This project
aims at developing formal methods and experimental settings for understanding the cell machinery and establishing computational paradigms in cell biology. It is based on the vision of **cells as machines, biochemical reaction networks as programs**, and on the use of concepts and tools from computer science to master the complexity of cell processes.

This project addresses fundamental research issues in computer science on the **interplay between structure and dynamics** in large interaction networks, and on **mixed analog-discrete computation**. We contribute to the theory of biochemical computation, and develop since 2002 a modelling, analysis and synthesis software, the Biochemical Abstract Machine, BIOCHAM. The reaction rule-based language of this system allows us to reason about biochemical reaction networks at different levels of abstraction, in the stochastic, differential, discrete, Boolean and hybrid semantics of reaction networks. We develop a variety of static analysis methods before going to simulations and dynamical analyses. We use **quantitative temporal logics** as a mean to formalise biological behaviours with imprecise data and to constrain model building or network synthesis.

A **tight integration between dry lab and wet lab efforts** is also essential for the success of the project. This is achieved through tight collaborations with biologists and experimentalists. Furthermore, half of Lifeware is in the InBio group at Institut Pasteur headed by Grégory Batt who develops an experimental platform for the closed-loop control of intracellular processes. This platform combines hardware (microfluidic device and microscope), software (cell tracking and model-based predictive control algorithms) and liveware (genetically modified living cells). The originality of this project thus also deals with the recourse to advanced microscopy and synthetic biology technologies to perform accurate observations, modifications and **real-time control** at both **single cell and cell population levels**.

Because of the importance of optimization techniques in our research, we keep some activity purely dedicated to optimization problems, in particular on constraint programming methods for computing with partial information systems and solving NP-hard static analysis problems, and on continuous optimization methods for dealing with continuous parameters.

Bridging the gap between the complexity of biological systems
and our capacity to model and **quantitatively predict system behaviors** is a central challenge in systems biology.
We believe that a deeper understanding of the concept and theory of biochemical computation
is necessary to tackle that challenge.
Progress in the theory is necessary for scaling, and enabling the application of static analysis,
module identification and decomposition, model reductions, parameter search, and model inference methods
to large biochemical reaction systems.
A measure of success on this route will be the production of better computational modeling tools
for elucidating the complex dynamics of natural biological processes,
designing synthetic biological circuits and biosensors,
developing novel therapy strategies, and optimizing patient-tailored therapeutics.

Progress on the **coupling of models to data** is also necessary.
Our approach based on quantitative temporal logics provides a powerful framework
for formalizing experimental observations and using them as formal specification in model building.
Key to success is a tight integration between *in vivo* and *in silico* work,
and on the mixing of dry and wet experiments, enabled by novel biotechnologies.
In particular, the use of micro-fluidic devices makes it possible
to measure behaviors at both single-cell and cell population levels *in vivo*,
provided innovative modeling, analysis and control methods are deployed *in silico*.

In synthetic biology, while the construction of simple intracellular circuits has shown feasible,
the design of larger, **multicellular systems** is a major open issue.
In engineered tissues for example, the behavior results from the subtle interplay
between intracellular processes (signal transduction, gene expression)
and intercellular processes (contact inhibition, gradient of diffusible molecule),
and the question is how should cells be genetically modified
such that the desired behavior robustly emerges from cell interactions.

Feinberg's chemical reaction network theory
and Thomas's influence network analyses
provide sufficient and/or necessary structural conditions for the existence of multiple steady states and oscillations
in regulatory networks.
Those conditions can be verified by static analyzers without knowing kinetic parameter values nor making any simulation.
In this domain, most of our work consists in analyzing the interplay between the **structure** (Petri net properties, influence graph, subgraph epimorphisms)
and the **dynamics** (Boolean, CTMC, ODE, time scale separations) of biochemical reaction systems.
In particular, our study of influence graphs of reaction systems,
our generalization of Thomas' conditions of multi-stationarity and Soulé's proof
to reaction systems

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)

biological model = transition system

dynamical behavior specification = temporal logic formula

model validation = model-checking

model reduction = sub-model-checking,

model prediction = formula enumeration,

static experiment design = symbolic model-checking, state

model synthesis = constraint solving

dynamic experiment design = constraint solving

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

The continuous nature of many protein interactions leads us to consider models of analog computation, and in particular,
the recent results in the theory of analog computability and complexity obtained by Amaury Pouly

Since nearly two decades, a significant interest has grown for getting a quantitative understanding of the functioning of biological systems at the cellular level. Given their complexity, proposing a model accounting for the observed cell responses, or better, predicting novel behaviors, is now regarded as an essential step to validate a proposed mechanism in systems biology. Moreover, the constant improvement of stimulation and observation tools creates a strong push for the development of methods that provide predictions that are increasingly precise (single cell precision) and robust (complex stimulation profiles).

It is now fully apparent that cells do not respond identically to a same stimulation, even when they are all genetically-identical. This phenotypic heterogeneity plays a significant role in a number of problems ranging from cell resistance to anticancer drug treatments to stress adaptation and bet hedging.

Dedicated modeling frameworks, notably **stochastic** modeling frameworks, such as chemical master equations, and **statistic** modeling frameworks, such as ensemble models, are then needed to capture biological variability.

Appropriate mathematical and computational tools should then be employed for the analysis of these models and their calibration to experimental data. One can notably mention **global optimization** tools to search for appropriate parameters within large spaces, **moment closure** approaches to efficiently approximate stochastic models **expectation maximization** algorithm for the identification of mixed-effects models

External control has been employed since many years to regulate culture growth and other physiological properties. Recently, taking inspiration from developments in synthetic biology, closed loop control has been applied to the regulation of intracellular processes. Such approaches offer unprecedented opportunities to investigate how a cell process dynamical information by maintaining it around specific operating points or driving it out of its standard operating conditions. They can also be used to complement and help the development of synthetic biology through the creation of hybrid systems resulting from the interconnection of in vivo and in silico computing devices.

In collaboration with Pascal Hersen (CNRS MSC lab), we developed a platform for gene expression control that enables to control protein concentrations in yeast cells. This platform integrates microfluidic devices enabling long-term observation and rapid change of the cells environment, microscopy for single cell measurements, and software for real-time signal quantification and model based control. We demonstrated in 2012 that this platform enables controlling the level of a fluorescent protein in cells with unprecedented accuracy and for many cell generations

More recently, motivated by an analogy with a benchmark control problem, the stabilization of an inverted pendulum, we investigated the possibility to balance a genetic toggle switch in the vicinity of its unstable equilibrium configuration. We searched for solutions to balance an individual cell and even an entire population of heterogeneous cells, each harboring a toggle switch

Independently, in collaboration with colleagues from IST Austria, we investigated the problem of controlling cells, one at a time, by constructing an integrated optogenetic-enabled microscopy platform. It enables experiments that bridge individual and population behaviors. We demonstrated: (i) population structuring by independent closed-loop control of gene expression in many individual cells, (ii) cell–cell variation control during antibiotic perturbation, (iii) hybrid bio-digital circuits in single cells, and freely specifiable digital communication between individual bacteria

Constraint solving and optimization methods are important in our research.
On the one hand, static analysis of biochemical reaction networks involves solving
hard combinatorial optimization problems,
for which **constraint programming** techniques
have shown particularly successful,
often beating dedicated algorithms
and allowing to solve large instances from model repositories. On the other hand, parameter search and model calibration problems involve similarly solving hard continuous optimization problems,
for which **evolutionary algorithms**, and especially the covariance matrix evolution strategy (CMA-ES)

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 predictive models of biological systems to answer them.
Furthermore, one important application of our research is the development of a **modeling software** for computational systems biology.

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**,
BIOCHAM is used by other groups either for building models, for comparing techniques, or for teaching (see statistics in software section).
BIOCHAM-web is a web application which makes it possible to use BIOCHAM without any installation.
We plan to continue developing BIOCHAM for these different purposes and improve the software quality.

Recent advances in cancer chronotherapy techniques support the evidence that there exist important links between the cell cycle and the circadian clock genes. One purpose for modeling these links is to better understand how to efficiently target malignant cells depending on the phase of the day and patient characterictics. These questions are at the heart of our collaboration with Franck Delaunay (CNRS Nice) and Francis Lévi (Univ. Warwick, GB, formerly INSERM Hopital Paul Brousse, Villejuif) and of our participation in the ANR Hyclock project
and in the submitted EU H2020 C2SyM proposal, following the former EU EraNet Sysbio C5Sys and FP6 TEMPO projects.
In the past, we developed a coupled model of the Cell Cycle, Circadian Clock, DNA Repair System,
Irinotecan Metabolism and Exposure Control under Temporal Logic Constraints

In collaboration with Franck Molina (CNRS, Sys2Diag, Montpellier)
and Jie-Hong Jiang (NTU, Taiwan) we ambition to apply our techniques
to the design and implementation of high-level functions in non-living vesicles
for medical applications, such as biosensors for medical diagnosis

Antibiotic resistance is becoming a problem of central importance at a global level. Two mechanisms are at the origin of non-susceptibility to antimicrobial treatments. The first one comes from adaptation of bacterial cells to antibacterial treatments, notably through the modification of efflux pumps or the expression of enzymes that degrade the antibiotics. Cells are individually resistant. The second one, typically found in resistances to

The functional characterization of these different effects is important for the best use of antibiotics (antibiotic stewardship). In collaboration with Lingchong You (Duke University) and with Philippe Glaser (Institut Pasteur), we develop experimental platforms, models, and optimal model calibration methods that gives precise estimations of individual resistance and collective resilience of bacterial populations to antibiotic treatments.

**Creation of a new team at Inria Paris**

At the end of 2019, the Lifeware team gave birth to a new Inria team, called InBio and affiliated to the Inria Paris research centre. So far, InBio was a Pasteur research unit that was hosting a fraction of the members of the Lifeware team on the campus of Institut Pasteur. So in 2020, InBio becomes a new Common Project-Team between Inria Paris and Pasteur Institute. This demonstrates that Inria is actively supporting research in the computational systems biology field.

**Launching of Inria Exploratory Action GRAM on chemical programming of artificial vesicles**

Chemical reaction networks are a computation paradigm used by natural cells to process information, take decisions and control their vital processes. The synthesis of artificial vesicles without DNA nor RNA but containing precise quantities of enzymes allows us today to implement high-level functions in proto-cells with numerous potential applications in health and the environment. Based on previous work of the Lifeware project-team on chemical analog computation theory and programming, of the CNRS-Alcediag Sys2diag laboratory on the synthesis of biosensors in artificial vesicles, and on the expertise of the Roscoff Biological Station on membrane transporters, we explore an original approach to analog chemical circuit design applied to the programming of high-level functions in chemical analog computers.

**Award Ceremony - La Recherche magazine 2019 - Information Sciences **

The ceremony for awards La Recherche magazine 2019 at University Paris-Dauphine was a great occasion to present our article “Strong Turing Completeness of Continuous Chemical Reaction Networks and Compilation of Mixed Analog-Digital Programs” by F. Fages, G. Le Guludec, O. Bournez and A. Pouly, Best Paper award at CMSB 2017, recipient of La Recherche magazine 2019 Award - Information Sciences.

*The Biochemical Abstract Machine*

Keywords: Systems Biology - Bioinformatics

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 Functional Description: – notebooks of Master classes – graphical user interface on top of Jupyter – synthesis of at most binary reactions with minimisation of the variables for negative values – detection of model reduction by subgraph epimorphism – option for partial tropical equilibrations

Participants: François Fages, David Coudrin, Sylvain Soliman and Mathieu Hemery

Contact: François Fages

*CellDesigner as SBML-Qual*

Keywords: SBML - Logical Framework - Knowledge representation

Functional Description: CaSQ transforms a big knowledge map encoded as an SBGN-compliant SBML file in CellDesigner into an executable Logical Model in SBML-Qual

Authors: Sylvain Soliman and Anna Niarakis

Partner: Université d'Evry-Val d'Essonne

Contact: Sylvain Soliman

Models play a central role in our work, either to test our understanding or to guide the design of novel systems. Model development and parameter calibration necessitate informative experiments. We develop methods to assist with the optimal design of experiments. In consequence, we have to perform, in sequence or in parallel, experiments with possibly complex input profiles. This led us to develop experimental platforms that allow for flexible and automated stimulations and measurements. Three platforms are being developed, based on (i) a microplate photometer, (ii) a bioreactor platform coupled with a flow cytometer, and (iii) a microscope equipped with microfluidic systems, respectively. In all cases, the real-time measurement and actuation capabilities allow for making reactive experiments, notably including real-time control experiments.

One goal of synthetic biology is to implement useful functions with biochemical reactions, either by reprogramming living cells or programming artificial vesicles. In this perspective, we consider Chemical Reaction Networks (CRN) as a programming language, and investigate the CRN program synthesis problem. Recent work has shown that CRN interpreted by differential equations are Turing-complete and can be seen as analog computers where the molecular concentrations play the role of information carriers. Any real function that is computable by a Turing machine in arbitrary precision can thus be computed by a CRN over a finite set of molecular species. The proof of this result gives a numerical method to generate a finite CRN for implementing a real function presented as the solution of a Polynomial Initial Values Problem (PIVP).

The compilation of high-level imperative programming languages in CRN requires however an efficient implementation of program control flows using threshold functions. The biochemical threshold function is also a crucial component in the biosensor circuits to be deployed in living cells or synthetic vesicles for disease diagnosis. In , based on the zero-order ultrasensitivity, we propose an economic biochemical implementation of threshold functions with reconfigurable threshold values. We show that the so-constructed threshold function module well approximates the unit step function and allows robust composition with other function modules for complex computation tasks. This is now implemented in BIOCHAM-4 for the compilation of sequentiality and conditionals in CRNs.

In , , we study an alternative method based on artificial evolution to build a CRN that approximates a real function given on finite sets of input values. We present a nested search algorithm that evolves the structure of the CRN and optimizes the kinetic parameters at each generation. We evaluate this algorithm on the Heaviside and Cosine functions both as functions of time and functions of input molecular species. We then compare the CRN obtained by artificial evolution both to the CRN generated by the numerical method from a PIVP definition of the function, and to the natural CRN found in the BioModels repository for switches and oscillators.

On a Heaviside function of time, the results obtained by artificial evolution lead to a remarkably simple CRN of 3 molecular species and 5 reactions with double catalysts which provide a very stiff transition although using mass action law kinetics. This solution is more economical than the CRN generated by the PIVP method for sigmoid functions. On a Heaviside function of input, the CRN found by evolution are slightly more complicated than the bistable switch found in cell cycle CRN for instance, but much less complex than the MAPK signaling network that plays a similar role.

On the cosine function of time, the best CRN found by evolution contains an annihilation reaction similar to the CRN generated by the numerical method for positive and negative variables, but one less reaction thanks to an intriguing non symmetric use of the two variables which preserves the limit cycle. Interestingly, the evolved and the PIVP generated structures could be compared to prokaryote and eukaryote models of the circadian clock found in BioModels.

On the cosine function of input, a CRN surprisingly emerges with the structure of the CRN for cosine function of time, using the same trick as for PIVP compilation to stop time at the desired input value.

With the automation of biological experiments and the increase of quality of single cell data that can now be obtained by phosphoproteomic and time lapse videomicroscopy, automating the building of
mechanistic models from these data time series becomes conceivable and
a necessity for many new applications. While learning numerical parameters to fit a given model structure to observed data is now a quite well
understood subject, learning the structure of the model is a more challenging problem that previous attempts failed to solve without relying
quite heavily on prior knowledge about that structure. In , , we
consider mechanistic models based on chemical reaction networks (CRN)
with their continuous dynamics based on ordinary differential equations,
and finite time series about the time evolution of concentration of molecular species for a given time horizon and a finite set of perturbed initial
conditions. We present a greedy heuristics unsupervised statistical learning algorithm to infer reactions with a time complexity for inferring one
reaction in

We have shown in the past
that model reduction relationships between CRNs can be detected on a large scale
by the graph matching notion of subgraph epimorphism

Furthermore, in the framework of the ANR-DFG SYMBIONT project
we investigate mathematical justification of SEPI reductions based on Tikhonov's theorem
and their computation using tropical algebra methods and constraint programming techniques

This year, beyond implementation work on hybrid simulations in BIOCHAM and on antithetic feedback control in CRNs, we have started the computational modelling of three biological systems with important potential applications in biomedicine.

The first is about erythrocytes (i.e. red blood cells). Their most obvious function concerns the respiratory system since erythrocytes allow gas exchanges at the level of the organism by transporting dioxygen and carbon dioxide between the lungs and the tissues. However, red blood cells also have an important buffer function in the blood, which is necessary to keep blood pH in the physiological range. Modelling the red blood cells with CRNs using BIOCHAM gives us insight as to which biological objects are necessary to allow the cell to process its functions correctly. At the level of Systems Biology, it also allows us to understand the links between the different biological functions of erythrocytes.

The second concerns microtubules and their post-translational modifications involved in major cellular processes such as: mitosis, cardiomyocyte contraction, and neuronal differentiation. More precisely, in neurons, the post-translational modifications of detyrosination and tyrosination are crucial for neuronal plasticity, axon regeneration, recruitment and transports of proteins and correct neuronal wiring. We hypothesize that the decrease of density and length of microtubules and the loss of neuronal structures such as synapses, dendritic spine and growth cone which are correlated with the progressive cognitive decline [9,10] may be the consequence of the dysregulation of the cycle detyrosination/tyrosination in neurodegenerative disorder. This hypothesis is investigated in collaboration with Servier by combining experimental approaches with mathematical modelling.

The third concerns inflammation processes in skin. Skin protects the body against external agents, for instance pathogens, irritants, or UV radiation, that can trigger inflammation. Inflammation is a complex phenomenon that is classified in two main types, acute and chronic. They are distinguished by different parameters such as the duration, the underlying mechanisms, the components involved like the type of immune cells, and the nature and intensity of the associated clinical signs. The computational models developed in collaboration with Johnson&Johnson France, combine mathematical and multi-agent modelling using BIOCHAM and EPISIM modelling tools.

Molecular interaction maps have emerged as a meaningful way of representing
biological mechanisms in a comprehensive and systematic manner. However, their
static nature provides limited insights to the emerging behavior of the
described biological system under different conditions. Computational
modeling provides the means to study dynamic properties through *in silico*
simulations and perturbations.

In collaboration with Anna Niarakis (Université d'Évry, GenHotel) we have started developing the CaSQ Python package, by defining simplification rules and logical formulas for the inferred Boolean models according to the topology and the annotations of the starting molecular interaction maps. We used CaSQ to produce executable files of existing molecular maps notably a big map of the Rheumatoid Arthritis that is at the core of Évry team's work.

A publication on the inference process has already been submitted to Bioinformatics but work continues on the applications side to fine-tune the automatically generated model and analyze its dynamical properties.

The growth of microorganisms is controlled by strategies for the dynamical allocation of available resources over different cellular functions. Synthetic biology approaches are considered nowadays to artificially modify these strategies and turn microbial populations into biotechnological factories for the production of metabolites of interest. In our recent work, we have studied dynamics of microbial resource allocation and growth in terms of coarse-grained self-replicator models described by ordinary differential equations, and proposed artificial control strategies for the optimization of metabolite production based on the reengineering of resource allocation. In this contribution, we elaborated on our earlier results and further investigate synthetic resource allocation control strategies . Using numerical simulation, we studied the effect on growth and bioproduction of the (biological or technological) costs associated with discontinuous control strategies, and of the time allotted to optimal substrate utilization. Results provided novel insight into the most favorable synthetic control strategies.

One of the most crippling problems in quantitative and synthetic biology is that models aiming to describe the real mechanisms of biochemical processes inside cells typically contain too many unknown parameters to be reliably inferable from available experimental data. Recent years, however, have seen immense progress in the development of experimental platforms that allow not only to measure biological systems more precisely but also to administer external control inputs to the cells. Optimal experimental design has been identified as a tool that can be used to decide how to best choose these control inputs so as to excite the systems in ways that are particularly useful for learning the biochemical rate constants from the corresponding data. Unfortunately, the experiment that is best to learn the parameters of a system depends on the precise values of these parameters, which are naturally unknown at the time at which experiments need to be designed. Here, we used a recently constructed genetic toggle switch as a case study to investigate how close to the best possible experiment we can hope to get with the most widely used optimal design approaches in the field. We found that, for strongly nonlinear systems such as the toggle switch, reliably predicting the information that can be gained from a priori fixed experiments can be difficult if the system parameters are not known very precisely . This suggests that a better strategy to guarantee informative experiments might be to use feedback control and to adjust the experimental plan in real time.

Mathematical models have been used successfully at diverse scales of biological organization, ranging from ecology and population dynamics to stochastic reaction events occurring between individual molecules in single cells. Generally, many biological processes unfold across multiple scales, with mutations being the best studied example of how stochasticity at the molecular scale can influence outcomes at the population scale. In many other contexts, however, an analogous link between micro-and macro-scale remains elusive, primarily due to the challenges involved in setting up and analyzing multi-scale models. In , we employed such a model to investigate how stochasticity propagates from individual biochemical reaction events in the bacterial innate immune system to the ecology of bacteria and bacterial viruses. We showed analytically how the dynamics of bacterial populations are shaped by the activities of immunity-conferring enzymes in single cells and how the ecological consequences imply optimal bacterial defense strategies against viruses. Our results suggest that bacterial populations in the presence of viruses can either optimize their initial growth rate or their population size, with the first strategy favoring simple immunity featuring a single restriction modification system and the second strategy favoring complex bacterial innate immunity featuring several simultaneously active restriction modification systems.

Across diverse biological systems - ranging from neural networks to intracellular signaling and genetic regulatory networks - the information about changes in the environment is frequently encoded in the full temporal dynamics of the network nodes. A pressing data-analysis challenge has thus been to efficiently estimate the amount of information that these dynamics convey from experimental data. In , we developed and evaluated decoding-based estimation methods to lower bound the mutual information about a finite set of inputs, encoded in single-cell high-dimensional time series data. For biological reaction networks governed by the chemical Master equation, we derived model-based information approximations and analytical upper bounds, against which we benchmarked our proposed model-free decoding estimators. In contrast to the frequently-used k-nearest-neighbor estimator, decoding-based estimators robustly extract a large fraction of the available information from high-dimensional trajectories with a realistic number of data samples. We applied these estimators to previously published data on Erk and Ca

In the framework of the Cifre PhD thesis of Jeremy Grignard at Servier, we work on the coupling between computational modeling and biological experiment design, and on chemical reaction network inference methods from data time series.

In the framework of the Cifre PhD thesis of Eléa Greugny at Johnson&Johnson Santé Beauté France, we work on the computational modeling of inflammatory process in the skin, using multi-scale modeling and multi-agent simulation.

ANR-FWF CyberCircuits (2018-2022): “Cybergenetic circuits to test composability of gene networks”, co-coordinated by C. Guet (IST Austria, Klosterneuburg, Austria) and J. Ruess (Inria EPI Lifeware);

ANR-DFG Symbiont (2018-2021) on “Symbolic Methods for Biological Systems”, coordinated by T. Sturm (CNRS, LORIA, Nancy, France) and A. Weber (Univ. Bonn, Germany) with F. Fages and F. Boulier (U. Lille), O. Radulescu (U. Montpellier), A. Schuppert (RWTH Aachen), S. Walcher (RWTH Aachen), W. Seiler (U. Kassel);

ANR-MOST Biopsy (2016-2020) on “Biochemical Programming System”, coordinated by F. Molina (CNRS, Sys2diag, Montpellier) and J.H. Jiang (National Taiwan University), with F. Fages;

ANR Memip (2016-2020) on “Mixed-Effects Models of Intracellular Processes”, coordinated by G. Batt, with P. Hersen, (CNRS/Paris7), E. Cinquemani (Inria EPI IBIS) and M. Lavielle (Inria/CNRS/Polytechnique, EPI XPOP);

ANR Cogex (2016-2019) on “Computer Aided Control of Gene Expression” coordinated by P. Hersen (MSC lab, CNRS/Paris7), with G. Batt and G. Truan (LISBP, CNRS/INSA);

IPL Cosy (2017-2021) on “real-time control of synthetic microbial communities”, coordinated by Eugenio Cinquemani (Ibis, Inria), with Jean-Luc Gouzé (Biocore, Inria), Grégory Batt, Frédéric Bonnans (Commands, Inria), Efimov Denis (Non-A, Inria), and Hans Geiselmann (BIOP, Université Grenoble-Alpes), Béatrice Laroche (Maiage, Inra Jouy-en-Josas).

H2020 FET-OPEN Cosy-Bio (2017-2020), on “Control Engineering of Biological Systems for Reliable Synthetic Biology Applications”, coordinated by Diego di Bernardo (Tigem), with Filippo Menolascina (Edinburgh U), Mario di Bernardo (Naples U), Pascal Hersen (Paris7 U), Mustafa Khammash (ETHZ), Grégory Batt, Guy-Bart Stan (Imperial College), and Lucia Marucci (Bristol U).

The following researchers have been invited for short visits:

Jean-Louis Lassez, retired IBM Yorktown, USA

Lucia Nasti, Univ. Pisa, Italy

Claudia Lopez Zazueta, NTNU, Norway

Orianne Bargain (TU Dresden Germany)

Elisabeth Degrand (KTH, Stockholm Sweden)

Chetan Aditya was the co-organiser of a thematic workshop entitled "From Bench to Bedside" at the Center for Interdisciplinary research, Paris, April 2019.

Grégory Batt was a co-organizer of the thematic session entitled "Predictive approaches for biological systems engineering" at the JOBIM conference, Nantes, July 2019.

François Fages and Sylvain Soliman were scientific co-chairs of

the Constraints and Life Sciences Track of CP'2019 25th International Conference on Principles and Practice of Constraint Programming, Stamford, CT, USA, September 30 to October 4, 2019.

François Fages was scientific co-chair of

Workshop on Computational Systems Biology for Complex Disease CSBCD, ENS Paris-Saclay, Cachan, France, 28-29 Nov. 2019.

Formal methods for the synthesis of biomolecular circuits Shonan Village, Japan, 2-6 Sep 2019.

France-Taiwan Summer School on New Strategies in Medical Diagnosis and Precision Medicine, NTU, Taipei, Taiwan, 9-10 July 2019.

Grégory Batt was a member of the program committee of the IFAC Conference on Foundations of Systems Biology in Engineering (FOSBE 2019), Valencia, Spain, and of the scientific committee of Advanced Lecture Course on Computational Systems Biology summer school, Aussois, France.

François Fages was PC member of

CIBCB'19 16th IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology – Certosa di Pontignano, Siena - Tuscany, Italy, July 9-11, 2019

CMSB'19 17th International Conference on Computational Methods in Systems Biology. Trieste, Italy, September 2019.

HSB'19, 6th International Workshop on Hybrid Systems and Biology, will be held at the Charles University, Prague (CZ) on the 6th and 7th April 2019, and is colocated with ETAPS 2019.

BIOINFORMATICS'19 10th International Conference on Bioinformatics Models, Methods and Algorithms” co-located with BIOSTEC'19, Prague, Czech republic, Feb 2019.

Jakob Ruess was a PC member of CMSB'19, the 17th International Conference on Computational Methods in Systems Biology. Trieste, Italy, September 2019.

Sylvain Soliman was a PC member of CSBio 2019 10th International Conference on Computational Systems-Biology and Bioinformatics — Nice (France), December 4–7, 2019

Jakob Ruess was a reviewer for two conferences: *European Control Conference (ECC)* and *Bioinformatics 2020*.

François Fages is member of

the Editorial Board of the Computer Science area of the Royal Society Open Science journal, since 2014;

the Editorial Board of the journal RAIRO OR Operations Research, since 2004.

Grégory Batt was a reviewer for *Bioinformatics* and *ACS Synthetic Biology* journals.

François Fages made reviews for the journals *Biosystems* and *Transactions on Computational Biology and Bioinformatics*.

Mathieu Hemery made 5 reviews for the journal *Physical Review E*, and was a reviewer for the journals *Physical Review Letters* and *Royal Society Open Science*.

Jakob Ruess was a reviewer for the journals *Entropy* and *Scientific Reports*.

Sylvain Soliman was a reviewer for *Briefings in Bioinformatics*, and for the
*Journal of Theoretical Biology*.

Chetan Aditya gave a presentation entitled "Synthetic optogenetic differentiation system for bioproduction in budding yeast" at the EMBL workshop "Creating is Understanding: Synthetic Biology Masters Complexity", September 2019, Heidelberg, Germany

Grégory Batt gave the following invited talks:

Driving cellular processes with quantitative accuracy using real-time control approaches, 6th Cross Disciplinary Genomics Meeting of Sorbonne University, Nov 2019, Paris, France

Driving cellular processes with quantitative accuracy using real-time control approaches, SynGen Series UK, Nov 2019, London, UK

An experimental platform for automated calibration of antimicrobial resistance models, APHP/IP AMR STORM, Oct 2019, Paris, France

Balancing a genetic toggle switch by real-time control and periodic stimulations, International Workshop on Control Engineering and Synthetic Biology, Sept 2019, Oxford, UK

Experimental and computational methods for modeling cellular processes, Shonan meeting on Formal methods for the synthesis of biomolecular circuits, Sept 2019, Shonan, Japan

Driving cellular processes with quantitative accuracy using real-time control approaches, Seminar of Unité Physico Chimie Curie, Jan 2019, Paris, France

François Bertaux gave an invited talk at the JOBIM thematic session entitled "Methods for single-cell omics data analysis", Nantes, July 2019.

Andjela Davidovic gave a presentation entitled "Calibration of stochastic biochemical models using single-cell video-microscopy experiments" at the Stochastic models for biology conference (BioHasard 2019), August 2019, Rennes, France

François Fages gave invited talk to

Turing-completeness of Chemical Reaction Networks in Natural cells and Artificial vesicles (keynote CSBio 2019) The 10th International Conference on Computational Systems-Biology and Bioinformatics. Nice, France. December 4 to 7, 2019

Cells as Analog Chemical Computers: Turing Completeness and Synthesis Formal methods for the synthesis of biomolecular circuits Shonan Village, Japan, 2-6 Sep 2019.

Modeling and Design of Biological Systems, France-Taiwan Summer School on New Strategies in Medical Diagnosis and Precision Medicin, NTU, Taipei, Taiwan, 9-10 July 2019.

La Cellule un Calculateur Chimique Journées scientifiques Inria, Lyon, 5-7 juin 2019.

La Cellule un Calculateur Chimique Dassault-Systèmes, Vélizy, June 4th 2019.

Calculs Analogiques dans les Programmes Biochimiques Naturels et Synthétiques Colloque d'ouverture 50 ans du Laboratoire Jacques-Louis Lions, Roscoff, France, 4-8 mars 2019.

Calculs analogiques dans les réseaux biochimiques naturels et synthétiques Prix La recherche - 15th edition. Université Paris Dauphine, 4 Feb. 2019.

Jakob Ruess gave an invited talk at the SYMBIONT meeting entitled "Molecular noise shapes bacteria-phage ecologies", Paris, April 2019.

Grégory Batt is co-animator of the working group on Symbolic Systems Biology (GT Bioss).

Grégory Batt is a member of

the Technical Committee on Systems Biology of IEEE and CSS societies

the scientific board of the French research network on Bioinformatics (GdR BIM)

the scientific committee of the Advanced Course on Computational Systems Biology summer school, in Aussois

François Fages is member of

the Steering Committee of the International Conference on Computational Methods in Systems Biology, CMSB, since 2008.

the Scientific Committee of the Doctorate School ED 474 FIRE, ex Frontiers in Life Sciences, FdV

the Scientific Committee of the Summer School Modélisation Formelle de Réseaux de Régulation Biologique Ile de Porquerolles du 23 au 28 juin 2019 .

François Fages was

member of the Jury for Inria Awards 2019

evaluator of two DFG grant proposals

reviewer of a program proposal from Institut Pascal - Paris Saclay

reviewer of professorship application, Indraprastha Institute of Information Technology - Delhi, India.

Grégory Batt is the deputy director of the department of Computational Biology at Institut Pasteur

François Fages is member of the “Comité des Projets du centre” Inria Saclay-IdF, and Inria representative for Doctorate School Institut Polytechnique de Paris

Sylvain Soliman is member of the “Commission Scientifique” of Inria Saclay-IdF

Summer school: François Fages (teacher 6h) Modélisation Formelle de Réseaux de Régulation Biologique, Ile de Porquerolles.

Summer school: Jakob Ruess (teacher 6h) Blackboard course on Optimally learning dynamical models from data at the "Advanced lecture course on computational systems biology" in Aussois, France, April 2019.

Master: Grégory Batt (coordinator and teacher: 35h) and Jakob Ruess (25h), *Computational Biology*, M1, Interdisciplinary Approaches to Life Sciences (AIRE-LiSc).

Master: Grégory Batt (3h) *Synthetic Biology and Control course in Molecular and Cellular Biology* Sorbonne Université, Paris.

Master: François Bertaux (co-coordinator and teacher: 20h), *Systems Biology*, M1, Master Interdisciplinary Approaches to Life Sciences (AIRE-LiSc).

Master: François Fages (coordinator module 24h and teacher 12h)
*C2-19 Biochemical Programming*, Master Parisien de Recherche en Informatique (MPRI), Paris.

Master: François Fages (co-coordinator module 36h and teacher 18h) and Sylvain Soliman (co-coordinator, teacher 18h) INF555 - Constraint-based Modeling and Algorithms for Decision Making Problems Master Artificial Intelligence, Ecole Polytechnique.

Bachelor 3: François Fages (co-coordinator module and teacher 4h30) and Sylvain Soliman (co-coordinator module, teacher 4h30) and Mathieu Hemery (TD 10h) CSE 301b: Constraint Logic Programming.

Bachelor 2: Eléonore Bellot (teacher 64h) *CSE201 Object-oriented Programming in C++* TD and project supervision

L1: Julien Martinelli (teacher 64h) Mathematics and Calculus, Univ. Paris Descartes

PhD in progress: Chetan Aditya, “Control of heterogenous synthetic microbial systems”, ED FdV, Université Sorbonne Paris Cité, Feb. 2018, Grégory Batt

PhD in progress: Virgile Andréani, Calibration efficace de modèles de résistance bactérienne aux antibiotiques à l’aide d’un plan d’expériences optimal, ED IPP, Ecole Polytechnique, Sept. 2016, Grégory Batt

PhD in progress : Eléonore Bellot, “Réduction de modèles différentiels par résolution de contraintes d’algèbre tropicale (min,+)”, ED IPP, Ecole Polytechnique, Sept. 2018, F. Fages & S. Soliman (50-50%)

PhD in progress: Arthur Carcano, “Iterative design of single-cell experiments to learn single-cell models of biological systems”, ED FdV, Université Sorbonne Paris Cité, Oct. 2018, Jakob Ruess and Grégory Batt

PhD in progress : Elisabeth Degrand, “Chemical Programming in Non-living Vesicles”, ED IPP, Ecole Polytechnique, Oct. 2019, F. Fages & S. Soliman (50-50%)

PhD in progress : Jérémy Grignard, “Apprentissage de modèles à partir de données pour la conception d’expériences de criblage et la recherche de médicaments”, ED IPP, Ecole Polytechnique, dec. 2018, F. Fages & T. Dorval, Servier (50-50%)

PhD in progress : Eléa Greugny, “Development and Implementation of a Mathematical Model of Inflammation in the Human Skin”, ED IPP, Ecole Polytechnique, Aug. 2019, F. Fages & J. Bensaci & G. Stamatas, Johnson&Johnson Santé Beauté France (1/3-2/3))

PhD in progress : Julien Martinelli, “Apprentissage de modèles mécanistes à partir de données temporelles, application à la personnalisation de la chronothérapie des cancers”, ED IPP, Ecole Polytechnique, Oct. 2018, F. Fages & A. Ballesta, Inserm (50-50%)

PhD in progress: Sebastian Sosa Carrillo, “Understanding the cost of protein production in yeast”, ED FdV, Université Sorbonne Paris Cité, Feb. 2018, Grégory Batt

Master's Thesis: Orianne Bargain, “Graph matching, theory and SAT implementation”, TU Dresden, Germany, Sep. 2019, F. Fages & S. Soliman (50-50%)

Master's Thesis: Elisabeth Degrand, “Evolving Chemical Reaction Networks”, KTH Stockholm, Sweden, June 2019, F. Fages & M. Hemery (50-50%)

Grégory Batt participated in the jurys of

PhD Antoine Barizien, Ecole Potytechnique and Institut Pasteur, *Rapporteur*, Paris, May 2019

PhD Mathilde Koch, Ecole Polytechnique and INRA, *Examinateur*, Paris, November 2019

François Fages participated in the jurys of

HDR Sabine Pérès, Université Paris-Saclay, *Reviewer*, 18 Nov. 2019

HDR David Safranek, Mazaryk Univ., Czech Republic *Reviewer*, July 2019

HDR Sriram Krishnamachary, Indraprastha Institute of Information Technology, Delhi, India, *Reviewer*, April 2019

PhD Adrien Husson, Université Paris-Diderot, *Rapporteur*, 16 Dec. 2019

PhD Jorgelindo da Viega Moreira, Université Paris-Saclay, *Rapporteur*, 18 April 2019

Master Thesis, Orianne Bargain, Graph matching, theory and SAT implementation, Technische Universität Dresden, Germany, *Superviser* October 2019

Master Thesis, Zi-Jun Lin, National Taiwan University, *Reviewer*, July 2019

Master Thesis, Wei-Chih Huang, National Taiwan University, *Reviewer*, July 2019

Master Thesis, Elisabeth Degrand, Evolving Chemical Reaction Networks, Kungliga tekniska högskolan, Stockholm, Sweden, *Superviser* April 2019,

For a Festschrift in honor of Catuscia Palamidessi, François Fages was unexpectedly inspired to compose a small music score and write a short essay on information leakage in music scores .

Jakob Ruess gave an interview for the Swiss Institute of Bioinformatics (SIB) as a past laureate of the SIB Award.

Eléonore Bellot, Mathieu Hemery and Elise Weill Duflos participated at Fête de la Science at Inria SIF, Oct. 2019

François Fages has received college school students for a visit to our research team with the question “Can we program any function ?”, answered negatively by proving Cantor's theorem.

Mathieu Hemery has animated debates with college school students on Science and Ethics at Inria and Association Arbre des Connaissance, Oct. 2019.

François Fages has created several BIOCHAM interactive notebooks that are integrated in the current release of BIOCAHM-4 to illustrate and exercise the main concepts of analog chemical computer and chemical programming, as teached in his Master course at MPRI.