The aim of our research is to enhance our understanding of the mechanisms underlying the behaviour of complex living systems. The goal is to gain a better explanation of how the complex dynamic behaviour of the system emerges from the interactions of individual parts. When solving such a non-trivial goal, the data have to be necessarily integrated with mathematical modelling and computer analysis. Since the complexity of biological networks is enormous due to the appearance of complicated feedback loops, the system dynamics is often counter-intuitive and cannot be directly predicted from the network structure.

Computer science technologies together with computer tools employing suitable mathematical and computer science methods can help to obtain exact description of the networks, and in consequence, to infer interesting predictions of systems dynamics evolved in an arbitrary environment. In order to cope with large-scale biological models describing the interactions in the scale of several functional layers of a living cell, the central requirement imposed on analysis tools is the scalability.

Research in the laboratory is organised under several partially overlapping themes.



Systems Biology models often have numerous parameters, such as kinetic constants, decay rates and drift/diffusion terms, which are unknown or only weakly constrained by existing experimental knowledge. A crucial problem for Systems Biology is that these parameters are often very difficult to measure directly. Furthermore, they may vary greatly according to their in vivo context. As a result, computational methods for the estimation or synthesis of these parameters are of great interest.

The goal of our research is to develop effective, fast, and scalable methods, techniques and tools for automated parameter synthesis for the computational analysis of biological systems.

Digital Bifurcation Analysis of Dynamical Systems

Bifurcation analysis is a central task of the analysis of parameterised high-dimensional dynamical systems that undergo transitions as parameters are changed. The classical numerical and analytical methods are typically limited to a small number of system parameters.

The goal of our research is to develop a novel approach to bifurcation analysis that is based on a suitable discrete abstraction of the system and employs model checking for discovering critical parameter values, referred to as bifurcation points, for which various kinds of behaviour (equilibrium, cycling) appear or disappear.

Model Discrimination and Selection

When given several models for the same biochemical process, which one is the best in the face of uncertainty about model structure and parameters governing model dynamics? Traditionally, this question has often been approached by model calibration. Our approach is to judge a model superior if there exist parameters (in its usually high-dimensional parameter space) that allow the model to mimic biologically observed behaviour more closely than other models.

The goal of our research is to develop formally sound definitions for model discrimination and to propose algorithmic techniques to automatically select appropriate models from a set of candidate models.

Comprehensive Modelling Platform

CMP is a general framework for public sharing, annotation, and visualisation of domain-specific biological models. For a selected organism, the framework is instantiated as a web-based application which allows capturing several aspects of biological models represented as biochemical reaction networks or ordinary differential equations. The key feature relies on mapping kinetic models to a precise textual and a schematic graphical representation of the related biological knowledge, thereby supporting the systems biological view of the modelled organism. Besides model repository and annotation, the platform includes basic model analysis procedures such as simulation and static analysis.

Currently, two instances E-Photosynthesis [] and E-Cyanobacterium [] are under development.