2025Activity​​ reportProject-TeamLIFEWARE

3.1​‌ Chemical Reaction Network (CRN)​​ Theory

Originally, Feinberg's mathematical​​​‌ theory of Chemical Reaction​ Networks (CRN), and Thomas's​‌ theory of influence networks,​​ were created to provide​​​‌ sufficient and/or necessary structural​ conditions for various dynamical​‌ properties such as perfect​​ adaptation, existence of multiple​​​‌ steady states, or of​ oscillations in complex interaction​‌ 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 interpretation​1. For example,​‌ our study of the​​ influence graphs of reaction​​​‌ systems 8 lead recently​ to sufficient graphical conditions​‌ ensuring rate-independence of CRN​​ 5, 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​‌ 3.

However, we​​ aim at development a​​​‌ computational theory of CRNs​ and biochemical programming. Our​‌ original method to infer​​ CRNs from ODEs 6​​​‌ 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 7 (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 mathematical functions. This​​​‌ is illustrated by a​ series of publications since​‌ that date 12 (best​​ paper award CMSB 2022),​​​‌ again this year 24​ , and innovative applications​‌ in synthetic biology 4​​.

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 $\mathrm{K‌}$, initial state $\mathrm{s}$

dynamical behavior specification =​​​‌ temporal logic formula $\mathrm{\varphi }$

model validation = model-checking​‌ $K,s\vDash ?\varphi$

model reduction​​ = sub-model-checking, $K^{\text{'}‌}\subset K$ s.t. ${\mathrm{K‌}}^{\text{'}}?,\mathrm{s‌‌}\vDash \varphi$

model prediction​​ = formula enumeration, $\mathrm{\varphi ‌}$ s.t. $K,\mathrm{s‌}\vDash \varphi ?$

static‌​‌ experiment design = symbolic​​ model-checking, $K,\mathrm{s‌}?\vDash \varphi$

model‌ synthesis = constraint solving‌​‌ $K?,\mathrm{s}\vDash \varphi$

model inference​​​‌ = constraint solving $\mathrm{K‌}?,s?‌‌\vDash \varphi$

In particular,​​ the definition of a​​​‌ continuous satisfaction degree for‌ first-order temporal logic formulae‌​‌ 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​​​‌ 34

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 domain‌5622,‌​‌ 21 and the pharmaceutical​​ industry 11, 20​​​‌, 19.

3.3‌ Constraint Logic Programming and‌​‌ Optimization

Constraint logic programming,​​ knowledge representation, 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 magnitude7‌​‌15, 23.​​

On the other hand,​​​‌ parameter search problems involve‌ solving hard continuous optimization‌​‌ problems, 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.

Furthermore, the paradigm​​​‌ of Logic Programming provides‌ the unavoidable foundations of‌​‌ knowledge representation systems, deductive​​ databases and argumentation frameworks,​​​‌ which are of particuler‌ relevance to systems biology,‌​‌ and the subject of​​ two of our courses​​​‌ at école polytechnique.

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. It is also​​ used in order to​​​‌ build hybrid models of​ metabolic networks and gene​‌ regulation in the MetaLo​​ platform.

Since 2020, we​​​‌ participate in the CoLoMoTo​ notebook platform, which​‌ provides an integrated collection​​ 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.

Our​‌ successful collaboration with Servier​​ on knowledge graphs and​​​‌ CRN model parameterizations using​ quantitative temporal logic for​‌ drug screening 11 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 19​, 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 dynamical models​​ 20.

As mentioned​​​‌ above, our long-standing collaboration​ with F. Molina's CNRS-ALCEN​‌ lab on the design,​​ optimization and industrialization of​​​‌ biochemical diagnosis vesicles 4​ continues, on our side,​‌ on a design methodology​​ of robust CRNs including​​​‌ analog functions and derivative​ estimation 13.

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 14​​​‌, 22 and their​ systemic regulators8.​‌

Thanks to the initiatives​​ of Anna Nirakis, first​​ during several years of​​​‌ "Delegation", our group became‌ very active in cooperative‌​‌ efforts at the European​​ scale to design large​​​‌ molecular interaction maps for‌ diseases such as Covid-19‌​‌ 16 or rheumatoid arthritis​​ 18 and derive from​​​‌ them Boolean dynamical models‌ using the CaSQ software‌​‌ as well as developing​​ hybrid models 1.​​​‌

5.1 Footprint of‌​‌ research activities

In contrast​​ to machine learning approaches​​​‌ to the solving of‌ digital health questions, our‌​‌ approach based on model​​ building or model inference​​​‌ do not use any‌ high energy consumption training‌​‌ process.

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 pharma industry.

6.1 Anna Niarakis's PEPR‌​‌ Santé Numérique DigiTREAT accepted​​ !

The PEPR Santé​​​‌ numérique project DigiTREAT,‌ "Building a Digital Twin‌​‌ for the personalised treatment​​ of RhEumatoid ArthriTis", accepted​​​‌ in December, was prepared‌ and submitted by Anna‌​‌ Niarakis during her secondment​​ for research with us​​​‌ in 2023-2024.

Rheumatoid Arthritis‌ (RA) is an autoimmune‌​‌ disease leading to joint​​ destruction. Despite the existence​​​‌ of 14 approved drugs,‌ up to 40% of‌​‌ patients do not respond​​ adequately to treatment, and​​​‌ no reliable biomarkers are‌ available to predict therapeutic‌​‌ responses. This emphasises the​​ need for new approaches​​​‌ to improve treatment personalisation.‌ Recent advances have brought‌​‌ medical digital twins (MDTs)​​ to the forefront, as​​​‌ they can integrate and‌ analyse complex, heterogeneous data.‌​‌ MDTs could enable the​​ development of personalised care​​​‌ by offering testable hypotheses.‌ The project DigiTREAT aims‌​‌ to develop the first​​ medical digital twin for​​​‌ RA (RA-DT), building on‌ recent advances in bioinformatics‌​‌ and computational biology. The​​ DigiTREAT project aims to​​​‌ create patient-specific models that‌ provide insights into disease‌​‌ heterogeneity and treatment responses.​​ Key objectives of DigiTREAT​​​‌ include:

1. Developing a multiscale,‌ multicellular model to simulate‌​‌ the interaction between immune​​ cells in the blood​​​‌ and resident cells in‌ the joints, aiming to‌​‌ identify biomarkers predicting disease​​ progression and treatment response.​​​‌
2. Testing predictions in a‌ mouse model of RA‌​‌ (collagen-induced arthritis, CIA) using​​ advanced spatial omics technologies.​​​‌
3. Validating the model’s predictions‌ and biomarkers through single-cell‌​‌ RNA sequencing (scRNAseq) in​​ blood samples from a​​​‌ cohort of RA patients‌ treated with three primary‌​‌ RA drugs (etanercept, tocilizumab,​​ and rituximab).
4. Linking predicted​​​‌ biomarkers to clinical symptoms‌ and systemic disease manifestations‌​‌ in patients.

The RA-DT​​ aims to provide personalised​​​‌ treatment recommendations by integrating‌ patient data and predicted‌​‌ treatment responses. A virtual​​ environment will be developed​​​‌ to host the RA-DT,‌ enabling data visualisation, simulation,‌​‌ and integration with clinical​​ data, making it accessible​​​‌ across clinical and preclinical‌ settings.

6.2 François Fages‌​‌ recipient of the Skolem​​​‌ award of the 30th​ Int. Conf. on Automated​‌ Deduction

The Skolem award​​ is a Best Paper​​​‌ Award delivered each year​ by the CADE conference​‌ since 2014, and a​​ Test of Time Award​​​‌ for the best papers​ of the first CADE​‌ conferences. This year, the​​ Test of Time Award​​​‌ was delivered at the​ 30th Conf. CADE 2025​‌ in Stuttgart for my​​ article at the 7th​​​‌ CADE Conf. in Napa​ Valley, 1984:

François Fages.​‌ Associative-Commutative Unification. In​​ Proc. of seventh Conf.​​​‌ on Automated Deduction CADE​ 1984, LNCS vol. 170.​‌ Springer-Verlag, 1984. (INRIA​​ Research Report).

In​​​‌ that article, I solved​ positively the conjecture of​‌ Peterson and Stickel about​​ the termination of Stickel's​​​‌ algorithm (1975) for solving​ equality constraints between terms​‌ in presence of associative-commutative​​ operators, a central problem​​​‌ for term rewriting systems.​ For this, I used​‌ a relatively simple complexity​​ measure which appears to​​​‌ be still a challenge​ for automated theorem provers.​‌

That was my second​​ participation to an international​​​‌ conference and the second​ major result of my​‌ PhD thesis defended one​​ year before at age​​​‌ 23. It was the​ time of pioneers in​‌ Computer Science and I​​ had the chance to​​​‌ meet at that conference,​ big names of mathematical​‌ logic and computer science​​ like Martin Davis, Youri​​​‌ Matiyasevich, Robert Shostak, Woody​ Bledsoe, Joe Goguen, and​‌ also to make various​​ memorizable visits with my​​​‌ PhD supervisor Gérard Huet.​

At the time of​‌ 2 years impact factor​​ measures, short term communications​​​‌ and electronic publications, I​ believe it is important​‌ to recall the importance​​ of the long term​​​‌ in research, and of​ the preservation of printed​‌ books in libraries.

7.1 Latest​ software developments

8.1 On BIOCHAM​​​‌ symbolic computation pipeline for‌ compiling mathematical functions into‌​‌ biochemistry

Participants: François Fages​​, Mathieu Hemery,​​​‌ Sylvain Soliman.

Chemical‌ Reaction Networks (CRNs) are‌​‌ a standard formalism used​​ in chemistry and biology​​​‌ to model complex molecular‌ interaction systems. In the‌​‌ perspective of systems biology,​​ they are a central​​​‌ tool to analyze the‌ high-level functions of the‌​‌ cell in terms of​​ their low-level molecular interactions.​​​‌ In the perspective of‌ synthetic biology, they constitute‌​‌ a target programming language​​ to implement in chemistry​​​‌ new functions either in‌ vitro, in artificial vesicles,‌​‌ or in living cells.​​

In 24, we​​​‌ describe the CRN synthesis‌ tool part of our‌​‌ CRN modeling and analysis​​ software BIOCHAM (Biochemical Abstract​​​‌ Machine). This compiler transforms‌ any elementary (resp. algebraic)‌​‌ real function into a​​ formal finite CRN to​​​‌ compute it (resp. with‌ absolute functional robustness), through‌​‌ a pipeline of symbolic​​​‌ computation steps, among which​ quadratization optimization plays a​‌ key role to restrict​​ to elementary reactions with​​​‌ at most two reactants​ and a minimum number​‌ of molecular species.

8.2​​ Graphical conditions ensuring equality​​​‌ between differential and mean​ stochastic dynamics

Participants: Guillaume​‌ Ballif, François Fages​​, Mathieu Hemery,​​​‌ Hélène Siboulet, Sylvain​ Soliman.

Complex systems​‌ can be advantageously modeled​​ by formal reaction systems​​​‌ (RS), a.k.a. chemical reaction​ networks in chemistry. Reaction-based​‌ models can indeed be​​ interpreted in a hierarchy​​​‌ of semantics, depending on​ the question at hand,​‌ most notably by Ordinary​​ Differential Equations (ODEs), Continuous​​​‌ Time Markov Chains (CTMCs),​ discrete Petri nets and​‌ asynchronous Boolean transition systems.​​ The last three semantics​​​‌ can be easily related​ in the framework of​‌ abstract interpretation. The first​​ two are classically related​​​‌ by Kurtz’s limit theorem​ which states that if​‌ reactions are density-dependent families,​​ then, as the volume​​​‌ goes to infinity, the​ mean reactant concentrations of​‌ the CTMC tends towards​​ the solution of the​​​‌ ODE. In the more​ realistic context of bounded​‌ volumes, it is easy​​ to show, by moment​​​‌ closure, that the restriction​ to reactions with at​‌ most one reactant ensures​​ similarly that the mean​​​‌ of the CTMC trajectories​ is equal to the​‌ solution of the ODE​​ at all time points.​​​‌

In a poster presented​ at CMSB 2025 30​‌, revisiting our previous​​ publication at CMSB 2024,​​​‌ we generalize that result​ in presence of polyreactant​‌ reactions, by introducing the​​ Stoichiometric Influence and Modification​​​‌ Graph (SIMG) of an​ RS, and by showing​‌ that the equality between​​ the two interpretations holds​​​‌ for the variables that​ belong to distinct SIMG​‌ ancestors of polyreactant reactions.​​ We illustrate this approach​​​‌ with several examples. Evaluation​ on BioModels reveals that​‌ the condition for all​​ variables is satisfied on​​​‌ models with no polymolecular​ reaction only. However, our​‌ theorem can be applied​​ selectively to certain variables​​​‌ of the model to​ provide insights into their​‌ behaviour within more complex​​ systems. Interestingly, we also​​​‌ show that the equality​ holds for a basic​‌ oscillatory RS implementing the​​ sine and cosine functions​​​‌ of time.

8.3 A​ partition method for bounding​‌ continuous-time Markov chain models​​ of general reaction network​​​‌

Participants: Guillaume Ballif,​ Jakob Ruess.

In​‌ 31, we present​​ a general method to​​​‌ establish properties of multi-dimensional​ continuous-time Markov chains representing​‌ stochastic reaction networks. This​​ method consists of grouping​​​‌ states together (via a​ partition of the state​‌ space), then constructing two​​ one-dimensional birth and death​​​‌ processes that lower and​ upper bound the initial​‌ process under simple assumptions​​ on the infinitesimal generators​​​‌ of the processes. The​ construction of these bounding​‌ processes is based on​​ coupling arguments and transport​​​‌ theory. The bounding processes​ are easy to analyse​‌ analytically and numerically and​​ allow us to derive​​​‌ properties on the initial​ continuous-time Markov chain. We​‌ focus on two important​​ properties: the behavior of​​​‌ the process at infinity​ through the existence of​‌ a stationary distribution and​​ the error in truncating​​ the state space to​​​‌ numerically solve the master‌ equation describing the time‌​‌ evolution of the probability​​ distribution of the process.​​​‌ We derive explicit formulas‌ for constructing the optimal‌​‌ bounding processes for a​​ given partition, making the​​​‌ method easy to use‌ in practice. We finally‌​‌ discuss the importance of​​ the choice of the​​​‌ partition to obtain relevant‌ results and illustrate the‌​‌ method on two examples​​ of chemical reaction network.​​​‌

8.4 Local sensitivity analysis‌ for stochastic reaction networks‌​‌ in growing cell populations​​

Participants: Henri Mermoz Kouye​​​‌, Jakob Ruess.‌

Stochastic chemical reaction networks‌​‌ operating inside single cells​​ are typically modeled as​​​‌ continuous-time Markov chains (CTMCs)‌ that track the number‌​‌ of molecules of the​​ reacting chemical species. CTMC​​​‌ models have been tremendously‌ helpful tools in the‌​‌ last decades to help​​ understand natural single-cell processes​​​‌ and to guide the‌ synthetic design of new‌​‌ gene circuits. Eventually, however,​​ the important question that​​​‌ needs to be answered‌ is how such stochastic‌​‌ single-cell models can be​​ tied back to the​​​‌ real context of living‌ cells, i.e. that of‌​‌ growing populations or microbial​​ communities. In recent years,​​​‌ first attempts have been‌ made to extend single-cell‌​‌ models to the population​​ scale but many of​​​‌ the tools for model‌ development and analysis that‌​‌ are established for pure​​ single-cell models have not​​​‌ been extended to a‌ multiscale context.

In 32‌​‌, we show how​​ stochastic models that couple​​​‌ single-cell and population dynamics‌ can be defined as‌​‌ measure-valued CTMCs and how​​ a random-time change (RTC)​​​‌ representation for such models‌ can be formulated. We‌​‌ then utilize the RTC​​ representation to propose generalizations​​​‌ to the multiscale context‌ of several classical methods‌​‌ for local sensitivity estimation​​ such as the finite-difference​​​‌ method with various couplings‌ and the Girsanov method.‌​‌ Our methods are demonstrated​​ on a tutorial case​​​‌ study and then utilized‌ to study a model‌​‌ of a bistable toggle​​ switch for which single-cell​​​‌ growth rates depend on‌ the stable state that‌​‌ cells occupy.

8.5 Bifurcation​​ analysis and optimal control​​​‌ of an infection age-structured‌ epidemic model with vaccination‌​‌ and treatment

Participants: Jakob​​ Ruess, Josue Tchouanti​​​‌.

In 25,‌ we address the problem‌​‌ of bifurcation analysis and​​ optimal control of an​​​‌ age-structured epidemic model. We‌ first develop and analyze‌​‌ a deterministic epidemiological model​​ for the transmission of​​​‌ an infectious disease described‌ by a susceptible–vaccinated–infected (SVI).‌​‌ The model takes into​​ account the age structure​​​‌ of the infection, imperfect‌ vaccination and treatment of‌​‌ infected people who become​​ susceptible again. The model​​​‌ without intervention strategies is‌ completely analyzed. We compute‌​‌ the disease-free equilibrium and​​ derive the basic reproduction​​​‌ number R0. The analysis‌ of the model reveals‌​‌ the existence of the​​ phenomenon of backward bifurcation,​​​‌ where a stable disease-free‌ equilibrium coexists with one‌​‌ or more stable endemic​​ equilibria when the associated​​​‌ basic reproduction number is‌ less than unity. In‌​‌ this case, controlling this​​ infectious disease remains extremely​​​‌ difficult. Therefore, based on‌ this continuous model, a‌​‌ control is formulated and​​​‌ solved as an optimal​ control theory problem, indicating​‌ how control terms on​​ vaccination and treatment should​​​‌ be introduced in the​ population to minimize the​‌ number of infected individuals​​ as well as the​​​‌ cost of applying controls.​ We establish the first-order​‌ necessary conditions for optimality​​ and characterize optimal controls.​​​‌ The theory is supported​ by numerical simulations, which​‌ further suggest that the​​ combination of intervention measures​​​‌ can significantly reduce the​ transmission dynamics of infectious​‌ diseases by keeping the​​ population of infected individuals​​​‌ relatively low.

8.6 Evolution​ of a trait distributed​‌ over a large fragmented​​ population: Propagation of chaos​​​‌ meets adaptive dynamics

Participants:​ Josue Tchouanti.

In​‌ 33, we consider​​ a metapopulation made up​​​‌ of $K$ demes, each​ containing $N$ individuals bearing​‌ a heritable quantitative trait.​​ Demes are connected by​​​‌ migration and undergo independent​ Moran processes with mutation​‌ and selection based on​​ trait values. Mutation and​​​‌ migration rates are tuned​ so that each deme​‌ receives a migrant or​​ a mutant in the​​​‌ same slow timescale and​ is thus essentially monomorphic​‌ at all times for​​ the trait (adaptive dynamics).​​​‌

In the timescale of​ mutation/migration, the metapopulation can​‌ then be seen as​​ a giant spatial Moran​​​‌ model with size $\mathrm{K}$ that we characterize. As​‌ $K\to \infty$ and​​ physical space becomes continuous,​​​‌ the empirical distribution of​ the trait (over the​‌ physical and trait spaces)​​ evolves deterministically according to​​​‌ an integro-differential evolution equation.​ In this limit, the​‌ trait of every migrant​​ is drawn from this​​​‌ global distribution, so that​ conditional on its initial​‌ state, traits from finitely​​ many demes evolve independently​​​‌ (propagation of chaos).

Under​ mean-field dispersal, the value​‌ $X_t$ of the​​ trait at time $\mathrm{t‌}$ and at any given​ location has a law​‌ denoted ${\mu }_t$ and​​ a jump kernel with​​​‌ two terms: a mutation-fixation​ term and a migration-fixation​‌ term involving ${\mu }_{\mathrm{t}-}$ (McKean-Vlasov equation).

In​​​‌ the limit where mutations​ have small effects and​‌ migration is further slowed​​ down accordingly, we obtain​​​‌ the convergence of $\mathrm{X}$, in the new​‌ migration timescale, to the​​ solution of a stochastic​​​‌ differential equation which can​ be referred to as​‌ a new canonical equation​​ of adaptive dynamics. This​​​‌ equation includes an advection​ term representing selection, a​‌ diffusive term due to​​ genetic drift, and a​​​‌ jump term, representing the​ effect of migration, to​‌ a state distributed according​​ to its own law.​​​‌

8.7 DigiDermA: Modeling Cellular​ and Molecular Interactions in​‌ Atopic Dermatitis

Participants: Anna​​ Niarakis, Sylvain Soliman​​​‌.

In 35,​ we analyzed publicly available​‌ single-cell RNA sequencing data​​ (GEO accession GSE147424) derived​​​‌ from human skin biopsies​ of lesional and non-lesional​‌ atopic dermatitis samples and​​ healthy controls. This dataset​​​‌ provided the basis for​ characterizing the cellular composition​‌ and identifying differentially expressed​​ genes between conditions. Functional​​​‌ enrichment analysis using KEGG​ pathways was performed to​‌ map gene expression changes​​ to biological processes. The​​​‌ results of this analysis​ were integrated into a​‌ conceptual model of the​​ skin microenvironment, which served​​ as the basis for​​​‌ computational modeling carried out‌ using the SimuScale modeling‌​‌ platform.

8.8 Generative modeling​​ of gene regulatory networks​​​‌

Participants: François Fages,‌ Alexandre Tan-Lhernould.

Gene‌​‌ Regulatory Networks (GRNs) constitute​​ a useful abstraction of​​​‌ complex molecular mechanisms responsible‌ for cell behaviors, cell‌​‌ differentiation, and various diseases.​​ Advances in high-throughput transcriptomics,​​​‌ such as single cell‌ RNA sequencing data, have‌​‌ enabled unprecedented access to​​ large-scale gene expression data.​​​‌ However, the manual reconstruction‌ of GRNs from such‌​‌ data and literature is​​ a hard task, and​​​‌ the automatic reconstruction by‌ GRN inference algorithms remains‌​‌ a major challenge to​​ analyze and validate, before​​​‌ scaling up to eukaryotic‌ transcriptomes. Previous work revealed‌​‌ that the performances of​​ GRN inference algorithms vary​​​‌ significantly according to network‌ topology, degree distribution and‌​‌ motif preponderance.

On a​​ poster at CMSB 2025​​​‌ 30, we presented‌ GRNgen, a tool to‌​‌ generate synthetic GRNs and​​ gene expression datasets under​​​‌ a wide range of‌ conditions, in order to‌​‌ evaluate GRN inference algorithms​​ more accurately, analyze their​​​‌ sensitivity to various GRN‌ properties, and drive future‌​‌ developments. We show the​​ capability of GRNgen to​​​‌ synthesize different types of‌ graphs similar to GRNs‌​‌ of the literature, from​​ E. Coli to Human,​​​‌ based on a set‌ of metrics combining both‌​‌ classical graph-theoretic properties with​​ degree and motif distributions.​​​‌

8.9 On the Boolean‌ network theory of Datalog-neg‌​‌

Participants: François Fages,​​ Sylvain Soliman, Van​​​‌ Giang Trinh.

Boolean‌ networks have been introduced‌​‌ in the 70's to​​ reason about gene regulatory​​​‌ networks and explain the‌ fact that cells with‌​‌ the same genes can​​ express very different cell​​​‌ types corresponding to different‌ stable states of gene‌​‌ activation. Boolean network theory​​ has developed Datalog-neg is​​​‌ a central formalism used‌ in a variety of‌​‌ domains ranging from deductive​​ databases and abstract argumentation​​​‌ frameworks to answer set‌ programming. Its model theory‌​‌ is the finite counterpart​​ of the logical semantics​​​‌ developed for normal logic‌ programs, mainly based on‌​‌ the notions of Clark's​​ completion and two-valued or​​​‌ three-valued canonical models including‌ supported, stable, regular and‌​‌ well-founded models.

In 34​​, we establish a​​​‌ formal link between Datalog-neg‌ and Boolean network theory.‌​‌ We show that in​​ the absence of odd​​​‌ cycles in a Datalog-neg‌ program, the regular models‌​‌ coincide with the stable​​ models, which entails the​​​‌ existence of stable models,‌ and in the absence‌​‌ of even cycles, we​​ prove the uniqueness of​​​‌ stable partial models and‌ regular models. This connection‌​‌ also gives new upper​​ bounds on the numbers​​​‌ of stable partial, regular,‌ and stable models of‌​‌ a Datalog¬ program using​​ the cardinality of a​​​‌ feedback vertex set in‌ its atom dependency graph.‌​‌ Interestingly, our connection to​​ Boolean network theory also​​​‌ points us to the‌ notion of trap spaces.‌​‌ In particular we show​​ the equivalence between subset-minimal​​​‌ stable trap spaces and‌ regular models.

8.10 On‌​‌ the Trap Space Semantics​​ of Normal Logic Programs.​​​‌

Participants: François Fages,‌ Sylvain Soliman, Van‌​‌ Giang Trinh.

The​​​‌ logical semantics of normal​ logic programs has traditionally​‌ been based on the​​ notions of Clark’s completion​​​‌ and two-valued or three-valued​ canonical models, including supported,​‌ stable, regular, and well-founded​​ models. Two-valued interpretations can​​​‌ also be seen as​ states evolving under a​‌ program’s update operator, producing​​ a transition graph whose​​​‌ fixed points and cycles​ capture stable and oscillatory​‌ behaviors, respectively. We refer​​ to this view as​​​‌ dynamical semantics since it​ characterizes the program’s meaning​‌ in terms of state-space​​ trajectories, as first introduced​​​‌ in the stable (supported)​ class semantics. Recently, we​‌ have established a formal​​ connection between Datalog¬ programs​​​‌ (i.e., normal logic programs​ without function symbols) and​‌ Boolean networks, leading to​​ the introduction of the​​​‌ trap space concept for​ Datalog¬ programs.

In 29​‌, we generalize the​​ trap space concept to​​​‌ arbitrary normal logic programs,​ introducing trap space semantics​‌ as a new approach​​ to their interpretation. This​​​‌ new semantics admits both​ model-theoretic and dynamical characterizations,​‌ providing a comprehensive approach​​ to understanding program behaviors.​​​‌ We establish the foundational​ properties of the trap​‌ space semantics and systematically​​ relate it to the​​​‌ established model-theoretic semantics, including​ the stable (supported), stable​‌ (supported) partial, regular, and​​ L-stable model semantics, as​​​‌ well as to the​ dynamical stable (supported) class​‌ semantics. Our results demonstrate​​ that the trap space​​​‌ semantics offers a unified​ and precise framework for​‌ proving the existence of​​ supported classes, strict stable​​​‌ (supported) classes, and regular​ models, in addition to​‌ uncovering and formalizing deeper​​ relationships among the existing​​​‌ semantics of normal logic​ programs.

8.11 Graphical conditions​‌ for the existence, unicity​​ and number of regular​​​‌ models

Participants: François Fages​, Sylvain Soliman,​‌ Van Giang Trinh.​​

The regular models of​​​‌ a normal logic program​ are a particular type​‌ of partial (i.e. 3-valued)​​ models which correspond to​​​‌ stable partial models with​ minimal undefinedness. In 28​‌, we explore graphical​​ conditions on the dependency​​​‌ graph of a finite​ ground normal logic program​‌ to analyze the existence,​​ unicity and number of​​​‌ regular models for the​ program. We show three​‌ main results: 1) a​​ necessary condition for the​​​‌ existence of non-trivial (i.e.​ non-2-valued) regular models, 2)​‌ a sufficient condition for​​ the unicity of regular​​​‌ models, and 3) two​ upper bounds for the​‌ number of regular models​​ based on positive feedback​​​‌ vertex sets. The first​ two conditions generalize the​‌ finite cases of the​​ two existing results obtained​​​‌ by You and Yuan​ (1994) for normal logic​‌ programs with well-founded stratification.​​ The third result is​​​‌ also new to the​ best of our knowledge.​‌ Key to our proofs​​ is a connection that​​​‌ we establish between finite​ ground normal logic programs​‌ and Boolean network theory.​​

8.12 Graphical Analysis of​​​‌ Abstract Argumentation Frameworks via​ Boolean Networks

Participants: Van​‌ Giang Trinh.

Abstract​​ Argumentation Frameworks (AFs) are​​​‌ the key formalism of​ abstract argumentation, which is​‌ one of the main​​ directions in argumentation research.​​​‌ An AF is mainly​ studied by means of​‌ its extensions, defined as​​ subsets of arguments. In​​ 27, we define​​​‌ a Boolean Network (BN)‌ encoding for AFs, where‌​‌ BNs are a simple​​ and efficient mathematical formalism​​​‌ that has a long‌ history of research. We‌​‌ then show that the​​ attack graph of an​​​‌ AF coincides with the‌ influence graph of its‌​‌ encoded BN, and in​​ particular preferred and stable​​​‌ extensions of this AF‌ one-to-one correspond to minimal‌​‌ trap spaces and fixed​​ points of the encoded​​​‌ BN, respectively. We also‌ define a new concept‌​‌ for BNs called complete​​ trap space, then show​​​‌ that complete trap spaces‌ (resp. the percolation of‌​‌ the special trap space​​ where all variables are​​​‌ free) in BNs one-to-one‌ correspond (resp. corresponds) to‌​‌ complete extensions (resp. the​​ grounded extension) in AFs.​​​‌ This connection opens the‌ promising application to gra‌​‌ phical analysis of AFs,​​ which is an interesting​​​‌ line of research with‌ many useful applications. More‌​‌ specifically, we use it​​ to explore many new​​​‌ results relating extensions of‌ an AF and (positive‌​‌ or negative) cycles in​​ its attack graph. In​​​‌ particular, we show new‌ upper bounds based on‌​‌ positive feedback vertex sets​​ for the numbers of​​​‌ stable, preferred, and complete‌ extensions.

8.13 Detecting Misleading‌​‌ Information with LLMs and​​ Explainable ASP

Participants: Van​​​‌ Giang Trinh.

Answer‌ Set Programming (ASP) is‌​‌ traditionally constrained by predefined​​ rule sets and domains,​​​‌ which limits the scalability‌ of ASP systems. While‌​‌ Large Language Models (LLMs)​​ exhibit remarkable capabilities in​​​‌ linguistic comprehension and information‌ representation, they are limited‌​‌ in logical reasoning which​​ is the notable strength​​​‌ of ASP. Hence, there‌ is growing research interest‌​‌ in integrating LLMs with​​ ASP to leverage these​​​‌ abilities. Although many models‌ combining LLMs and ASP‌​‌ have demonstrated competitive results,​​ issues related to misleading​​​‌ input information which directly‌ affect the incorrect solutions‌​‌ produced by these models​​ have not been adequately​​​‌ addressed.

In 26,‌ we propose a method‌​‌ integrating LLMs with explainable​​ ASP to trace back​​​‌ and identify misleading segments‌ in the provided input.‌​‌ Experiments conducted on the​​ CLUTRR dataset show promising​​​‌ results, laying a foundation‌ for future research on‌​‌ error correction to enhance​​ the accuracy of question-answering​​​‌ m odels. Furthermore, we‌ discuss current challenges, potential‌​‌ advancements, and issues related​​ to the utilization of​​​‌ hybrid AI systems.

9.1 Bilateral​​ contracts with industry

10.1 International‌ initiatives

10.2​​ International research visitors

10.3​ European initiatives

10.4 National initiatives​

11.1​‌ Promoting scientific activities

11.2 Teaching -‌​‌ Supervision - Juries -​​ Educational and pedagogical outreach​​​‌

11.3 Popularization​​​‌

12.1​​ Major publications

• 1 article​​​‌S.Sahar Aghakhani,​ S. E.Sacha E​‌ Silva-Saffar, S.Sylvain​​ Soliman and A.Anna​​​‌ Niarakis. Hybrid computational​ modeling highlights reverse warburg​‌ effect in breast cancer-associated​​ fibroblasts.Computational and​​​‌ Structural Biotechnology Journal21​August 2023, 4196-4206​‌HALDOIback to​​ text
• 2 articleS.​​​‌Sahar Aghakhani, S.​Sylvain Soliman and A.​‌Anna Niarakis. Metabolic​​ Reprogramming in Rheumatoid Arthritis​​​‌ Synovial Fibroblasts: a Hybrid​ Modeling Approach.PLoS​‌ Computational Biology1812​​December 2022, e1010408​​​‌HALDOI
• 3 article​A.Adrien Baudier,​‌ F.François Fages and​​ S.Sylvain Soliman.​​​‌ Graphical Requirements for Multistationarity​ in Reaction Networks and​‌ their Verification in BioModels​​.Journal of Theoretical​​​‌ Biology459December 2018​, 79--89HALDOI​‌back to text
• 4​​ articleA.Alexis Courbet​​​‌, P.Patrick Amar​, F.François Fages​‌, E.Eric Renard​​ and F.Franck Molina​​. Computer‐aided biochemical programming​​​‌ of synthetic microreactors as‌ diagnostic devices.Molecular‌​‌ Systems Biology144​​April 2018HALDOI​​​‌back to textback‌ to text
• 5 inproceedings‌​‌E.Elisabeth Degrand,​​ F.François Fages and​​​‌ S.Sylvain Soliman.‌ Graphical Conditions for Rate‌​‌ Independence in Chemical Reaction​​ Networks.Proceedings CMSB​​​‌ 2020: The 18th International‌ Conference on Computational Methods‌​‌ in Systems BiologyCMSB​​ 2020 - 18th International​​​‌ Conference on Computational Methods‌ in Systems BiologyKonstanz‌​‌ / Online, GermanySeptember​​ 2020HALback to​​​‌ text
• 6 articleF.‌François Fages, S.‌​‌Steven Gay and S.​​Sylvain Soliman. Inferring​​​‌ reaction systems from ordinary‌ differential equations.Theoretical‌​‌ Computer Science599September​​ 2015, 64--78HAL​​​‌DOIback to text‌
• 7 inproceedingsF.François‌​‌ Fages, G.Guillaume​​ Le Guludec, O.​​​‌Olivier Bournez and A.‌Amaury Pouly. Strong‌​‌ Turing Completeness of Continuous​​ Chemical Reaction Networks and​​​‌ Compilation of Mixed Analog-Digital‌ Programs.CMSB 2017‌​‌ - 15th International Conference​​ on Computational Methods in​​​‌ Systems BiologyLecture Notes‌ in Computer ScienceProceedings‌​‌ of the fiveteen international​​ conference on Computational Methods​​​‌ in Systems Biology, CMSB‌ 201710545Darmstadt, Germany‌​‌September 2017, 108-127​​HALback to text​​​‌back to text
• 8‌ articleF.François Fages‌​‌, T.Thierry Martinez​​, D. A.David​​​‌ A Rosenblueth and S.‌Sylvain Soliman. Influence‌​‌ Networks compared with Reaction​​ Networks: Semantics, Expressivity and​​​‌ Attractors.IEEE/ACM Transactions‌ on Computational Biology and‌​‌ BioinformaticsPP992018​​, 1-14HALDOI​​​‌back to text
• 9‌ inbookF.François Fages‌​‌ and F.Franck Molina​​. The Cell: A​​​‌ Chemical Analog Calculator.‌Symbolic Approaches to Modeling‌​‌ and Analysis of Biological​​ Systems1WileyAugust​​​‌ 2023HALDOIback‌ to text
• 10 thesis‌​‌E.Eléa Greugny.​​ Computational modeling approaches to​​​‌ multifactorial aspects of atopic‌ dermatitis.Institut Polytechnique‌​‌ de ParisOctober 2022​​HAL
• 11 articleJ.​​​‌Jeremy Grignard, V.‌Véronique Lamamy, E.‌​‌Eva Vermersch, P.​​Philippe Delagrange, J.-P.​​​‌Jean-Philippe Stephan, T.‌Thierry Dorval and F.‌​‌François Fages. Mathematical​​ modeling of the microtubule​​​‌ detyrosination/tyrosination cycle for cell-based‌ drug screening design.‌​‌PLoS Computational Biology18​​6June 2022,​​​‌ e1010236HALDOIback‌ to textback to‌​‌ text
• 12 inproceedingsM.​​Mathieu Hemery and F.​​​‌François Fages. Algebraic‌ Biochemistry: a Framework for‌​‌ Analog Online Computation in​​ Cells.CMSB 2022​​​‌ - International Conference on‌ Computational Methods in Systems‌​‌ BiologyBucarest, RomaniaSeptember​​ 2022HALback to​​​‌ text
• 13 inproceedingsM.‌Mathieu Hemery and F.‌​‌François Fages. On​​ Estimating Derivatives of Input​​​‌ Signals in Biochemistry.‌CMSB 2023 - 21st‌​‌ International Conference on Computational​​ Methods in Systems Biology​​​‌Luxembourg City, Luxembourg2023‌HALback to text‌​‌
• 14 thesisJ.Julien​​ Martinelli. On learning​​​‌ mechanistic models from time‌ series data with applications‌​‌ to personalised chronotherapies.​​Institut Polytechnique de Paris​​​‌February 2022HALback‌ to text
• 15 article‌​‌F.Faten Nabli,​​​‌ T.Thierry Martinez,​ F.François Fages and​‌ S.Sylvain Soliman.​​ On Enumerating Minimal Siphons​​​‌ in Petri nets using​ CLP and SAT solvers:​‌ Theoretical and Practical Complexity​​.Constraints212​​​‌April 2016, 251--276​HALDOIback to​‌ text
• 16 articleM.​​Marek Ostaszewski, A.​​​‌Anna Niarakis, A.​Alexander Mazein, I.​‌Inna Kuperstein, R.​​Robert Phair, A.​​​‌Aurelio Orta-Resendiz, V.​Vidisha Singh, S.​‌ S.Sara Sadat Aghamiri​​, M. L.Marcio​​​‌ Luis Acencio, E.​Enrico Glaab, A.​‌Andreas Ruepp, G.​​Gisela Fobo, C.​​​‌Corinna Montrone, B.​Barbara Brauner, G.​‌Goar Frishman, L.​​ C.Luis Cristóbal Monraz​​​‌ Gómez, J.Julia​ Somers, M.Matti​‌ Hoch, S.Shailendra​​ Kumar Gupta, J.​​​‌Julia Scheel, H.​Hanna Borlinghaus, T.​‌Tobias Czauderna, F.​​Falk Schreiber, A.​​​‌Arnau Montagud, M.​Miguel Ponce de Leon​‌, A.Akira Funahashi​​, Y.Yusuke Hiki​​​‌, N.Noriko Hiroi​, T. G.Takahiro​‌ G Yamada, A.​​Andreas Dräger, A.​​​‌Alina Renz, M.​Muhammad Naveez, Z.​‌Zsolt Bocskei, F.​​Francesco Messina, D.​​​‌Daniela Börnigen, L.​Liam Fergusson, M.​‌Marta Conti, M.​​Marius Rameil, V.​​​‌Vanessa Nakonecnij, J.​Jakob Vanhoefer, L.​‌Leonard Schmiester, M.​​Muying Wang, E.​​​‌ E.Emily E Ackerman​, J. E.Jason​‌ E Shoemaker, J.​​Jeremy Zucker, K.​​​‌Kristie Oxford, J.​Jeremy Teuton, E.​‌Ebru Kocakaya, G.​​Gökçe Ya Gmur Summak​​​‌, K.Kristina Hanspers​, M.Martina Kutmon​‌, S.Susan Coort​​, L.Lars Eijssen​​​‌, F.Friederike Ehrhart​, D.Devasahayam Arokia​‌, B.Balaya Rex​​, D.Denise Slenter​​​‌, M.Marvin Martens​, N.Nhung Pham​‌, R.Robin Haw​​, B.Bijay Jassal​​​‌, L.Lisa Matthews​, M.Marija Orlic-Milacic​‌, A. S.Andrea​​ Senff Ribeiro, K.​​​‌Karen Rothfels, V.​Veronica Shamovsky, R.​‌Ralf Stephan, C.​​Cristoffer Sevilla, T.​​​‌Thawfeek Varusai, J.-M.​Jean-Marie Ravel, R.​‌Rupsha Fraser, V.​​Vera Ortseifen, S.​​​‌Silvia Marchesi, P.​Piotr Gawron, E.​‌Ewa Smula, L.​​Laurent Heirendt, V.​​​‌Venkata Satagopam, G.​Guanming Wu, A.​‌Anders Riutta, M.​​Martin Golebiewski, S.​​​‌Stuart Owen, C.​Carole Goble, X.​‌Xiaoming Hu, R.​​ W.Rupert W Overall​​​‌, D.Dieter Maier​, A.Angela Bauch​‌, B. M.Benjamin​​ M Gyori, J.​​​‌ A.John A Bachman​, C.Carlos Vega​‌, V.Valentin Grouès​​, M.Miguel Vazquez​​​‌, P.Pablo Porras​, L.Luana Licata​‌, M.Marta Iannuccelli​​, F.Francesca Sacco​​​‌, A.Anastasia Nesterova​, A.Anton Yuryev​‌, A.Anita De​​ Waard, D.Denes​​​‌ Turei, A.Augustin​ Luna, O.Ozgun​‌ Babur, S.Sylvain​​ Soliman, A.Alberto​​ Valdeolivas, M.Marina​​​‌ Esteban-Medina, M.Maria‌ Peña-Chilet, K.Kinza‌​‌ Rian, T.Tomáš​​ Helikar, B.Bhanwar​​​‌ Lal Puniya, D.‌Dezso Modos, A.‌​‌Agatha Treveil, M.​​Marton Olbei, B.​​​‌Bertrand De Meulder,‌ S.Stephane Ballereau,‌​‌ A.Aurélien Dugourd,​​ A.Aurélien Naldi,​​​‌ V.Vincent Noël,‌ L.Laurence Calzone,‌​‌ C.Chris Sander,​​ E.Emek Demir,​​​‌ T.Tamas Korcsmaros,‌ T. C.Tom C‌​‌ Freeman, F.Franck​​ Augé, J. S.​​​‌Jacques S Beckmann,‌ J.Jan Hasenauer,‌​‌ O.Olaf Wolkenhauer,​​ E. L.Egon L​​​‌ Wilighagen, A. R.‌Alexander R Pico,‌​‌ C. T.Chris T​​ Evelo, M. E.​​​‌Marc E Gillespie,‌ L. D.Lincoln D‌​‌ Stein, H.Henning​​ Hermjakob, P.Peter​​​‌ D'eustachio, J.Julio‌ Saez-Rodriguez, J.Joaquin‌​‌ Dopazo, A.Alfonso​​ Valencia, H.Hiroaki​​​‌ Kitano, E.Emmanuel‌ Barillot, C.Charles‌​‌ Auffray, R.Rudi​​ Balling, R.Reinhard​​​‌ Schneider and C.-1.COVID-19‌ Disease Map Community.‌​‌ COVID19 Disease Map, a​​ computational knowledge repository of​​​‌ virus-host interaction mechanisms.‌Molecular Systems Biology17‌​‌10October 2021,​​ e10387HALDOIback​​​‌ to text
• 17 article‌J.Jakob Ruess,‌​‌ G.Guillaume Ballif and​​ C.Chetan Aditya.​​​‌ Stochastic chemical kinetics of‌ cell fate decision systems:‌​‌ from single cells to​​ populations and back.​​​‌The Journal of Chemical‌ Physics15918November‌​‌ 2023HALDOI
• 18​​ articleV.Vidisha Singh​​​‌, A.Aurélien Naldi‌, S.Sylvain Soliman‌​‌ and A.Anna Niarakis​​. A large-scale Boolean​​​‌ model of the rheumatoid‌ arthritis fibroblast-like synoviocytes predicts‌​‌ drug synergies in the​​ arthritic joint.npj​​​‌ Systems Biology and Applications‌933July 2023‌​‌HALDOIback to​​ text
• 19 articleE.​​​‌Eléa Thibault Greugny,‌ J.Jalil Bensaci,‌​‌ F.François Fages and​​ G.Georgios Stamatas.​​​‌ Computational modeling predicts impaired‌ barrier function and higher‌​‌ sensitivity to skin inflammation​​ following pH elevation.​​​‌Experimental DermatologyNovember 2022‌HALDOIback to‌​‌ textback to text​​
• 20 articleE.Eléa​​​‌ Thibault Greugny, F.‌François Fages, O.‌​‌Ovidiu Radulescu, P.​​Peter Szmolyan and G.​​​‌Georgios Stamatas. A‌ Skin Microbiome Model with‌​‌ AMP interactions and Analysis​​ of Quasi-Stability vs Stability​​​‌ in Population Dynamics.‌Theoretical Computer ScienceNovember‌​‌ 2023, 114294HAL​​DOIback to text​​​‌back to textback‌ to text
• 21 article‌​‌P.Pauline Traynard,​​ A.Adrien Fauré,​​​‌ F.François Fages and‌ D.Denis Thieffry.‌​‌ Logical model specification aided​​ by model- checking techniques:​​​‌ application to the mammalian‌ cell cycle regulation.‌​‌Bioinformatics32172016​​, i772-i780HALDOI​​​‌back to text
• 22‌ articleP.Pauline Traynard‌​‌, C.Céline Feillet​​, S.Sylvain Soliman​​​‌, F.Franck Delaunay‌ and F.François Fages‌​‌. Model-based investigation of​​ the circadian clock and​​​‌ cell cycle coupling in‌ mouse embryonic fibroblasts: Prediction‌​‌ of RevErb-α up-regulation during​​​‌ mitosis.BioSystems149​November 2016, 59--69​‌HALDOIback to​​ textback to text​​​‌
• 23 articleV.-G.Van-Giang​ Trinh, B.Belaid​‌ Benhamou and S.Sylvain​​ Soliman. Trap spaces​​​‌ of Boolean networks are​ conflict-free siphons of their​‌ Petri net encoding.​​Theoretical Computer Science971​​​‌September 2023, 114073​HALDOIback to​‌ text

12.2 Publications of​​ the year