion spectroscopy (TDS), a development of Diffusion-Weighted-
Magnetic Resonance Imaging, has recently enabled identification of appearant diffusion coefficients that
may permit to probe balooning non-invasively. The short-coming of the methodology so far is that the
interpretation of the obtained signals is difficult. We developed first model prototypes to simulate the
movement of magnetic moments inside tissue microarchitectures represented within the framework of
agent-based models using the team’s novel simulation software CompuTiX, and to subsequently mimic
the measurements of MRI-signals within a modeling approach, to study, how the MRI-signals relate to
the tissue microarchitecture. The conceptual approach is reminiscent of comparing direct and inverse
methodologies in tissue perfusion [14].

Collaborators: P. Garteiser, B. Van Beers (Inserm)

8.1.4 Microvascular modeling for medical imaging and toxicity assessment

Participants:

Jérôme Kowalski, Dirk Drasdo, Peter Kottman, Jules Dichamp,
Irene Vignon-Clementel.

Dynamic-contrast-enhanced medical imaging can be used to detect non-invasively tissue abnormalities
like tumors, to evaluate organ function, or to assess drug efficacy. Tracers injected into the body may
reflect tissue perfusion and function, depending on whether they are taken up and/or metabolized by
the cells or not. Distinguishing spatial and functional contributions to the signal obtained from in-vivo
analyses is challenging, which is why we develop in-silico models for better image interpretation, again
following a conceptual strategy as in ref. [14].
In organs such as the liver, cells are nourished by networks of capillaries, small fenestrated vessels called
sinusoids. Remodeling of liver microarchitecture during chronic liver disease may modify the fenestration
of the network resulting in an impaired liver function, such as the modification of detoxification of the
blood from potentially hepatotoxic drugs. On the other hand, hepatotoxicity of most drugs is dose-
dependent, which requires to determine the critical concentration of no-observed-adverse-effect levels
(NOAEL) at the location of the hepatocytes. Recent findings indicate that the tissue microarchitecture
and the spatial-temporal distribution of the drug may play an important role in the determination of
the NOAEL [11]. In-vitro experiments permit to determine the concentration at which a substance a
cell is exposed to is hepatotoxic (the pharmadynamics), but to know the concentration distribution,
information on the microarchitecture of the liver, the drug kinetics, and the distribution of a drug in the
liver needs to be known. This seems feasible with the help of mathematical models addressing flow and
transport in liver tissue microarchitecture.
We assessed three in-silico models that, based on a common mathematical framework, simulate blood
flow and drug transport in and out of a diseased micro-vascular network, and their effect on cells [18] .
To that end, the main pillars of multi-phase multi-species flow and transport models are first derived
from primary principles and then transcribed for a vascularized tissue. The chapter provides overall
a methodology to link changes in vascular network architectures, microcirculation consequences and
some macroscale biomarkers relevant for dynamic medical imaging or toxicity predictions.

8.1.5 Physics informed tissue architecture reconstruction

Participants:

Jiˇrí Pešek, Mathieu de Langlard, Dirk Drasdo.

Project SIMBIOTX

13

Microscopic architecture of tissue influences the outcome of many cell processes from signalling,
proliferation and, ultimately, the cell fate. In order to study these processes, it is imperative to obtain
a realistic and detailed microarchitecture of the tissue. Despite recent advances in imaging and image
analysis techniques, they are still insufficient to provide all necessary data for a successful detailed
tissue microarchitecture reconstruction directly from the experimental data. This issue is even more
pronounced in complex tissues like liver, with many different structures that have to be taken into
account. We offer an alternative method where the traditional image analysis is augmented with a model
driven tissue reconstruction. In this method, the physical and mechanical cell properties are used to
complement the information obtained from a classical image analysis in order to recover cell shapes and
consequently the overall tissue microarchitecture. We demonstrate1 that such method offers a higher
accuracy of reconstructed cell shapes and tissue organization when compared to traditional methods like
morphological watershed.

8.1.6 A digital liver twin to demonstrate the significance of disease-related remodeling of liver archi-

tecture in ammonia detoxification

Participants:

Jules Dichamp, Dirk Drasdo, Geraldine Cellière, Noémie Boissier.

Hyperammonemia i.e., a critically elevated ammonia blood concentration, can lead to encephalo-
pathy and patients’ death and constitutes the major reason for acute liver failure. Recent works guided by
compartment models led to the identification of a previously unrecognized ammonia sink mechanism
that could be used as a treatment option in hyperammonemia. However, compartment models are not
well suited to mimic hyperammonemia in chronic liver diseases such as fibrosis or cirrhosis, as they
lack architectural representation. We thus introduce a digital liver twin that reflects the remodeling
of architecture and all key processes relevant in ammonia detoxification during fibrosis development.
We demonstrate, that the architectural changes alone can explain most of the experimentally observed
changes in ammonia detoxification during fibrosis. Our findings suggest a novel modeling strategy in
toxicology modeling in tissues during acute liver tissue damage or disease development, by first studying
the effect of tissue remodeling of a toxic substance, and subsequently adapt intracellular processes to
capture the observed concentrations of toxic substances. A manuscript is in preparation.

Collaborators: J.G. Hengstler, A. Ghallab and coworkers from Leibnitz Institute IFADO, Dortmund,

Germany.

8.2 Liver: From the micro- to the mesoscale 3D

8.2.1 3D digital histopathology: a new methodology for morphological characterization of the hu-

man liver

Participants: Mathieu de Langlard, Irene Vignon-Clementel, Dirk Drasdo.

2D histopathology is a common technique for the diagnosis and study of diseases of the liver tissue.
However, the complexity of the liver organization calls for richer and more consistent data representation,
hence leading to spatially resolved 3D visualization and analysis. It would enable better understanding
and earlier diagnosis of liver diseases. We developed an automatic pipeline of 3D histology reconstruction
based on image registration and segmentation to analyze large and consistent liver sample volumes
from the micro- to the mesoscale. Morphological quantifications emerging from such an analysis can
furthermore inform computational models permitting to probe the possible functional consequences of
architectural alterations. Within our pipeline, micro- and mesoscale vascular systems and bile ducts were
visualized in 3D, with topology described using the diameter-defined Strahler system. Morphological
analysis included computing mean diameter and length, showing an exponential law for vessel segments.

1Manuscript in preparation.

14

Inria Annual Report 2023

This work achieved the first-time 3D reconstruction and quantification of a large human liver tissue
volume reconstructed from 2D histological images. The method, evaluated on healthy tissue, can be
applied to pathological cases, offering consistent three-dimensional analysis for detailed morphological
alterations across liver disease stages .

Collaborators: Olivier Trassard, Anne Dubart-Kupperschmitt, Jean-Charles Duclos-Vallée, Cather-

ine Guettier-Bouttier, APHP (hopital Paul Brousse, Le Kremlin-Bicetre), INSERM U1193.

8.3 Liver: Organ-scale

In this section, we present a number of computational modeling results of flow and transport of particles
or injected substances. Such biophysical approach is complemented by AI approaches. The common
goal is to create digital twins to help design or assess the risk of liver cancer treatment.

8.3.1 Automatic Generation of Liver Digital Twins with Artificial Intelligence: Application to Liver

Resection Complexity Prediction

Participants: Omar Ali, Amaury Facque, Irene Vignon-Clementel.

Liver resection (LR) is the most prevalent curative treatment for primary liver cancer, yet overall
mortality/morbidity rates remain remains elevated, particularly for complex/difficult liver resections
in non-specialized centers. The conventional evaluation of the liver resection complexity occurs at the
preoperative stage, continues to require significant medical expertise and/or extended clinical tests.
The conventional definition and classification of LR complexity (LRC) considers the number of liver
segments to be removed, while lacking the analysis of the tumor’s position with respect to the major
liver vessels. These major vessels are defined to be up to the 2nd bifurcation in the portal vein, and the
emergence of the three hepatic veins. Therefore, we propose an automatic digital tool to predict the LRC
at the intraoperative stage, solely from the portal phase of preoperative CT scans, after: (1) transforming
the liver region into a quantifiable space, (2) identifying the tumors and major liver vessels, and (3)
generating a reference frame based on a new concept, the hepatic central zone. This tool can help orient
patients towards appropriate medical centers depending on the predicted level of surgery complexity.
The 3D reconstructions of the liver, tumors, and hepatic venous vessels from preoperative CT scans are
generated with in-house trained deep learning models. A topological analysis of the reconstructed vessels
is performed on the generated vessel segmentations to identify the major liver vessels and generate the
hepatic central zone (HCZ). Imaging biomarkers such as the depth and proximity of the tumor to the
HCZ are leveraged to predict the LRC. The LRC predictions obtained with the proposed automatic digital
tool accurately predict intraoperative LRC (AUC = 0.85) and outperform the predictions of five trained
liver surgeons (3 experts, 1 junior, and 1 resident). This tool gives a basis for liver surgery planning and
patient orientation towards centers adequate to treat their pathology.

Collaborators: A. Bône and M-M Rohe at Guerbet, C. Accardo, B. Acidi, P. Valleur, C. Salloum and E.

Vibert at APHP-hopital Paul Brousse.

8.3.2 Geometric and Hemodynamic Study of the Portal Vein: Identification of Predictive Factors for
Portal Thrombosis through 3D Numerical Simulation after Extended Liver Resection

Participants:

Amaury Facque, Ana Vlasceanu, Weiqiang Liu,
Clementel.

Irene Vignon-

Extended liver resection is the only curative treatment of cholangiocarcinoma which is a liver tumor
that develops from the biliary tract, and it is a surgery that is associated with high morbidity and mor-
tality. A relatively common but very serious complication is the portal vein thrombosis, which is the
development of a blood clot in the portal vein leading to liver failure and potentially to the death of the pa-
tient. Thrombosis formation is likely influenced by vessel geometry, nevertheless there is poor literature
regarding the analysis the correlation between portal vein geometry and formation of thrombosis.

Project SIMBIOTX

15

Our project aims to study real patient models of the portal vein in order to simulate the blood flow
in the portal vessels and analyse the impact of geometry on post-operative thrombosis of the portal
vein. The study was conducted using a clinical database as a support for combined 3D-Modeling and
Computational Fluid Dynamics (CFD) analysis. The preliminary results of the study have shown a
correlation between vessel diameter discrepancy and thrombosis occurrence. We could also observe a
link between the angle formed by the branches of the portal vein and the thrombosis and the onset of
thrombosis. We are now confirming these hypotheses that could change the way the surgery is performed
in order to lower the risk of thrombosis after partial hepatectomy.

Collaborators: N. Golse, E. Vibert at APHP-hopital Paul Brousse.; L. Sala at INRAE

8.3.3 Hemodynamics modeling for liver surgery: digital twins

Participants:

Roel Meiburg, Kevin Hakkakian, Ramdane Bessaïd, Clémence Finotto,
Irene Vignon-Clementel.

Recently a lumped-parameter model of the cardiovascular system was proposed to simulate the
hemodynamics response to partial hepatectomy and evaluate the risk of portal hypertension due to this
surgery, where model parameters are tuned based on each patient data. In [13, 17], we focus on a global
sensitivity analysis (SA) study of such model to better understand the main drivers of the clinical outputs
of interest. The analysis suggests which parameters should be considered patient-specific and which
can be assumed constant without losing in accuracy in the predictions. While performing the SA, model
outputs need to be constrained to physiological ranges. An innovative approach exploits the features
of the polynomial chaos expansion method to reduce the overall computational cost. The computed
results give new insights on how to improve the calibration of some model parameters. Moreover
the final parameter distributions enable the creation of a virtual population available for future works
(openAire, ). A further identifiability analysis revealed that certain importaint cardiac parameters were
unidentifiable with the current workflow. The protocol was therefore amended to included additional
cardiac measurements, namely ejection fraction via ultrasound. These measurements are part of clinical
routine and therefore do not pose an additional burden.

Collaborators: N. Golse, A. Joosten, and E. Vibert at APHP-hopital Paul Brousse.; L. Sala at INRAE

8.3.4 Numerical investigation of particle aggregate steering with magnetic resonance navigation for

targeted embolization

Participants: Mahdi Rezaei Adariani, Jiˇrí Pešek, Irene Vignon-Clementel.

Magnetic resonance navigation (MRN) of medicinal substances is gaining popularity in the treatment
of liver cancer. This method relies on a cluster of particles formed by dipole-dipole interaction; the
latter originates from the magnetic moments generated by the MRI scanner static magnetic field. These
aggregates are then injected into the controlled blood flow where they are steered into the target branch
by the combination of the magnetic gradient force and gravity.

The success of the MRN procedure depends on the aggregates shape, which determines their mobility
and stability. In this part of our research, we deploy a computational model to investigate the dynamics of
MRN clusters and to optimize their design for improved treatment efficacy. Using a modified version of
Maxey-Riley equation and the immersed boundary method, the hydrodynamic forces acting on clusters
were simulated and validated against experimental data and the bead-chain drag model (BDM).

Preliminary results show that the immersed boundary method is suitable for determining the drag
as its results are in a good agreement with the bead drag model and the experiments reported in the
literature. However as the bead drag model is limited to a rigid chain of spherical particles, additional
research is required to generalize the effect of the hydrodynamic force on aggregates of different shapes
in a realistic arterial blood flow field.

Collaborators: Gilles Soulez (CR-CHUM, Montreal, Canada), Charlotte Debbaut (group bioMMeda,

UGent, Belgium)

16

Inria Annual Report 2023

8.4 Digital twins for a better understanding and leveraging of medical dynamic

imaging data

8.4.1 A proof-of-concept pipeline to guide evaluation of tumor tissue perfusion by dynamic contrast-

agent imaging: Direct simulation and inverse tracer-kinetic procedures

Participants:

Irene Vignon-Clementel,
Dirk Drasdo.

Jules Dichamp,

Jérôme Kowalski,

Dynamic contrast-enhanced (DCE) perfusion imaging has shown great potential to non-invasively assess
cancer development and its treatment by their characteristic tissue signatures. Different tracer kinetics
models are being applied to estimate tissue and tumor perfusion parameters from DCE perfusion imaging.
The work published in [14] provides an in silico model-based pipeline to evaluate how these DCE imaging
parameters may relate to the true tissue parameters. As histology data provides detailed microstructural
but not functional parameters, the presented work can also help to better interpret such data. To this
aim in silico vasculatures are constructed and the spread of contrast agent in the tissue is simulated.
As a proof of principle we show the evaluation procedure of two tracer kinetic models from in silico
contrast-agent perfusion data after a bolus injection. Representative microvascular arterial and venous
trees are constructed in silico. Blood flow is computed in the different vessels. Contrast-agent input
in the feeding artery, intra-vascular transport, intra-extravascular exchange and diffusion within the
interstitial space are modeled. From this spatiotemporal model, intensity maps are computed leading
to in silico dynamic perfusion images. Various tumor vascularizations (architecture and function) are
studied and show spatiotemporal contrast imaging dynamics characteristic of in vivo tumor morphotypes.
The Brix II also called 2CXM, and extended Tofts tracer-kinetics models common in DCE imaging are
then applied to recover perfusion parameters that are compared with the ground truth parameters of the
in silico spatiotemporal models. The results show that tumor features can be well identified for a certain
permeability range. The simulation results in this work indicate that taking into account space explicitly
to estimate perfusion parameters may lead to significant improvements in the perfusion interpretation of
the current tracer-kinetics models.

Collaborators: N. Jagiella, W. Lederle (RWTH Aachen, Germany), H. Laue (Fraunhofer MEVIS,
Institute for Digital Medicine, Bremen, Germany), F. Kiessling (RWTH Aachen, Germany), O. Sedlaczek
(DFKZ Heidelberg, Germany)

8.4.2 Whole-body vascular transport and pharmacokinetics models: application to imaging

Participants:

Jérôme Kowalski, Ihsane Hadeg, Dirk Drasdo, Irene Vignon-Clementel.

A crucial aspect in the surgical decision process is organ perfusion and functional assessment. In
this context, a very active medical imaging field is dynamic functional imaging. Functional imaging
often involves a tracer, which is transported in the blood circulation, with a certain time-dynamics as
it goes through the different components of the circulatory system. However, following a disease or a
major surgery, the time-dynamics of the different components is subject to change. A better knowledge
of the impact of a disease on the time-dynamics of a tracer would enhance the interpretation of the tracer
measured signals and thus help radiologists and surgeons to detect abnormal behaviors. This PhD thesis
aims at understanding these dynamical signals, by a combination of mathematical modelling of flow and
transport, numerical simulations, determination of model parameter from imaging data and machine
learning.

Work has been conducted towards the understanding of the transport of a chemical species through a
vascular tree [15]. Building on top of the insilico model developed last year, a framework for analyzing
real vasculatures has been introduced along with precise metrics which aim at quantitatively interpreting
the relation between a change in the geometry (e.g. following a major surgery or the development of a
disease) and the hemodynamic and transport behaviors. A collaboration has started with Gent University

Project SIMBIOTX

17

(Belgium) to analyze the development of a hepatic cirrhosis and relate architectural modifications of the
rat liver vascular trees with blood flow and transport of chemical species in it.

As a first clinical application, a focus was given on the estimation of the blood perfusion in the legs
following a revascularization surgery, using Indoycianine-Green (ICG) signals ; in collaboration with
vascular surgeons form Leiden University Medical Center (LUMC, the Netherlands). A simple model for
the closed-loop blood circulation and transport of ICG has been developed and later refined to help the
interpretability by the surgeons. A preliminary validation of the model was performed based on clinical
data.

Collaborators: L. Sala (Inrae), Jan R. van der Vorst (LUMC, The Netherlands), C. Debbaut (UGent,

Belgium)

8.5 Digital twins of blood flow for disease or treatment assessment

In the section below, we first present two studies where the aim is to replace invasive or challenging
medical exams of the heart by computational modeling. The heart often adjusts to diseases. Numerical
results are then presented to better understand vasculopathy in sickle-cell patients, whose cardiac output
typically increases to compensate for insufficient oxygen delivery in the brain. Finally, diseases in the
pulmonary circulation and their pallation typically affect the right pump of the heart. Different aspects
are presented in a bookchapter, an imaging study and a proof-of-concept multiscale digital twin study.
All these studies are conducted in collaboration with clinicians (radiologists, cardiologists, surgeons, ...).

8.5.1 Myocardial perfusion simulation for coronary artery disease: combination of Machine Learning

and physical simulation

Participants:

Raoul Sallé de Chou, Mohamed Ali Srir, Irene Vignon-Clementel.

Coronary arteries feed the heart muscles with nutrients and oxygen and as such are some of the most
critical blood vessel in the entire body. Coronary disease is difficult to diagnose especially when it affects
the smaller branches of these vessels, because direct imaging of these vessels is infeasible with current
medical imaging technology. Instead, blood perfusion through the myocardium can be imaged and is
correlated with both arterial and myocardium disease. However, blood perfusion imaging is challenging
and expensive.

A previous model was developed for myocardial perfusion simulation for coronary artery disease in
[30] to replace the actual exam with a numerical twin and conduct it via simulations. The model aims
at reproducing [15O]H2O PET imaging exam using only CT scans as input. The simulation is based on
: 1. Detection, segmentation and simulation of blood flow through the coronary vessels visible on the
injected CT scan; 2. A patient-specific method for generating small 3D vessels consistent with the vessels
detected. The rules for the growth of these vessels are based on physiology and simulated blood flows; 3.
a perfusion simulation model that considers the myocardium as a porous medium. A Darcy model is used
to simulate blood flow through the porous medium. However, in addition to a high computational cost,
the simulation fails to accurately reproduce some diseases, particularly those that affect medium-size
coronary branches.

The main goal of this project is to combine Machine Learning (ML) methods with physical simulations
in order to improve the current simulation pipeline. ML algorithms are used to learn from PET imaging
exams while being guided by simulation hypothesis, thereby diminishing the dependency on patient
data. To achieve this, each part of the simulation is to be replaced by an ML model. Following successful
replication of simulation outcomes, the model will undergoes refinement using patient data.

A finite volume informed graph neural network was developed to solve the Darcy equations on irregu-
lar shapes serving as a substitute for the myocardium component in perfusion simulation. Preliminary
results indicate superior performance of this model in terms of accuracy and generalization compared
to classical ML approaches. In [16], we introduced a novel optimization framework for the generation
of the synthetic small vessels utilizing the constructed constrained optimization (CCO) method. Our
new approach simulated similar 2D vascular trees as the original CCO method in terms of morphometry

18

Inria Annual Report 2023

while producing better optimal solutions at lower computational cost. This new approach is expected to
be more readily reproducible using ML methods compared to the original CCO technique. Additionally,
work has been conducted towards the determination of the myocardium perfusion regions. Determining
these regions, and their associated vessel is a crucial step in current simulation pipeline. However, the
current calculation method is inaccurate and highly sensitive to the resolution of segmented vessels. A
more robust and accurate model, employing graph neural networks (GNNs), has been developed for the
determination of these regions.

Collaborators: L. Najman (ESIEE - U Gustave Eiffel), H. Talbot (CentraleSupelec, INRIA OPIS),

Lazaros Papamanolis (Stanford university, USA, California), Heartflow inc. (USA, California).

8.5.2 Potts Shunt as a palliative treatment option for suprasystemic idiopathic Pulmonary Arterial

Hypertension: an in-silico modelling study

Participants:

Pavlos Varsos, Irene Vignon-Clementel.

The Potts shunt emerges as a palliative treatment option for pediatric patients, facing unsuccessful
drug treatments for suprasystemic Pulmonary Arterial Hypertension, and for whom lung transplantation
is not yet feasible. Despite its intent, a considerable number of surgeries prove unsuccessful, leading
eventually to mortality, with the failure mechanism still eluding comprehension. This study aims to
investigate the underlying factors that influence the success or failure of the procedure, while exploring
various shunt designs, ranging from anastomoses to conduits, and with or without the integration of
a unidirectional valve. A multiscale model (0D-3D) was initially developed, for which, an extensive
sensitivity analysis was conducted. Preliminary findings suggest that pre-operative selection of shunt
geometry is crucial for surgery success. Patient-specific factors, including hemodynamic conditions
as well as cardiac chamber structural characteristics, may prove to be prohibitive for shunt placement.
Results show that different shunt designs maintain oxygen saturation in the upper body, but larger shunt
diameters lead to decreased oxygenation in the lower body. Post-operative hemodynamics indicate
increased right ventricular workload due to higher stroke volume, although ejection fraction improves to
normal levels across all cases.

Collaborators: S. Pant, Necker-Enfants Malades Hospital

8.5.3 In-silico modeling in deciphering stroke risk factors in children with sickle cell disease.

Participants: Weiqiang Liu, Irene Vignon-Clementel.

Sickle cell disease (SCD) is the most prevalent inherited blood disorder in the world, caused by a single
mutation leading to pathological hemoglobin (HbS). HbS polymerization during deoxygenated states
attributes to the characteristic sickled shape of red blood cells (RBCs). The deformability and rigidity
changes of RBCs lead to impaired oxygen delivery and vaso-occlusion, resulting in anemia, multisystem
damage and long-term inflammation. The incidence of cerebral vasculopathy (CV) during the first decade
of life in pediatric patients with sickle cell disease establishes SCD as the first cause of stroke in children
worldwide. The main known risk factor for CV is a pathologically elevated intracranial arterial velocity.
However, the causes of these elevated velocities and their relationship with CV are not well understood.
In this study, we simulated in-silico blood flow in the internal carotid arteries of SCD patients from
different age groups. Results revealed that pathological velocities can only be reached in children, even
for moderate levels of blood flow. The occurrence of such elevated velocities was most frequent in the
distal part of the internal carotid, aligning with the positions where stenosis typically manifests. The
Dean number, a fluid mechanics concept combining both flow and geometry, revealed that high flow
rate, small diameter and large curvature contribute together to extreme blood velocities. Given that
the high oxygen requirement for cerebral growth in children combined with anemia lead to elevated
cardiac output, we hypothesize the following underlying mechanism: high cardiac output, together with

Project SIMBIOTX

19

geometrical characteristics, lead to elevated blood velocities in SCD. The resulting complex flow would
subsequently injure the endothelium and activate platelets, leading to CV. One journal paper is under
submission.

Collaborators: Suzanne Verlhac(MD, Hôpital Robert Debré AP-HP), Pablo Bartolucci and team
(MD, Univ Paris Est Créteil/Hôpitaux Universitaires Henri Mondor AP-HP, Créteil), Lazaros Papaman-
olis (Stanford university, USA, California)

8.5.4 Multiscale modeling of feto-placental hemodynamics

Participants:

Pascalle Wijntjes, Jérôme Kowalski, Irene Vignon-Clementel.

Pregnancy induced hypertension (PIH) is one the most prevalent pregnancy complication. It often
leads to more complex pregnancy complications as fetal growth restriction, pre-eclampsia, HELLP. Later
in life, these women have a 5 to 10 times higher chance of cardiovascular diseases. Therefore, early
diagnosis and treatment would reduce the morbidity of worse outcomes.

This study focused on modelling hemodynamical behaviour of the feto-placental vasculature to
gain insights into the cause of complications. To tackle this, numerical problems and stability issues for
hemodynamic modelling of the multiscale placental vasculature were encountered and solved within the
research visit of six months. At the end of the visit, the hemodynamics model is capable of dealing with
both the bigger and smaller vessels in the placental system.

Collaborators: F.N. van de Vosse, W. Huberts (TU/e, The Netherlands), M.B. van der Hout-van der

Jagt (TU/e & MMC, The Netherlands)

8.5.5 Methodology on blood flow model parameter estimation

In [19], we published an overview in a bookchapter for the French speaking community on how to set up
blood flow simulations boundary conditions (3D models) or parameters (0D models) depending on the
available clinical data and the resistance to flow in the 3D domain. It is also explained how this enables
predictive simulations, i.e. for intervention planning.

9 Bilateral contracts and grants with industry

9.1 Bilateral contracts with industry

9.1.1 Guerbet

Participants: Omar Ali, Yassine Machta, Irene Vignon-Clementel (correspondant).

This project in collaboration with E. Vibert (APHP-Hop Paul Brousse, France) and the company Guerbet
is on AI as a decision support tool for the curative treatment of primary liver cancer. See the PhD thesis of
Omar Ali for more information ().

9.1.2 Heartflow

Participants: Mohamed Ali Srir, Raoul Sallé de Chou, Irene Vignon-Clementel (cor-

respondant).

This project is in collaboration with Hugues Talbot (CentraleSupelec & INRIA OPIS), Laurent Najmann
(ESIEE/G Eiffel University) and the company Heartflow. The goal is to generate heart perfusion maps by
machine learning. See the PhD thesis of Raoul Sallé de Chou for more information ().

20

Inria Annual Report 2023

10 Partnerships and cooperations

10.1 International research visitors

10.1.1 Visits of international scientists

Prof. Eduard Rohan

Status Professor

Institution of origin West Bohemia University

Country Czech Republic

Dates 3 days (May 2023) and 3 days (November 2023)

Context of the visit co-advising of PhD of Peter Kottman, ERC MoDeLLiver

Dr. Yi Yin

Status Postdoctoral research associate

Institution of origin University of Oxford

Country United Kingdom

Dates 3 days (May 2023)

Context of the visit Simbiotx weekly seminar, ERC MoDeLLiver

10.1.2 Visits to international teams

Research stays abroad Jérôme Kowalski visited the group of C. Debbaut, BIOMMEDA, U. of Ghent (1
week, September 2023) in the context of the ERC MoDeLLiver.

10.2 European initiatives

10.2.1 H2020 projects

• EDITH project

Participants: Dirk Drasdo, Maxime Sermesant (INRIA Sophia-Antipolis),

Irene Vignon-Clementel.

H2020 EDITH, Ecosystem for Digital Twins in Healthcare, Coordination and Support Action (CSA).
This project aims at developing a vision for the integrated human digital twin, based on standardised
(meta-)data and models, and a roadmap to realise that vision, together with concrete proof-of-
concept examples. 10/2022-09/2024

• H2020 ERC consolidator grant MoDeLLiver

Participants:

Jules Dichamp, Dirk Drasdo, Amaury Facque, Kevin Hakkakian,
Peter Kottman, Jerome Kowalski, Mathieu De Langlard, Weiqi-
ang Liu, Roel Meiburg, Ramdane Bessaid, Jiri Pesek, Mahdi Rez-
aei Adariani, Pavlos Varsos, Ana Vlasceanu, Irene Vignon-
Clementel (correspondant; grant holder)
.

Project SIMBIOTX

21

This project is about ’Numerical modelling of hemodynamics and pharmacokinetics for clinical
translation’ . Surgical interventions are based on patient data, and although they require careful
planning, they may be revised during surgery. To better predict surgery outcome, several aspects
must be considered, including the local point of intervention, whole organ perfusion and function
as well as their interaction with the entire circulation. To address this complexity, the EU-funded
MoDeLLiver project aims to develop a haemodynamic model to guide surgical interventions in
the lung and liver. Researchers will also employ an injected substance model to unravel the link
between non-invasive medical imaging and organ perfusion and function: this will be very useful
to parameterise the model prior to the patient’s intervention. The new modelling tool is expected
to bring personalised surgical simulation a step closer to reality. 10/2020-09/2025

Collaborators are the groups of E. Vibert, N. Golse (Chair BOPA and APHP-Hop. P Brousse, France),
E. Rohan (U of West Bohemia, Czech Republic), G. Soulez (CHUM, Canada), C. Debbaut (U. Ghent,
Belgium), J.r. Van der Vorst (LUMC, The Netherland).

10.2.2 Other european programs/initiatives

BMBF-LiSyM-Cancer

Participants:

Jieling Zhao, Dirk Drasdo (correspondant).

BMBF “LiSyM-CANCER” (liver systems medicine of cancer). This project followed the project LiSyM and
establishes liver systems medicine approaches to understand progression in chronic liver disease towards
Hepatocellular cancer. The project is a large network project linking many partners all over Germany.

Collaborators include the groups of Steven Dooley (University Hospital Mannheim, Germany), Jan
Hengstler (Leibniz Institute IFADO, Dortmund, Germany), Johannes Bode (University Hospital Düsseldorf,
Germany)

10.3 National initiatives

• ANR ABM-EPISPREAD

Participants:

Jules Dichamp, Marwan Bourdim, Dirk Drasdo.

The project established a stochastic modeling framework of epidemic spread both individual agent-
based and population-based in space of time. It permits to easily introduce many stratifications,
super spreaders, traveling etc. 04/2020 - 12/2023.

• ANR STEDI-NASH

Participants:

Jiri Pesek, Irene Vignon Clementel, Dirk Drasdo (Correspondant).

The project led by Philippe Garteiser (Inserm, Hopital Beaujon) aims at a Temporal diffusion
spectroscopy (TDS) approach, a development of Diffusion-Weighted-Magnetic Resonance Imaging,
to extract histological information in NASH (non-alcoholic steatohepatitis) non-invasively. 10/2020
- 03/2024.

11 Dissemination

11.1 Promoting scientific activities

• I. Vignon-Clementel represented Inria at the inauguration of a new pre-exascale EuroHPC super-

computer in the Barcelona Supercomputer center, Dec 2023

22

Inria Annual Report 2023

• I. Vignon-Clementel represented Inria at the European Virtual Human Twins Initiative launch event
on Thursday 21st Dec 2023. She represented Academia at the Round Table on why do we need a
European Virtual Human Twins Initiative?

11.1.1 Scientific events: organisation

A. Facque and I. Vignon-Clementel organized with D. Garcia (INSERM) a day of courses for clinicians ,
28th Nov 2023: L’Art de Soigner, la Science de Comprendre. Cours de Biomécanique Médicale

General chair, scientific chair

• L. Sala and I. Vignon-Clementel: chair of the session "C-02: Clinical Biomechanics & translational

research II", CMBBE 2023 Symposium, Paris, May 2023

• L. Sala, I. Vignon-Clementel, D. Drasdo: Organization of the session "D-07: Digital twin of different
scales and biological processes: the example of liver", CMBBE 2023 Symposium, Paris, May 2023

Member of the organizing committees
the French working group GDR mecabio santé

I. Vignon-Clementel, Member of the organizing committee of

11.1.2 Scientific events: selection

Member of the conference program committees

• Irene Vignon-Clementel, Program committee member, Computational and Mathematical Biomed-

ical Engineering Conference

• Dirk Drasdo, Program committee for "Systems Biology of Mammalian Cells" 2024

Reviewer

• I. Vignon-Clementel, reviewer for ESB, SBM conferences.

11.1.3 Journal

Member of the editorial boards

• Irene Vignon-Clementel is Associate Editor of the International Journal for Numerical Methods in

Biomedical Engineering and Royal Society Open

• Dirk Drasdo is Associated Editor for Journal of Theoretical Biology.

Reviewer - reviewing activities

• The most senior team members have reviewed articles for a number of journals.

• I. Vignon-Clementel, Review Committee for the European Society of Biomechanics Huiskes Medal

11.1.4 Conferences and Talks

• Omar Ali, Poster presentation, CMBBE 2023 Symposium, Paris, May 2023

• Mathieu de Langlard, Poster presentation, EASL Congress 2023, in session Hepatocyte Biology,

Vienna, June 2023.

• Jules Dichamp, Oral presentation, CMBBE 2023 Symposium, in session Structures and systems

biomechanics, Paris, May 2023

• Dirk Drasdo, invited presentation, "Towards a digital liver", santé globale et bio-ingénierie, INSA

Lyon 25/04/2023

Project SIMBIOTX

23

• Dirk Drasdo, invited presentation, "Towards a digital liver twin - and where the immune system

matters", Building Immune Digital Twins”, Saclay campus, Paris area, France, June, 2023

• Dirk Drasdo, invited presentation (keynote), "Towards a full digital liver twin: injury, regeneration

and disease progression", EMBO workshop, Girona, November 2023

• Ihsane Hadeg, Oral presentation, GDR Mécabio Santé 2023, in session Hémodynamique, Lyon,

December 2023.

• Peter Kottman, Oral presentation, ICCB 2023, in session Multiscale modelling of flows and transport

in tissues, TU Wien, September 2023.

• Peter Kottman, Oral presentation, GDR Mécabio Santé 2023, in session Hémodynamique, Lyon,

December 2023.

• Jérôme Kowalski, Oral presentation, CMBBE 2023 Symposium, in session Structures and systems

biomechanics, Paris, May 2023

• Jérôme Kowalski, Poster presentation, 7th VPHi Summer School, Barcelona, June 2023.

• Jérôme Kowalski, Poster presentation, Engineering 4 Health Annual Forum, Palaiseau, July 2023.

• Jérôme Kowalski, Oral presentation, Complex Networks 2023, in session Biological networks,

Menton, November 2023

• Jérôme Kowalski, Poster presentation, Journée de l’école doctorale IP-Paris, in session Ingénierie,

Mécanique, Énergétique, Palaiseau, December 2023.

• Weiqiang Liu, Oral presentation, CMBBE 2023 Symposium, in session Clinical Biomechanics and

Translational Research VI, Paris, May 2023.

• Yassine Machta, Oral presentation, GDR Mécabio Santé 2023, in session Vasculaire, Lyon, December

2023.

• Roel Meiburg, Oral presentation, EHRA 2023, in session Young Investigator Award - Basic and

Translational Science, Barcelona, April 2023.

• Roel Meiburg, Poster presentation, Engineering 4 Health Annual Forum, Palaiseau, July 2023

• Roel Meiburg, Poster presentation, Digital Twins 4 Health, Leuven, September 2023.

• Roel Meiburg, Oral presentation, GDR Mécabio Santé 2023, in session Hémodynamique, Lyon,

December 2023.

• Jiˇrí Pešek, Oral presentation, CMBBE 2023 Symposium, in session Mechanobiology III, Paris, May

2023.

• Mahdi Rezaei Adariani, Poster presentation, Engineering 4 Health Annual Forum, Palaiseau, July

2023.

• Mahdi Rezaei Adariani, Oral presentation, EuroMech 2023, in session Effect of particle shape, Nice,

June 2023.

• Mahdi Rezaei Adariani, Oral presentation, ESB 2023, in session Biofluids and transport, Maastricht,

July 2023.

• Mahdi Rezaei Adariani, Oral presentation, APS 2023, in session Particle-Laden Flow: Non-Spherical

Particles, Washington, Nov 2023.

• Lorenzo Sala: Oral presentation, CMBBE 2023 Symposium, in session Clinical biomechanics and

translational research. Paris, May 2023.

24

Inria Annual Report 2023

• Mohamed Ali Srir, Oral presentation, GDR Mécabio Santé 2023, in session Vasculaire, Lyon, Decem-

ber 2023.

• Pavlos Varsos, Oral presentation, GDR Mécabio Santé 2023, in session Hemodynamique, Lyon,

December 2023.

• Pavlos Varsos, Attended, Workshop on Scientific Machine Learning at CWI, Amsterdam, October

2023.

• Pascalle Wijntjes, Poster presentation, BME-NL 2023, Egmond aan Zee, January 2023.

• Pascalle Wijntjes, Poster presentation, CMBBE 2023 Symposium, in session Methods in mechanics

for biology and medicine, Paris, May 2023.

• Pascalle Wijntjes, Poster presentation, Engineering 4 Health Annual Forum, Palaiseau, July 2023.

• Pascalle Wijntjes, Oral presentation, ESB 2023, in session Cardiovascular biomechanics, Maastricht,

July 2023.

• Pascalle Wijntjes, Attended, Workshop on Scientific Machine Learning at CWI, Amsterdam, October

2023.

• Pascalle Wijntjes, Poster presentation, DUCOMS-day 2023, in session Multiscale Modeling, Utrecht,

November 2023.

• Jieling Zhao, Oral presentation, GASL 2023, Bochum, January 2023.

• Jieling Zhao, Oral presentation, CMBBE 2023, in session Digital twin of different scales and biological

processes: the example of liver, Paris, May 2023.

• Jieling Zhao, Oral presentation, ENUMATH 2023, in session Mathematical and computational

models of cells, cell-populations, and applications thereof, Lisbon, September 2023.

• Irene Vignon-Clementel, presentation BOPA for the new French Agency Innovation Santé, Jan 5th

2023

• Irene Vignon-Clementel, BME faculty seminar, Jan 11th 2023, IP Paris, France

• Irene Vignon-Clementel, round table on Digital Twin, Symposium on Digital Twin, Feb 9th 2023,

Ecole CentraleSupelec Saclay, France

• Irene Vignon-Clementel, Seminar, Feb 16th 2023, Ghent U., Belgium

• Irene Vignon-Clementel, Keynote speaker, CFC, April 25th-28th 2023, Cannes, France

• Irene Vignon-Clementel, invited presentation, CMBBE, May 3rd-5th 2023, Paris, France

• Irene Vignon-Clementel, invited presentation, Immune System Digital Twins workshop, May 23rd

2023, Institut Blaise Pascal, Orsay, France

• Irene Vignon-Clementel, Webinar initiative Biomeca2023 promotion M&SN pour les dispositifs

médicaux, June 13th 2023, online

• Irene Vignon-Clementel, WIC (surgical innovation week-end), June 23rd-25th 2023

• Irene Vignon-Clementel, STIMULATE Colloquium, Universty of Magdeburg, June 29th 2023, Ger-

many (online)

• Irene Vignon-Clementel, E4H forum, round table, July 5th 2023, IP Paris, France

• Irene Vignon-Clementel, invited talk, ESB 2023, July 10-12th, Maastricht, The Netherland

• Irene Vignon-Clementel, round table on numerical twin, TERATEC forum, Sept 26th 2023, Paris

• Irene Vignon-Clementel, conférence de réanimation pré hospitalière « Mardi du Val » de la BSPP

(Firefighters of Paris), Oct 10th 2023, Paris

• Irene Vignon-Clementel, invited talk, GDR mecabio santé, Nov 29th-Dec 1rst 2023, Lyon

Project SIMBIOTX

25

11.1.5 Leadership within the scientific community

• D. Drasdo is member of the scientific leadership team of the German LiSyM-Cancer network

project.

• I. Vignon-Clementel, Member of the Société de Biomécanique, the European Society of Biomech-

anics and VPH institute.

• I. Vignon-Clementel is a member of the Board of Directors, VPHi (virtual physiological human

institute)

11.1.6 Scientific expertise

• I. Vignon-Clementel is member of the Advisory Board, EPSRC Healthcare Technologies NetworkPlus

– BIOREME project (UK), since Sept 2021

• I. Vignon-Clementel for INRAE: WG to write a report on the concept of digital twin (metaprogramme

DIGIT-BIO)

11.1.7 Research administration

• D. Drasdo is modeling coordinator of German LiSyM-Cancer network project.

• D. Drasdo is associated with IfADo Leibniz Institute, having directed a postdocs from that institute.

11.2 Teaching - Supervision - Juries

11.2.1 Teaching

Practical and lab work supervision:

• Bachelor: R. Salle de Chou, "Probability", 24h, L3, ESIEE, Universite Paris Gustave Eiffel, France.

• Bachelor: R. Salle de Chou, "Introduction to object-oriented programming", 26h, L1, ESIEE, Uni-

versite Paris Gustave Eiffel, France.

Student project supervision:

• Master: R. Salle de Chou, "NLP methods for sentiment analysis on twitter", 34h, M1, ESIEE,

Universite Paris Gustave Eiffel, France.

• Master: J. Pešek, J. Dichamp, "Modélisation et simulation de colonies de bactéries et de biofilms", 5

students, 1 day per week, Sept. 2022 – April 2023, École polytechnique, Saclay, Île de France.

Focused Interventions:

• Master: D. Drasdo, "Étapes vers un foie VIRTUEL ", 1,5 h, UE Initiation à la bio-ingéniérie, Master

de Sciences, technologies et Santé Mention Biologie Integrative, Sorbonne U., France.

• Master: D. Drasdo, "Integrated and spatial-temporal multiscale modeling of liver guide in vivo
experiments in healthy and chronic disease states: a blue print for systems medicine", 1h, M2, MEC
550 - Biofluid Mechanics and Mass Transport, Ecole Polytechnique (engineering school), France

• Jiˇrí Pešek, GDR Mécabio Santé 2023, Mechanics of life - Simulations of tissues and cellular interac-

tions, Lyon, December 2023.

• Master: I. Vignon-Clementel, "Examples of data-based multiscale cardiovascular and respiratory
models and applications", 1h, M2, MEC 550 - Biofluid Mechanics and Mass Transport, Ecole
Polytechnique (engineering school), France

• Master: I. Vignon-Clementel, "Modélisation numérique des écoulements biofluides", 1,5 h, UE
Initiation à la bio-ingéniérie, Master de Sciences, technologies et Santé Mention Biologie Integrative,
Sorbonne U., France.

26

Inria Annual Report 2023

• Master: I. Vignon-Clementel, "Modélisation hémodynamique and simulation numérique comme

outil pour la chirurgie”, 1h, M2 Sciences Chirurgicales de l’Université Paris Sud, France

• Bachelor: I. Vignon-Clementel, "Modélisation numérique des écoulements biofluides", continuum

mechanics class at AgroParisTech (engineering school), France

• Bachelor: I. Vignon-Clementel, "Digital twins of hemodynamics modelling for clinical applications:
are we there yet?", Classes European Week, 1,5h, April 6th 2023, Ecole CentraleSupelec, France

• Bachelor : I. Vignon-Clementel, Table ronde numerique Robotique Cachan, June 6th 2023, IUT

Cachan, France

11.2.2 Supervision

• CentraleSupélec Parcours Recherche project in progress: C. Finotto: "Improved personalization of
a 0D hemodynamic model for liver surgery outcome", Oct. 2023 - present, supervisors: R. Meiburg,
I. Vignon-Clementel

• Master Internship: M. Srir, "Graph Neural Network for perfusion regions prediction from uncom-
plete vesssels", July 2023 - Jan. 2024, supervisors: I. Vignon-Clementel & H. Talbot & R. Sallé de
Chou

• Master Internship: I. Hadeg, "Numerical modeling for stenosis characterization", June - Oct. 2023,

supervisors: I. Vignon-Clementel & J. Kowalski.

• Master Internship: Y. Machta, "Enhancing liver vessel connectivity and properties in 3D segmenta-

tion models", June - Nov. 2023, supervisors: I. Vignon-Clementel & O. Ali

• PhD in progress: P. Kottman, "Multilevel modeling of flow and transport in liver lobules in health
and disease", Sep. 2023 - present, supervisors: I. Vignon-Clementel, D. Drasdo & E. Rohan (UWB
Pilsen, Czech Republic).

• PhD in progress: P. Varsos, "Multi-fidelity modelling of vascular shunts and clinical applications",

Jul. 2023 - present, supervisors: I. Vignon-Clementel, S. Pant, N. Golse.

• PhD in progress: J. Kowalski, ”Whole-body vascular transport and pharmaco-kinetics models:
application to imaging, in particular of liver ”, Dec. 2022 - present , supervisors: I. Vignon-Clementel,
D. Drasdo & L. Sala (INRAE).

• PhD in progress: R. Sallé de Chou, ”Machine Learning based prediction of heart perfusion maps”,
Oct. 2021 - present, supervisors: H. Talbot (CentraleSupelec and INRIA Opis team), I. Vignon-
Clementel, L. Najman (ESIEE Paris)

• PhD in progress: M. Rezaei Adariani, ”Flow Dynamic Modelling to Assess the Accurate Forces
Scheme of Magnetic Drug Eluting Beads Navigated by Magnetic Resonance Imaging”, Sep. 2021 -
present, supervisors: G. Soulez (CR-CHUM, Montreal, Canada), I. Vignon-Clementel

11.2.3 Juries

• Irene Vignon-Clementel, PhD defense committee:

– Mahdi Daei-Daei, IP Paris, Jan 11th 2023 (president)

– Charles Jabour, INSA Lyon, Feb 10th 2023 (reviewer)

– Luc Bakker, TU Eindhoven, The Netherland,April 14th 2023 (reviewer)

– Megane Decroocq, INSA Lyon, May 31rst (president)

– Omar Ali, Université Paris Saclay, June 26th 2023 (advisor)

• Irene Vignon-Clementel, PhD monitoring committee:

Project SIMBIOTX

27

– Quentin Vanderbecq, Sorbonne Université, July 20th 2023

– Alice Peyraut, IP Paris, Sept 18th 2023

– Alexandra Hauguel, IP Paris, Oct 5th 2023

– Littisha Lawrance, Université Paris Saclay, Oct 11th 2023

• Irene Vignon-Clementel, hiring committee:

– Handicap CRCN (Junior permanent research position) for Inria, July 3rd 2023, Rocquencourt,

France

• Dirk Drasdo, PhD defense committee:

– Zheyi Yang, ENSTA / Ecole Polytechnique, 19/12/2023 (member)

11.3 Popularization

11.3.1 Internal or external Inria responsibilities

I. Vignon-Clementel, in charge of public outreach for the Inria Saclay IDF research center (since Oct.
2023)

11.3.2 Articles and contents

I. Vignon-Clementel: Interview with the French journal Capital: A Glimpse into Healthcare’s Future,
“Santé - Les cinq révolutions qui vont tout changer” (Health - The Five Revolutions That Will Change
Everything), Aug 2023

11.3.3 Education

• SIMBIOTX group: Hosted at Inria a group of junior high school students (December 2023)

• J Kowalski and I Vignon-Clementel: helped at the Fête de la Science events (6-14th October 2023)

11.3.4 Interventions

• I. Vignon-Clementel: RJMI girls high school speed meeting, Feb 21rst 2023, Inria Saclay

• Jérôme Kowalski: CHICHE !, meeting and presentation to highschool female students for "la
semaine du numérique et des sciences informatiques". At Inria Rocquencourt, Dec. 8th 2023

• I. Vignon-Clementel: CHICHE !, meeting and presentation to highschool students. At Inria Roc-

quencourt, June 2nd 2023

• O. Ali and I. Vignon-Clementel: Predicting Liver Surgery Complexity With AI-Based Digital Twins

presented on Twin+, 9th Feb 2023

• I. Vignon-Clementel: Artificial intelligence and digital twins on FranceInter live interview (a major

French national radio). 12th April 2023, Podcast

• I. Vignon-Clementel, Sept 16th, National Open Day Journée du Patrimoine, presentation on numer-

ical twins for health, Inria Rocquencourt

• I. Vignon-Clementel, roundtable on career in research after a PhD, Université Paris Saclay, Cent-

raleSupelec, Sept 27th 2023

28

Inria Annual Report 2023

12 Scientific production

12.1 Major publications

[1]

J. Dichamp, G. Cellière, A. Ghallab, R. Hassan, N. Boissier, U. Hofmann, J. Reinders, S. Sezgin, S.
Zühlke, J. Hengstler and D. Drasdo. ‘In-vitro to in-vivo acetaminophen hepatotoxicity extrapolation
using classical schemes, pharmaco-dynamic models and a multiscale spatial-temporal liver twin’.
In: Frontiers in Bioengineering and Biotechnology (2023). URL: https://hal.science/hal-0394
1544.

[2] D. Drasdo, S. Hoehme and J. G. Hengstler. ‘How predictive quantitative modeling of tissue organiz-
ation can inform liver disease pathogenesis.’ In: Journal of Hepatology 61.4 (Oct. 2014), pp. 951–956.
DOI: 10.1016/j.jhep.2014.06.013. URL: https://hal.inria.fr/hal-01110644.

[3] A. Ghallab, G. Celliere, S. Henkel, D. Driesch, S. Hoehme, U. Hofmann, S. Zellmer, P. Godoy, A.
Sachinidis, M. Blaszkewicz, R. Reif, R. Marchan, L. Kuepfer, D. Häussinger, D. Drasdo, G. Gebhardt
and J. G. Hengstler. ‘Model-guided identification of a therapeutic strategy to reduce hyperam-
monemia in liver diseases’. In: Journal of Hepatology 64.4 (27th Nov. 2015), pp. 860–871. DOI:
10.1016/j.jhep.2015.11.018. URL: https://hal.archives-ouvertes.fr/hal-01257127.

[4] N. Golse, F. Joly, P. Combari, M. Lewin, Q. Nicolas, C. Audebert, D. Samuel, M.-A. Allard, A. Sa
Cunha, D. Castaing, D. Cherqui, R. Adam, E. Vibert and I. Vignon-Clementel. ‘Predicting the risk
of post-hepatectomy portal hypertension using a digital twin: A clinical proof of concept’. In:
Journal of Hepatology 74.3 (Mar. 2021), pp. 661–669. DOI: 10.1016/j.jhep.2020.10.036. URL:
https://hal.archives-ouvertes.fr/hal-03523641.

[5] F. Joly, G. Soulez, S. Lessard, C. Kauffmann and I. Vignon-Clementel. ‘A cohort longitudinal study
identifies morphology and hemodynamics predictors of abdominal aortic aneurysm growth’. In:
Annals of Biomedical Engineering (1st Oct. 2019). DOI: 10.1007/s10439- 019- 02375- 1. URL:
https://hal.archives-ouvertes.fr/hal-02302464.

[6]

S. Pant, B. Fabrèges, J.-F. Gerbeau and I. Vignon-Clementel. ‘A methodological paradigm for patient-
specific multi-scale CFD simulations: from clinical measurements to parameter estimates for
individual analysis’. In: International Journal for Numerical Methods in Biomedical Engineering
30.12 (Dec. 2014), pp. 1614–1648. DOI: 10.1002/cnm.2692. URL: https://hal.inria.fr/hal-0
1093879.

[7] P. Van Liedekerke, J. Neitsch, T. Johann, K. Alessandri, P. Nassoy and D. Drasdo. ‘Quantitative cell-
based model predicts mechanical stress response of growing tumor spheroids over various growth
conditions and cell lines’. In: PLoS Computational Biology 15.3 (8th Mar. 2019), e1006273. DOI: 10
.1371/journal.pcbi.1006273. URL: https://hal.archives-ouvertes.fr/hal-02371813.

[8] P. Van Liedekerke, M. Palm, N. Jagiella and D. Drasdo. ‘Simulating tissue mechanics with Agent
Based Models: concepts and perspectives’. In: Computational Particle Mechanics 2.4 (25th Nov.
2015), pp. 401–444. DOI: 10.1007/s40571-015-0082-3. URL: https://hal.inria.fr/hal-01
220539.

[9] N. Vartak, G. Guenther, F. Joly, A. Damle-Vartak, G. Wibbelt, J. Fickel, S. Jörs, B. Begher-Tibbe,
A. Friebel, K. Wansing, A. Ghallab, M. Rosselin, N. Boissier, I. Vignon-Clementel, C. Hedberg,
F. Geisler, H. Hofer, P. Jansen, S. Hoehme, D. Drasdo and J. Hengstler. ‘Intravital dynamic and
correlative imaging reveals diffusion-dominated canalicular and flow-augmented ductular bile
flux’. In: Hepatology 73.4 (Apr. 2021), pp. 1531–1550. DOI: 10.1002/hep.31422. URL: https://ha
l.inria.fr/hal-03135253.

[10]

I. Vignon-Clementel, N. Jagiella, J. Dichamp, J. Kowalski, W. Lederle, H. Laue, F. Kiessling, O.
Sedlaczek and D. Drasdo. ‘A proof-of-concept pipeline to guide evaluation of tumor tissue perfusion
by dynamic contrast-agent imaging: Direct simulation and inverse tracer-kinetic procedures’. In:
Frontiers in Bioinformatics 3 (13th Apr. 2023). DOI: 10.3389/fbinf.2023.977228. URL: https:
//hal.science/hal-04068296.

Project SIMBIOTX

29

12.2 Publications of the year

International journals

[11]

J. Dichamp, G. Cellière, A. Ghallab, R. Hassan, N. Boissier, U. Hofmann, J. Reinders, S. Sezgin, S.
Zühlke, J. Hengstler and D. Drasdo. ‘In-vitro to in-vivo acetaminophen hepatotoxicity extrapolation
using classical schemes, pharmaco-dynamic models and a multiscale spatial-temporal liver twin’.
In: Frontiers in Bioengineering and Biotechnology 11 (2023). DOI: 10.3389/fbioe.2023.1049564.
URL: https://hal.science/hal-03941544.

[12] D. Drasdo and J. Zhao. ‘An integrative experimental and computational twin modeling approach to
understand clonal dynamics in the normal liver’. In: Journal of Hepatology 79.2 (Aug. 2023), pp. 273–
276. DOI: 10.1016/j.jhep.2023.05.016. URL: https://inria.hal.science/hal-03512915.

[13] L. Sala, N. Golse, A. Joosten, E. Vibert and I. Vignon-Clementel. ‘Sensitivity Analysis of a Mathemat-
ical Model Simulating the Post-Hepatectomy Hemodynamics Response’. In: Annals of Biomedical
Engineering (Jan. 2023). DOI: 10.1007/s10439-022-03098-6. URL: https://hal.science/ha
l-03839072.

[14]

I. Vignon-Clementel, N. Jagiella, J. Dichamp, J. Kowalski, W. Lederle, H. Laue, F. Kiessling, O.
Sedlaczek and D. Drasdo. ‘A proof-of-concept pipeline to guide evaluation of tumor tissue perfusion
by dynamic contrast-agent imaging: Direct simulation and inverse tracer-kinetic procedures’. In:
Frontiers in Bioinformatics 3 (13th Apr. 2023). DOI: 10.3389/fbinf.2023.977228. URL: https:
//hal.science/hal-04068296.

International peer-reviewed conferences

[15]

J. Kowalski, L. Sala, D. Drasdo and I. Vignon-Clementel. ‘Understanding Organ Vascular Behavior
Through their Architecture’. In: The 12th International Conference on Complex Networks and their
Applications. Menton, France, 2023. URL: https://hal.inrae.fr/hal-04275318.

[16] R. Sallé de Chou, M. A. Srir, L. Najman, N. Passat, H. Talbot and I. Vignon-Clementel. ‘Convex
optimization for binary tree-based transport networks’. In: International Conference on Discrete
Geometry and Mathematical Morphology (DGMM). Florence, Italy, 2024. URL: https://inria.h
al.science/hal-04359833.

Conferences without proceedings

[17] L. Sala, N. Golse, A. Joosten, E. Vibert and I. Vignon-Clementel. ‘Sensitivity Analysis of a Mathemat-
ical Model Simulating the Post-Hepatectomy Hemodynamics Response’. In: CMBBE 2023 - 18th
International Symposium on Computer Methods in Biomechanics and Biomedical Engineering.
Vol. 51. Paris, France, 3rd Nov. 2022, pp. 270–289. URL: https://hal.inrae.fr/hal-04075698.

Scientific book chapters

[18]

[19]

J. Kowalski, D. Drasdo, P. Kottman, J. Dichamp and I. Vignon-Clementel. ‘Microvascular modeling
for medical imaging and toxicity assessment’. In: Quantitative approaches to microcirculation:
mathematical models, computational methods, measurements and data analysis. SEMA SIMAI
Springer Series. Springer, 2023. URL: https://hal.science/hal-04465901.

I. Vignon-Clementel and S. Pant. ‘Simulations hémodynamiques : paramétrage, données cliniques,
planification d’interventions’. In: Écoulements biologiques dans les grands vaisseaux: dialogue
entre modélisations numériques et études expérimentales in vitro/in vivo. ISTE, 1st Mar. 2023. URL:
https://hal.science/hal-04148714.

Reports & preprints

[20]

J. Zhao, S. Hammad, M. de Langlard, P. Erdoesi, Y. Li, P. van Liedekerke, A. Buttenschoen, M. Winkler,
S. W. Kürschner, P.-S. Koch, N. Grabe, J. G. Hengstler, M. P. Ebert, S. Dooley and D. Drasdo. A digital
liver twin demonstrating the interplay between biomechanics and cell kinetics can explain fibrotic
scar formation. 19th Sept. 2023. URL: https://hal.science/hal-04211418.

30

Inria Annual Report 2023

12.3 Cited publications

[21] C. Audebert, P. Bucur, M. Bekheit, E. Vibert, I. Vignon-Clementel and J.-F. Gerbeau. ‘Kinetic scheme
for arterial and venous blood flow, and application to partial hepatectomy modeling.’ In: Computer
Methods in Applied Mechanics and Engineering 314 (Feb. 2017), pp. 102–125. DOI: 10.1016/j.cma
.2016.07.009. URL: https://hal.archives-ouvertes.fr/hal-01347500.

[22] C. Audebert and I. Vignon-Clementel. ‘Model and methods to assess hepatic function from in-
docyanine green fluorescence dynamical measurements of liver tissue’. In: European Journal of
Pharmaceutical Sciences (2018). DOI: 10.1016/j.ejps.2018.01.008. URL: https://hal.archi
ves-ouvertes.fr/hal-01696064.

[23] N. Boissier, D. Drasdo and I. Vignon-Clementel. ‘Simulation of a detoxifying organ function: Focus
on hemodynamics modeling and convection-reaction numerical simulation in microcirculatory
networks’. In: International Journal for Numerical Methods in Biomedical Engineering 37.2 (2021).
DOI: 10.1002/cnm.3422. URL: https://hal.inria.fr/hal-03135175.

[24] D. Drasdo, S. Hoehme and J. G. Hengstler. ‘How predictive quantitative modeling of tissue organiz-
ation can inform liver disease pathogenesis.’ In: Journal of Hepatology 61.4 (Oct. 2014), pp. 951–956.
DOI: 10.1016/j.jhep.2014.06.013. URL: https://hal.inria.fr/hal-01110644.

[25] A. Friebel, T. Johann, D. Drasdo and S. Hoehme. ‘Guided interactive image segmentation using
machine learning and color-based image set clustering’. In: Bioinformatics 38.19 (2022), pp. 4622–
4628. DOI: 10.1093/bioinformatics/btac547. URL: https://inria.hal.science/hal-037
02928.

[26] A. Friebel, J. Neitsch, T. Johann, S. Hammad, D. Drasdo and S. Hoehme. ‘TiQuant: software for tissue
analysis, quantification and surface reconstruction’. In: Bioinformatics 31.19 (June 2015), pp. 3234–
3236. DOI: 10.1093/bioinformatics/btv346. URL: https://hal.inria.fr/hal-01257137.

[27] N. Jagiella, B. Müller, M. Müller, I. E. Vignon-Clementel and D. Drasdo. ‘Inferring Growth Control
Mechanisms in Growing Multi-cellular Spheroids of NSCLC Cells from Spatial-Temporal Image
Data’. In: PLoS Computational Biology 12.2 (2016), e1004412. DOI: 10.1371/journal.pcbi.1004
412. URL: https://hal.sorbonne-universite.fr/hal-01244593.

[28] M. Leist, A. Ghallab, R. Graepel, R. Marchan, R. Hassan, S. H. Bennekou, A. Limonciel, M. Vinken,
S. Schildknecht, T. Waldmann et al. ‘Adverse outcome pathways: opportunities, limitations and
open questions’. In: Archives of Toxicology 91.11 (Nov. 2017), pp. 3477–3505. DOI: 10.1007/s00204
-017-2045-3. URL: https://hal.inria.fr/hal-01968849.

[29]

S. Pant, C. Corsini, C. Baker, T.-Y. Hsia, G. Pennati and I. Vignon-Clementel. ‘A Lumped Parameter
Model to Study Atrioventricular Valve Regurgitation in Stage 1 and Changes Across Stage 2 Surgery
in Single Ventricle Patients’. In: IEEE Transactions on Biomedical Engineering 65.11 (Nov. 2018),
pp. 2450–2458. DOI: 10.1109/tbme.2018.2797999. URL: https://hal.inria.fr/hal-019547
84.

[30] L. Papamanolis, H. J. Kim, C. Jaquet, M. Sinclair, M. Schaap, I. Danad, P. van Diemen, P. Knaapen,
L. Najman, H. Talbot, C. A. Taylor and I. Vignon-Clementel. ‘Myocardial Perfusion Simulation for
Coronary Artery Disease: A Coupled Patient-Specific Multiscale Model’. In: Annals of Biomedical
Engineering 49 (May 2021), pp. 1432–1447. DOI: 10.1007/s10439-020-02681-z. URL: https:
//hal.science/hal-03036457.

[31] N. Pozin, S. Montesantos, I. Katz, M. Pichelin, I. Vignon-Clementel and C. Grandmont. ‘Predicted
airway obstruction distribution based on dynamical lung ventilation data: a coupled modeling-
machine learning methodology’. In: International Journal for Numerical Methods in Biomedical
Engineering 34.9 (May 2018). DOI: 10.1002/cnm.3108. URL: https://hal.archives-ouvertes
.fr/hal-01568065.

[32] F. Schliess, S. Hoehme, S. Henkel, A. Ghallab, D. Driesch, J. Böttger, R. Guthke, M. Pfaff, J. Hengstler,
R. Gebhardt, D. Häussinger, D. Drasdo and S. Zellmer. ‘Integrated metabolic spatial-temporal
model for the prediction of ammonia detoxification during liver damage and regeneration’. In:
Hepatology 60.6 (Dec. 2014), pp. 2040–2051. DOI: 10.1002/hep.27136. URL: https://hal.inri
a.fr/hal-01110646.

Project SIMBIOTX

31

[33] P. Van Liedekerke, J. Neitsch, T. Johann, K. Alessandri, P. Nassoy and D. Drasdo. ‘Quantitative cell-
based model predicts mechanical stress response of growing tumor spheroids over various growth
conditions and cell lines’. In: PLoS Computational Biology 15.3 (Mar. 2019), e1006273. DOI: 10.137
1/journal.pcbi.1006273. URL: https://hal.archives-ouvertes.fr/hal-02371813.

[34] P. Van Liedekerke, M. Palm, N. Jagiella and D. Drasdo. ‘Simulating tissue mechanics with Agent
Based Models: concepts and perspectives’. In: Computational Particle Mechanics 2.4 (Nov. 2015),
pp. 401–444. DOI: 10.1007/s40571-015-0082-3. URL: https://hal.inria.fr/hal-0122053
9.

[35] Y. Yin, O. Sedlaczek, B. Müller, A. Warth, M. González-Vallinas, B. Lahrmann, N. Grabe, H.-U.
Kauczor, K. Breuhahn, I. Vignon-Clementel and D. Drasdo. ‘Tumor cell load and heterogeneity
estimation from diffusion-weighted MRI calibrated with histological data: an example from lung
cancer’. In: IEEE Transactions on Medical Imaging (2017). DOI: 10.1109/TMI.2017.2698525. URL:
https://hal.inria.fr/hal-01421398.
<scratchpad>
- Main topic: SIMBIOTX research center and its activities in systems medicine, systems toxicology, and systems biotechnology.
- Key points: SIMBIOTX aims to develop computational models and tools for guiding clinical and experimental designs and decisions in systems medicine, systems toxicology, and systems biotechnology. The center focuses on liver disease modeling, including liver tissue architecture, perfusion, and function. It also explores applications in congenital heart disease, in vitro cell populations, tumors, and cancer.
- Structure: The document is organized into sections, including activity report, project-team, domain, theme, contents, and more.
- Potential artifacts to ignore: Links, HTML tags, and web-related elements.
</scratchpad>

<final_summary>
The SIMBIOTX research center focuses on developing computational models and tools for systems medicine, systems toxicology, and systems biotechnology. The center's main activities include modeling liver disease, including tissue architecture, perfusion, and function, as well as exploring applications in congenital heart disease, in vitro cell populations, tumors, and cancer. SIMBIOTX aims to develop digital twins to guide clinical and experimental designs and decisions, with a focus on improving health outcomes for patients. The center's research also includes exploring applications in systems toxicology and systems biotechnology, with a particular emphasis on liver and liver cells.
The SIMBIOTX research center focuses on developing computational models and tools for systems medicine, systems toxicology, and systems biotechnology. The center's main activities include modeling liver disease, including tissue architecture, perfusion, and function, as well as exploring applications in congenital heart disease, in vitro cell populations, tumors, and cancer. SIMBIOTX aims to develop digital twins to guide clinical and experimental designs and decisions, with a focus on improving health outcomes for patients. The center's research also includes exploring applications in systems toxicology and systems biotechnology, with a particular emphasis on liver and liver cells.