Research and Projects

The aim of our group is to apply and to develop new computational methods in order to understand complex dynamical phenomena in living systems. A specific focus is on constructive dynamical systems, where new components can appear which may change the dynamics of the whole system. Current research is concerned with a theory of chemical organization, autonomous experimentation, and semiotics of dynamical systems.

Research Areas

  • Computational Systems Biology
  • Bioinformatics
  • Artificial Chemistry
  • Systems Analysis
  • Simulation and Modeling
  • Systems Theory

Research Directions

  • Theory of constructive dynamical systems
  • Structural evolution of complex networks
  • Computational modeling of large-scale bio-chemical systems
  • Artificial dynamical networks
  • Natural information processing



Chemical Organization Theory

Chemical organization theory offers a new way to anlyze complex dynamical networks. It aims espically at constructive chemical reaction systems, but can also be applied to chemical-like systems found in domains like popluation dynamics and infection biology. more ...


Systems Biology of the Cell Cycle

The cell cycle is controlled by a highly complex bio-chemical system. In this project we investigate the mitotic transition control mechanisms. In order to capture their complexity on a systems level, we apply mathematical and computational methods. more ...


The Chemical Metaphor as a Programming Paradigm for Organic Computing

In this project we develop a theoretical and practical framework to exploit the bio-chemical information processing metaphor as a programming paradigm for organic computing. By doing so, we expect to make available a technology that allows to create computational systems with the properties of their biological counterpart. more ...


ESIGNET: Evolving Cell Signalling Networks

In this project we develop methods to evolve cell signalling networks in silico. Using these evolutionary techniques together with theoretical methods we investigate potential structures and mechanisms of bio-chemical information processing. more ...


SEMBIOTICS: Formal Semantics of Bio-Models

Systems Biology reconstructs biological phenomena in order to develop explanatory models of living systems. These models are represented precisely in terms of mathematical expressions. However, the meaning of a model usually is not formally specified but only described in natural language. In this project we develop a formal framework for specifying the meaning of bio-models. more ...


Seceder Model

The seceder model shows how the local tendency to be different gives rise to the formation of groups. The model consists of a population of simple entities which reproduce and die. In a single reproduction event three individuals are chosen randomly and the individual which possesses the largest distance to their center is reproduced by creating a mutated offspring. The offspring replaces a randomly chosen individual of the population. This simple algorithm generates a complex group formation behavior as for example shown in the figure on the left-hand side. more ...


Autonomous Experimentation / Scouting

Given an experimental system or large simulation model of the dynamics of a biological system, we usually face the problem that there are many parameters to adjust. How does the system behaves when we change a parameter? If the number of parameters is large, this question can not be answered by systematic test (e.g., n-factorial designs). In this project we developed autonomous experimentation techniques (called scouting), which allow to explore the dynamics of a certain class of experimental systems. more ...


Artificial Gene Expression as a Realistic Test-Platform for Systems Biology

In systems biology a large number of new algorithms are developed that extract information from high throughput gene expression data. In order to evaluate these new algorithms, we are developing an artificial gene expression data source, which is able to deliver an arbitrary large amount of artificial gene expression data, where the underlying network is precisely known in all details. Currently the system consists of an artificial gene expression network including protein-protein interaction dynamics. The user can specify the characteristics and complexity of the internal dynamics of the network. more ...

"Jun 26 2009" Peter Dittrich