To understand an object or a phenomenon, it is relevant to study similar yet different objects or phenomena. To understand humans, scientists study primates. To understand ecosystems, this internship suggests studying cells, and identifying their (functioning) differences with any ecosystem. Each cell is composed of a large number of organelles playing differential roles, all necessary to its survival and evolution. Biologists model the cell for a long time, but they succeed only recently to build an integrated model combining a large number of components of different natures (Karr et al. 2012). Yet, biology is still waiting for a conceptual framework providing a coherent understanding for any cell (Gaucherel et al. 2019). Here, the goal is to develop such a framework on the basis of discrete event models.

Discrete event models developed in theoretical computer sciences, indeed, have demonstrated their ability to model highly complex systems with minimal assumptions and a rigorous control (Machado et al. 2009, Fages et al. 2018). In particular, qualitative models based on Petri nets or symbolic methods allow combining a large number of components and processes and exhaustively compute the fates of the system, as recently illustrated in complex ecosystems (Gaucherel and Pommereau 2019). Such attempt is based on a graph representation of the system, carried by a theoretical view of life (Gaucherel et al. 2019), and provides a so-called state space summarizing the system dynamics. This internship aims at replicating such an integrated modeling for an idealized cell, and to try recovering well-known dynamics (e.g. mitosis, meiosis, Karr et al. 2012).

Allowance and conditions:

Practically, the work will consist in a combination of literature survey (to list cell processes), of modeling tasks (a prototype of the code is already available), and of biological interpretations. We look for a theoretical biologist mastering mathematics and/or computer sciences or an applied mathematician and computer specialist interested in biological topics. The internship will be done in the AMAP laboratory, Montpellier, with a Master allowance.


Fages, F., T. Martinez, D. Rosenblueth, and S. Soliman. 2018. Influence Networks compared with Reaction Networks: Semantics, Expressivity and Attractors. IEEE/ACM Transactions on Computational Biology and Bioinformatics, Institute of Electrical and Electronics Engineers 99:1-14.
Gaucherel, C., P. H. Gouyon, and J. L. Dessalles. 2019. Information, the hidden side of life. ISTE, Wiley, London, UK.
Gaucherel, C. and F. Pommereau. 2019. Using discrete systems to exhaustively characterize the dynamics of an integrated ecosystem. Methods in Ecology and Evolution 00:1–13.
Karr, J. R., J. C. Sanghvi, D. N. Macklin, M. V. Gutschow, B. Bolival, N. Assad-Garcia, J. I. Glass, and M. W. Covert. 2012. A whole-cell computational model predicts phenotype from genotype. Cell 150:389-401.
Machado, D., R. S. Costa, M. Rocha, I. Rocha, B. Tidor, and E. C. Ferreira. 2009. A Critical Review on Modelling Formalisms and Simulation Tools in Computational Biosystems. Pages 1063-1070 in A. I. IWANN : Distributed Computing, Bioinformatics, Soft Computing, and Ambient Assisted Living, editor. International Work-Conference on Artificial Neural Networks. Part of the Lecture Notes in Computer Science book series (LNCS).

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