Disentangling the Complexity of HGF Signaling by Combining Qualitative and Quantitative Modeling.

Signaling pathways are characterized by crosstalk, feedback and feedforward mechanisms giving rise to highly complex and cell-context specific signaling networks. Dissecting the underlying relations is crucial to predict the impact of targeted perturbations. However, a major challenge in identifying cell-context specific signaling networks is the enormous number of potentially possible interactions.

Detecting and removing inconsistencies between experimental data and signaling network topologies using integer linear programming on interaction graphs.

Cross-referencing experimental data with our current knowledge of signaling network topologies is one central goal of mathematical modeling of cellular signal transduction networks. We present a new methodology for data-driven interrogation and training of signaling networks. While most published methods for signaling network inference operate on Bayesian, Boolean, or ODE models, our approach uses integer linear programming (ILP) on interaction graphs to encode constraints on the qualitative behavior of the nodes.

Modeling approaches for qualitative and semi-quantitative analysis of cellular signaling networks.

A central goal of systems biology is the construction of predictive models of bio-molecular networks. Cellular networks of moderate size have been modeled successfully in a quantitative way based on differential equations. However, in large-scale networks, knowledge of mechanistic details and kinetic parameters is often too limited to allow for the set-up of predictive quantitative models.

The Process-Interaction-Model: a common representation of rule-based and logical models allows studying signal transduction on different levels of detail.

Background: Signaling systems typically involve large, structured molecules each consisting of a large number of subunits called molecule domains. In modeling such systems these domains can be considered as the main players. In order to handle the resulting combinatorial complexity, rule-based modeling has been established as the tool of choice.

Large-scale network models of IL-1 and IL-6 signalling and their hepatocellular specification.

The pro-inflammatory cytokines interleukin 1 (IL-1) and 6 (IL-6) are crucially involved in the regulation of a multitude of physiological processes, in particular coordinating the immune response upon bacterial infection and tissue injury. Both interleukins induce complex signalling cascades and trigger the production of mitogenic, pro-proliferative, anti-apoptotic, chemotactic, and pro-angiogenic factors thereby affecting the delicate balance between regeneration vs. invasive growth, tumourigenesis and metastasis.

Computing combinatorial intervention strategies and failure modes in signaling networks.

The identification of combinatorial intervention strategies and the elucidation of failure modes that may cause aberrant behavior of cellular signaling networks are highly relevant topics in cell biology, medicine, and pharmaceutical industry. We have recently introduced the concept of minimal intervention sets (MISs)--minimal combinations of knock-ins and knock-outs provoking a desired/observed response in certain target nodes--to tackle those problems within a Boolean/logical framework. We first generalize the notion of MISs and then present several techniques for search space reduction facilitating the enumeration of MISs in networks of realistic size.

Discrete logic modelling as a means to link protein signalling networks with functional analysis of mammalian signal transduction.

Large-scale protein signalling networks are useful for exploring complex biochemical pathways but do not reveal how pathways respond to specific stimuli. Such specificity is critical for understanding disease and designing drugs. Here we describe a computational approach--implemented in the free CNO software--for turning signalling networks into logical models and calibrating the models against experimental data.

The logic of EGFR/ErbB signaling: theoretical properties and analysis of high-throughput data.

The epidermal growth factor receptor (EGFR) signaling pathway is probably the best-studied receptor system in mammalian cells, and it also has become a popular example for employing mathematical modeling to cellular signaling networks. Dynamic models have the highest explanatory and predictive potential; however, the lack of kinetic information restricts current models of EGFR signaling to smaller sub-networks. This work aims to provide a large-scale qualitative model that comprises the main and also the side routes of EGFR/ErbB signaling and that still enables one to derive important functional properties and predictions.