Crosstalk and Signalling Pathway Complexity - A Case Study on Synthetic Models

Abstract

Crosstalk between signalling pathways have been intensively studied in wet laboratory experiments. More and more experimental evidences show that crosstalk is a very important component for maintaining biology systems robustness. In wet laboratory experiments, crosstalk are normally predicted by applying specific stimulus, i.e., various extra-cellular cues or individual gene suppressors or protein inhibitors. If significant difference between a control group without a specific stimulus and an experimental group with a specific stimulus is found, crosstalk is predicted. In terms of time complexity and cost, such experiments are commonly limited to small scales by using very few sampling time points. At the same time, few mathematical models have been proposed to analyse or predict crosstalk. This work investigates how crosstalk affects signalling pathway complexity and if such effect is significant for discrimination purpose, hence providing evidence for crosstalk prediction. Two crosstalk activities (positive and negative) based on simple synthetic transcription models are used for study. The study has found that crosstalk can change the steady-state gene expression order, hence making signalling pathway complex. The finding indicates that crosstalk is predictable using computer programs in top-down systems biology research.