Gsmodutils: a python based framework for test-driven genome scale metabolic model development.

James Gilbert,Jamie Twycross,Rupert Norman,Nigel Minton,John King,Thomas Millat,Nicole Pearcy,Klaus Winzer,Charlie Hodgman,Russell Schwartz

doi:10.1093/bioinformatics/btz088

James Gilbert, Jamie Twycross + Show 8 more

Open Access

https://doi.org/10.1093/bioinformatics/btz088

Copy DOI

Abstract

MotivationGenome scale metabolic models (GSMMs) are increasingly important for systems biology and metabolic engineering research as they are capable of simulating complex steady-state behaviour. Constraints based models of this form can include thousands of reactions and metabolites, with many crucial pathways that only become activated in specific simulation settings. However, despite their widespread use, power and the availability of tools to aid with the construction and analysis of large scale models, little methodology is suggested for their continued management. For example, when genome annotations are updated or new understanding regarding behaviour is discovered, models often need to be altered to reflect this. This is quickly becoming an issue for industrial systems and synthetic biotechnology applications, which require good quality reusable models integral to the design, build, test and learn cycle.ResultsAs part of an ongoing effort to improve genome scale metabolic analysis, we have developed a test-driven development methodology for the continuous integration of validation data from different sources. Contributing to the open source technology based around COBRApy, we have developed the gsmodutils modelling framework placing an emphasis on test-driven design of models through defined test cases. Crucially, different conditions are configurable allowing users to examine how different designs or curation impact a wide range of system behaviours, minimizing error between model versions.Availability and implementationThe software framework described within this paper is open source and freely available from http://github.com/SBRCNottingham/gsmodutils.Supplementary information Supplementary data are available at Bioinformatics online.

Highlights

Stoichiometric constraints based modelling for biological systems has been a mainstay of systems biology for several decades (Fell and Small, 1986; Varma and Palsson, 1994)
Such large scale reconstructions are often referred to as genome scale metabolic models (GSMMs), as the processes is significantly aided through the advent of relatively inexpensive genome sequencing (Land et al, 2015; O’Brien et al, 2015)
Owing to their ability to model complex aspects of metabolism, GSMMs have been widely adopted as a standard to elucidate and optimize industrial biotechnology processes (Kim et al, 2017)

Summary

Introduction

Stoichiometric constraints based modelling for biological systems has been a mainstay of systems biology for several decades (Fell and Small, 1986; Varma and Palsson, 1994). Such large scale reconstructions are often referred to as genome scale metabolic models (GSMMs), as the processes is significantly aided through the advent of relatively inexpensive genome sequencing (Land et al, 2015; O’Brien et al, 2015). Owing to their ability to model complex aspects of metabolism, GSMMs have been widely adopted as a standard to elucidate and optimize industrial biotechnology processes (Kim et al, 2017)

Objectives

Results

Conclusion