Activation-repression connectivity pattern of transcriptional regulatory networks and their impact on robustness
Faiyaz Zamal , McGill SOCS

Area: Bioinformatics

Abstract:

Transcriptional regulatory networks, the biochemical systems controlling the transcription of genes into RNA in response to activating or repressing inputs from transcription factor molecules, are able to robustly retain their functionality against a wide array of environmental perturbations and evolutionary mutations. However, what injects such robustness in these systems remains largely unexplained. Previous studies have principally focused on identifying topological features of these networks that are unlikely to occur in random networks and then determining the impact of these features on robustness and other dynamical aspects of the system. While this approach has yielded significant insights into the design principles of robust biological systems, a comprehensive analysis of how these topological features act in conjunction with the parameters of the system has not been conducted yet. In this ongoing project, we first analyze how the activating and repressing connections are distributed within the E Coli transcriptional network to identify some features which deviate from the expected behaviour under random connectivity and then, through generation of random ensembles of networks and simulating their dynamics using a standard discrete time boolean network dynamics model, determine the robustness induction effect of these features. Our result thus far indicates that both the first and second order aspects of this connectivity pattern exert impacts on the robustness of the network, suggesting a synergy between the topological features and parametric features defining the system towards attaining robustness.