The ICSMB researchers cover a very broad range of fundaments of the theory of dynamical systems, chaos and its impact and relevance to physics, biology, engineering and medical sciences, complex networks and relativistic quantum chaos. We apply the ideas and methods of physics and mathematics to understand biological systems.
We use the theories of dynamical systems and statistical physics to model molecular biology processes and networks like DNA replication, DNA damage and repair, transcription, translation and the interaction amongst them, metabolic responses to stress of microorganisms and combinatorial stress, the dynamics of molecular assembly on cell membrane, gene regulatory networks, circadian and neuronal networks and synthetic genetic networks with cell-to-cell communication.
The aims of our models are to explain the mechanisms underlying molecular interaction networks and to uncover design principles like multi-stability and multi-rhythmicity that lead to increased robustness, higher flexibility or better adaptability to changing environmental factors. Applying evolutionary models to simulate the development of interaction networks under random mutations and selective pressure, we strive at understanding how characteristic structural properties of complex networks may have emerged.
We apply differential equations, convex algebra, stochastic equations, bifurcation analysis and other mathematical and numerical concepts to solve and evaluate the complex models.
We are interested in a basic understanding of how and how much information a chaotic network is able to transmit, the network capacity. The very general approach allows us to make generally admitted statements to water channels, electrical power grids, internet or transportation networks e.g. to avoid network failures, built reliable communication and transport networks or to estimate the likelihood of a collapse.
The analyses of experimental data from external collaborators accomplish the model construction and validation. We see the System Biology as a mutual interaction between theory, models and purpose tailored experiments. We exploit modern linear and non-linear analysis tools like recurrence plots and enhance these methods.