Negative interactions and feedback regulations are required for transient cellular response.

Signal transduction is a process required to conduct information from a receptor to the nucleus. This process is vital for the control of cellular function and fate. The dynamics of signaling activation and inhibition determine processes such as apoptosis, proliferation, and differentiation.

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.

Simulated evolution of signal transduction networks.

Signal transduction is the process of routing information inside cells when receiving stimuli from their environment that modulate the behavior and function. In such biological processes, the receptors, after receiving the corresponding signals, activate a number of biomolecules which eventually transduce the signal to the nucleus. The main objective of our work is to develop a theoretical approach which will help to better understand the behavior of signal transduction networks due to changes in kinetic parameters and network topology.

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.

Multiscale modeling of cell mechanics and tissue organization.

Nowadays, experimental biology gathers a large number of molecular and genetic data to understand the processes in living systems. Many of these data are evaluated on the level of cells, resulting in a changed phenotype of cells. Tools are required to translate the information on the cellular scale to the whole tissue, where multiple interacting cell types are involved.

Cell transmembrane receptors determine tissue pattern stability.

The analysis of biological systems requires mathematical tools that represent their complexity from the molecular scale up to the tissue level. The formation of cell aggregates by chemotaxis is investigated using Delaunay object dynamics. It is found that when cells migrate fast such that the chemokine distribution is far from equilibrium, the details of the chemokine receptor dynamics can induce an internalization driven instability of cell aggregates.

Mechanisms of organogenesis of primary lymphoid follicles.

Primary lymphoid follicles (PLFs) in secondary lymphoid tissue (SLT) of mammals are the backbone for the formation of follicular dendritic cell (FDC) networks. These are important for germinal center reactions during which affinity maturation creates optimized antibodies in adaptive immune responses. In the context of organogenesis, molecular requirements for the formation of follicles have been identified.

Modeling emergent tissue organization involving high-speed migrating cells in a flow equilibrium.

There is increasing interest in the analysis of biological tissue, its organization and its dynamics with the help of mathematical models. In the ideal case emergent properties on the tissue scale can be derived from the cellular scale. However, this has been achieved in rare examples only, in particular, when involving high-speed migration of cells.

Conclusions from two model concepts on germinal center dynamics and morphology.

Germinal centers (GC) are an essential part of the humoral immune response. They develop a clear structure during maturation: Centroblasts and centrocytes are separated into two zones, the dark and the light zone. The mechanisms leading to this specific morphology as well as the reason for zone-depletion during a later phase of the GC reaction have not clearly been revealed in experiment.

The type of seeder cells determines the efficiency of germinal center reactions.

During humoral immune responses some germinal centers (GCs) develop very well and give rise to a large number of high affinity antibody producing plasma cells. Other GC reactions develop poorly, somatic mutation is reduced, and the output production is practically absent. This led to the hypothesis that two classes of GCs exist, and that GCs show an all-or-none behaviour.

A possible role of chemotaxis in germinal center formation.

During the germinal center (GC) reaction a characteristic morphology is developed. In the framework of a recently developed space-time model for the GC, a mechanism for the formation of dark and light zones has been proposed. There, the mechanism is based on a diffusing differentiation signal which is distinguished by follicular dendritic cells (FDC).