**Declarative approaches and
tools for constructing and analyzing biological networks**

**Context and goal**

In a
post-genomic perspective, understanding biological networks is a
fundamental
issue. Modelling such networks is becoming an essential part of the
work of
today’s biologists. Predictions deduced from such models help them to
go
further in the analysis, particularly in designing new experiments.

Our goal is to
develop software tools to assist biologists for constructing such
models.
Difficulties mainly arise because that part of the information to
construct a
model is not available. The usual way to proceed is rather empirical:
modelers
make “reasonable guesses”, for example to value parameters, and if the
resulting model does not verify an observation, they try to adjust this
unique
model so that it verifies the observation. On the contrary, our
AI-oriented
approach relies on specifying all available knowledge in terms of
constraints
(logical formulae). In this way we can get automatically the set of the
models
which are consistent with the current level of knowledge: they are the
solutions of the constraints. The predictions which can be obtained
from the
modeling are theorems deduced from the constraints. If the set of
models is
empty, it means that the constraints are inconsistent, in which case
the
constraints can be revised: for example by removing automatically as
few constraints
as possible or by adding/suppressing interactions or species. Also,
some
special classes of models can be specified according to some optimality
criterions like the number of interactions. Finally, according to the
resulting
predictions biologists could devise appropriate experiments. If a
property is
predicted (true for all models), checking it experimentally means
verifying its
validity. If not, it means going ahead in the analysis by expelling,
according
to the result of the experiment, a subset of the models.

**Scientific
domains**

They concern mainly
three topics:

- Mathematical
modeling of biological networks. We focus mainly on discrete modeling
of
genetic networks. From Thomas’s network formalism as a starting point,
we want
to extend it to take into account i) the notion of time (delay) and of
spatiality, 2) the composition of models and of their properties. On
the
prediction side, in addition to check that already identified
properties are
theorems, it is essential, for obtaining new knowledge, to provide a
framework
for designing high level languages expressing biological meaningful
properties.
The design of these languages needs the help of biologists as they must
be
relevant to future experiments. Their framework could be based for
example on
propositional logic or on CTL (Computational Tree Language). Coll. with
G.
Bernot et al. (U. de Nice).

- Computer
implementation. Consistency checking or consistency repairing could be
very
much time consuming. We study how to
overcome
this difficulty with new approaches like ASP (Answer Set Programming),
a very
efficient logical programming technology based on a non monotonous
logic which
allows to specify defaults. Also, we intend to study CHR (Constraint
Handling
Rules, for implementing specific constraints solver), global
constraints
(specific to certain classes of problems) or continuous/stochastic
constraints
(for continuous variables). Coll. with Y. Hamadi et al. (Microsoft,
Cambridge),
Jacques Cohen (Brandeis U., Boston), Jacques Nicolas et al. (IRISA,
Rennes), T.
Schaub et al. (Postdam U.).

- Biological
applications. We have collaborations with biologists on different
systems like the carbon**
**starvation response in *E. coli *(Delphine
Ropers et al., INRIA, Grenoble), the* Drosophila *Gap-gene
system
(Denis Thieffry et al., ENS, Paris) or the mammalian iron regulation
system
(Jean-Marc Moulis, CEA, Grenoble).