Aneta Koseska

Cellular Cognition



Contact

Phone:+49 (231) 133 - 2252
Fax:+49 (231) 133 - 2299

Research concept

<strong>Figure 1: </strong>Schematic representation how chemical communication between cells can give rise to heterogeneity in a multicellular population. The underlying mechanism is a Turing-like symmetry breaking principle that leads to the formation of an inhomogenous steady state. Zoom Image
Figure 1: Schematic representation how chemical communication between cells can give rise to heterogeneity in a multicellular population. The underlying mechanism is a Turing-like symmetry breaking principle that leads to the formation of an inhomogenous steady state. [less]

Living systems continuously exchange energy and matter to sustain life processes and thereby dynamically maintain their behavior that is non-uniform in space and time. Since the molecular components in the cells are distributed in specific localization patterns, their collective behavior is extended to form regions of coordinated, but different action over space. We are interested how information is processed through the spatial-temporal dynamics of protein interaction networks. Studying theoretically and experimentally the relation between the topology of the signaling networks and their dynamics in minimal model systems including receptor tyrosine kinase – protein tyrosine phosphatase (RTK-PTP) and GTPase networks, we study the plasticity in responses in terms of their biochemical behavior, characterized as different stable dynamical solutions. Which of these dynamical solutions is chosen however depends on the local context or microenvironment in which the cells are embedded. We therefore develop theoretical approaches to study how chemical communication between cells can give rise to heterogeneity in a multicellular population (Koseska et al., J. Theor. Biol., 2010; Phys. Rev. Lett. 2013; Phys. Reports 2013). Combining this with experimental studies gives as the means to understand how intercellular communication allows to establish and maintain differentiated entities in tissues.
Since signaling networks are inherently multistable, the positioning in parameter space determines their response to various stimuli and the balance between exploration and stability. We thus develop theories and mathematical tools to study whether signaling networks dynamically self-organize in parameter space to efficiently sample out the environment and “calculate” the necessary response. The experimental implementations on RTK and MAPK signaling modules allows us to further correlate the specific network topology to the observed dynamical organization principles. In this context, we also develop and apply novel methods that will direct experimentally accessible network reconstruction from single cell data.



 

 
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