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.
Original language | English |
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Pages (from-to) | 2035-2046 |
Number of pages | 12 |
Journal | Biophysical Journal |
Volume | 116 |
Issue number | 10 |
Early online date | 19 Apr 2019 |
DOIs | |
Publication status | Published - 21 May 2019 |
Bibliographical note
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- Biophysics