Recent advances on constraint-based models by integrating machine learning

[Display omitted] •Machine learning has already been successfully applied to facets of constraint-based modeling.•Further development of the combined framework has great potential to elucidate both model parameters and masked biological relationships.•Iterative integrative machine learning schemes c...

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Bibliographic Details
Published in:Current opinion in biotechnology 2020-08, Vol.64 (C), p.85-91
Main Authors: Rana, Pratip, Berry, Carter, Ghosh, Preetam, Fong, Stephen S
Format: Article
Language:English
Subjects:
Computer Simulation
Machine Learning
Phenotype
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Summary:[Display omitted] •Machine learning has already been successfully applied to facets of constraint-based modeling.•Further development of the combined framework has great potential to elucidate both model parameters and masked biological relationships.•Iterative integrative machine learning schemes can be seamlessly applied to the established constraint-based modeling pipeline. Research that meaningfully integrates constraint-based modeling with machine learning is at its infancy but holds much promise. Here, we consider where machine learning has been implemented within the constraint-based modeling reconstruction framework and highlight the need to develop approaches that can identify meaningful features from large-scale data and connect them to biological mechanisms to establish causality to connect genotype to phenotype. We motivate the construction of iterative integrative schemes where machine learning can fine-tune the input constraints in a constraint-based model or contrarily, constraint-based model simulation results are analyzed by machine learning and reconciled with experimental data. This can iteratively refine a constraint-based model until there is consistency between experimental data, machine learning results, and constraint-based model simulations.
ISSN:0958-1669
1879-0429
DOI:10.1016/j.copbio.2019.11.007