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Modeling of biological systems without kinetic parameters: Petri nets in theory and application
The presentation aims to make listeners familiar with the main concepts of Petri nets and their adaption to systems biology, particularly, to analyze signaling pathways. Signaling pathways are crucial in complex, multi‐genetic diseases such as cancer. For computational modeling, there are several bottlenecks in modeling signaling pathways. The main issue is the incompleteness and quality of the data. Thus, the integration of multi‐scale data from different experiments becomes more and more important.
To build quantitative, kinetic models, the data has to be complete and of sufficient quality, particularly the kinetic parameters. That is in many cases not possible because of experimental and also ethical reasons, especially for medical applications. However, a huge amount of qualitative data is available from high‐throughput techniques. Based on this data, we can predict and thus, may understand the dynamics of the system, using qualitative and semi‐quantitative approaches, for example, Petri nets and logical modeling methods.
Here, we present Petri nets ‐ a mathematical formalism initially developed for modeling technical systems with concurrent processes, which is meanwhile used in different biological applications. Besides an intuitive visualization, Petri nets provide sound mathematically proven analysis techniques. First, we will introduce the basic terms using biological examples. We focus on place/transition nets and their invariants. We define transition invariants, place invariants, and manatee invariants and discuss their biological interpretation. For signaling pathways, the concept of manatee invariants enables us to predict all complete, meaning from receptor activation to cell response, signaling pathways of a system. Based on a verified model, we can easily perform in‐silico knockout analyses, for single as well as multiple knockouts.
As a case study, we consider a Petri net model of the TNFR1‐induced signaling pathway that is of great importance in cancer as it decides about cell survival or cell death via apoptosis or necroptosis. To find and characterize molecular switch points, we apply in‐silico knockout analyses based on manatee invariants. We finalize with the consideration of the advantages and limitations of the Petri net formalism. If there is still time left, we will give examples for further Petri net‐based developments, such as for stochastic modeling and the combination with agent‐based concepts.
About the speaker
After graduating with a diploma in quantum chemistry from the University of Leipzig, Ina Koch worked on expert systems for predicting chemical reactions and protein structure topologies, applying graph-theoretical methods. In 1997, she received her PhD in theoretical computer science, focusing on a graph theory-based algorithm for protein structure comparison. In the group of Jens Reich, she began using Petri nets to model biochemical systems. Additionally, she investigated the structural impact of alternative splicing and single nucleotide polymorphism in the group of Martin Vingron.
During her professorships at the University of Applied Sciences Berlin, the University of Jena, and since 2010 as a full professor at Goethe University Frankfurt, she deepened her research in systems biology by applying Petri nets to various systems, including metabolic, gene regulatory, and signaling pathways. Her contributions to the field include the development of methods for network reduction and network decomposition, all based on the Petri net formalism.
The Causal Analysis of Biomedical Data Lecture Series is supported by the Luxembourg National Research Fund (FNR) RESCOM Program.
