- Hidde de Jong, INRIA, Qualitative simulation of bacterial stress responses

proteins, metabolites, and their mutual interactions. We have analyzed the network of global transcription regulators controlling the

adaptation of the bacterium Escherichia coli to environmental stress conditions. Even though E. coli is one of the best studied model

organisms, it is currently little understood how a stress signal is sensed and propagated throughout the network of global regulators, and

leads the cell to respond in an adequate way. Using a qualitative method that is able to overcome the current lack of quantitative data

on kinetic parameters and molecular concentrations, we have modeled the carbon starvation response network and simulated the response of

E. coli cells to carbon deprivation. This has allowed us to identify essential features of the transition between exponential and

stationary phase and to make new predictions on the qualitative system behavior following a carbon upshift. The model predictions have been

tested experimentally by means of gene reporter systems.

- Frédéric Boyer, CEA Grenoble, A comparative transcriptomics approach for extracting transcriptional regulation knowledge about heavy metals stress-induced response in Arabidopsis thaliana

Data
generated by global approaches
such transcriptomics, proteomics or metabolomics give only a partial
vision of
the biological systems

if
they are not thoroughly examined and are still considered
as a major hurdle. Within this context, our biological investigation
focus on
the

transcriptional
regulation study of the sulphur metabolism pathway in
the *Arabidopsis* *thaliana*
model
plant in response to a metal stress

induced by cadmium. The purpose of
the
study is to identify the molecular factors involved in the cell
adaptation to
the metal stress.

Precisely, our aim is to extract coarse grain
knowledge about the transcriptional
network coordinating the plant response and to discriminate

the
metal-induced
specific response from the generic stress responses. As a first step,
awaited
results are the following: i. targeting transcriptional

modules
containing
metal responsive genes; ii. identification of putative transcriptional
regulators coordinating the metal response

and iii. associated putative
regulatory cis-elements.

To
achieve these, several approaches have been
already proposed that rely on different methodological framework but
all
requiring a large amount

of measurements. In our case, only few
microarray datasets
about metal stress response are available today. At a first stage, we
choose to
perform

a comparative exploration of a large number of *Arabidopsis*
expression microarray datasets among various
experimental conditions (including

cadmium stress condition). The main
idea is
to select conditions where a subset of genes exhibits a similar pattern
of
regulation. In this talk,

I will mainly focus on the methodological
approach we
have retained and I will present our preliminary results and discuss
opening
questions.

Joint work
with J. Bourguignon and Y. Vandenbrouck

- Grégory Batt, INRIA, Robustness analysis and tuning of synthetic gene networks

gene networks implementing simple functions has demonstrated the feasibility of this approach. However, the design of these networks is

difficult, notably because existing techniques and tools are not adapted to deal with uncertainties on molecular concentrations and parameter values.

We propose an approach for the analysis of a class of uncertain piecewise-multiaffine differential equation models. This

modeling framework is well-adapted to the experimental data currently-available. Moreover, these models present interesting

mathematical properties that allow the development of efficient algorithms for solving robustness analyses and tuning problems.

These algorithms are implemented in the tool RoVerGeNe, and their practical applicability and biological relevance are demonstrated on the

analysis of the tuning of a synthetic transcriptional cascade built in E. coli.

Joint work with C. Belta and R. Weiss

- Thao Dang, Verimag, Reachability techniques for the analysis of dynamical systems models in biology

dynamical systems models in biology. In particular, we focus on a reachability technique for systems with polynomial differential equations,

which are a useful model for a variety of biological systems. The essence behind the technique we propose can be described as extending

traditional numerical integration to set integration, and set computations in numerical schemes are performed using techniques from

computer aided geometric design, such as the Bezier techniques.

- Eric Fanchon, CNRS-TIMC, Constraints-based methods for the qualitative modeling of biological networks

with feedback loops. In addition some kind of data are generally lacking, especially kinetic parameters, and the way several

interactions combine on a given node is often ill-defined. In this context, our goal is to provide formal tools to assist in reasoning,

inference of model parameters, model revision, hypothesis generation. Constraints represent a natural frame to work with complex systems.

Constraint programming is a family of computer science technologies which allow to describe a problem in terms of mathematical relationships or equations.

It is a declarative approach in which all knowledge about structure and behaviour is described. If parameters are unkown, they are considered as

problem variables. No 'reasonable choices' need be done, contrary to what is done when performing simulations. We are currently using two constraint

technologies: Constraint Logic Programming (CLP) and boolean satisfiability (SAT). They differ by their expressiveness and their

underlying solvers. In SAT all knowledge must be represented in propositional (boolean) logic, which is weakly expressive, but very

efficient solvers exist to ckeck the (un)satisfiability of large boolean formulae.

We focus on a particular kind of networks with sigmoidal interactions. Many regulatory systems can be described in this way.

These interactions can be approximated by step functions, and the behaviour of such system can be described by discrete equations in

place of ordinary differential equations (ODEs), resulting in the so-called Thomas networks, and their generalization by de Jong and colleagues.

The discrete nature of this formalism leads to a representation of all the possible behaviours in the form of a transition graph.

When model parameters are unkown, a set of transition graphs has to be considered.

When all knowledge and hypotheses have been represented as contraints, queries can be asked to the model. If inconsistencies appear in the

resulting constraint system, critical constraints must be identified in order to revise the model. This approach is illustrated with a

model of nutritional stress in E. coli. The discrete model deduced from the ODEs allows to identify the origin of a discrepancy between

model and observations. From this we can go back to the differential representation and propose new biological hypotheses.

Joint work with Fabien Corblin, Sébastien Tripodi, Laurent Trilling

- Sol Efroni, NIH, Identification of key processes underlying cancer phenotypes using biologic pathway analysis

An increasingly deep catalogue of canonical networks details the specific molecular interaction of genes and their products.

However, mapping of disease phenotypes to alterations of these networks of interactions is accomplished indirectly and non-systematically.

Here we objectively identify pathways associated with malignancy, staging, and outcome in cancer through application of an analytic approach that

systematically evaluates differences in the activity and consistency of interactions within canonical biologic processes.

Using large collections of publicly accessible genome-wide gene expression, we identify small, common sets of pathways – Trka Receptor, Apoptosis

response to DNA Damage, Ceramide, Telomerase, CD40L and Calcineurin – whose differences robustly distinguish diverse tumor types from

corresponding normal samples, predict tumor grade, and distinguish phenotypes such as estrogen receptor status and p53 mutation state.

Pathways identified through this analysis perform as well or better than phenotypes used in the original studies in predicting cancer outcome.

This approach provides a means to use genome-wide characterizations to map key biological processes to important clinical features in disease.

- René Thomas, ULB, Frontier diagrams: a global view of the structure of phase space

- Denis Thieffry, Marseille Univ, Dynamical analysis of logical models of genetic regulatory networks

and to obtain insight in their dynamical behaviours. One qualitative approach consists in modelling regulatory networks in terms of logical

equations, using Boolean or multi-level variables. Recently, we have proposed a novel implementation of the multi-level logical

modelling approach by means of Multi-valued Decision Diagrams. This representation enabled the development of two efficient

algorithms for the dynamical analysis of parameterised regulatory graphs. A first algorithm allows the identification of all stable

states without generating the state transition graph. A second algorithm assess the conditions insuring the functionality of the

feedback circuits found in the regulatory graph. These algorithms have been implemented into a novel development version of our logical

modelling software GINsim. Their application to logical models of T cell activation and differentiation will be briefly presented

- Jacques Demongeot, UJF-TIMC, Evolution and RNA relics

interest for questions on the origin of life. Nevertheless, a gap continues to exist between the research on prebiotic chemistry and

molecule generation, on one hand, and the study of molecular fossils preserved in genomes, on the other. Here we attempt to fill this gap

by using some assumptions about the prebiotic scenario (including a strong stereochemical basis for the genetic code) to determine the RNA

sequences more likely to appear and subsist. A set of minimal RNA rings is exhaustively determined; a subset of them is then selected

through stability arguments, and a particular ring ("AL ring") is finally singled out as the most likely winner of this prebiotic game.

The rings happen to have several structural and statistical properties of modern genes: a repeated AUG codon appears spontaneously

(and is thus made available for becoming a start signal), the form AUG/STOP emerges, and frequency patterns resemble those of present genes.

The whole set of rings was also compared to a database of tRNAs, considering the conserved positions (located in the free parts of the

molecule, essentially the loops); the ring that most closely matched tRNA sequences ?and matched, in fact, the consensus of tRNA at all the

aligned positions? was AL, the same ring independently selected before. The unselected emergence of gene-like features through two

simple selection steps, and the close similarity between the finally selected ring and tRNA (including some remarkable features of the

resulting alignment), suggest a possible link between the prebiotic world and the first biological molecules, which is amenable for

experimental testing. Even if our scenario is partially wrong, the unlikely coincidences should provide useful hints for other efforts.

- Gilles Bernot, Polytech'Nice, A discrete approach to model gene regulatory networks and the use of formal logic to propose new wet experiments

temporal properties of biological regulatory networks, expressed in Computational Tree Logic.

It is then possible to automatically compute all the models whose behaviour satisfies a set of given temporal properties. The chosen temporal properties

can reflect established knowledge about the model as well as hypotheses which motivate the biological research.

If the set of computed models is not empty, then we can manipulate the temporal formulae which formalize the hypotheses in a computer aided manner,

according to some logical rules. This allows us to derive a set of sensible wet experiments capable to refute or validate the hypotheses.

Our approach is illustrated on the cytotoxicity example in Pseudomonas aeruginosa.

- Heike Siebert, Berlin Univ, Logical modeling with time delays

data. Among others, R. Thomas introduced a discrete formalism that captures the structure and the qualitative behavior of a

system. However, the resulting representation of the network's dynamics is coarse and non-deterministic due to the restrictive data

incorporated in the model. A more detailed description of the dynamics is possible if we allow for the integration of temporal information on

the different processes involved in the system's behavior. In this talk, we present an extension of the logical Thomas formalism using

the framework of timed automata, and discuss advantages and difficulties of this approach.

- Koret Hirschberg, Tel-Aviv Univ, Kinetic modeling and in vivo
*imaging applied to studying intracellular membrane transport and organelle dynamics*

Intracellular protein and membrane transport can now be studied using microscopy of intact living cells. This approach allows the direct

qualitative and quantitative analysis of the dynamics of a wide range of membrane transport processes. The living cell is thus emerging as a

remarkably complex experimental system, even in the case where a simple unidirectional route of a single fluorescent protein is visualized and

analyzed. In this talk I will try to summarize a decade-long study of the transport dynamics of a fluorescently tagged membrane cargo protein

called VSVG. This protein travels “upon request” owing to a shift to permissive temperature that promotes its folding and export from the

endoplasmic reticulum, through the Golgi apparatus to the cell surface, by membrane bound transport carriers. Studying the intracellular

transport of this single protein has provided us with a wealth of information on the dynamic properties of intracellular transport.

Quantitative kinetic modeling allowed us for the first time to obtain the precise kinetic parameters that accurately describe the entire

intracellular route of VSVG using a series of simple mono-exponential equations. These and other emerging dynamic properties, prompted us to

challenge well established dogmas related to transport mechanisms as well as morphological-functional properties of the Golgi apparatus, a

central secretory organelle. Thus far, these are still modest steps in the long journey towards unraveling the mechanisms underlying the

formation and maintenance of cellular complexity.

- David Harel, Weizmann, Beyond the gene

in which that concept finds itself. After briefly reviewing these problems, we propose an alternative to both the concept and the word gene – an

alternative that, like the gene, is intended to capture the essence of inheritance, but which is both richer and more expressive. It is also

clearer in its separation of what the organism statically is (what it tangibly inherits) and what it dynamically does (its functionality and

behavior). Our proposal of a genetic functor, or genitor, is a sweeping extension of the classical genotype/phenotype paradigm, yet it appears to be

faithful to the findings of contemporary biology, encompassing many of the recently emerging, and surprisingly complex, links between structure and

functionality.

Joint work with Evelyn Fox Keller.

- Hans Geiselmann, UJF, From gene expression to genetic regulatory networks

therefore like to (i) determine the connections within the genetic regulatory network and (ii) predict the resulting dynamics gene

expression. Extensive and expensive experiments molecular genetics can, of course, provide this information. However, we would like to

derive the relevant properties of the system from much more easily obtained expression data. We have used this approach in bacterial

model systems. We will present a very simple method for reconstructing a genetic regulatory network in the special case of a closed and

linear system: the mutual regulation of the five sigma factors of the cyanobacterium Synechocystis. More recently, we have acquired time

series expression data during growth transitions in the model bacterium Escherichia coli. We will show how to obtain sufficiently

informative time series of gene expression that can be used to explore the underlying genetic regulatory network of the organism.

- Adam Halasz, UPenn, Mesoscopic and stochastic phenomena illustrated in the lac operon

relevance of this aspect vary widely and is probably one of the most illustrative 'cultural' issues in the field of systems biology modeling.

On one hand it is clear that the size of cells forces us to at least consider effects due to the small numbers of molecules involved. On the

other, there are results that show that at least in some cases, Nature goes to long distances to control or eliminate the effects of noise and

stochasticity. Either way, ignoring noise and stochasticity is not realistic. There are several well established and quite transparent methods

to treat these effects and I will attempt to cover some of them. I will illustrate the effect of including noise and how some of these methods

work (or not) on the well studied lac operon.

- Michel Thellier, Rouen Univ, Functioning-dependent structures and the coordination between and within metabolic and signalling pathways

We have wondered whether this coordination might
involve what we term *functioning-dependent structures* (FDSs), an
FDS being

an assembly of proteins that associate with one another when
performing a task and that disassociate when the task is over. In this
investigation,

we have studied numerically the steady-state kinetics of a model
system of FDS made of two sequential monomeric enzymes. Our calculation
has

shown that such a FDS can display kinetic properties [Thellier et al., FEBS J., 2006] that
the individual
enzymes cannot [Legent et
al., C.R.Biol.,
2006].

These include basic input/output characteristics found in
electronic circuits such as linearity, invariance, pulsing and
switching.
Hence, FDSs can

generate kinetics that might regulate and coordinate metabolism
and signalling. Finally, we suggest that the occurrence of terms
representative
of

the assembly and disassembly of FDSs in the classical expression of the
density of entropy production is characteristic of living systems.

Joint work with G. Legent, P. Amar, V. Norris and C. Ripoll

- Oded Maler, CNRS-VERIMAG, The potential roles of informatics in systems biology

- John Lygeros, ETH Zurich, Stochastic hybrid models for DNA replication in the fission yeast

it has been observed that several biological processes exhibit the interaction of continuous and discrete phenomena. It has also been

recognized that many biological processes are intrinsically uncertain; stochastic phenomena have in fact been shown to be instrumental in

improving the robustness of certain biological processes, or in inducing variability. In this talk we will describe the development of

a stochastic hybrid model for DNA replication, one of the most fundamental processes behind the life of every cell. We will discuss

how the model was instantiated for the fission yeast and present analysis results that suggest that the predictions of the model do not

match conventional biological wisdom and experimental evidence. Interestingly, the problem appears to be not in the model,

but in conventional biological wisdom. This has motivated follow-on experiments (in vitro and in silico) to test two competing biological

hypotheses that could explain the mismatch.

- Amir Pnueli, Weizmann, Specifying Biological Systems as Reactive Systems: Some Observations

- Jasmin Fisher, Microsoft Research Cambridge, Executable Biology: Successes and challenges

their full complexity. We distinguish between two types of biological models – mathematical and computational – which differ in their

representations of biological phenomena. We call the approach of constructing computational models of biological systems Executable

Biology, as it focuses on the design of executable computer algorithms that mimic biological phenomena. In this talk I will survey the main

modeling efforts in this direction, emphasize the applicability and benefits of executable models in biological research, and highlight

some of the main challenges that executable biology poses for Biology and Computer Science.

Joint work with Thomas Henzinger

- Yves Pommier, NIH, Molecular interaction diagrams

detailed information has not. This gap between data accumulation and understanding concerns the cancer research community in particular,

because many of the signaling pathways involved in cellular growth are mutated in cancer and some of the major targets of cancer therapy are

proteins that interact with the complex molecular networks. To better understand and simulate the complex regulatory pathways involved in

cancers, one of us (Kurt W. Kohn) has developed a diagrammatic notation, which we refer to as the Molecular Interaction Map (MIM) language.

In recent years, we have created MIMs of various cellular signaling pathways (p53, apoptosis, HIF, EGFR, cell cycle, ATM-Chk2).

The MIMs have attracted wide interest from diverse laboratories around the globe for their powerful ways to organize biological information.

The MIMs have since evolved toward computer simulation and we have developed the first prototypes of electronic MIMs, which are available

on the Internet and allow easy access to annotations and databases. These maps can operate as educational tools, but can also serve as guides

to simulation-based studies aimed to understand the control principles underlying bioregulatory networks.

Joint work with Kurt W. Kohn, Mirit Aladjem, and John N. Weinstein

- François Fages, INRIA, Formal verification and inference of biochemical reaction models

qualitatively and quantitatively. Such a formal specification of the behaviors of the system, under various conditions, opens the way to the use

of automated reasoning tools, not only for validating models and their refinements (e.g. by model-checking techniques) but also for infering

parameter values and reaction rules from temporal properties. We report on our experience in the design of the Biochemical Abstract Machine environment

BIOCHAM and in its use in models of signal transduction and of the cell cycle.

- Nicolas
Thierry-Mieg, CNRS-TIMC, Smart-pooling for interactome
mapping

projects. It consists in assaying well-chosen pools of probes, such that each probe is present in several pools, hence tested several

times. The goal is to construct the pools so that the positive probes can usually be identified from the pattern of positive pools, despite

the occurrence of false positives and false negatives. While striving for this goal, several interesting mathematical or computational

problems emerge. In this talk I will discuss these questions and our contributions, from the pooling problem (how should the pools be

designed?), to the decoding problem (how to interpret the outcomes?) and finally to an experimental validation in the context of Y2H

interactome mapping.

- Hillel Kugler, Microsoft Research Cambridge, Modeling C. elegans Development - Progress and Challenge

processes that are conserved also in more advanced organisms. This talk surveys efforts to model C. elegans behavior over the past few years,

describes insights gained through the modeling process and outlines some of the main challenges remaining to make such modeling efforts scalable

to large systems and accessible to biologists.