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Constraint-based models predict metabolic and associated cellular functions

Key Points

  • Understanding the genotype–phenotype relationship of a cell has been a long-standing goal in biology. The availability of genome sequencing and of other high-throughput 'omic' data sets provides an opportunity to parameterize various statistical and mechanistic models.

  • Genome-scale metabolic network reconstructions contain comprehensively curated and systematized information on the cellular metabolism of an organism. These networks can be converted into a mathematical format that is amenable to constraint-based modelling.

  • Biological phenotype is constrained by the complex relationships between the genotype of a cell, its environment and physico-chemical laws. Rather than deriving a single solution to a problem, the philosophy of constraint-based modelling is to impose known constraints on physiological function to study the set of possible solutions.

  • Constraint-based modelling has a 30-year history, which can be separated into four phases — from initial theoretical interest and conception to maturing to predictive biological practice. In the current fourth phase, there is a critical mass of studies that combine high-throughput data and constraint-based models (CBMs) to answer relevant biological questions in a prospective manner.

  • Key successes in this field of research include deriving underlying principles for both optimal flux states and protein evolution; experimentally discovering cancer drug targets and antibiotics; and designing organisms that overproduce metabolic precursors of commodity chemicals.

  • Integrated modelling efforts combine CBMs with alternative modelling frameworks that are more amenable to capture other cellular processes in order to expand predictive scope outside metabolism. Expansions for other cellular features — such as transcription and translation machinery, transcriptional regulation and structural protein properties — have been constructed.

  • The formulation of high-dimensional models is enabled by the availability of genome-sequences (that is, a parts list), high-throughput omic data (that is, a functional readout) and CBMs (that is, a mechanistic framework to reconcile network topology, primary literature and omic data). These models can be used to quantitatively study the genotype–phenotype relationship for cellular metabolism, which will lead to a new generation of genome-scale science.

Abstract

The prediction of cellular function from a genotype is a fundamental goal in biology. For metabolism, constraint-based modelling methods systematize biochemical, genetic and genomic knowledge into a mathematical framework that enables a mechanistic description of metabolic physiology. The use of constraint-based approaches has evolved over ~30 years, and an increasing number of studies have recently combined models with high-throughput data sets for prospective experimentation. These studies have led to validation of increasingly important and relevant biological predictions. As reviewed here, these recent successes have tangible implications in the fields of microbial evolution, interaction networks, genetic engineering and drug discovery.

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Figure 1: The multiple uses of high-throughput data in constraint-based models.
Figure 2: Predictive case studies in understanding underlying principles of interaction networks.
Figure 3: Predictive case studies in metabolic engineering and drug targeting.
Figure 4: Expanding predictive scope through integrative modelling.

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Acknowledgements

The authors thank D. Zielinski, J. Lerman, N. E. Lewis and H. Nagarajan for their criticisms and comments on the manuscript. This work was supported by the US National Institutes of Health grants GM068837 and GM057089, and by the Novo Nordisk Foundation. Z.A.K. is supported through the US National Science Foundation Graduate Research Fellowship (DGE-1144086).

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Glossary

Metabolite overflows

Biological phenomena whereby the rate of substrate use by a cell for growth is lower than the rates of uptake and conversion of the substrate, which results in production of side metabolites (for example, acetate in Escherichia coli).

Metabolic fluxes

The rates of turnover or movement of metabolites through a reaction or a pathway.

Objective functions

The particular variables, or metabolic reactions, that are being maximized or minimized for by the linear programme. In flux-balance analysis, the objective function is often a pseudoreaction for biomass generation that represents cellular growth.

Metabolic pathways

In the context of this Review, sets of pathways that are calculated by metabolic network-based pathway analysis tools such as Extreme Pathways and Elementary Flux Modes.

Metabolic engineering

The practice of improving cellular production of target compounds of interest by modifying and optimizing genetic, regulatory and environmental parameters of cellular metabolism.

Genome-scale models

The formulation, using mathematical models, of genome-scale metabolic network reconstructions. They are synonymous with constraint-based models in the context of this Review.

Pathway enrichment analysis

A high-throughput data analysis technique to understand more global changes in an experiment by grouping individual measurements of biological components (for example, genes and proteins) into a context that is based on various pathway databases (for example, Kyoto Encyclopedia of Genes and Genomes, BioCyc and Gene Ontology).

Metabolic flux analysis

An experimental approach to identify metabolic fluxes using isotopically labelled metabolites and computational software that reconciles experimental data with network topology.

Flux distributions

Sets of calculated flux values for all reactions in a constraint-based model.

Pareto surface

The space that is formed when multiple objective functions are modelled at once; it represents a set of optimal solutions, in which increasing the value of one of the objectives results in a trade-off with other objective values.

Central carbon metabolism

The metabolic pathways and reactions that convert sugars into the metabolic precursors that are required for growth. It is typically comprised of glycolysis, pentose phosphate pathways and the tricarboxylic acid cycle.

Solution space

The range of all feasible values for variables in a constraint-based model, which represents all potential metabolic reaction flux distributions on the basis of the given constraints.

Machine learning method

A method that applies statistical methods to discover generalizable rules and patterns in complex data sets.

Gap-filling

Pertaining to a procedure for targeted expansion of metabolic knowledge, whereby prospective experiments are designed on the basis of discrepancies in experimental data and model predictions.

Auxotrophies

Metabolic limitations that impair the ability of a cell or organism to synthesize a particular metabolite that is essential for growth, which force the cell or organism to rely on an exogenous source of the nutrient.

Reaction bounds

User-defined constraints on the minimum and maximum allowable flux values for a particular metabolic reaction in a constraint-based model.

Metabolite essentiality analysis

A metabolite-centric approach to determine essential components for cellular growth. To computationally test the essentiality of a metabolite, the consuming reactions of the particular metabolites are constrained to zero.

Coupling constraints

Constraints that enforce strict relationships between model biochemical transformations, thereby connecting the fluxes for different cellular processes (such as transcription, translation, and tRNA and protein use for a metabolic reaction).

Linear programming

A mathematical optimization technique that calculates the maximum or minimum value of a particular variable (that is, the objective function) on the basis of a set of linear constraints; an example of this is flux-balance analysis.

Consensus sequences

Conserved sequences of nucleotides or amino acids that represent the target for a biomolecular event, often for proteins binding to the genome.

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Bordbar, A., Monk, J., King, Z. et al. Constraint-based models predict metabolic and associated cellular functions. Nat Rev Genet 15, 107–120 (2014). https://doi.org/10.1038/nrg3643

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