Download appel a pre-projets de recherche 2007

APPEL A IDEES 2007
dans le domaine des Systèmes Complexes
Formulaire de demande de soutien financier
DESCRIPTION DU PROJET
Titre du projet
Mathematical theory of biological robustness
with experimental applications in cancer systems biology
Titre court ou acronyme :
MATBRAC
Nature de la proposition (projet ou événement) :
PROJET
Mots-clés :
robustness ; cancer ; mathematical models ; model reduction
Secteurs disciplinaires :
BIOLOGICAL SYSTEMS ; SYSTEMS BIOLOGY
Coordinateur du projet :
Nom : Zinovyev
Prénom : Andrei
Institution : Institut Curie
Laboratoire/Equipe : Service Bioinformatique
e-mail : [email protected]
Tél : 01-53-10-70-59
Adresse postale : 26, rue d’Ulm Paris 75248
Nom des principaux participants au projet
Nom
Prénom
Institution
Laboratoire/Equipe
e-mail
Zinovyev
Radulescu
Andrei
Ovidiu
Service Bioinformatique
IRMAR, IRISA
Barillot
Emmanuel
Institut Curie
Univ.Rennes,
INRIA
Institut Curie
Service Bioinformatique
[email protected]
[email protected]
[email protected]
Radvanyi
François
UMR 144 CNRS/Institut Curie
[email protected]
Delattre
Olivier
Laurence
Alain
Tatiana
Unité 830 INSERM/
Institut Curie
Service Bioinformatique
EA300 Université Paris 7
IRMAR
[email protected]
Calzone
Lilienbaum
Baumuratova
Institut Curie
CNRS
Institut Curie
INSERM
Institut Curie
Univ. Paris 7
Univ.Rennes
[email protected]
[email protected]
[email protected]
Nombre de personnes impliquées dans ce projet :
Chercheurs et enseignants-chercheurs __ 5 _____
Post-doctorant(s)___ 2 ____
Doctorant(s) _________
Ingénieurs et techniciens __ 1 _____
Le projet aura une durée maximale d’un an et devra se terminer au plus tard le 31
décembre 2008.
Date de début souhaitée __ 01.01.2008 ___
Un court compte-rendu d’activité et la participation à une réunion commune seront
demandés.
Autres demandes de soutien envisagées pour le projet :
1) O. Radulescu submitted an application for partial support of the objectives of this project to the
prospective program of region Bretagne
2) O. Radulescu and A. Zinovyev jointly submitted an application for partial support of the objectives
of this project to
(IFCPAR/CEFIPRA)
Indo-French
Centre
for
the
Promotion
of
Advanced
Research
Mathematical theory of biological robustness
with experimental applications in cancer systems biology
Modeling molecular mechanisms gives better understanding of biological processes by using precise and
unambiguous mathematical language. Mathematical modeling in molecular biology promises to accelerate
testing biological hypotheses, integrate and explain heterogeneous data and develop new efficient drugs.
However, application of mathematical tools at the current stage meets such obstacles as incomplete knowledge
of the systems including lack of quantitative parameters knowledge. There is very few methodological
principles that were proposed to bypass these difficulties, and one of the most fruitful is the recent suggestion
[Kitano 2004] that feasible model designs should be robust. This principle is justified by a universal
observation that biological systems stably perform their functions in highly variable external (environment) and
internal (cellular content, including genetic variations) conditions.
Being necessary for stable normal cell functioning, biological robustness poses problems for intervention
strategies, in particular, in the treatment of various systemic diseases such as immune diseases or cancer. Thus,
in the case of cancer, the robust design of cell cycle mechanism makes it difficult or impossible to invent a
universal “magic bullet” targeting malignant cells and preventing them from proliferation [Kitano 2004].
Although robustness of a biological system or a complex mathematical model can be tested in vivo (by varying
conditions) or in silico (by sensitivity analysis), currently there exists no general theory to explain why a system
is robust or what modifications should be introduced to achieve or to avoid robust behavior. Moreover, the
notion of robustness itself should be formulated more precisely. Most of existing ideas such as distributed
robustness or “robust yet fragile” concepts still have semi-phenomenological character [Wagner, 2005;
Carlson and Doyle, 2000].
The long-term objectives of our project are
1) make the notion of biological robustness more precise and specific to cancer processes
2) develop and implement theoretical tools for identification, classification and correction of robust
behavior in mathematical models of molecular mechanisms important for cancer
3) perform several case studies of biological robustness in mathematical models of signal transduction
pathways controlling cell cycle and apoptosis: NfκB pathway, TGFβ pathway, RB/E2F pathway
4) use available high-throughput data on genome structure (CGH) and transcriptome (microarrays) of
tumor samples (of breast, bladder cancer, Ewing’s sarcoma in which the participants have strong
expertise) to validate our conclusions
5) develop our conclusions up to testable hypotheses, and as far as possible (if other sources of funding
will be obtained), verify them in a laboratory experiment
The participants of the project have expertise in complementary fields (mathematics, bioinformatics, computer
science, biology) which creates required synergy to achieve these goals. Recently, the participants developed
mathematical models of NfκB [Radulescu,2007] and RB/E2F (http://bioinfo.curie.fr/projects/rbpathway)
pathways that are the most detailed models of these important cellular pathways available, and made first
analysis of their robustness properties [Gorban and Radulescu, 2007]. The participants have strong expertise in
analyzing heterogeneous high-throughput data [Rapaport et al, 2007].
The theoretical tools that will be developed in this project will include but not be limited to
1) Notion of r-robustness: it was mathematically formulated in [Gorban and Radulescu, 2007] and
verified on NfκB pathway model;
2) Notion of multiscale system (system with hierarchical time scales): such systems have properties
allowing to study them analytically, yet they can approximate large class of biological models [Gorban
and Radulescu, 2007];
3) Classification of robust behavior: in [Gorban and Radulescu, 2007] it was suggested that robust
behavior can have different origins and types (of cube and simplex type, the former being related to
the law of big numbers while the later is related to order statistics. Both are particular cases of more
general Gromov’s measure concentration theory [Gromov,1999]);
4) Systematic model reduction of large networks: the first draft of our model reduction approach based
on combination of Clarke’s method with idempotent algebra was proposed in [Radulescu, Zinovyev,
Lilienbaum 2007]. The method allows to generate families of mathematical models of decreasing
structural complexity but qualitatively similar dynamical behavior;
5) Procedures for estimating system dynamics intrinsic dimension: in our theory of robustness we
claim that the intrinsic dimension of complex system dynamics is tightly related to its robust
properties. Estimating intrinsic dimension based on the notion of invariant manifold was proposed by
the project participants in [Radulescu et al., 2007] and [Gorban, Karlin, Zinovyev 2005].
All these contributions are original and constructive, allowing to apply them in modeling real biological
processes. In Institut Curie there is a large bank of data available for validating these tools in the area of cancer
systems biology. We intend to test the robust properties of cancer cells at various stages, by considering
mathematical models of pathways involved in cell proliferation and apoptosis. Tumor samples for which both
CGH data (on genomic alterations) and transcriptome data (gene expression) are available, present particular
interest, since the data will allow to test the hypothesis that in tumor cells such genome modifications are
selected that allow to achieve less controllable (hence, more robust) proliferation. Our theoretical analysis will
potentially allow to develop counter-strategy of interventions breaking such malignant robustness. This will
suggest experimental designs on tumorigenic cell cultures. The team of O. Delattre recently developed a unique
Ewing’s tumor inducible cellular model particularly suitable for such purposes.
The project funds if obtained will support
1) Missions (participants travelling Paris-Rennes and participation in one international conference)
2) Common meeting organisation
3) Access to databases of cellular pathways (BioBase, Ingenuity)
4) Licensing MATLAB working environment, including Bioinformatics and Systems Biology toolboxes
5) Paying publication fees in open-access journals (PLOS, BMC)
6) Limited support of experimental verification of the theoretical predictions (chemical reagents for
manipulations on cell cultures)
The theoretical tools developed in this project are potentially applicable to large classes of complex systems,
therefore the project will make a contribution to the general theory of robust complex systems. Since the
objectives formulated are far more ambitious than what can be done in one year, after some progress and first
applied results, the project can lead to bigger and more focused national or international project.
References
1. Wagner A. Robustness and Evolvability in Living Systems (2005) Princeton University Press,
Princeton, NJ
2. Kitano H. (2004) Cancer as a robust system: implications for anticancer therapy. Nature Reviews
Cancer 4, 227-235
3. Gromov M. Metric structures for Riemannian and non-Riemannian spaces, Progr.Math. 152.
Birkhauser, Boston, 1999.
4. Carlson J.M. and Doyle J. Highly Optimized Tolerance: Robustness and Design in Complex Systems.
Phys. Rev. Lett. 84: 2529 - 2532, 2000.
Relevant publications of the project participants :
1) Radulescu O., Zinovyev A., Lilienbaum A. Model reduction and model comparison for NFkB
signaling. In Proceedings of Foundations of Systems Biology in Engineering, September 2007,
Stuttgart, Germany
2) Gorban A., Radulescu O. Dynamical robustness of biological networks with hierarchical distribution
of time scales. IET Systems Biology (2007) 1: 238-246
3) Rapaport F., Zinovyev A., Dutreix M., Barillot E., Vert J.-P. Classification of microarray data using
gene networks.BMC Bioinformatics. (2007) Feb 1;8:35
4) Gorban A., Karlin I., Zinovyev A. Invariant grids: method of complexity reduction in reaction
networks. 2005. ComPlexUs 2004-05;2:110-127
5) Chen KC, Calzone L, Csikasz-Nagy A, Cross FR, Novak B, Tyson JJ. Integrative analysis of cell cycle
control in budding yeast. Mol Biol Cell. 2004 Aug;15 (8):3841-62.