Elucidating the Design Principles of Signal Transduction Networks: Application to Dopamine and Calcium signaling in reinforcement learning
2011 (English)In: Proceedings of IFAC World Congress 2011, Milan, Italy, 2011, 10436-10441 p.Conference paper (Refereed)
The biochemical networks underlying biological functions are in general highly complex. An important aim of systems biology is to provide mechanistic insight into how the different interactions within the network give rise to specific behaviors and properties. In this paper we consider the use of structured dynamic perturbations applied to the network nodes and edges for elucidating the most important interactions in signal transduction networks. Signal transduction networks mediate extracellular and intracellular signals to the nucleus, resulting in an appropriate response by the gene regulatory network. The most important characteristic of signal transduction networks is usually the specific temporal amplification of signals. As a case study we consider the intracellular signaling pathways that underlie reinforcement learning in striatum brain cells. It has recently been found that these networks respond to Dopamine and Calcium signals in a fashion which is strongly dependent on the signal shape, and the hypothesis is that this is related to the existence of a resonant feedback loop within the network. By systematically perturbing the nodes and edges of the network using general dynamic perturbations, affecting both the strength and phase lag of the direct interactions within the network, we are able to identify the most important components and interactions underlying the ''resonant'' signal amplification. Based on this we derive a reduced order model of the network, with retained physical states, from which we can show that the apparant resonance is caused by two parallel pathways with opposing effects and widely different time-constants. We postulate that this is a sound architecture for signal amplification of mid-frequency signals based on the fact that the robustness can be made almost arbitrarily large, as compared to resonant feedback loops that are inherently unrobust.
Place, publisher, year, edition, pages
2011. 10436-10441 p.
Biochemical Networks, Robustness, Modeling, Pathways
Control Engineering Bioinformatics and Systems Biology
IdentifiersURN: urn:nbn:se:kth:diva-87207DOI: 10.3182/20110828-6-IT-1002.03693ScopusID: 2-s2.0-84866769503OAI: oai:DiVA.org:kth-87207DiVA: diva2:502040
IFAC World Congress 2011, Milan, Italy
QC 201409012012-02-142012-02-142014-10-02Bibliographically approved