RESEARCH CENTRE

Inria Saclay Centre

2023
ACTIVITY REPORT

Project-Team

LIFEWARE

Computational systems biology and
optimization

DOMAIN

Digital Health, Biology and Earth

THEME

Computational Biology

Contents

Project-Team LIFEWARE

1 Team members, visitors, external collaborators

2 Overall objectives

3 Research program

3.1 Chemical Reaction Network (CRN) Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2 Logical Paradigm for Systems Biology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3 Constraint Solving and Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

4 Application domains

4.1 Preamble . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2 Modeling software for systems and synthetic biology at the cellular level
. . . . . . . . . . .
4.3 Biomedical applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

5 Social and environmental responsibility

5.1 Footprint of research activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.2 Impact of research results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

6 Highlights of the year

6.1 Trap spaces of Boolean networks are conflict-free siphons of their Petri net encoding . . . .
6.2 Stochastic chemical kinetics of cell fate decision systems: from single cells to populations
and back . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.3 A Skin Microbiome Model with AMP interactions and Analysis of Quasi-Stability vs Stability
in Population Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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7 New software, platforms, open data

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7.1 New software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9
7.1.1 BIOCHAM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.1.2 CaSQ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
7.1.3 CoLoMoTo Notebook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

8 New results

11

8.1 Stochastic chemical kinetics of cell fate decision systems: from single cells to populations

and back . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

8.2 A Skin Microbiome Model with AMP interactions and Analysis of Quasi-Stability vs Stability

in Population Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
8.3 On Estimating Derivatives of Input Signals in Biochemistry . . . . . . . . . . . . . . . . . . . 12
8.4 Trap spaces of Boolean networks are conflict-free siphons of their Petri net encoding . . . . 12
8.5 Efficient Enumeration of Fixed Points in Complex Boolean Networks Using Answer Set

Programming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

8.6 Hybrid computational modeling highlights reverse warburg effect in breast cancer-associated

fibroblasts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

8.7 A large-scale Boolean model of the rheumatoid arthritis fibroblast-like synoviocytes predicts

drug synergies in the arthritic joint . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

8.8 Reactmine: a statistical search algorithm for inferring chemical reactions from time series

data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
8.9 Neural-based classification rule learning for sequential data . . . . . . . . . . . . . . . . . . 14

9 Bilateral contracts and grants with industry

15
9.1 Bilateral contracts with industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
9.1.1 Institut de Recherches Servier
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
9.1.2 Johnson&Johnson Santé Beauté France . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
9.1.3 IBM research, France . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

10 Partnerships and cooperations

10.2 European initiatives

10.1 International research visitors

15
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
10.1.1 Visits of international scientists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
10.2.1 ERC Starting Grant BridgingScales . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
10.3 National initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
10.3.1 ANR Project: Opt-MC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
10.3.2 ANR Project: Difference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

11 Dissemination

16
11.1 Promoting scientific activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
11.1.1 Scientific events: organisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
11.1.2 Journal
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
11.1.3 Invited talks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
11.1.4 Scientific expertise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
11.2 Teaching - Supervision - Juries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
11.2.1 Teaching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
11.2.2 Juries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
11.3 Popularization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
11.3.1 Internal or external Inria responsibilities . . . . . . . . . . . . . . . . . . . . . . . . . . 18
11.3.2 Articles and contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
11.3.3 Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
11.3.4 Interventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

12 Scientific production

19
12.1 Major publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
12.2 Publications of the year

Project LIFEWARE

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Project-Team LIFEWARE

Creation of the Project-Team: 2015 April 01

Keywords

Computer sciences and digital sciences

A2.1.1. – Semantics of programming languages

A2.1.5. – Constraint programming

A2.1.10. – Domain-specific languages

A2.2.1. – Static analysis

A2.3.2. – Cyber-physical systems

A2.4. – Formal method for verification, reliability, certification

A2.4.1. – Analysis

A2.4.2. – Model-checking

A2.4.3. – Proofs

A3.4.2. – Unsupervised learning

A3.4.4. – Optimization and 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

A6.2.4. – Statistical methods

A6.2.6. – Optimization

A6.3.1. – Inverse problems

A6.3.4. – Model reduction

A7.2. – Logic in Computer Science

A8.1. – Discrete mathematics, combinatorics

A8.2. – Optimization

A8.7. – Graph theory

A9.7. – AI algorithmics

Other research topics and application domains

B1. – Life sciences

B1.1.2. – Molecular and cellular biology

B1.1.7. – Bioinformatics

B1.1.8. – Mathematical biology

B1.1.10. – Systems and synthetic biology

B2.2.3. – Cancer

B2.2.6. – Neurodegenerative diseases

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Inria Annual Report 2023

B2.4.1. – Pharmaco kinetics and dynamics

B9. – Society and Knowledge

Project LIFEWARE

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1 Team members, visitors, external collaborators

Research Scientists

• François Fages [Team leader, INRIA, Senior Researcher, HDR]

• Jakob Ruess [INRIA, Researcher]

• Sylvain Soliman [INRIA, Researcher, HDR]

Faculty Members

• Anna Niarakis [UNIV TOULOUSE III, Professor, from Sep 2023, HDR]

• Anna Niarakis [Univ EVRY, Associate Professor, until Aug 2023, Univ EVRY Val d’Essonne Paris-

Saclay, HDR]

Post-Doctoral Fellows

• Guillaume Ballif [INRIA, Post-Doctoral Fellow, from Jul 2023]

• Henri Mermoz Kouye [INRIA, Post-Doctoral Fellow, from Mar 2023]

PhD Students

• Sahar Aghakhani [UNIV EVRY, until Sep 2023]

• Marine Collery [IBM, until Sep 2023]

• Alexandre Tan-Lhernould [SERVIER, from Oct 2023]

Technical Staff

• Mathieu Hemery [INRIA, Engineer, until Aug 2023]

Interns and Apprentices

• Hugo Buscemi [ENS PARIS-SACLAY, Intern, from Mar 2023 until Jul 2023]

Administrative Assistant

• Melanie Da Silva [INRIA, from May 2023]

External Collaborators

• Guillaume Ballif [INSTITUT PASTEUR, until Jun 2023]

• Julien Martinelli [UNIV AALTO]

• Denis Thieffry [ENS PARIS, HDR]

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Inria Annual Report 2023

2 Overall objectives

This project aims at developing formal methods for understanding the cell machinery and establishing
computational paradigms in cell biology. It is based on the vision of
cells as machines,
chemical reaction networks as programs,

and on the use of concepts from computer science to master the complexity of cell biochemical

processes [25].

We contribute to the development of a computational theory of chemical reaction networks (CRNs),
by addressing fundamental research issues in computer science on the concepts of analog computation
and analog computational complexity in biochemistry, and on the interplay between structure and
dynamics in CRNs.

Since 2002, we develop a software platform, called the Biochemical Abstract Machine (BIOCHAM),
for modeling, analyzing and now synthesizing CRNs, with some unique algorithmic contributions.
The reaction rule-based language of BIOCHAM allows us to reason about CRNs at different levels of
abstraction in the hierarchy of their stochastic, differential, Boolean and hybrid semantics. Various static
analysis methods, most of them based on constraint solving or graph theory, provide useful information
before going to simulations and dynamical analyses, for which quantitative temporal logic is used to
formalize cell behaviors with imprecise data, and to constrain model building.

A tight integration between dry lab and wet lab efforts is also essential for the success of the project.
This is achieved through collaborations with biologists and experimentalists, including partners from the
pharmaceutical industry, on concrete biological and biomedical questions.

Because of the importance of constraint solving and optimization techniques in our approach, and
the need of rapid prototyping for our software developments, we keep some research and teaching activity
in relational programming (i.e. logic programming, constraint programming, interval arithmetic) as a
general paradigm for computing with partial information systems, and solving practical instances of
NP-hard problems.

3 Research program

3.1 Chemical Reaction Network (CRN) Theory

Originally, Feinberg’s mathematical theory of Chemical Reaction Networks (CRN) and Thomas’s influence
networks, were created to provide sufficient and/or necessary structural conditions for the existence
of multiple steady states and oscillations in complex gene networks. Those conditions can be verified
by static analyzers without knowing kinetic parameter values, nor making any simulation. In this first
approach, most of our work consists in considering the hypergraph structure of a CRN (Petri net
invariants, species-reaction labelled hypergraph, influence graph, reductions by subgraph epimorphisms)
and analysing their interplay with the dynamics of CRNs in their different interpretations (Boolean, CTMC,
ODE, time scale separations) which can be related in the framework of abstract interpretation1. For
example, our study of the influence graphs of reaction systems [7] lead recently to sufficient graphical
conditions ensuring rate-independence of CRN [4], or some time ago to the non-trivial generalization
to reaction systems of Thomas’ conditions of multi-stationarity and Soulé’s proof given for influence
systems, with much greater efficiency by several orders of magnitude for testing them when compared to
current symbolic computation methods [2].

However, we aim at development a computational theory of CRNs and biochemical programming.
Our original method to infer CRNs from ODEs [5] showed the generality of CRNs, and lead us to prove
the Turing-completeness of continuous CRNs over a finite set of molecular species, showing that any
computable function over the real numbers (i.e. computable in arbitrary precision by a Turing machine)
can be computed by a CRN over a finite set of molecular species [6]. (Best paper award CMSB 2017, Prix
La Recherche 2019). This result closed the last open problem on the computation power of CRNs in
their different semantics and has opened a whole research avenue on CRN design by compilation of

1François Fages, Sylvain Soliman. Abstract Interpretation and Types for Systems Biology. Theoretical Computer Science,

403(1):52–70, 2008. (preprint)

Project LIFEWARE

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Figure 1: Overall picture of our Lifeware project centered on the development of a Computational Theory
of Chemical Reaction Networks, in both perspectives of understanding natural cell processes in systems
biology, and programming high-level functions in biochemistry and synthetic biology.

         Biomedical questions   - Immune response to SARS CoV2 infection - Metabolic reprogramming of fibroblasts - Inflammation process in skin - Microtubule tyrosination activation  - Drug screening design - Patient-tailored chronotherapies - Synthetic biosensor design  Biochemical process models - Cell cycle & circadian clock coupling - Multiple timescale models - Cell signal transduction - Biosensors  - Macroscale input/output function - Microtubule tyrosination cycle - Multiscale skin models Background  knowledge - Ontologies - Knowledge graphs - Interaction maps - Covid19 disease map - Model repositories      Experimental data - Macroscale - Microscale - Unicellular/population - Time series - Videomicroscopy - Microfluidic control  Software tools - Simulations - Proofs of reachability, model-checking, cmultistability, onstraint satisfaction  - Parameter optimization - Robustness optimization  - Program-CRN compiler  - CRN design by artificial evolution  - CRN learning Computational Theory of  Chemical Reaction Networks - Semantics hierarchy - Graph-theoretic static analyses  - Temporal logic dynamical analyses - Turing-completeness and analog complexity - CRN synthesis from specification/data  6

Inria Annual Report 2023

mathematical functions. This is illustrated by a series of publications since that date [10] (best paper
award CMSB 2022), again this year [23] , and innovative applications in synthetic biology [3].

3.2 Logical Paradigm for Systems Biology

Our group was among the first ones in 2002 to apply model-checking methods to systems biology in
order to reason on large molecular interaction networks, such as Kohn’s map of the mammalian cell cycle
(800 reactions over 500 molecules) 2. The logical paradigm for systems biology that we have subsequently
developed for quantitative models can be summarized by the following identifications :

biological model = transition system K , initial state s
dynamical behavior specification = temporal logic formula φ
model validation = model-checking K , s |=? φ
′
′ ⊂ K s.t. K

model reduction = sub-model-checking, K

?, s |= φ

model prediction = formula enumeration, φ s.t. K , s |= φ?
static experiment design = symbolic model-checking, K , s? |= φ
model synthesis = constraint solving K ?, s |= φ
model inference = constraint solving K ?, s? |= φ

In particular, the definition of a continuous satisfaction degree for first-order temporal logic for-
mulae with constraints over the reals, was the key to generalize this approach to quantitative models,
opening up the field of model-checking to model optimization 3 4

This line of research continues with the development of temporal logic constraint patterns with
efficient solvers, and their use for model building, in partnership with biologists to answer concrete
questions in the biomedical domain5 6 [16, 15] and the pharmaceutical industry [9, 20, 14, 20].

3.3 Constraint Solving and Optimization

Constraint solving and optimization algorithms are important in our research. On the one hand, static
analyses of CRNs often involve solving hard combinatorial optimization problems, for which we have
shown that constraint logic programming techniques, including Answer Set Programming (ASP), are
particularly successful, often beating dedicated algorithms on real-size instances from model repositories
by orders of magnitude [12, 21] 7.

On the other hand, parameter search problems involve solving hard continuous optimization prob-
lems, for which evolutionary algorithms, and especially the covariance matrix evolution strategy (CMA-
ES) (EPI RANDOPT) have shown to provide best results in our context. Constraint-based models and
efficient constraint solvers are thus instrumental in our approach for building quantitative models,
gaining model-based insights, revisiting biological hypotheses, and contributing to biological knowledge.

2N. Chabrier-Rivier, M. Chiaverini, V. Danos, F. Fages, V. Schächter. Modeling and querying biochemical interaction networks.

Theoretical Computer Science, 325(1):25–44, 2004.

3Aurélien Rizk, Grégory Batt, François Fages, Sylvain Soliman. Continuous Valuations of Temporal Logic Specifications with

applications to Parameter Optimization and Robustness Measures. Theoretical Computer Science, 412(26):2827–2839, 2011.

4Aurélien Rizk, Grégory Batt, François Fages, Sylvain Soliman. A general computational method for robustness analysis with

applications to synthetic gene networks. Bioinformatics, 12(25):il69–il78, 2009.

5Domitille Heitzler, Guillaume Durand, Nathalie Gallay, Aurélien Rizk, Seungkirl Ahn, Jihee Kim, Jonathan D. Violin, Laurence
Dupuy and Christophe Gauthier, Vincent Piketty, Pascale Crépieux, Anne Poupon, Frédérique Clément, François Fages, Robert
J. Lefkowitz, Eric Reiter. Competing G protein-coupled receptor kinases balance G protein and β-arrestin signaling. Molecular
Systems Biology, 8(590), 2012.

6Elisabetta De Maria, François Fages, Aurélien Rizk, Sylvain Soliman. Design, Optimization, and Predictions of a Coupled
Model of the Cell Cycle, Circadian Clock, DNA Repair System, Irinotecan Metabolism and Exposure Control under Temporal Logic
Constraints. Theoretical Computer Science, 412(21):2108–2127, 2011.

7Steven Gay, François Fages, Thierry Martinez, Sylvain Soliman, Christine Solnon. On the subgraph Epimorphism Problem.

Discrete Applied Mathematics, 162:214–228, 2014 (preprint).

Project LIFEWARE

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4 Application domains

4.1 Preamble

Our collaborative work on biological applications is expected to serve as a basis for groundbreaking
advances in cell functioning understanding, cell monitoring and control, and novel therapy design and
optimization. Our collaborations with biologists are focused on concrete biological questions, and on
the building of mechanistic models of biological systems to answer them. Furthermore, one important
application of our research is the development and distribution of a modeling software for computational
systems biology and synthetic biology.

4.2 Modeling software for systems and synthetic biology at the cellular level

Since 2002, we develop an open-source software environment for modeling and analyzing biochemical
reaction systems. This software, called the Biochemical Abstract Machine (BIOCHAM), is compatible
with SBML for importing and exporting models from repositories such as BioModels. It can perform a
variety of static analyses, specify behaviors in Boolean or quantitative temporal logics, search parameter
values satisfying temporal constraints, and make various simulations. While the primary reason of this
development effort is to be able to implement our ideas and experiment them quickly on a large scale,
using rapid prototyping techniques based on constraint logic programming, BIOCHAM is distributed
and used by other groups worldwide, for building CRN models, for comparing CRN analysis/synthesis
techniques, and for teaching computational systems biology. A JUPYTER BIOCHAM KERNEL has been
developed to use BIOCHAM on our web server without any installation which is heavily used for teaching.
We plan to continue developing BIOCHAM for these different purposes with the recruitment of a research
engineer to improve the software quality and animation of the community of users.

Since 2018, the CaSQ software complements this effort by providing an interface to import large
interaction maps written in SBML using the CellDesigner tool and translate them into Boolean influence
models with various tools compatible with the SBML-qual standard.

Since 2020, we participate in the CoLoMoTo notebook platform, which provides an integrated collec-
tion of software tools for the analysis of qualitative models, including CaSQ. This platform encourages
the reproducibility of analysis by combining Docker images (reproducible software environment) and
Jupyter notebooks (reproducible and shareable workflows).

These two last efforts play a central role in the global Covid-19 Disease Maps project.

4.3 Biomedical applications

We plan to continue to tackle challenging concrete biomedical questions with academic and industrial
partners in an opportunist way, according to both the scope of the question on the international scene,
and the relevance of our theoretical approaches to the question.

As mentioned above, our long-standing collaboration with F. Molina’s CNRS-ALCEN lab on the
design, optimization and industrialization of biochemical diagnosis vesicles [3] continues on a design
methodology of robust CRNs including analog functions and derivative estimation [23].

Our successful collaboration with Servier on knowledge graphs and CRN model parameterizations
using quantitative temporal logic for drug screening [9] continues with a new CIFRE thesis with Servier
started this fall on CRN model learning from experimental data.

Our similarly successful collaboration with Johnson& Johnson Santé Beauté France on multi-scale
modeling of the epidermis and multifactorial aspects of atopic dermatitis [14, 20] has revealed complex
metastability behaviors in our population dynamics models and further emerging properties when
combined with a multi-agent model at the tissue level. This research continued with O. Radulescu in
Montpellier on the tropical algebraic analysis of quasi-stability in our model has led to a generalization of
this phenomenon in population dynamicas models [20].

We also keep ready to continue our long-standing collaborations on chronotherapies as specialists of

coupled models of the cell cycle and the circadian clock [11, 16] and their systemic regulators8.

8Elisabetta De Maria, François Fages, Aurélien Rizk, Sylvain Soliman. Design, Optimization, and Predictions of a Coupled
Model of the Cell Cycle, Circadian Clock, DNA Repair System, Irinotecan Metabolism and Exposure Control under Temporal Logic

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Inria Annual Report 2023

Last but not least, our group became very active in cooperative efforts at the European scale to design
large molecular interaction maps for diseases such as Covid-19 [13] or rheumatoid arthritis [19] and
derive from them Boolean dynamical models using the CaSQ software as well as developing hybrid
models [17].

5 Social and environmental responsibility

5.1 Footprint of research activities

In synthetic biology, our approach based on analog computation target enzymatic reactions with proteins
in artificial DNA-free RNA-free vesicles. This is an original approach to solving major safety issue for
applications in medicine and the environment.

5.2 Impact of research results

Our multidisciplinary research rooted in fundamental computer science aims at contributing to biology
and medicine by going quite far in the applications with partners from academia and industry, in
particular with the pharma industry.

6 Highlights of the year

We consider as major results for our project the following results published this year and described in
Section 8

6.1 Trap spaces of Boolean networks are conflict-free siphons of their Petri net

encoding

Participants:

Sylvain Soliman.

Boolean network modeling of gene regulation but also of post-transcriptomic systems has proven over
the years that it can bring powerful analyses and corresponding insight to the many cases where precise
biological data is not sufficiently available to build a detailed quantitative model. Besides simulation, the
analysis of such models is mostly based on attractor computation, since those correspond roughly to
observable biological phenotypes. The recent use of trap spaces made a real breakthrough in that field
allowing to consider medium-sized models that used to be out of reach. However, with the continuing
increase in model size and complexity of Boolean update functions, the state-of-the-art computation
of minimal trap spaces based on prime implicants shows its limits due to the difficulty of the prime-
implicant computation.

In [21], we explore and prove for the first time a connection between trap spaces of a general Boolean
network and siphons of its Petri net encoding. Besides important theoretical applications in studying
properties of trap spaces, the connection enables us to propose an alternative approach to compute
minimal trap spaces, and hence complex attractors, of a general Boolean network. It replaces the need
for prime implicants by a completely different technique, namely the enumeration of maximal siphons in
the Petri net encoding of the original model. We then demonstrate its efficiency and compare it to the
state-of-the-art methods on a large collection of real-world and randomly generated models.

6.2 Stochastic chemical kinetics of cell fate decision systems: from single cells to

populations and back

Constraints. Theoretical Computer Science, 412(21):2108 2127, 2011.

Project LIFEWARE

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Participants: Guillaume Ballif, Jakob Ruess.

Stochastic chemical kinetics is a widely used formalism for studying stochasticity of chemical reactions
inside single cells. Experimental studies of reaction networks are generally performed with cells that are
part of a growing population, yet the population context is rarely taken into account when models are
developed. Models that neglect the population context lose their validity whenever the studied system
influences traits of cells that can be selected in the population, a property that naturally arises in the
complex interplay between single-cell and population dynamics of cell fate decision systems.

In [18], we represent such systems as absorbing continuous-time Markov chains. We show that
conditioning on non-absorption allows one to derive a modified master equation that tracks the time
evolution of the expected population composition within a growing population. This allows us to derive
consistent population dynamics models from a specification of the single-cell process. We use this
approach to classify cell fate decision systems into two types that lead to different characteristic phases
in emerging population dynamics. Subsequently, we deploy the gained insights to experimentally study a
recurrent problem in biology: how to link plasmid copy number fluctuations and plasmid loss events
inside single cells to growth of cell populations in dynamically changing environments.

6.3 A Skin Microbiome Model with AMP interactions and Analysis of Quasi-Stability

vs Stability in Population Dynamics

Participants:

François Fages, Elea Thibault-Greugny.

The skin microbiome plays an important role in the maintenance of a healthy skin. It is an ecosystem,
composed of several species, competing for resources and interacting with the skin cells. Imbalance in the
cutaneous microbiome, also called dysbiosis, has been correlated with several skin conditions, including
acne and atopic dermatitis. Generally, dysbiosis is linked to colonization of the skin by a population
of opportunistic pathogenic bacteria. Treatments consisting in non-specific elimination of cutaneous
microflora have shown conflicting results.

In [20], we introduce a mathematical model based on ordinary differential equations, with 2 types
of bacteria populations (skin commensals and opportunistic pathogens) and including the production
of antimicrobial peptides to study the mechanisms driving the dominance of one population over the
other. By using published experimental data, assumed to correspond to the observation of stable states
in our model, we reduce the number of parameters of the model from 13 to 5. We then use a formal
specification in quantitative temporal logic to calibrate our model by global parameter optimization and
perform sensitivity analyses. On the time scale of 2 days of the experiments, the model predicts that
certain changes of the environment, like the elevation of skin surface pH, create favorable conditions
for the emergence and colonization of the skin by the opportunistic pathogen population, while the
production of human AMPs has non-linear effect on the balance between pathogens and commensals.
Surprisingly, simulations on longer time scales reveal that the equilibrium reached around 2 days can in
fact be a quasi-stable state followed by the reaching of a reversed stable state after 12 days or more. We
analyse the conditions of quasi-stability observed in this model using tropical algebraic methods, and
show their non-generic character in contrast to slow-fast systems. These conditions are then generalized
to a large class of population dynamics models over any number of species.

7 New software, platforms, open data

7.1 New software

7.1.1 BIOCHAM

Name: The Biochemical Abstract Machine

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Inria Annual Report 2023

Keywords: Bioinformatics, Systems Biology, Computational biology

Functional Description: The Biochemical Abstract Machine (BIOCHAM) is a software environment for
modeling, analyzing and synthesizing biochemical reaction networks (CRNs) with respect to a
formal specification of the observed or desired behavior of a biochemical system. BIOCHAM is
compatible with the Systems Biology Markup Language (SBML) and contains some unique features
about formal specifications in quantitative temporal logic, sensitivity and robustness analyses and
parameter search in high dimension w.r.t. behavioral specifications, static analyses, and synthesis
of CRNs.

Release Contributions: – compilation pipeline of elementary mathematical functions in at most bi-
molecular reaction networks over a finite set of abstract molecular species – improved heuristic
quadratization algorithm to generate at most bi-molecular reactions – notebooks of Master classes
in Bioinfomratics of molecular interaction networks – multiple improvements of the commands
and documentation

URL: http://lifeware.inria.fr/biocham4/

Contact: François Fages

Participants: François Fages, Mathieu Hemery, Sylvain Soliman

7.1.2 CaSQ

Name: CellDesigner as SBML-Qual

Keywords: SBML, Logical Framework, Knowledge representation

Functional Description: CaSQ is a tool that can convert a molecular interaction map built with CellDesigner,

or any similar SBML-capable tool, to an executable Boolean model. CaSQ is developed in Python
(download and install instructions can be found on the Python package index) and uses as source
the xml file of CellDesigner, in order to infer preliminary Boolean rules based solely on network
topology and semantic annotations (e.g., certain arcs are noted as catalysis, inhibition, etc.). The
aim is to convert a Process Description representation, i.e., a reaction model, into a full logical
model. The resulting structure is closer to an Activity Flow diagram, though not in a strict SBGN-PD
to SBGN-AF notion. Moreover logical rules that make the model executable are also obtained.
CaSQ was used on maps of the Rheumatoïd Arthritis, of the MAP-Kinase cascade, etc. and is now
being used by the Covid-19 DiseaseMaps consortium to automatically obtain logical models from
maps [2].

CaSQ has recently been added to the CoLoMoTo Docker image and can be used in such a notebook.

URL: https://casq.readthedocs.io/en/stable/

Publications: hal-03385317, hal-02590714

Contact: Sylvain Soliman

Participants: Sylvain Soliman, Anna Niarakis, Aurélien Naldi

Partner: Université d’Evry-Val d’Essonne

7.1.3 CoLoMoTo Notebook

Keywords: Systems Biology, Computational biology

Functional Description: Dynamical models are widely used to summarize our understanding of bio-
logical networks, to challenge its consistency, and to guide the generation of realistic hypothesis
before experimental validation. Qualitative models in particular enable the study of the dynamical
properties that do not depend on kinetic parameters, which can be difficult to obtain. This platform

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integrates a wide collection of complementary software tools, along with interoperability features
and a consistent Programming interface based on the popular Python language.

The platform is distributed as a Docker image, ensuring the reproducibility of the software environ-
ment. The use of Jupyter Notebooks provides a good balance between ease of use and flexibility.
These notebooks can be further used to share precise analysis workflows, ensuring the dissemina-
tion of fully reproducible research work using this platform.

URL: http://colomoto.org/notebook/

Publication: hal-01794294

Contact: Aurélien Naldi

Participants: Sylvain Soliman, Aurélien Naldi, Denis Thieffry

Partners: LaBRI, Institut Curie, ENS Paris, LRI - Laboratoire de Recherche en Informatique

8 New results

8.1 Stochastic chemical kinetics of cell fate decision systems: from single cells to

populations and back

Participants: Guillaume Ballif, Henri Mermoz Kouyé, Jakob Ruess.

Stochastic chemical kinetics is a widely used formalism for studying stochasticity of chemical reactions
inside single cells. Experimental studies of reaction networks are generally performed with cells that are
part of a growing population, yet the population context is rarely taken into account when models are
developed. Models that neglect the population context lose their validity whenever the studied system
influences traits of cells that can be selected in the population, a property that naturally arises in the
complex interplay between single-cell and population dynamics of cell fate decision systems.

In [18], we represent such systems as absorbing continuous-time Markov chains. We show that
conditioning on non-absorption allows one to derive a modified master equation that tracks the time
evolution of the expected population composition within a growing population. This allows us to derive
consistent population dynamics models from a specification of the single-cell process. We use this
approach to classify cell fate decision systems into two types that lead to different characteristic phases
in emerging population dynamics. Subsequently, we deploy the gained insights to experimentally study a
recurrent problem in biology: how to link plasmid copy number fluctuations and plasmid loss events
inside single cells to growth of cell populations in dynamically changing environments.

8.2 A Skin Microbiome Model with AMP interactions and Analysis of Quasi-Stability

vs Stability in Population Dynamics

Participants:

François Fages.

The skin microbiome plays an important role in the maintenance of a healthy skin. It is an ecosystem,
composed of several species, competing for resources and interacting with the skin cells. Imbalance in the
cutaneous microbiome, also called dysbiosis, has been correlated with several skin conditions, including
acne and atopic dermatitis. Generally, dysbiosis is linked to colonization of the skin by a population
of opportunistic pathogenic bacteria. Treatments consisting in non-specific elimination of cutaneous
microflora have shown conflicting results.

In [20], we introduce a mathematical model based on ordinary differential equations, with 2 types
of bacteria populations (skin commensals and opportunistic pathogens) and including the production

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of antimicrobial peptides to study the mechanisms driving the dominance of one population over the
other. By using published experimental data, assumed to correspond to the observation of stable states
in our model, we reduce the number of parameters of the model from 13 to 5. We then use a formal
specification in quantitative temporal logic to calibrate our model by global parameter optimization and
perform sensitivity analyses. On the time scale of 2 days of the experiments, the model predicts that
certain changes of the environment, like the elevation of skin surface pH, create favorable conditions
for the emergence and colonization of the skin by the opportunistic pathogen population, while the
production of human AMPs has non-linear effect on the balance between pathogens and commensals.
Surprisingly, simulations on longer time scales reveal that the equilibrium reached around 2 days can in
fact be a quasi-stable state followed by the reaching of a reversed stable state after 12 days or more. We
analyse the conditions of quasi-stability observed in this model using tropical algebraic methods, and
show their non-generic character in contrast to slow-fast systems. These conditions are then generalized
to a large class of population dynamics models over any number of species.

In [8], this is combined with a multi-agent model of the skin for further analyses of the multifactorial

factors leading to atopic dermatitis.

8.3 On Estimating Derivatives of Input Signals in Biochemistry

Participants:

François Fages, Mathieu Hemery.

The online estimation of the derivative of an input signal is widespread in control theory and en-
gineering. In the realm of chemical reaction networks (CRN), this raises however a number of specific
issues on the different ways to achieve it. A CRN pattern for implementing a derivative block has already
been proposed for the PID control of biochemical processes, and proved correct using Tikhonov’s limit
theorem.

In [23], we give a detailed mathematical analysis of that CRN, thus clarifying the computed quantity
and quantifying the error done as a function of the reaction kinetic parameters. In a synthetic biology
perspective, we show how this can be used to design error correcting terms to compute online functions
involving derivatives with CRNs. In the systems biology perspective, we give the list of models in BioMod-
els containing (in the sense of subgraph epimorphisms) the core derivative CRN, most of which being
models of oscillators and control systems in the cell, and discuss in detail two such examples: one model
of the circadian clock and one model of a bistable switch.

This provides one more brick to our vision of the cell as an analog chemical computer, calculating

with proteins, and changing of programs according to gene expression [25].

8.4 Trap spaces of Boolean networks are conflict-free siphons of their Petri net

encoding

Participants:

Sylvain Soliman.

Boolean network modeling of gene regulation but also of post-transcriptomic systems has proven over
the years that it can bring powerful analyses and corresponding insight to the many cases where precise
biological data is not sufficiently available to build a detailed quantitative model. Besides simulation, the
analysis of such models is mostly based on attractor computation, since those correspond roughly to
observable biological phenotypes. The recent use of trap spaces made a real breakthrough in that field
allowing to consider medium-sized models that used to be out of reach. However, with the continuing
increase in model size and complexity of Boolean update functions, the state-of-the-art computation
of minimal trap spaces based on prime implicants shows its limits due to the difficulty of the prime-
implicant computation.

In [21], we explore and prove for the first time a connection between trap spaces of a general Boolean
network and siphons of its Petri net encoding. Besides important theoretical applications in studying

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properties of trap spaces, the connection enables us to propose an alternative approach to compute
minimal trap spaces, and hence complex attractors, of a general Boolean network. It replaces the need
for prime implicants by a completely different technique, namely the enumeration of maximal siphons in
the Petri net encoding of the original model. We then demonstrate its efficiency and compare it to the
state-of-the-art methods on a large collection of real-world and randomly generated models.

8.5 Efficient Enumeration of Fixed Points in Complex Boolean Networks Using

Answer Set Programming

Participants:

Sylvain Soliman.

Boolean Networks (BNs) are an efficient modeling formalism with applications in various research
fields such as mathematics, computer science, and more recently systems biology. One crucial problem in
the BN research is to enumerate all fixed points, which has been proven crucial in the analysis and control
of biological systems. Indeed, in that field, BNs originated from the pioneering work of R. Thomas on gene
regulation and from the start were characterized by their asymptotic behavior: complex attractors and
fixed points. The former being notably more difficult to compute exactly, and specific to certain biological
systems, the computation of stable states (fixed points) has been the standard way to analyze those
BNs for years. However, with the increase in model size and complexity of Boolean update functions,
the existing methods for this problem show their limitations. To our knowledge, the most efficient
state-of-the-art methods for the fixed point enumeration problem rely on Answer Set Programming
(ASP).

In [24], we propose two new efficient ASP-based methods to solve this problem. We evaluate them on
both real-world and pseudo-random models, showing that they vastly outperform four state-of-the-art
methods as well as can handle very large and complex models.

8.6 Hybrid computational modeling highlights reverse warburg effect in breast

cancer-associated fibroblasts

Participants:

Sahar Aghakhani, Anna Niarakis, Sylvain Soliman.

Cancer-associated fibroblasts (CAFs) are amongst the key players of the tumor microenvironment
(TME) and are involved in cancer initiation, progression, and resistance to therapy. They exhibit aggressive
phenotypes affecting extracellular matrix remodeling, angiogenesis, immune system modulation, tumor
growth, and proliferation. CAFs phenotypic changes appear to be associated with metabolic alterations,
notably a reverse Warburg effect that may drive fibroblasts transformation. However, its precise molecular
mechanisms and regulatory drivers are still under investigation. Deciphering the reverse Warburg effect
in breast CAFs may contribute to a better understanding of the interplay between TME and tumor cells,
leading to new treatment strategies. In this regard, dynamic modeling approaches able to span multiple
biological layers are essential to capture the emergent properties of various biological entities when
complex and intertwined pathways are involved.

In [17], we present the first hybrid large-scale computational model for breast CAFs covering major
cellular signaling, gene regulation, and metabolic processes. It was generated by combining a cell-and
diseasespecific asynchronous Boolean model with a generic core metabolic network leveraging both
data-driven and manual curation approaches. This model reproduces the experimentally observed
reverse Warburg effect in breast CAFs and further identifies Hypoxia-Inducible Factor 1 (HIF-1) as its key
molecular driver. Targeting HIF-1 as part of a TME-centered therapeutic strategy may prove beneficial
in the treatment of breast cancer by addressing the reverse Warburg effect. Such findings in CAFs, in
light of our previously published results in rheumatoid arthritis synovial fibroblasts, point to a common
HIF-1-driven metabolic reprogramming of fibroblasts in breast cancer and rheumatoid arthritis.

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Inria Annual Report 2023

In [27], this is generalized to computational modeling of metabolic reprogramming in both rheumat-

oid arthritis synovial fibroblasts and cancer associated fibroblasts.

8.7 A large-scale Boolean model of the rheumatoid arthritis fibroblast-like synovio-

cytes predicts drug synergies in the arthritic joint

Participants:

Anna Niarakis, Sylvain Soliman.

Rheumatoid arthritis (RA) is a complex autoimmune disease with an unknown aetiology. However,
rheumatoid arthritis fibroblast-like synoviocytes (RA-FLS) play a significant role in initiating and perpetu-
ating destructive joint inflammation by expressing immuno-modulating cytokines, adhesion molecules,
and matrix remodelling enzymes. In addition, RA-FLS are primary drivers of inflammation, displaying
high proliferative rates and an apoptosis-resistant phenotype. Thus, RA-FLS-directed therapies could
become a complementary approach to immune-directed therapies by predicting the optimal conditions
that would favour RA-FLS apoptosis, limit inflammation, slow the proliferation rate and minimise bone
erosion and cartilage destruction.

In [19], we present a large-scale Boolean model for RA-FLS that consists of five submodels focusing on
apoptosis, cell proliferation, matrix degradation, bone erosion and inflammation. The five-phenotype-
specific submodels can be simulated independently or as a global model. In silico simulations and
perturbations reproduced the expected biological behaviour of the system under defined initial condi-
tions and input values. The model was then used to mimic the effect of mono or combined therapeutic
treatments and predict novel targets and drug candidates through drug repurposing analysis.

8.8 Reactmine: a statistical search algorithm for inferring chemical reactions from

time series data

Participants:

François Fages, Julien Martinelli, Sylvain Soliman.

Inferring chemical reaction networks (CRN) from concentration time series is a challenge encouraged
by the growing availability of quantitative temporal data at the cellular level. This motivates the design
of algorithms to infer the preponderant reactions between the molecular species observed in a given
biochemical process, and build CRN structure and kinetics models. Existing ODE-based inference
methods such as SINDy resort to least square regression combined with sparsity-enforcing penalization,
such as Lasso. However, we observe that these methods fail to learn sparse models when the input time
series are only available in wild type conditions, i.e. without the possibility to play with combinations of
zeroes in the initial conditions.

In [31], we present a CRN inference algorithm which enforces sparsity by inferring reactions in a
sequential fashion within a search tree of bounded depth, ranking the inferred reaction candidates
according to the variance of their kinetics on their supporting transitions, and re-optimizing the kinetic
parameters of the CRN candidates on the whole trace in a final pass. We show that Reactmine succeeds
both on simulation data by retrieving hidden CRNs where SINDy fails, and on two real datasets, one of
fluorescence videomicroscopy of cell cycle and circadian clock markers, the other one of biomedical
measurements of systemic circadian biomarkers possibly acting on clock gene expression in peripheral
organs, by inferring preponderant regulations in agreement with previous model-based analyses. The
code is available at together with introductory notebooks. , a statistical constraint-based search algorithm
is introduced to learn chemical reaction networks from single time series data, and compared to state-of-
the-art SINDy algorithm to learn differential equations from time series data.

8.9 Neural-based classification rule learning for sequential data

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Participants: Marine Collery, François Fages.

Discovering interpretable patterns for classification of sequential data is of key importance for a variety

of fields, ranging from genomics to fraud detection or more generally interpretable decision-making.

In [22], we propose a novel differentiable fully interpretable method to discover both local and global
patterns (i.e. catching a relative or absolute temporal dependency) for rule-based binary classification. It
consists of a convolutional binary neural network with an interpretable neural filter and a training strategy
based on dynamically-enforced sparsity. We demonstrate the validity and usefulness of the approach on
synthetic datasets and on an open-source peptides dataset. Key to this end-to-end differentiable method
is that the expressive patterns used in the rules are learned alongside the rules themselves.

In [28], this approach is compared to other approaches to rule learning from sequential data.

9 Bilateral contracts and grants with industry

9.1 Bilateral contracts with industry

9.1.1 Institut de Recherches Servier

Participants:

François Fages, Alexandre Than-Lhernould, Mathieu Hemery, Ju-
lien Martinelli, Sylvain Soliman.

Cifre PhD thesis of Alexandre Than-Lhernould on "Inference of biological models from temporal data

for drug screening".

9.1.2 Johnson&Johnson Santé Beauté France

Participants:

François Fages, Elea Greugny, Mathieu Hemery, Sylvain Soliman.

Cifre PhD thesis of Eléa Thibault-Greugny on "Computational modeling approaches to multifactorial

aspects of atopic dermatitis" defended in 2022.

9.1.3 IBM research, France

Participants: Marine Collery, François Fages, Sylvain Soliman.

PhD thesis of Marine Collery at IBM France on "Expressive classification rule learning with an

emphasis on learning from sequential data."[28].

10 Partnerships and cooperations

10.1 International research visitors

10.1.1 Visits of international scientists

We had the visit of Corentin Briat, ETH Zurich, Switzerland.

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Inria Annual Report 2023

10.2 European initiatives

10.2.1 ERC Starting Grant BridgingScales

Participants: Guillaume Ballif, Henri Mermoz Kouyé, Jakob Ruess.

ERC Starting Grant BridgingScales - From single cells to microbial consortia: bridging the gaps
between synthetic circuit design and emerging dynamics of heterogeneous populations (2023-2028)
coord. Jakob Ruess.

10.3 National initiatives

10.3.1 ANR Project: Opt-MC

Participants: Guillaume Ballif, Henri Mermoz Kouyé, Jakob Ruess.

ANR Opt-MC - Optogenetic control of microbial communities (2023-2026) coord. Jakob Ruess, with

Frédéric Bonnans, Laurent Pfeiffer (DISCO).

10.3.2 ANR Project: Difference

Participants:

François Fages, Mathieu Hemery, Sylvain Soliman.

ANR δIFFERENCE on "Complexity theory with discrete ODEs", coordinated by Olivier Bournez, Ecole
Polytechnique, with F. Fages, F. Chyzak (MATEXP), A. Durand Univ. Paris-Diderot, F. Madelaine P. Valarché
Univ. Créteil, Jerôme Durand-Lose, Orléans -Moulay Barkatou Thomas Cluzeau, Limoges - Mathieu
Sablik, Toulouse.

11 Dissemination

Participants:

François Fages, Mathieu Hemery, Henri Mermoz Kouyé, Anna Niarakis,
Jakob Ruess, Sylvain Soliman.

11.1 Promoting scientific activities

11.1.1 Scientific events: organisation

Jakob Ruess was organizer of the department seminar series of the Department of Computational Biology
at Institut Pasteur.

General chair, scientific chair

• François Fages is chairman of the Steering Committee of CMSB international conference.

• Anna Niaraki was co-chair of the Digital Twin Symposium at Institut Pascal, Orsay.

Member of the organizing committees François Fages was member of the scientific committee of the
summer school Modélisation formelle de réseaux de régulation biologique, Ile de Porquerolles, 4-9 June
2023.

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Member of the conference program committees

• François Fages and Jakob Ruess were members of the program committee of CMSB 2023.

• Sylvain Soliman was member of the program committee of BIOINFORMATICS (BIOSTEC) 2023.

Reviewer

• Guillaume Ballif, François Fages, Henri Mermoz Kouyé and Jakob Ruess made reviews for CMSB

2023.

• Jakob Ruess made reviews for CDC 2023.

11.1.2 Journal

Member of the editorial boards François Fages is member of the Editorial board of Royal Society Open
Science

Reviewer - reviewing activities

• François Fages made reviews for JACM, RSOS.

• Mathieu Hemery made reviews for Natural Computing, RSOS, Acta Biotheoretica, PR Research.

• Jakob Ruess was reviewer for Automatica.

• Sylvain Soliman was a reviewer for JOMB, Bioinformatics, PLoS Computational Biology.

11.1.3 Invited talks

• Guillaume Ballif gave an invited presentation at the Mathematical Seminar of the Inria team

MUSCA.

• François Fages gave invited talks at the Thematic workshop on networks and biological models
inference, CIRM, Marseille, at the Digital Twin Symposium, Institut Pascal, Orsay, at the Scryer
Prolog meeting in Düsseldorf, Germany, and at the chemical reation network visioconference
meeting in Bangalore, India.

• Mathieu Hemery gave an invited talk at the Thematic workshop on networks and biological models

inference, CIRM, Marseille.

• Henri Mermoz Kouyé gave an invited presentation at the INRAE team MAIAGE.

• Jakob Ruess gave invited presentations at the 10th International Workshop on Applied Probabil-
ity (Thessaloniki, Greece), at the Second SOS Workshop on Self-organization in space and time
(Darmstadt, Germany), and in the Seminar on Biological Control Systems (online).

• Sylvain Soliman gave an invited talk at the Digital Twin Symposium, Institut Pascal, Orsay.

11.1.4 Scientific expertise

François Fages evaluated one CIFRE PhD thesis proposal, and was member of the jury for the recruitment
of one Maître de conférence at the University of Nice Alpes Provence côte d’azur.

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Inria Annual Report 2023

11.2 Teaching - Supervision - Juries

11.2.1 Teaching

• Summer school: François Fages gave two lectures at the summer school Modélisation formelle de

réseaux de régulation biologique, Ile de Porquerolles, 4-9 June 2023.

• Master 2: François Fages (coordinator 24h and teacher 12h), Jérôme Feret (12h) C2-19 - Biochemical

Programming, Master Parisien de Recherche en Informatique (MPRI), Paris.

• Cycle ingénieur Ecole Polytechnique, Master 1: François Fages (coordinator, 18h lectures, 14h TD),
Mathieu Hemery (6h TD) INF555 - Constraint-based Modeling and Algorithms for Decision Making
Problems Master Artificial Intelligence, Master Science and Technology, Ecole Polytechnique.

• Bachelor 3: François Fages (co-coordinator, 12h lectures), Mathieu Hemery (12h TD), CSE 307 -

Relational Programming, Ecole Polytechnique.

• Anna Niarakis is the pedagogical responsible for the Double Bachelor in Life Sciences and Inform-

atics at Paris Saclay University.

• François Fages supervised the Master 2 internship of Hugo Buscemi.

11.2.2 Juries

• François Fages was reviewer of the PhD theses of Albin Salazar (ENS Paris), Athénaïs Vaginay

(Nancy), and examiner of the PhD thesis of Maxime Mahout (UPSaclay).

• François Fages was member of the TAC committee of Mitsuhiro Odaka (Univ. Tokyo, Japan, and

Nantes).

• Jakob Ruess is member of the TAC committee of Emrys Reginato (Inria Grenoble).

• Jakob Ruess was examiner of the PhD thesis and defense of Kaan Ocal (University of Edinburgh).

• Sylvain Soliman was reviewer of the PhD thesis of Mitsuhiro Okada (SOKENDAI, Japan, and École
Centrale Nantes, France), member of the CRCN-TH 2023 (handicapped researcher) jury for Inria.

11.3 Popularization

11.3.1 Internal or external Inria responsibilities

Mathieu Hemery took the co-direction of the scientific mediation of the Centre de Saclay along with Irene
Vignon-Clementel (EPI SIMBIOTX).

11.3.2 Articles and contents

François Fages published a large audience chapter on the foundations of biochemical programming
which makes of programming a natural science... [25].

11.3.3 Education

François Fages received five 12yo scool students to investigate the question of whether one can program
any function on the integer numbers ? And showing that the answer is negative by proving Cantor’s
theorem using diagonal argument, an easy proof of a hard theorem they can redo to their Professor of
Mathematics.

11.3.4 Interventions

Mathieu Hemery participated to the "Fête de la Science" on the IPP campus.

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12 Scientific production

12.1 Major publications

[1] S. Aghakhani, S. Soliman and A. Niarakis. ‘Metabolic Reprogramming in Rheumatoid Arthritis
Synovial Fibroblasts: a Hybrid Modeling Approach’. In: PLoS Computational Biology 18.12 (12th Dec.
2022), e1010408. DOI: 10.1371/journal.pcbi.1010408. URL: https://inria.hal.science
/hal-03880870.

[2] A. Baudier, F. Fages and S. Soliman. ‘Graphical Requirements for Multistationarity in Reaction
Networks and their Verification in BioModels’. In: Journal of Theoretical Biology 459 (14th Dec.
2018), pp. 79–89. DOI: 10.1016/j.jtbi.2018.09.024. URL: https://hal.archives-ouverte
s.fr/hal-01879735.

[3] A. Courbet, P. Amar, F. Fages, E. Renard and F. Molina. ‘Computer-aided biochemical programming
of synthetic microreactors as diagnostic devices’. In: Molecular Systems Biology 14.4 (26th Apr.
2018). DOI: 10.15252/msb.20177845. URL: https://hal.inria.fr/hal-01779791.

[4] E. Degrand, F. Fages and S. Soliman. ‘Graphical Conditions for Rate Independence in Chemical
Reaction Networks’. In: Proceedings CMSB 2020: The 18th International Conference on Computa-
tional Methods in Systems Biology. CMSB 2020 - 18th International Conference on Computational
Methods in Systems Biology. Konstanz / Online, Germany, 23rd Sept. 2020. URL: https://hal.in
ria.fr/hal-02900758.

[5] F. Fages, S. Gay and S. Soliman. ‘Inferring reaction systems from ordinary differential equations’.
In: Theoretical Computer Science 599 (Sept. 2015), pp. 64–78. DOI: 10.1016/j.tcs.2014.07.032.
URL: https://hal.inria.fr/hal-01103692.

[6] F. Fages, G. Le Guludec, O. Bournez and A. Pouly. ‘Strong Turing Completeness of Continuous
Chemical Reaction Networks and Compilation of Mixed Analog-Digital Programs’. In: CMSB 2017 -
15th International Conference on Computational Methods in Systems Biology. Vol. Lecture Notes
in Computer Science. Proceedings of the fiveteen international conference on Computational
Methods in Systems Biology, CMSB 2017 10545. Darmstadt, Germany, 27th Sept. 2017, pp. 108–127.
URL: https://hal.inria.fr/hal-01519828.

[7] F. Fages, T. Martinez, D. A. Rosenblueth and S. Soliman. ‘Influence Networks compared with
Reaction Networks: Semantics, Expressivity and Attractors’. In: IEEE/ACM Transactions on Compu-
tational Biology and Bioinformatics PP.99 (2018), pp. 1–14. DOI: 10.1109/TCBB.2018.2805686.
URL: https://hal.inria.fr/hal-01510216.

[8] E. Greugny. ‘Computational modeling approaches to multifactorial aspects of atopic dermatitis’.
Institut Polytechnique de Paris, 4th Oct. 2022. URL: https://pastel.hal.science/tel-03906
902.

[9]

J. Grignard, V. Lamamy, E. Vermersch, P. Delagrange, J.-P. Stephan, T. Dorval and F. Fages. ‘Mathem-
atical modeling of the microtubule detyrosination/tyrosination cycle for cell-based drug screening
design’. In: PLoS Computational Biology 18.6 (27th June 2022), e1010236. DOI: 10.1371/journal
.pcbi.1010236. URL: https://inria.hal.science/hal-03707809.

[10] M. Hemery and F. Fages. ‘Algebraic Biochemistry: a Framework for Analog Online Computation in
Cells’. In: CMSB 2022 - International Conference on Computational Methods in Systems Biology.
Bucarest, Romania, 14th Sept. 2022. URL: https://hal.science/hal-03696273.

[11]

J. Martinelli. ‘On learning mechanistic models from time series data with applications to personal-
ised chronotherapies’. Institut Polytechnique de Paris, 18th Feb. 2022. URL: https://theses.hal
.science/tel-03686289.

[12] F. Nabli, T. Martinez, F. Fages and S. Soliman. ‘On Enumerating Minimal Siphons in Petri nets
using CLP and SAT solvers: Theoretical and Practical Complexity’. In: Constraints 21.2 (Apr. 2016),
pp. 251–276. DOI: 10.1007/s10601-015-9190-1. URL: https://hal.archives-ouvertes.fr
/hal-01170962.

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Inria Annual Report 2023

[13] M. Ostaszewski, A. Niarakis, A. Mazein, I. Kuperstein, R. Phair, A. Orta-Resendiz, V. Singh, S. S.
Aghamiri, M. L. Acencio, E. Glaab et al. ‘COVID19 Disease Map, a computational knowledge
repository of virus-host interaction mechanisms’. In: Molecular Systems Biology 17.10 (1st Oct.
2021), e10387. DOI: 10.15252/msb.202110387. URL: https://hal.inria.fr/hal-03385317.

[14] E. Thibault Greugny, J. Bensaci, F. Fages and G. Stamatas. ‘Computational modeling predicts
impaired barrier function and higher sensitivity to skin inflammation following pH elevation’. In:
Experimental Dermatology (2nd Nov. 2022). DOI: 10.1111/exd.14698. URL: https://inria.ha
l.science/hal-03909897.

[15] P. Traynard, A. Fauré, F. Fages and D. Thieffry. ‘Logical model specification aided by model- checking
techniques: application to the mammalian cell cycle regulation’. In: Bioinformatics 32.17 (2016),
pp. i772–i780. DOI: 10.1093/bioinformatics/btw457. URL: https://hal.archives-ouvert
es.fr/hal-01378465.

[16] P. Traynard, C. Feillet, S. Soliman, F. Delaunay and F. Fages. ‘Model-based investigation of the
circadian clock and cell cycle coupling in mouse embryonic fibroblasts: Prediction of RevErb-α
up-regulation during mitosis’. In: BioSystems 149 (Nov. 2016), pp. 59–69. DOI: 10.1016/j.biosys
tems.2016.07.003. URL: https://hal.archives-ouvertes.fr/hal-01378466.

12.2 Publications of the year

International journals

[17] S. Aghakhani, S. E. Silva-Saffar, S. Soliman and A. Niarakis. ‘Hybrid computational modeling
highlights reverse warburg effect in breast cancer-associated fibroblasts’. In: Computational and
Structural Biotechnology Journal 21 (19th Aug. 2023), pp. 4196–4206. DOI: 10.1016/j.csbj.2023
.08.015. URL: https://inria.hal.science/hal-04192259.

[18]

J. Ruess, G. Ballif and C. Aditya. ‘Stochastic chemical kinetics of cell fate decision systems: from
single cells to populations and back’. In: Journal of Chemical Physics 159.18 (8th Nov. 2023). DOI:
10.1063/5.0160529. URL: https://inria.hal.science/hal-04275681.

[19] V. Singh, A. Naldi, S. Soliman and A. Niarakis. ‘A large-scale Boolean model of the rheumatoid
arthritis fibroblast-like synoviocytes predicts drug synergies in the arthritic joint’. In: npj Systems
Biology and Applications 9.33 (15th July 2023). DOI: 10 . 1038 / s41540 - 023 - 00294 - 5. URL:
https://inria.hal.science/hal-04187223.

[20] E. Thibault Greugny, F. Fages, O. Radulescu, P. Szmolyan and G. Stamatas. ‘A Skin Microbiome
Model with AMP interactions and Analysis of Quasi-Stability vs Stability in Population Dynamics’.
In: Theoretical Computer Science (Nov. 2023), p. 114294. DOI: 10.1016/j.tcs.2023.114294. URL:
https://inria.hal.science/hal-04253028.

[21] V.-G. Trinh, B. Benhamou and S. Soliman. ‘Trap spaces of Boolean networks are conflict-free siphons
of their Petri net encoding’. In: Theoretical Computer Science 971 (6th Sept. 2023), p. 114073. DOI:
10.1016/j.tcs.2023.114073. URL: https://amu.hal.science/hal-04167028.

International peer-reviewed conferences

[22] M. Collery, P. Bonnard, F. Fages and R. Kusters. ‘Neural-based classification rule learning for se-
quential data’. In: ICLR 2023 - The Eleventh International Conference on Learning Representations.
Kigali, Rwanda, 22nd Feb. 2023. DOI: 10.48550/arXiv.2302.11286. URL: https://inria.hal
.science/hal-04093337.

[23] M. Hemery and F. Fages. ‘On Estimating Derivatives of Input Signals in Biochemistry’. In: CMSB
2023 - 21st International Conference on Computational Methods in Systems Biology. Luxembourg
City, Luxembourg, 2023. URL: https://inria.hal.science/hal-04154923.

Project LIFEWARE

21

[24] V.-G. Trinh, B. Benhamou and S. Soliman. ‘Efficient Enumeration of Fixed Points in Complex
Boolean Networks Using Answer Set Programming’. In: 29th International Conference on Principles
and Practice of Constraint Programming (CP 2023). Vol. 280. Toronto, Canada: Schloss Dagstuhl -
Leibniz-Zentrum für Informatik, 22nd Sept. 2023, 35:1–35:19. DOI: 10.4230/LIPIcs.CP.2023.35.
URL: https://amu.hal.science/hal-04209296.

Scientific book chapters

[25] F. Molina and F. Fages. ‘The Cell: A Chemical Analog Calculator’. In: Symbolic Approaches to Model-
ing and Analysis of Biological Systems. 1. Wiley, 10th Aug. 2023. DOI: 10.1002/9781394229086.ch
7. URL: https://hal.science/hal-04310569.

Edition (books, proceedings, special issue of a journal)

[26] C. Frioux, S. Huet, S. Labarthe, J. Martinelli, T. Malou, D. Sherman, M.-L. Taupin and P. Ugalde-
Salas, eds. Accelerating metabolic models evaluation with statistical metamodels: application to
Salmonella infection models. Vol. 73. CEMRACS 2021 - Data Assimilation and Reduced Modeling for
High Dimensional Problems. EDP Sciences, 2023, pp. 187–217. DOI: 10.1051/proc/202373187.
URL: https://hal.inrae.fr/hal-03635862.

Doctoral dissertations and habilitation theses

[27] S. Aghakhani. ‘Computational modeling of metabolic reprogramming in rheumatoid arthritis
synovial fibroblasts and cancer- associated fibroblasts’. Université Paris-Saclay, 22nd Sept. 2023.
URL: https://theses.hal.science/tel-04224645.

[28] M. Collery. ‘Expressive classification rule learning with an emphasis on learning from sequential
data’. Institut Polytechnique de Paris, 12th Oct. 2023. URL: https://theses.hal.science/tel-
04415715.

Reports & preprints

[29] V. Bansaye, P.-Y. Boëlle, S. Cauchemez, B. Cazelles, P. Crepey, L. Cristancho-Fajardo, J.-S. Dhersin,
S. Dumitrescu, P. Ezanno, A. Flahault, J.-L. Golmard, P. Hoscheit, H. Mermoz Kouye, M. Kubasch,
C. Laredo, A. Mallet, L. Opatowski, C. Prieur and A. Véber. Hommage à Elisabeta Vergu. 29th Nov.
2023. URL: https://hal.science/hal-04230307.

[30] M. Hemery and F. Fages. On a model of online analog computation in the cell with absolute func-
tional robustness: algebraic characterization, function compiler and error control. 21st Oct. 2023.
URL: https://inria.hal.science/hal-04252933.

[31]

J. Martinelli, J. Grignard, S. Soliman, A. Ballesta and F. Fages. Reactmine: a statistical search al-
gorithm for inferring chemical reactions from time series data. 21st Jan. 2023. URL: https://hal.s
cience/hal-03769872.
<scratchpad>
- Main topic: Inria Saclay Centre's LIFEWARE project, focusing on computational systems biology and optimization
- Key points:
  - Developing formal methods for understanding cell machinery and establishing computational paradigms in cell biology
  - Research on chemical reaction networks (CRNs), logical paradigms for systems biology, and constraint solving and optimization
  - Application domains include digital health, biology, and earth; biomedical applications include drug screening design, patient-tailored chronotherapies, and synthetic biosensor design
  - Social and environmental responsibility includes considering the footprint of research activities and impact of research results
- Structure: The document is divided into sections for project-team members, activity report, domain, theme, contents, highlights of the year, new software, platforms, open data, and more
- Potential artifacts to ignore: web-related elements such as links, HTML tags, and other web-related elements
</scratchpad>

<final_summary>
The LIFEWARE project, led by Inria Saclay Centre, focuses on developing formal methods for understanding cell machinery and establishing computational paradigms in cell biology. The project involves research on chemical reaction networks (CRNs), logical paradigms for systems biology, and constraint solving and optimization. The project aims to apply its findings to various domains, including digital health, biology, and earth, as well as biomedical applications such as drug screening design, patient-tailored chronotherapies, and synthetic biosensor design. The project also considers its social and environmental responsibility, including the footprint of research activities and impact of research results. Overall, the LIFEWARE project seeks to advance our understanding of cell biology and its applications in various fields.
The LIFEWARE project, led by Inria Saclay Centre, focuses on developing formal methods for understanding cell machinery and establishing computational paradigms in cell biology. The project involves research on chemical reaction networks (CRNs), logical paradigms for systems biology, and constraint solving and optimization. The project aims to apply its findings to various domains, including digital health, biology, and earth, as well as biomedical applications such as drug screening design, patient-tailored chronotherapies, and synthetic biosensor design. The project also considers its social and environmental responsibility, including the footprint of research activities and impact of research results. Overall, the LIFEWARE project seeks to advance our understanding of cell biology and its applications in various fields.