Inferring metabolic networks from time series data
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AbstractMetabolic networks comprise the chemical reactions of metabolism and the regulatory interactions that guide these reactions. Metabolic network behavior is commonly modeled by a set of differential equations that govern the time evolution of the metabolites. We focus on the description using biochemical system theory (BST) in the presence of noise��̇��=����Π��=1������������−����Π��=1������ℎ����+���� ,��=1,2,….,��Here ���� is the concentration of metabolite ��, ���� and ���� are respectively the overall rate constants of the processes that increase and decrease ���� , ������ and ℎ���� are parameters, known as kinetic orders, for such processes that involve metabolite ��, and ���� is a Gaussian white noise with ����(��)����(��′)= ��2��������(��−��′). We address the challenge of reconstructing such metabolic networks using only time series data ����(��). Such systems approach a stable fixed point ���� in the noise-free limit. In the presence of weak nose, by linearizing the dynamics about the fixed point, one can derive a relation����=���������� , ������=����Π��=1������ℎ����(������−ℎ����)/����,where ���� denotes the time-lagged covariance matrix of the measurements taken at two times separated by a time interval �� and ���� denotes the covariance matrix of measurements taken at the same time. Using glycolysis, the metabolic pathway that breaks down glucose to extract energy in living organisms, as an example we discuss how to use this relation to reconstruct the links of the network, namely to find which ������ and ℎ���� are nonzero, and the limitations of this method.
Acceptance Date12/10/2016
All Author(s) ListEmily S.C. Ching, Chumin Sun
Name of ConferenceDynamics Days Asia Pacific 9
Start Date of Conference14/12/2016
End Date of Conference17/12/2016
Place of ConferenceHKBU, HKUST
Country/Region of ConferenceHong Kong
Proceedings TitleDDAP9 Programme Book
Series TitleProgramme Schedule – Posters
Number in SeriesP20
Pages11 - 11
LanguagesEnglish-United States

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