Abstract
Signalling network inference is a central problem in system biology. Previous studies investigate this problem by independently inferring local signalling networks and then linking them together via crosstalk. Since a cellular signalling system is in fact indivisible, this reductionistic approach may have an impact on the accuracy of the inference results. Preferably, a cell-scale signalling 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: 1) variations of concentrations are sparse due to separations of timescales; 2) several species can be measured together using cross-reactivity. We propose a method, CCELL, for cell-scale signalling 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.
Highlights
Inferring signalling networks from time series aims at revealing the mechanisms behind biological processes and is an important research subject in systems biology
Step 2: Network inference This paper focuses on the biological networks that can be modelled by a system of linear equations: xt~Axt{1 zN(0,s2s I ): ð9Þ
We propose a method, which is called CCELL, to solve this problem
Summary
Inferring signalling networks from time series aims at revealing the mechanisms behind biological processes and is an important research subject in systems biology. In some inflammatory diseases, such as severe asthma, the effect of GR as an anti-inflammatory regulator is dramatically impaired when the p38 MAPK is over-activated This suggests that this is crosstalk between the p38 MAPK and GR pathways, which can potentially explain the reduced responsiveness to glucocorticoids in chronic inflammation at the molecular level. A cell-scale signalling network can be inferred using existing methods, such as maximum likelihood estimation [2], least-squares estimation [10,11], non-linear optimization [12], Kalman filters [13,14] and approximate Bayesian computation [15,16] This holistic approach suffers from three practical issues, which limits the applications of the existing methods:
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
