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Learning time-varying interaction networks

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Licentiate thesis

Most biological systems consist of several subcomponents whichinteract with each other. These interactions govern the overall behaviourof the system; and in turn vary over time and in response to internaland external stress during the course of an experiment. Identifying such time-varying networks promises new insight into transient interactionsand their role in the biological process. Traditional methods havefocussed on identifying a single interaction network based on time series data, ignoring the dynamic rewiring ofthe underlying network.This thesis studies the problem of inferring time-varying interactionsin gene interaction networks based on gene microarray expressiondata. With the advent of next generation sequencing techologies,the amount of publicly available microarray expression data as well as other omicsdata has grown tremendously. Further, the microarray data is often generatedfrom different experimental conditions or under networkperturbations. One of the current challenges in systems biology isintegration of data generated from different experimental conditionsand under different stresses towards understanding of the dynamicinteractome. NETGEM, the first study included in this thesis describes a method for inference oftime-varying gene interaction network based on microarray expressiondata under network perturbation. The method presents a probabilistic generativemodel under the assumption that the changes in the interactionnetwork are caused by the changing functional roles of the interaction genesduring the course of a biological process. This is used to infertime-varying interactions for a perturbation study in {\emSaccharomyces cerevisiae\/}~(Baker's Yeast) under nutrient stress. Theinferred network agrees with experimental evidence aswell as identifying key transient interactions during the course of the experiment. In the subsequent study, we present a survey chapter describing current approaches forinference of time-varying biological networks based on nodeobservations. We give an overview of different methods in terms of theunderlying model assumptions and applicability under differentconditions. We also describe how recent advances in theory ofcompressed sensing have led to development of new network inference methods with mild assumptions on network dynamics.

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Page updated: 2013-04-01 23:58

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