ArticlePloS one2012
MultiMetEval: comparative and multi-objective analysis of genome-scale metabolic models.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed, 35 citations in OpenAlex.
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- Re-classification ofiScience · 2021Article
- Synthetic biology approaches to actinomycete strain improvement.FEMS microbiology letters · 2021Review
- Systems Biology Approaches Toward Understanding Primary Mitochondrial Diseases.Frontiers in genetics · 2019Review
- Human Systems Biology and Metabolic Modelling: A Review-From Disease Metabolism to Precision Medicine.BioMed research international · 2019Review
- Development and validation of an updated computational model of Streptomyces coelicolor primary and secondary metabolism.BMC genomics · 2018Article
- Development of fungal cell factories for the production of secondary metabolites: Linking genomics and metabolism.Synthetic and systems biotechnology · 2017Review
- Synthetic Biology of Natural Products.Cold Spring Harbor perspectives in biology · 2016Review
- Bioinformatics for the synthetic biology of natural products: integrating across the Design-Build-Test cycle.Natural product reports · 2016Review
- Multiplex methods provide effective integration of multi-omic data in genome-scale models.BMC bioinformatics · 2016Article
- Synthetic biology advances for pharmaceutical production.Current opinion in biotechnology · 2015Review
- From cultured to uncultured genome sequences: metagenomics and modeling microbial ecosystems.Cellular and molecular life sciences : CMLS · 2015Review
- antiSMASH 3.0-a comprehensive resource for the genome mining of biosynthetic gene clusters.Nucleic acids research · 2015Article
- Cell scale host-pathogen modeling: another branch in the evolution of constraint-based methods.Frontiers in microbiology · 2015Article
- A novel nutritional predictor links microbial fastidiousness with lowered ubiquity, growth rate, and cooperativeness.PLoS computational biology · 2014Article
- Promise and reality in the expanding field of network interaction analysis: metabolic networks.Bioinformatics and biology insights · 2014Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Comparative metabolic modelling is emerging as a novel field, supported by the development of reliable and standardized approaches for constructing genome-scale metabolic models in high throughput. New software solutions are needed to allow efficient comparative analysis of multiple models in the context of multiple cellular objectives. Here, we present the user-friendly software framework Multi-Metabolic Evaluator (MultiMetEval), built upon SurreyFBA, which allows the user to compose collections of metabolic models that together can be subjected to flux balance analysis. Additionally, MultiMetEval implements functionalities for multi-objective analysis by calculating the Pareto front between two cellular objectives. Using a previously generated dataset of 38 actinobacterial genome-scale metabolic models, we show how these approaches can lead to exciting novel insights. Firstly, after incorporating several pathways for the biosynthesis of natural products into each of these models, comparative flux balance analysis predicted that species like Streptomyces that harbour the highest diversity of secondary metabolite biosynthetic gene clusters in their genomes do not necessarily have the metabolic network topology most suitable for compound overproduction. Secondly, multi-objective analysis of biomass production and natural product biosynthesis in these actinobacteria shows that the well-studied occurrence of discrete metabolic switches during the change of cellular objectives is inherent to their metabolic network architecture. Comparative and multi-objective modelling can lead to insights that could not be obtained by normal flux balance analyses. MultiMetEval provides a powerful platform that makes these analyses straightforward for biologists. Sources and binaries of MultiMetEval are freely available from https://github.com/PiotrZakrzewski/MetEval/downloads.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.