Research Scientist (Chargé de Recherche)
I use different frameworks for modeling dynamical systems: continuous (ordinary differential equations) and discrete systems (Boolean networks), as well as a hybrid formulation combining discrete synthesis terms with linear degradation (piecewise affine systems).
A description of some of my recent topics is given below:
For a quick overview
A paper showing how to combine continuous, hybrid and Boolean networks to obtain a rich, informative and predictive model of a given physical system (here the circadian clock mechanism in cyanobacteria)
A theoretical paper, introducing an original idea on how to compute the attractors of a large network through two smaller networks; an exact algorithm which is highly computational cost-saving.
A paper on the construction of original control strategies that are defined in a qualitative form, for piecewise affine systems (controls are constant within each state space region).
Member of Editorial Board
I am the coordinator of this new ANR project (the French National Research Agency), started in February 2017.
‣ Signalife (2012 -- 2020) Network for innovation on signal transduction pathways in life sciences.
A Labex (Laboratory of Excellence), hosted by the University of Nice Sophia Antipolis, it is composed of 49 local teams that study diverse aspects of signaling pathways. Labex funded by Investissements d'Avenir, 2011.
‣ RESET (2012 -- 2016) Arrest and restart of the gene expression machinery in bacteria: from mathematical models to biotechnological applications.
A project coordinated by Hidde de Jong and funded by Investissements d'Avenir, Bioinformatique. Started in October 2012, and runs for four years.
‣ GeMCo (2010 -- 2013) Model reduction, experimental validation, and control for the gene expression machinery in E. coli.
I am the coordinator of this ANR funded project (the French National Research Agency).
‣ ColAge (2009 -- 2014) Natural and engineering solutions to the control of bacterial growth and aging.
A joint research initiative (Inria Project Lab), co-funded by INRIA and INSERM (the French Institute for Medicine and Health), coordinated by Hugues Berry and Hidde de Jong.
PhD students at Inria
Applications to the modeling and analysis of genetic networks
The cellular division cycle is an essential process to ensure healthy tissue homeostasis, which can, due to its periodicity, be interpreted as a biological oscillator. In the context of project ICycle, we develop and calibrate a 2D model based on post-translational modifications of cyclin B-cdk1 and in its degradation by the APC:cdc20 complex. The behavior of the model is studied in a simple open-loop control configuration, showing that it can exhibit either bi-stability or oscillations.
In this work, we use several formalisms to model the Kai ABC oscillator, which is at the core of the cyanobacterial circadian clock. We start with a logical model, that contains the qualitative information available on the system. Building upon this structure, we then develop quantitative ``minimal'' models---with the smallest number of parameters and state variables---that are able to describe the dynamical properties observed in experiments. The resulting model successfully reproduces many of the defining properties of circadian rhythms, including the entrainment by the ``environment'', showing that it can synchronize with daily cycles, and it is robust to perturbations in the phase of oscillation.
Apoptosis is a form of programed cell death which enables the organism to remove unwanted or damaged cells. This is an essential biological function in a healthy living organism. The complex Nuclear Factor kB pathway interacts with the caspase cascade at the heart of apoptosis. I have worked on a Boolean model to study apoptosis regulation by the NFkB pathway. This is a system that exhibits bistability.
These are the genes responsible for establishing the anterior-posterior polarity of segments in the embryo of the fruit fly. I have been working on the analysis of a Boolean model of the segment polarity network, introducing asynchronous updating rules, and other methods that allow a more realistic analysis of discrete models with respect to the time scales of the various regulatory processes (transcription, translation, or post-translational modifications). One can also study the effect of cell division on the robustness of the network.
For the continuous model of the segment polarity network proposed by von Dassow et. al., we have fully characterized the feasible parameter space, as a hierarchy of intervals.
The binding of a ligand to a cell receptor triggers a sequence of biochemical reactions, that ultimately lead to a cell response (such as contraction, motility, proliferation). I am interested in the modeling of various stages in this process, from receptor-ligand binding, to cascades of protein activation, to cell signaling in the form of changes in the levels of cytosolic calcium, cAMP,... One of the goals would be to obtain validated models that help us in predicting and controlling the response of the cell to given stimulus.
Regulation and control of dynamical systems
In the context of project GeMCo, we developed a minimal synthetic gene circuit, that describes part of the gene expression machinery in Escherichia coli, and enables the control of the growth rate of the cells during the exponential phase. The model is a piecewise non-linear system, its dynamics and the bifurcation diagram with respect to the input are studied, and we obtain an analytic expression of the growth rate during the exponential phase as function of the input. Some problems include the identifiability of the parameters of the model supposing noisy measurements.
A biological network can be schematically described as an input/output Boolean module: that is, both the states, the outputs and inputs are Boolean. The interconnection of two Boolean modules can be characterized in terms of a new object, called the asymptotic graph , whose nodes are cross-products of the attractors of each module. We have shown that the attractors of the feedback interconnection of two Boolean modules can be fully identified in terms of the attractors of the asymptotic graph. Based on this result, a model reduction technique can be proposed, to predict the asymptotic dynamics of high-dimensional biological networks through the computation of the dynamics of two isolated smaller subnetworks.
The concentrations of proteins and mRNAs in genetic regulatory networks are often characterized as ``strongly'' or ``weakly'' expressed. To deal with this type of measurements we explore a qualitative method to control piecewise affine differential systems, by considering systems whose outputs and inputs are of a qualitative form. The control laws are piecewise constant functions in each region and in time, and are only allowed to take three qualitative values corresponding to no control (u=1), high synthesis rates (u=umax) or low synthesis rates (u=umin). An application is to control the bistable switch system to either of its steady states.
Gene expression can impact metabolic levels through changes in enzyme concentration and, conversely, metabolic species can influence gene transcription and hence modulate enzyme synthesis. To analyze these interactions, we have studied an unbranched metabolic chain (described by classical kinetic equations) with one metabolite acting as a global regulator of enzyme expression (described by PWA systems). Under the hypothesis that metabolic reactions happen in a faster time scale when compared to gene transcription or translation, we develop geometric criteria to characterize the equilibria (mono- or bi-stability) or possible oscillations of the network.
Many signaling pathways eventually trigger transcription of groups of genes. But, conversely, the signaling pathway may be regulated by gene expression patterns. We propose a simple model for such a regulatory mechanism, exploring the idea that the dynamics of a signal transduction pathway is must faster than the dynamics of gene transcription/translation.
Zero-deficiency chemical networksare a class of nonlinear systems which model general biochemical networks with mass-action kinetics. The formalism was first introduced and studied by M. Feinberg, F. Horn and R. Jackson. In my thesis I designed globablly asymptotically convergent observers for this class of systems, where other standard observers (eg. Kalman filters) may fail. I have extended the analysis of zero-deficiency networks to the case of time-dependent kinetic parameters - these are interpreted as inputs into the system. Such system satisfies an input-to-state stability (ISS) property, which guarantees robustness with respect to small perturbations in the kinetic parameters, such as temperature fluctuations, or various external factors and stimuli.