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Systems Biology

Mathematical modeling of biochemical networks has attracted considerable attention in recent years and is recognized as one of the major challenges facing the biology research community today. Most of the models available in the literature can be classified into two families: models with deterministic dynamics (e.g. ordinary differential equation models for the time evolution of the average amount of molecules in a cell population) and models with stochastic dynamics (e.g. continuous-time Markov chains which capture the dynamics of the amount of molecules in individual cells). Even though it is widely recognized that many biochemical processes involve inherent randomness, there have been very few attempts to construct such stochastic models from data. The objective of the group is, on the one hand, to develop new methods for the identification of such models from single-cell measurements and, on the other hand, to use stochastic modeling to learn about and to control real biological system.

One of the defining changes in molecular biology over the last decade has been the massive scaling up of its experimental techniques. The sequencing of the entire genome of organisms, the determination of the expression level of genes in a cell by means of DNA micro-arrays or flow cytometry and the identification of proteins and their interactions by high-throughput proteomic methods have produced enormous amounts of data on different aspects of the development and functioning of cells. A consensus is now emerging among biologists that to exploit this data to its full potential one needs to complement experimental results with formal models of biochemical networks. Mathematical models that describe gene and protein interactions in a precise and unambiguous manner can play an instrumental role in shaping the future of biology. For example, mathematical models allow computer-based simulation and analysis of biochemical networks. Such in silico experiments can be used for massive and rapid verification or falsification of biological hypotheses, replacing in certain cases costly and time-consuming in vitro or in vivo experiments. Moreover, in silico, in vitro and in vivo experiments can be used together in a feedback arrangement: mathematical model predictions can assist in the design of in vitro and in vivo experiments, the results of which can in turn be used to improve the fidelity of the mathematical models.


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