Systems-Theory-in-Systems-Biology

Systems Theory in Systems Biology

Institute for Systems Theory and Automatic Control (IST)

Pfaffenwaldring 9, 70569 Stuttgart

Repository of the Systems Theory in Systems Biology Group of the University of Stuttgart.

Head of Group is Prof. Dr. rer. nat. Nicole Radde.

Ph.D. researchers: Viviane Klingel, Vincent Wagner, Sebastian Höpfl

Our research focuses on the modeling and analysis of intracellular regulation processes. The ultimate goal is the development and application of mathematical methods to facilitate a better systemic understanding of these processes for particular subsystems and the organization of molecular regulation mechanisms in general. I.e. these methods try to catch the dynamics of the underlying system, the stochasticity of the process, population phenomens and capture the uncertainty in the model parameters and predictions.To achieve this goal our group works in close collaboration with partners from molecular and cellular biology and mathematics.

news

Oct 4, 2023	International Conference on Data-Integrated Simulation Science (SimTech2023)
Jan 16, 2023	SRCSB: Dagmar Waltemath - Be FAIR! Ways to improve model reuse and interoperability in Systems Medicine
Nov 7, 2015	Workshop on Computational Models in Biology and Medicine 2023

selected publications

SiCaSMA: An Alternative Stochastic Description via Concatenation of Markov Processes for a Class of Catalytic Systems

Vincent Wagner, and Nicole Erika Radde

Mathematics 2021

Abs

The Chemical Master Equation is a standard approach to model biochemical reaction networks. It consists of a system of linear differential equations, in which each state corresponds to a possible configuration of the reaction system, and the solution describes a time-dependent probability distribution over all configurations. The Stochastic Simulation Algorithm (SSA) is a method to simulate sample paths from this stochastic process. Both approaches are only applicable for small systems, characterized by few reactions and small numbers of molecules. For larger systems, the CME is computationally intractable due to a large number of possible configurations, and the SSA suffers from large reaction propensities. In our study, we focus on catalytic reaction systems, in which substrates are converted by catalytic molecules. We present an alternative description of these systems, called SiCaSMA, in which the full system is subdivided into smaller subsystems with one catalyst molecule each. These single catalyst subsystems can be analyzed individually, and their solutions are concatenated to give the solution of the full system. We show the validity of our approach by applying it to two test-bed reaction systems, a reversible switch of a molecule and methyltransferase-mediated DNA methylation.