World-class research outputs lies at the heart of ICSMB. To view and search through our publications list please follow the link below to the University's central publications portal:
ICSMB researchers cover a very broad range of fundamentals 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 interactions between 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 to understand 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 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. This very general approach allows us to make general statements relevant to water channels, electrical power grids, and internet or transportation networks. This is useful to avoid network failures, build reliable communication and transport networks, or to estimate the likelihood of a collapse.
Analysis of experimental data from external collaborators is crucial for model construction and validation. We see Systems Biology as a mutual interaction between theory, models, and purposely-tailored experiments.