A probabilistic model for genetic regulation of metabolic networks

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dc.contributor.authorKallus, Jonatan
dc.contributor.authorWilsson, Joel
dc.contributor.departmentChalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers)sv
dc.contributor.departmentChalmers University of Technology / Department of Computer Science and Engineering (Chalmers)en
dc.date.accessioned2019-07-03T13:07:50Z
dc.date.available2019-07-03T13:07:50Z
dc.date.issued2013
dc.description.abstractRecent advancements in gene expression pro ling and measurement of metabolic reaction rates have led to increased interest in predicting metabolic reaction rates. In this thesis we present a principled approach for using gene expression pro les to improve predictions of metabolic reaction rates. A probabilistic graphical model is presented, which addresses inherent weaknesses in the current state of the art method for data-driven reconstruction of regulatory-metabolic networks. Our model combines methods from systems biology and machine learning, and is shown to outperform the current state of the art on synthetic data. Results on real data from S. cerevisiae and M. tuberculosis are also presented.
dc.identifier.urihttps://hdl.handle.net/20.500.12380/174171
dc.language.isoeng
dc.setspec.uppsokTechnology
dc.subjectData- och informationsvetenskap
dc.subjectComputer and Information Science
dc.titleA probabilistic model for genetic regulation of metabolic networks
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster Thesisen
dc.type.uppsokH
local.programmeComplex adaptive systems (MPCAS), MSc
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