Section: Research Program

Systems Biology

Participants : Patrick Amar, Sarah Cohen-Boulakia, Alain Denise, Christine Froidevaux, Loic Paulevé, Sabine Pérès, Jean-Marc Steyaert, Erwan Bigan, Adrien Rougny.

Systems Biology involves the systematic study of complex interactions in biological systems using an integrative approach. The goal is to find new emergent properties that may arise from the systemic view in order to understand the wide variety of processes that happen in a biological system. Systems Biology activity can be seen as a cycle composed of theory, computational modelling to propose a hypothesis about a biological process, experimental validation, and use of the experimental results to refine or invalidate the computational model (or even the whole theory). During the past five years, new questions and research domains have been identified, and some members of the team have reoriented a part of their activities on these questions.

Three main types of problems have been studied: metabolic networks, signaling networks and more recently synthetic biology. Networks - have become popular since many crucial problems, coming from biology, medecine, pharmacology, are nowadays stated in these terms: a great number of them are issued from the cancer phenomenom and the will to enhance our understanding in order to propose more efficient therapeutic issues. Metabolism has received the major attention since it concerns a large variety of topics and several methods that have been proposed. Depending on the nature of the biological problem, several methods can be used : discrete deterministic, stochastic, combinatorial, up to continuous differential. Also, the recent rise of synthetic biology proposes similar challenges aiming at improving the production of energy by means of biological systems or at getting more efficient medicamental treatments, for instance.

Topological analysis of metabolic networks

Participant : Sabine Pérès.

Elementary flux mode analysis is a powerful tool for the theoretical study of simple metabolic networks. However, when the networks are complex, the determination of elementary flux modes leads to a combinatorial explosion of their number which prevents from drawing simple conclusions from their analysis. Since the concept of elementary flux mode analysis was introduced in 1994, there has been an important and ongoing effort to develop more efficient algorithms. However, these methods share a common bottleneck: they enumerate all the elementary flux modes which make the computation impossible when the metabolic network is large and only few works try to search only elementary flux mode with specific properties. We have shown that enumerating all the elementary flux modes is not necessary in many cases and it is possible to directly query the network instead with an appropriate tool. For ensuring a good query time, we have relied on a state of the art SAT solver, working on a propositional encoding of elementary flux mode, and enriched with a simple SMT-like solver ensuring elementary flux mode consistency with stoichiometric constraints. We have illustrated our new framework by providing experimental evidences of almost immediate answer times on a non trivial metabolic network  [45] , [70] .

Signaling networks

Participants : Sarah Cohen-Boulakia, Christine Froidevaux, Adrien Rougny.

Signaling pathways involving G protein-coupled receptors (GPCR) are excellent targets in pharmacogenomics research. Large amounts of experiments are available in this context while globally interpreting all the experimental data remains a very challenging task for biologists. Our goal is to help the understanding of signaling pathways involving (GPCR) and to provide means to semi-automatically construct the signaling networks.

We have introduced a logic-based method to infer molecular networks and show how it allows inferring signaling networks from the design of a knowledge base. Provenance of inferred data has been carefully collected, allowing quality evaluation. Our method (i) takes into account various kinds of biological experiments and their origin; (ii) mimics the scientist’s reasoning within a first-order logic setting; (iii) specifies precisely the kind of interaction between the molecules; (iv) provides the user with the provenance of each interaction; (v) automatically builds and draws the inferred network [43] .

Observe that a logic-based formalisation is used as in some works carried out in Inria team Dyliss . Amib aim is different, as the design of the network lies on a knowledge-based system describing experimental facts and ontological relationships on backgound knowledge, together with a set of generic and expressive rules, that mimick the expert's reasoning.

This is a collaboration with A. Poupon (Inra-Bios , Tours) that was supported by an Inra-Inria starting grant in 2011-2012.

Modelling and Simulation

Participants : Patrick Amar, Sarah Cohen-Boulakia, Loic Paulevé, Jean-Marc Steyaert, Erwan Bigan.

A great number of methods have been proposed for the study of the behavior of large biological systems. The first one is based on a discrete and direct simulation of the various interactions between the reactants using an entity-centered approach; the second one implements a very efficient variant of the Gillespie stochastic algorithm that can be mixed with the entity-centered method to get the best of both worlds; the third one uses differential equations automatically generated from the set of reactions defining the network.

These three methods have been implemented in an integrated tool, the Hsim system  [41] . It mimics the interactions of biomolecules in an environment modelling the membranes and compartments found in real cells. It has been applied to the modelling of the circadian clock of the cyanobacterium, and we have shown pertinent results regarding the spontaneous appearance of oscillations and the factors governing their period  [42] .

Synthetic biology

Synthetic biology begins to be a very popular domain of research. Genetic engineering is a good example of synthetic biology, organisms are artificially modified to boost the production of compounds that might be used in the medical or industrial domains. We have been focused on using synthetic biology for medical diagnostic purposes. In a collaboration with the Sysdiag Lab (UMR 3145 ) at Montpellier, P. Amar participates at the CompuBioTic project. The goal is to design, test and build an artificial embedded biological nano-computer in order to detect the biological markers of some human pathologies (colorectal cancer, diabetic nephropathy, etc.). This nano-computer is a small vesicle containing specific enzymes and membrane receptors. These components are chosen in a way that their interactions can sense and report the presence in the environment of molecules involved in the human pathologies tageted. We plan to design a dedicated software suite to help the design and validation of this artificial nano-computer. Hsim is used to help the design and to test qualitatively and quantitatively this "biological computer" before in vitro.

Evaluating metabolic networks

It is now well established in the medical world that the metabolism of organs depends crucially of the way the calls consume oxygene, glucose and the various metabolites that allow them to grow and duplicate. A particular variety of cells, tumour cells, is of major interest. In collaboration with L. Schwartz (AP-HP ) and biologists from Inserm-INRA Clermont-Theix we have started a project aiming at identifying the important points in the metabolic machinery that command the changes in behaviour. The main difficulties come from the fact that biologists have listed dozens of concurrent cycles that can be activated alternatively or simultaneously, and that the dynamic characteristics of the chemical reactions are not known accurately.

Given the set of biochemical reactions that describe a metabolic function (e.g. glycolysis, phospholipids' synthesis, etc.) we translate them into a set of o.d.e's whose general form is most often of the Michaelis-Menten type but whose coefficients are usuall very badly determined. The challenge is therefore to extract information as to the system's behavior while making reasonable asumptions on the ranges of values of the parameters. It is sometimes possible to prove mathematically the global stability, but it is also possible to establish it locally in large subdomains by means of simulations.Our program Mpas (Metabolic Pathway Analyser Software) renders the translation in terms of a systems of o.d.e's automatic, leading to easy, almost automatic simulations. Furthermore we have developed a method of systematic analysis of the systems in order to characterize those reactants which determine the possible behaviors: usually they are enzymes whose high or low concentrations force the activation of one of the possible branches of the metabolic pathways. A first set of situations has been validated with a research Inserm-Inra team based in Clermont-Ferrand. In her PhD thesis, defended in 2011, M. Behzadi proved mathematically the decisive influence of the enzyme PEMT on the Choline/Ethylamine cycles.

Comparison of Metabolic Networks

We study the interest of fungi for biomass transformation. Cellulose, hemicellulose and lignin are the main components of plant biomass. Their transformation represent a key energy challenges of the 21st century and should eventually allow the production of high value new compounds, such as wood or liquid biofuels (gas or bioethanol). Among the boring organisms, two groups of fungi differ in how they destroy the wood compounds. Analysing new fungi genomes can allow the discover of new species of high interest for bio-transformation. For a better understanding of how the fungal enzymes facilitates degradation of plant biomass, we conduct a large-scale analysis of the metabolism of fungi. Machine learning approaches such like hierarchical rules prediction are being studied to find new enzymes allowing the transformation of biomass. The Kegg database http://www.genome.jp/kegg/ contains pathways related to fungi and other species. By analysing these known pathways with rules mining approaches, we aim to predict new enzymes activities.