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Dynamic elementary mode modelling of non-steady state flux data
Abel Folch-Fortuny1,2, Bas Teusink3, Huub C J Hoefsloot4
1Grupo de Ingeniería Estadística Multivariante, Departamento de Estadística e IO Aplicadas y Calidad, Universitat Politècnica de València, Valencia, Spain. abfolfor@upv.es.
BMC Systems Biology
|June 20, 2018
Summary
This study introduces dynamic elementary modes (dynEMs) for analyzing metabolic fluxes under non-steady conditions. This novel framework helps identify key metabolic pathways driving cellular state changes in dynamic biological systems.
Area of Science:
- Biochemistry
- Systems Biology
- Metabolic Engineering
Background:
- Metabolic flux analysis traditionally focuses on steady-state conditions.
- Analyzing dynamic metabolic changes is crucial for understanding cellular responses to environmental shifts.
- Existing methods often struggle to capture time-dependent metabolic activity.
Purpose of the Study:
- To develop a novel framework for analyzing metabolic fluxes in non-steady state conditions.
- To introduce the concept of dynamic elementary modes (dynEMs) for time-dependent metabolic analysis.
- To enable the identification of metabolic pathways that change over time or in response to experimental conditions.
Main Methods:
- Proposed a novel framework based on dynamic elementary modes (dynEMs).
- Introduced dynamic elementary mode analysis (dynEMA) and dynamic elementary mode regression discriminant analysis (dynEMR-DA).
- Extended principal elementary mode analysis (PEMA) from steady-state to non-steady-state scenarios.
Main Results:
- Demonstrated the application of dynEMA and dynEMR-DA using Saccharomyces cerevisiae.
- Utilized both simulated and real concentration data for flux analysis.
- Highlighted the benefits of dynamic modeling in capturing time-varying metabolic behavior.
Conclusions:
- The methodology allows for the analysis of metabolic fluxes at early stages.
- Enables the creation of reduced dynamic models from flux data.
- Facilitates the identification of critical metabolic pathways driving organismal state transitions under changing environmental conditions.
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