[Frontiers in Bioscience S5, 149-166, January 1, 2013]

Discrete, qualitative models of interaction networks

Kathrin Ballerstein1, Utz-Uwe Haus1, Jonathan Axel Lindquist1, Tilo Beyer2, Burkhart Schraven2, Robert Weismantel1

1Institute of Operations Research, Department of Mathematics, ETH Zurich, Raemistrasse 101, CH-8092 Zurich, Switzerland, 2Institute for Molecular and Clinical Immunology, Medical Faculty, Otto-von-Guericke-Universitaet, Leipziger Str. 44, Building 26, 39120 Magdeburg, Germany


1. Abstract
2. Introduction
2.1. Propagation techniques
2.2. Interaction graphs
2.3. Kinetic logic and Petri nets
3. Logic framework for interaction networks
3.1. From blots to formulas
3.2. Structural analysis
3.3. Functional analysis
4. Dynamic model
5. Multiple activation levels
6. Conclusions
7. References


Logical models for cellular signaling networks are recently attracting wide interest: Their ability to integrate qualitative information at different biological levels, from receptor-ligand interactions to gene-regulatory networks, is becoming essential for understanding complex signaling behavior. We present an overview of Boolean modeling paradigms and discuss in detail an approach based on causal logical interactions that yields descriptive and predictive signaling network models. Our approach offers a mathematically well-defined concept, improving the efficiency of analytical tools to meet the demand of large-scale data sets, and can be extended into various directions to include timing information as well as multiple discrete values for components.