Partner: Sarah Filippi

Imperial College London (GB)

Recent publications
1.Jetka T., Nienałtowski K., Filippi S., Stumpf M.P.H., Komorowski M., An information-theoretic framework for deciphering pleiotropic and noisy biochemical signaling, Nature Communications, ISSN: 2041-1723, DOI: 10.1038/s41467-018-07085-1, Vol.9, pp.4591-1-9, 2018
Abstract:

Many components of signaling pathways are functionally pleiotropic, and signaling responses are marked with substantial cell-to-cell heterogeneity. Therefore, biochemical descriptions of signaling require quantitative support to explain how complex stimuli (inputs) are encoded in distinct activities of pathways effectors (outputs). A unique perspective of information theory cannot be fully utilized due to lack of modeling tools that account for the complexity of biochemical signaling, specifically for multiple inputs and outputs. Here, we develop a modeling framework of information theory that allows for efficient analysis of models with multiple inputs and outputs; accounts for temporal dynamics of signaling; enables analysis of how signals flow through shared network components; and is not restricted by limited variability of responses. The framework allows us to explain how identity and quantity of type I and type III interferon variants could be recognized by cells despite activating the same signaling effectors.

Affiliations:
Jetka T.-IPPT PAN
Nienałtowski K.-IPPT PAN
Filippi S.-Imperial College London (GB)
Stumpf M.P.H.-Imperial College London (GB)
Komorowski M.-IPPT PAN
2.Liepe J., Filippi S., Komorowski M., Stumpf M.P.H., Maximising the information content of experiments in systems biology, PLOS COMPUTATIONAL BIOLOGY, ISSN: 1553-7358, DOI: 10.1371/journal.pcbi.1002888, Vol.9, No.1, pp.e1002888-1-13, 2013
Abstract:

Our understanding of most biological systems is in its infancy. Learning their structure and intricacies is fraught with challenges, and often side-stepped in favour of studying the function of different gene products in isolation from their physiological context. Constructing and inferring global mathematical models from experimental data is, however, central to systems biology. Different experimental setups provide different insights into such systems. Here we show how we can combine concepts from Bayesian inference and information theory in order to identify experiments that maximize the information content of the resulting data. This approach allows us to incorporate preliminary information; it is global and not constrained to some local neighbourhood in parameter space and it readily yields information on parameter robustness and confidence. Here we develop the theoretical framework and apply it to a range of exemplary problems that highlight how we can improve experimental investigations into the structure and dynamics of biological systems and their behavior.

Affiliations:
Liepe J.-Imperial College London (GB)
Filippi S.-Imperial College London (GB)
Komorowski M.-IPPT PAN
Stumpf M.P.H.-Imperial College London (GB)