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Biological Networks - From Structure to Dynamical Diversity

Intro

An important part of IRIDIA researches is focused on the analysis of generic properties of biological networks, i.e. properties that are shared by networks and that can be found at different levels of organization. Our main aim is a qualitative and quantitative characterization of the networks and the study of how differences in the network structure (i.e. its connectivity) influence the repertoire of dynamical behaviours of the entire network, its stability and adjustment to environmental variations


Compartmentalization and networking are two primary design principles in biological systems. While compartmentalization has been well studied for more than a decade leading to one of the cornerstones of modern biology and medicine, networks have only recently attracted the attention of researchers. The network organization is almost ubiquituous in biological systems and can be found on all structural levels, from physiological, immune and neural networks to social organization of insects, birds, mammals and man up to the all encompassing ecological networks at the highest hierarchical level.
Despite many theoretical and experimental efforts, the current knowledge of networks remains rather limited. One possible explanation, besides the complexity of the subject, lies in the fact that researchers have tended to emphasize those aspects of behaviours of networks that distinguish them - instead of considering them from a single perspective and thus identifying their common features.
Only recently has it become clear that there are many common properties, shared by various types of networks.


whatís a biological network

A network can be viewed as a set of largely identical subunits that interact, i.e. communicate, with each other. Once the collection of these sub-units has been identified, three important properties that govern the behaviour of a network can be distinguished: (a) the connectivity of the network that determines which subunits interact with which other subunits, (b) the strength and nature of these interactions, and (c) the total size of the network. The acknowledgement that all networks can be seen from this common perspective constitutes a considerable reduction of the problem's complexity. When the problem is faced in this way, it is possible to concentrate on precisely these parameters and the types of sub-units under investigation, and define further the classes of similarity into which the diverse types of networks can be placed. This is the conceptual basis that enables us to discover and examine common rules governing different biological networks. If this can be realized, we can expect to develop a theoretical framework for biological networks which plays the same role as the modern theory of phase transitions does for physical networks.


What to study

In our collaborative study we particularly approach the following set of questions:


How to connect computer simulations with experimental data

There are two ways to obtain data for the behaviour of biological networks: computer simulations and experimental work. Computer simulations are necessary in order to guarantee "clean" and reproducible conditions in which it is possible to investigate problems that cannot be solved in real laboratory or field experiments. They are especially suited to uncover generic network properties such as scaling and phase transitions in such a way as to fulfill demanding scientific requirements such as reproducibility. Our work is basically theoretical and our task is to devise a mathematical theory. However, close links with experimental groups are maintained.

Our research aims at unveiling these parallels and at developing tools and concepts to foster a generalized theoretical framework for these systems. Hence, at present the project represents purely fundamental research. It is highly desirable to put the expected results to work in an application oriented framework someday. However one has to distinguish between application in general and involvement of industry in particular. Several aspects of our research that are oriented towards the elucidation of ecological networks may find useful applications at the level of environmental management and may help to define decision criteria for governmental use and industrial and societal implementaion. There will certainly be important issues at the level of the biochemical networks that we plan to study and that can find application in medical research and the pharmaceutical industry. We do expect that contacts with industry will become more successful, after the goals we try to achieve in this research - i.e. an increased understanding of complex networks that goes along with the development of tools to manipumate these s ystems - have been reached.

Researchers in IRIDIA have been working together for many years as members of the Paris-Brussels group on Theoretical Immunology in which scientists of the Ecole Polytechnique in Paris (Varela, Calenbuhr), the Pasteur Institute (Coutinho, Stewart, Carneiro) and the UniversitÈ Libre de Bruxelles (Bersini, Calenbuhr, Detours) participate. IRIDIA was basically involved in modeling complex dynamical aspects of the immune system in relation to auto-immune disease and other pathological disarrays as well as in relation to early development of the immune system in young individuals. The approaches and techniques used are differential equations, qualititative study of complex systems, bifurcation theory, computer simulation and cellular automata.


[ Hugues Bersini | Vera Calenbuhr | Vincent Detours ]

Selected references

Detours, V., Bersini, H., Stewart, J. and F. Varela :
Development of an Idiotypic Network in Shape Space.
in Journal of Theoretical Biology - 1994 - 170.
Bersini, H. and V. Calenbuhr :
Frustration Induced Chaos in a System of Coupled ODE's.
in Chaos, Soliton and Fractals - 1995 - August.
Calenbuhr, V., Bersini, H., Stewart, J. and F. Varela. :
Natural Tolerance in a Simple Immune Network
Accepted for Publication in Journal of Theoretical Biology. 1995

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Last updated Jan 15, 1996 | Comments to webmaster