Modeling and analyzing the effects of crosstalk in a biochemical pathway: A study on human mTOR signaling pathway

Article Type

Research Article

Publication Title

Current Bioinformatics

Abstract

Background: Crosstalk is the phenomenon in which two or more biochemical pathways interact with each other. In the presence of many inputs (cross talk) to a signaling pathway, there is a high chance of getting it excess activation. Therefore, to put a ‘brake’ over excessive activation, it has to put extra efforts in the form of regulatory loops. Objective: Design the crosstalk modeling study to analyze the effect of crosstalk on a biochemical pathway under study, and comparison of mTOR signaling pathway with and with no crosstalk. Methodology: We have modeled the crosstalk phenomenon in a signaling pathway, where the interacting pathway has been considered as a hypothetical interacting entity, termed as a ‘crosstalk node’. We have first implemented the methodology, viz., Flux Balance Analysis (FBA) over a synthetic system with feedback inhibition and crosstalk then on human mTOR signaling pathway to investigate the effect of crosstalk, along with feedback inhibition. Apart from analyzing crosstalk, we have also explored the idea of a ‘critical node’ in the form of complex TSC1/TSC2, for the first time, in mTOR pathway. We have modeled the crosstalk among the mTOR, Insulin, Wnt and MAPK pathways, and we represent the latter as ‘crosstalk nodes’. Results: We have obtained higher concentration for the regulators of the reactions, which induce feedback inhibition in the pathway, with crosstalk nodes, in comparison with the pathway having no crosstalk nodes. We have validated the results with existing experimental evidences. Conclusion: This is a novel way for pathway analysis, where one can integrate and model two pathway processes simultaneously to capture the impact of a pathway process on the other one. The major difference with the typical FBA is incorporation of concentration factor, feedback inhibition and crosstalk simultaneously into modeling aspect, which is the significance of this study.

First Page

578

Last Page

589

DOI

10.2174/1574893611666160808115416

Publication Date

11-1-2016

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