Abstract
In this article, we present a protocol for generating a complete (genome-scale) metabolic resource allocation model, as well as a proposal for how to represent such models in the systems biology markup language (SBML). Such models are used to investigate enzyme levels and achievable growth rates in large-scale metabolic networks. Although the idea of metabolic resource allocation studies has been present in the field of systems biology for some years, no guidelines for generating such a model have been published up to now. This paper presents step-by-step instructions for building a (dynamic) resource allocation model, starting with prerequisites such as a genome-scale metabolic reconstruction, through building protein and noncatalytic biomass synthesis reactions and assigning turnover rates for each reaction. In addition, we explain how one can use SBML level 3 in combination with the flux balance constraints and our resource allocation modeling annotation to represent such models.
Highlights
In recent years, the systems biology of metabolism has moved more and more from classical metabolic network study towards the study of growth as a result of an optimized cellular economy.This idea of studying growth strategies using resource allocation models has been initiated by Molenaar et al [1] in 2009
Specification, called resource allocation modeling (RAM) [13]. This specification allows encoding such models in the systems biology markup language (SBML) format using the Flux Balance Constraints extension [14]. In addition to this protocol, we provide software in Python 2.7 as well as MATLAB R2016a for reading and writing resource allocation models using our SBML specification as well as for solving deFBA problems
Dynamic resource allocation models have emerged in recent years as a means of extending the predictive capabilities of constraint-based models
Summary
The systems biology of metabolism has moved more and more from classical metabolic network study towards the study of growth as a result of an optimized cellular economy. Experimental studies focused on relating absolute protein abundances to how metabolic pathways balance production costs and activity requirements [7] These formalisms have been taken a step forward, towards understanding how resources are distributed in a dynamically changing environment by means of a dynamic enzyme-cost flux balance. Metabolites 2017, 7, 47 analysis (deFBA) [8] and conditional flux balance analysis (cFBA) [9] This has been taken to the genome scale by studying the optimal glycogen and metabolite partitioning dynamics under a day-night cycle in a cyanobacterium using a dynamic resource allocation model [10]. To facilitate exchange among researchers, we propose a new SBML specification, called resource allocation modeling (RAM) [13] This specification allows encoding such models in the SBML format using the Flux Balance Constraints extension [14]. //pdb101.rcsb.org/motm/10 [19], https://swissmodel.expasy.org/repository/uniprot/P04806 [20]
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