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A Protocol for Generating and Exchanging (Genome-Scale) Metabolic Resource Allocation Models.

Alexandra-M Reimers1,2, Henning Lindhorst3, Steffen Waldherr4

  • 1Department of Mathematics and Computer Science, Freie Universit&#228;t Berlin, 14195 Berlin, Germany. alexandra.reimers@fu-berlin.de.

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|September 8, 2017
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Summary

This study provides a protocol for creating genome-scale metabolic resource allocation models and representing them in the Systems Biology Markup Language (SBML). This enables investigation of enzyme levels and growth rates in metabolic networks.

Keywords:
SBMLconstraint-based modelingmetabolic networksoptimality

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Area of Science:

  • Systems Biology
  • Metabolic Engineering
  • Computational Biology

Background:

  • Metabolic resource allocation models are crucial for understanding enzyme levels and growth rates in large-scale metabolic networks.
  • Existing systems biology approaches lack standardized protocols for generating these comprehensive models.

Purpose of the Study:

  • To present a detailed protocol for generating genome-scale metabolic resource allocation models.
  • To propose a method for representing these models using the Systems Biology Markup Language (SBML).

Main Methods:

  • Developing step-by-step instructions for building dynamic resource allocation models.
  • Incorporating genome-scale metabolic reconstructions as a prerequisite.
  • Defining protein and non-catalytic biomass synthesis reactions.
  • Assigning turnover rates for each reaction.
  • Utilizing SBML level 3, flux balance constraints, and custom annotation for model representation.

Main Results:

  • A comprehensive protocol for constructing genome-scale metabolic resource allocation models is established.
  • A standardized method for representing these models in SBML is proposed.
  • The protocol guides users from initial reconstruction to assigning reaction turnover rates.

Conclusions:

  • This work fills a critical gap by providing the first published guidelines for generating metabolic resource allocation models.
  • The proposed SBML representation facilitates model sharing and interoperability within the systems biology community.
  • The protocol enables robust investigation of enzyme efficiency and growth rate predictions in complex metabolic networks.