Inferring cell-scale signaling networks via compressive sensing

6^th International Conference on Bioinformatics & Systems Biology

August 22-23, 2016 Philadelphia, USA

Yike Guo

Imperial College London, UK

Posters & Accepted Abstracts: J Comput Sci Syst Biol

Abstract :

Signaling network inference is a central problem in system biology. Previous studies investigate this problem by independently inferring local signaling networks and then linking them together via crosstalk. Since a cellular signaling system is in fact indivisible, this reductionistic approach may have an impact on the accuracy of the inference results. Preferably, a cell-scale signaling network should be inferred as a whole. However, the holistic approach suffers from three practical issues: Scalability, measurement and overfitting. Here we make this approach feasible based on two key observations: Variations of concentrations are sparse due to separations of timescales; several species can be measured together using cross-reactivity. We propose a method, CCELL for cellscale signaling network inference from time series generated by immunoprecipitation using Bayesian compressive sensing. A set of benchmark networks with varying numbers of time-variant species is used to demonstrate the effectiveness of our method. Instead of exhaustively measuring all individual species, high accuracy is achieved from relatively few measurements.

Biography :

Email: y.guo@imperial.ac.uk