Luty 1, 2013

Systems biology of biochemical signalling research

Deciphering cytokine signalling

Statistical inference

In recent years, increasingly more dynamical systems studied in the biological sciences are modelled by ordinary and stochastic differential equations. For the vast majority of such systems, we lack reliable information about parameters and frequently have several competing models for the structure of the underlying equations. Moreover, the biological experimental data are often scarce and incomplete, and the likelihood surface of large models are complex. We intend to develop new tools for the analysis of such dynamical systems required to build more realistic, quantitative, and predictive models.





Optimal design of experiments

A recent progress  in time-lapse measurement techniques such as fluorescent microscopy, mass spectrometry and transcriptomics can significantly increase our understanding of biochemical dynamics. The complexity of biochemical networks implies however that conventional measurement can only partially increase our knowledge. Complementary information must be obtained by careful selection of experimental conditions. The development of microfluidic devices creates unprecedented possibilities to control the single cell experiments. Systems biology experimentation will benefit greatly from data obtained in experiments with mathematically optimized stimuli executed using microfluidic devices. We are focused on designing optimal experimental protocols for microfluidic devices to study the NF-κB signal transduction pathway.



Biochemical signal transduction and information processing

Many proteins in living cells have as their primary function the transfer and processing of information. Such proteins are linked through various mechanism into biochemical circuits that perform a variety of tasks including, most importantly, information storage, amplification and integration of signals. In unicellular organisms protein-based circuits act in place of a nervous system. A set of biochemical reactions that deal with signal detection, transduction and decision making is often considered as cellular information processing. The wiring of the information processing networks depends on diffusion-limited encounters between molecules resulting in a distortion of transferred information. Therefore, the stochasticity in information processing can be understood as noise. Despite noisiness many crucial biological processes operate surprisingly well engaging only small number of molecules. We are interested in using mathematical tools of stochastic processes and information theory to describe design principles of signal transduction networks.