Information processing in molecular motifs and signal transduction

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Title: Information processing in molecular motifs and signal transduction
Authors: Mc Mahon, Siobhan
Item Type: Thesis or dissertation
Abstract: Sensing and responding to the environment is a crucial function in all living organisms. The molecular mechanisms that facilitate these essential processes are however subject to a range of random effects and stochastic processes, which jointly affect the reliability of information transmission between receptors and e.g. the physiological downstream response. We employ an information theoretic framework to capture and characterise how extrinsic and intrinsic noise affect the transmission of signals along simple motifs of molecular interaction networks, and cell fate decision making pathways. We observe that the biomolecular information processing efficiency is profoundly but differently affected by the various sources of noise. In particular extrinsic variability is apt to generate “apparent” information that can in extreme cases mask the actual information that would flow between the different molecular components. We show how this artificial inflation in information arises, and how the effects of different types of noise alone and in combination can be understood. By applying the same approach to the ERK signalling pathway, we determine the best mechanistic model for this system in question and shed light on the noise filtering capabilities of the system. We also consider the pathway under conditions of abnormal regulation, similar to those associated with tumerogenesis. Next we investigate the Akt pathway, using a highly simplified model under different EGF input signal conditions and in the presence of difference sources of variability, in order to determine weather, under these conditions, an information theoretic approach can still be used. We find that such an approach cannot replace the use of mechanistic models to understand the molecular dynamics. Overall, these results are a stepping stone towards understanding the complexities of cellular information processing.
Content Version: Open Access
Issue Date: Sep-2015
Date Awarded: Jan-2016
Supervisor: Stumpf, Michael
Department: Life Sciences
Publisher: Imperial College London
Qualification Level: Doctoral
Qualification Name: Doctor of Philosophy (PhD)
Appears in Collections:Life Sciences PhD theses

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