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Computational analysis of multi-stable signalling biochemical networks
- Prelegent(ci)
- Agnieszka Dziekańska
- Afiliacja
- alumni of University College Dublin, Systems Biology, Ireland
- Termin
- 2 kwietnia 2014 14:15
- Pokój
- p. 4050
- Seminarium
- Seminarium Zakładu Biomatematyki i Teorii Gier
In this talk, two novel approaches to investigate systemic properties of signalling biochemical networks will be introduced.
The first approach focuses on the analysis of information processing in signalling networks performed by the application of information theory. Molecular components of any signalling network are constantly subject to intracellular fluctuations in gene expression. The study applies the concept of Shannon’s entropy and channel capacity to investigate how fluctuations in the level of network components affect information processing capacity in biochemical networks. A sensitivity analysis of channel capacity could be applied to detect the nodes whose perturbations do not translate to significant changes in channel capacity and therefore do not play an important role in information transduction across the network. However, such a detailed analysis can be performed only in presence of high quality mathematical models of signalling networks. Lack of such models poses a main obstacle in current applications of IT in Systems Biology.
The second approach concentrates on computation of steady-states of biochemical networks by the application of the concept of the biochemical landscape. The biochemical landscape quantifies the propensity of the system to settle in any of the possible steady-states. The study introduces two methods of computation of the biochemical landscape. The first method applies the concept of the quasi-potential in order to compute the gradient of trajectory of a dynamic system as it evolves to its steady-state. The second method applies stochastic simulations (Gibbs sampling) for the purpose of deriving the probability densities that correspond to steady-states of the system. Calculation of landscapes for gene regulatory or signalling networks can determine the relative stability of steady-states to fluctuations present in the network. The concept of an attractor positioned on the landscape surface could represent the discrete phenotypes of many human diseases such as cancer. It could also provide an explanation of the origin and development of the diseases and influence the current method of drug discovery.
The first approach focuses on the analysis of information processing in signalling networks performed by the application of information theory. Molecular components of any signalling network are constantly subject to intracellular fluctuations in gene expression. The study applies the concept of Shannon’s entropy and channel capacity to investigate how fluctuations in the level of network components affect information processing capacity in biochemical networks. A sensitivity analysis of channel capacity could be applied to detect the nodes whose perturbations do not translate to significant changes in channel capacity and therefore do not play an important role in information transduction across the network. However, such a detailed analysis can be performed only in presence of high quality mathematical models of signalling networks. Lack of such models poses a main obstacle in current applications of IT in Systems Biology.
The second approach concentrates on computation of steady-states of biochemical networks by the application of the concept of the biochemical landscape. The biochemical landscape quantifies the propensity of the system to settle in any of the possible steady-states. The study introduces two methods of computation of the biochemical landscape. The first method applies the concept of the quasi-potential in order to compute the gradient of trajectory of a dynamic system as it evolves to its steady-state. The second method applies stochastic simulations (Gibbs sampling) for the purpose of deriving the probability densities that correspond to steady-states of the system. Calculation of landscapes for gene regulatory or signalling networks can determine the relative stability of steady-states to fluctuations present in the network. The concept of an attractor positioned on the landscape surface could represent the discrete phenotypes of many human diseases such as cancer. It could also provide an explanation of the origin and development of the diseases and influence the current method of drug discovery.
