Discrete, qualitative models of interaction networks.

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.

Minimal cut sets in a metabolic network are elementary modes in a dual network.

MOTIVATION: Elementary modes (EMs) and minimal cut sets (MCSs) provide important techniques for metabolic network modeling. Whereas EMs describe minimal subnetworks that can function in steady state, MCSs are sets of reactions whose removal will disable certain network functions. Effective algorithms were developed for EM computation while calculation of MCSs is typically addressed by indirect methods requiring the computation of EMs as initial step. RESULTS: In this contribution, we provide a method that determines MCSs directly without calculating the EMs. We introduce a duality framework for metabolic networks where the enumeration of MCSs in the original network is reduced to identifying the EMs in a dual network. As a further extension, we propose a generalization of MCSs in metabolic networks by allowing the combination of inhomogeneous constraints on reaction rates. This framework provides a promising tool to open the concept of EMs and MCSs to a wider class of applications. CONTACT: utz-uwe.haus@math.ethz.ch; klamt@mpi-magdeburg.mpg.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Integrating signals from the T-cell receptor and the interleukin-2 receptor.

T cells orchestrate the adaptive immune response, making them targets for immunotherapy. Although immunosuppressive therapies prevent disease progression, they also leave patients susceptible to opportunistic infections. To identify novel drug targets, we established a logical model describing T-cell receptor (TCR) signaling. However, to have a model that is able to predict new therapeutic approaches, the current drug targets must be included. Therefore, as a next step we generated the interleukin-2 receptor (IL-2R) signaling network and developed a tool to merge logical models. For IL-2R signaling, we show that STAT activation is independent of both Src- and PI3-kinases, while ERK activation depends upon both kinases and additionally requires novel PKCs. In addition, our merged model correctly predicted TCR-induced STAT activation. The combined network also allows information transfer from one receptor to add detail to another, thereby predicting that LAT mediates JNK activation in IL-2R signaling. In summary, the merged model not only enables us to unravel potential cross-talk, but it also suggests new experimental designs and provides a critical step towards designing strategies to reprogram T cells.