Quantifying Dynamic Regulation in Metabolic Pathways with Nonparametric Flux Inference.

Abstract:

:One of the central tasks in systems biology is to understand how cells regulate their metabolism. Hierarchical regulation analysis is a powerful tool to study this regulation at the metabolic, gene-expression, and signaling levels. It has been widely applied to study steady-state regulation, but analysis of the metabolic dynamics remains challenging because it is difficult to measure time-dependent metabolic flux. Here, we develop a nonparametric method that uses Gaussian processes to accurately infer the dynamics of a metabolic pathway based only on metabolite measurements; from this, we then go on to obtain a dynamical view of the hierarchical regulation processes invoked over time to control the activity in a pathway. Our approach allows us to use hierarchical regulation analysis in a dynamic setting but without the need for explicitly time-dependent flux measurements.

journal_name

Biophys J

journal_title

Biophysical journal

authors

He F,Stumpf MPH

doi

10.1016/j.bpj.2019.04.009

subject

Has Abstract

pub_date

2019-05-21 00:00:00

pages

2035-2046

issue

10

eissn

0006-3495

issn

1542-0086

pii

S0006-3495(19)30327-3

journal_volume

116

pub_type

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