Program


 Monday, 13.09


 
09:00-09:25 Registration
09:25-09:30 welcoming speech
09:30-10:15 O. Martin Modeling and computations for biological networks
10:15-10:45 coffee break
10:45-11:30 H. Isambert Non-adaptive constraints in biological network evolution
11:30-12:15 L. Peliti TBA
12:15 lunch
14:00-14:45 O. Rivoire A model for the value of biological information
14:45-15:30 C. Furthlener TBA
15:30-16:00 coffee break
16:00-16:45 J.P. Nadal Neural coding of categories: from e_cient coding to reaction times
16:45-17:30 A. Barra A statistical mechanics approach to immune networks
18:30 Conference dinner


 

Tuesday, 14.09


 
09:30-10:15 V. Hakim Computational identification of  cis-regulatory motifs and modules underlying cell-specific gene expression
10:15-10:45 coffee break
10:45-11:30 F. Massucci The Storm and Nelson's model for polymer stretching revisited
11:30-12:15 M. Weigt The identification of residue-contacts in proteins, and the inverse Potts problem
12:15 lunch
14:00-14:45 J. Berg Stochastic gene expression and the statistics of rare évents
14:45-15:30 A. De Martino Constrained Markovian dynamics of random graphs
15:30-16:00 coffee break
16:00-16:45 E. Marinari The Solution Space of Metabolic Network
16:45-17:30 P. Lió System Biology Analysis and Optimization of Bacterial Metabolism and Photosynthesis


 

Wednesday, 15.09


 
09:30-10:15 M. Marsili Luck, institutions and property rights from spontaneous symmetry breaking in systems of interacting agents
10:15-10:45 coffee break
10:45-11:30 A. Carbone Reading mechanical and functional properties of proteins from the topology of phylogenetic trees
11:30-12:15 I. Junier TBA
12:15 lunch
14:00-14:45 A. Fouquier d'Herouel Simple and efficient screening for ncRNAs
14:45-15:30 A. Scardicchio TBA
15:30-16:00 coffee break
16:00-16:45 F. Krzakala Inference in particle tracking experiments by passing messages between images

 

Abstracts

Olivier Martin

Modeling and computations for biological networks

It is often claimed that biological networks are particularly robust or modular, or have remarkable features such as being scale free. To separate consequences of evolutionary dynamics from constraints due to functionality, we first construct a microscopic model of the genotype to phenotype map which then specifies the effects of these constraints. Given such a framework, we show how Monte Carlo sampling reveals the exceptionality or not of different biological networks.

Hervé Isambert

Non-adaptive constraints in biological network evolution

We are interested in the role of non-adaptive constraints in the evolution of biomolecular networks. I will show, in particular, that duplication-divergence processes, at the level of individual genes and whole genome duplication, bring not only genetic novelty but also evolutionary constraints restricting by construction the emerging properties of biomolecular networks. I will also discuss the role of non-adaptive constraints on the evolution of signaling pathways implicated in cancer development. All in all, evolutionary constraints appear to have largely controlled the overall topology and scale-dependent conservation of biomolecular networks.

Luca Peliti

TBA

Olivier Rivoire

A model for the value of biological information

The notion of information pervades informal descriptions of biological systems, but formal treatments face the problem of defining a quantitative measure of information rooted in a concept of fitness, which is itself an elusive notion. I will present a model where this problem is amenable to a mathematical analysis. In a limiting case of this model, the mutual information emerges as the relevant measure of information, as in Shannon’s communication theory. In general however, the analysis of the model leads to extensions of the concept of mutual information in three different directions, to account for biological features absent from Shannon's model of communication. (work in collaboration with Stanislas Leibler)

Cyril Furthlener

Sampling the Pareto front of Multi-Objective 3-SAT problems with message passing techniques

We consider a family of multi-objective 3-SAT problems and propose a way  to sample the so-called Pareto front, i.e. the set of Pareto optimal solutions associated to such problems. The method consists first in  defining a Markov random field  well suited to approximate the distribution of Pareto optimal  solutions, by enforcing locally a Pareto requirement criteria. Then we derive the survey-propagation equations corresponding  to this measure and finally set up an algorithm able to sample solutions close to the Pareto front, which combines an elimination procedure of clauses with the usual decimation of variables used in the survey propagation algorithm,  to explore different regions of the front (work in coll. with Marc Schauenauer).

Jean Pierre Nadal

Neural coding of categories: from e_cient coding to reaction times

When dealing with a categorization task, the brain has to face two different sources of uncertainty: categorization uncertainty and neuronal uncertainty. The latter stems from neuronal noise, whereas the former is intrinsic to the category structure: categories like phonemes or colors typically overlap in stimulus space, so that a given stimulus might corresponds to different categories. Here, we propose a general neural theory of category coding, in which these two sources of uncertainty are quantified by means of information theoretic tools. We analytically show [1, 2] how these two quantities combine at both coding and decoding stages of the information process. From the hypothesis of optimal information processing, we derive formulae which capture different psychophysical consequences of category learning -- namely, a better discrimination between categories, and longer reaction times to identify the category of a stimulus lying at the category boundary. The main outcome of this work is thus to exhibit, in both quantitative and qualitative terms, the interplay between discrimination and identification. Our results are confronted with experimental data taken from both the psycholinguistic and neuroscience literature.

References:

Adriano Barra

A statistical mechanics approach to immune networks

join work with Elena Agliari (Dipartimento di Fisica Università di Parma)

In this talk, at first, a short introduction to theoretical immunology will be presented. Then the two main approaches (Burnet and Jerne theories) will be bridged respectively to one-body and two-body models in statistical mechanics offering a new unifying perspective for these strands. Furthermore, close to Varela viewpoint, focusing on topological properties of the immune network (in our model built up by antibody interactions) a proposal for the explanation of several properties of the immune system (namely low dose tolerance and self-non self discrimination) will be presented in the statics. Turning to the dynamics, other properties as the improvement of the secondary response and the bell shaped functions will be achieved. 

Vincent Hakim

Computational identification of  cis-regulatory motifs and modules underlying cell-specific gene expression

Cell specificity depends in part  on the interpretation of the genomic  cis-regulatory information by sequence-specific factors, which results in the establishment of specific transcriptional programs of gene expression. Decoding this information in sequence genome is an important issue. I will describe a computational method that we recently developed to try and computationally map the cis-regulatory elements that control gene expression in a set of co-regulated genes with no a priori information on the transcriptional factors involved. The input is formed by a small collection of known Cis-Regulatory Modules (CRMs) in a species of interest, that serves as a training set. Extensive use is made of orthologous sequences in other sequenced genomes. As a first application,the method was applied to the gene expression program active in fly sensory organ precursor cells (SOPs),a specific type of neural progenitor cells.

Work performed with H Rouault, in collaboration with K Mazouni, L Couturier and F Schweisguth at Institut Pasteur.

Francesco Massucci

The Storm and Nelson's model for polymer stretching revisited

The development of the experimental techniques involved in molecular biology allows nowadays the manipulation of single molecules, among which a primary role is played by molecule stretching. To get meaningful insights on the mechanical properties of the observed molecules it is then necessary to provide physical models which can fit the data returned by the experiments. In this talk I will discuss a phenomenological model which reproduces the behaviour of a molecule of double stranded DNA (dsDNA) under tension. Introducing two slightly different versions of the model we are able to study both the low and high forces regimes, which differ because of the existence of the so-called overstretching transition. Tackling the problem via the cavity method we are able to fit our solution to the experimental measurements and hence to measure the molecular units' length, the molecule's bending stiffness and its elasticity.

Martin Weigt

The identification of residue-contacts in proteins, and the inverse Potts problem.

The structure of proteins puts constraints on the evolution of their sequence. A random mutation of a residue in a protein (or an interacting protein pair) may lead to conflicts with the chemical and physical properties of its directly neighboring residues in the folded protein (or the assembled protein complex) and decrease the stability of the protein (or protein complex). As a consequence, correlations in the amino-acid occupation of residues in contact emerge in the course of evolution. Seen the huge increase in sequenced genomes, I will discuss the inverse problem: Given large multiple-sequence alignments of evolutionarily related (homologous) proteins, can we identify residue contacts by statistical analysis of amino acid frequencies? As already explained at the Les Houches meeting in March, a maximum-entropy model leads directly to the inverse Potts problem, i.e., to infer the coupling constants of a disordered Potts model from its equilibrium configurations. I will formulate several approaches to solve this problem (mean field, TAP, susceptibility propagation), and will compare their relative performance in predicting residue contacts. I will also provide evidence that at least about 1000 aligned sequences are needed to identify contacts with high precision.

Johannes Berg

Stochastic gene expression and the statistics of rare events

Gene expression refers to the fundamental process by which genetic information is read out to produce functional molecules such as proteins. Gene expression is intrinsically noisy due to low copy numbers of the molecules involved. Recent experimental progress in the field of biological imaging of single cells has shed light on the different sources of noise in gene expression and has spurred corresponding theoretical efforts. In this talk, I will discuss the phenomenology of noise in gene expression and discuss the use of semiclassical methods to describe the statistics of rare events.

Andrea de Martino

Constrained Markovian dynamics of random graphs

A statistical mechanics formalism for the study of constrained graph evolution as a Markovian stochastic process is introduced. As an application we analyze the properties of degree-preserving Markov chains based on elementary edge switchings. We give an exact yet simple formula for the mobility (the number of allowed swaps) of any graph in terms of the graph’s adjacency matrix and its spectrum. This formula allows us to define acceptance probabilities for edge switchings, such that the Markov chains become controlled Glauber-type detailed balance processes, designed to evolve to any required invariant measure. We also derive a condition in terms of simple degree statistics, sufficient to guarantee that, in the limit where the number of nodes diverges, even for state-independent acceptance probabilities of proposed moves the invariant measure of the process will be uniform. We test our theory on synthetic graphs and on realistic larger graphs as studied in cellular biology.

Enzo Marinaro

The Solution Space of Metabolic Network

Understanding the organization of reaction fluxes in cellular metabolism from the stoichiometry and the topology of the underlying biochemical network is a central issue in systems biology. In this task, it is important to devise reasonable approximation schemes that rely on the stoichiometric data only, because full-scale kinetic approaches are computationally affordable only for small networks (e.g. red blood cells, about 50 reactions). Methods commonly employed are based on finding the stationary flux configurations that satisfy mass-balance conditions for metabolites, often coupling them to local optimization rules (e.g. maximization of biomass production) to reduce the size of the solution space to a single point. Such methods have been widely applied and have proven able to reproduce experimental findings for relatively simple organisms in specific conditions. Here we define and study a constraint-based model of cellular metabolism where neither mass balance nor flux stationarity are postulated, and where the relevant flux configurations optimize the global growth of the system.  In the case of {\it E. coli}, steady flux states are recovered as solutions, though mass-balance conditions are violated for some metabolites, implying a non-zero net production of the latter. Such solutions furthermore turn out to provide the correct statistics of fluxes for the bacterium {\it E. coli} in different environments and compare well with the available experimental evidence on individual fluxes. Conserved metabolic pools play a key role in determining growth rate and flux variability. Finally, we are able to connect phenomenological gene essentiality with `frozen' fluxes (i.e. fluxes with smaller allowed variability) in {\it E. coli} metabolism.

Pietro Lio

System Biology Analysis and Optimization of Bacterial Metabolism and Photosynthesis

Matteo Marsili

Luck, institutions and property rights from spontaneous symmetry breaking in systems of interacting agents

Consider a generic situation where a population of agents asynchronously accesses a number of resources. Usage of resources is exclusive: if an agent is using a resource, other agents cannot use it. Examples include searching for parking, establishing colonies and animals trying to establish a territory or a position in pecking order. Nash equilibria can be of two types: Symmetric, when each agent adopts the same strategy in order to search for unexploited resources, and asymmetric, where different agents play differently. When the preferences of different agents over resources are uncorrelated, the problem is one of coordination. When preferences are aligned, i.e. all agents rank resources in the same way, coordination intertwine with competition for the best resources in non-trivial ways. Then, when asymmetric outcomes prevail, some agents turn out to occupy more frequently the best resources, as if they were lucky, or if they had property rights on those resources. I discuss, in simple models, the transition from symmetric to asymmetric states, how it materializes and its determinants.

Alessandra Carbone

Reading mechanical and functional properties of proteins from the topology of phylogenetic trees

Given a family of protein sequences and the associated distance tree, we shall explain how to predict important information on protein binding sites and on mechanical and allosteric properties by using a fine reading of the conservation and co-evolution signals between residues in the sequences. This information is encoded in the tree topology.

Ivan Junier

TBA

Aymeric Fouquier d’Herouel

Simple and efficient screening for ncRNAs

Non-coding (nc) RNAs have emerged as important and ubiquitous players in gene regulation across all kingdoms, adding new layers of complexity to the regulatory machinery. Challenging views about the junk content of higher organisms' genomes and partially explaining the high complexity vs low gene number observed in many organisms, ncRNAs exhibit a broad range of functions, raising exciting questions to an interdisciplinary community. We have performed a genome wide computational prediction of ncRNAs and their putative targets in Enterococcus faecalis, a commensal but opportunistic bacteria and major nosocomial pathogen. 45 selected loci were assessed experimentally using a newly developed method which allows us to verify the presence of a given transcript, while at the same time discerning primary from processed ones. We have discovered 29 novel ncRNAs, 10 putative novel mRNAs, and 16 antisense organizations. Functional analysis is ongoing on these candidates, yet first results exhibit highly interesting regulatory properties.

Antonello Scardicchio

TBA

Florent Krzakala

Inference in particle tracking experiments by passing messages between images

Methods to extract information from the tracking of mobile objects/particles have broad interest in biological and physical sciences. Techniques based on simple criteria of proximity in time-consecutive snapshots are useful to identify the trajectories of the particles. However, they become problematic as the motility and/or the density of the particles increases due to uncertainties on the trajectories that particles followed during the images' acquisition time. Here, we report an efficient method for learning parameters of the dynamics of the particles from their positions in time-consecutive images. Our algorithm belongs to the class of message-passing algorithms, known in computer science, information theory and statistical physics as Belief Propagation (BP). The algorithm is distributed, thus allowing parallel implementation suitable for computations on multiple machines without significant inter-machine overhead. We test our method on the model example of particle tracking in turbulent flows, which is particularly challenging due to the strong transport that those flows produce. Our numerical experiments show that the BP algorithm compares in quality with exact Markov Chain Monte-Carlo algorithms, yet BP is far superior in speed. We also suggest and analyze a random-distance model that provides theoretical justification for BP accuracy. Methods developed here systematically formulate the problem of particle tracking and provide fast and reliable tools for its extensive range of applications.

Invited speakers: