Article series: Modelling

Constraint-based models predict metabolic and associated cellular functions

Journal name:
Nature Reviews Genetics
Year published:
Published online


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.

At a glance


  1. The multiple uses of high-throughput data in constraint-based models.
    Figure 1: The multiple uses of high-throughput data in constraint-based models.

    Constraint-based modelling can be used to interpret and augment omic data sets by using an underlying cellular network that has been biochemically validated. Metabolites are represented by circles. a | Similarly to pathway enrichment analysis and interaction networks, high-throughput data can be integrated with the metabolic network topology to determine enriched regions and even significantly perturbed metabolites32. b | Omic data add an additional layer of constraints for reaction fluxes. One study48 integrated expression profiling data to determine context-specific flux distributions (pathway shown in red), which increases the fidelity of the data (represented as bars) as well as the accuracy of flux predictions (upper panel). In addition, two other studies77, 78 used omic data to build cell- and tissue-specific models of human metabolism by removing unexpressed reactions (shown as discoloured reactions) from the global human metabolic network (lower panel). Differences in these networks can be exploited to learn unique features of each network. c | Constraint-based analysis predictions can be compared and validated against high-throughput data sets. One study41 compared flux-balance analysis solutions of different objectives against 13C fluxomic data to find a combination of objectives that best fit the in vivo fluxes.

  2. Predictive case studies in understanding underlying principles of interaction networks.
    Figure 2: Predictive case studies in understanding underlying principles of interaction networks.

    Many network types are used to represent cellular behaviour. Recent studies have compared the properties of interaction networks against constraint-based models (CBMs) to learn global principles. a | One study55 compared an experimental set of genetic interactions for metabolic genes against interactions that were predicted by flux-balance analysis (FBA). The CBM was able to recapitulate many of the in vivo principles. However, there was a high number of incorrect model predictions. Using machine learning techniques, key changes to the metabolic network that would improve model accuracy were identified. Using growth screens, the authors validated that the synthesis of NAD+ from amino acids was only possible from L-tryptophan (L-trp) but not from L-aspartate (L-asp). Δbna refers to any of the genes that are related to the kynurenine pathway, including bna1, bna2, bna4 and bna5. b | Another study57 calculated metabolic pathways — Elementary Flux Patterns — for the network. Elementary Flux Patterns decompose the metabolic network into distinct functional pathways (shown by different colours). The degree of co-regulation of the genes of each pathway was calculated, which reveals that some pathways are highly correlated, whereas others are not. Variation in co-regulation was attributed to the 'cost' that is needed for building the proteins in a particular pathway.

  3. Predictive case studies in metabolic engineering and drug targeting.
    Figure 3: Predictive case studies in metabolic engineering and drug targeting.

    Constraint-based models have been used for answering important questions in translational research. a | One study70 used multiple computational and experimental tools to design an Escherichia coli strain that produces 1,4-butanediol (BDO). An unengineered wild-type (WT) strain trades off metabolite production with cellular growth (shown by the solid line in the solution space). Using the OptKnock algorithm, BDO production was 'coupled' with the growth objective of the cell by forcing the synthetic BDO pathway to be the sole route for E. coli to maintain redox balance (shown by black arrows). Thus, the solution space is modified such that BDO production is linked to cellular growth (shown by the dashed line in the solution space). b | In one study81, researchers took an alternative, metabolite-centric approach to drug targeting, which computationally removes consuming reactions of a particular metabolite. The approach was experimentally confirmed for Vibrio vulnificus by a structural analogue of the endogenous metabolite, which also acts as a small-molecule inhibitor. c | Metabolic reactions in the E. coli model were augmented to capture the generation of reactive oxygen species (ROS), which allowed the use of flux-balance analysis to predict ROS production in one study82. In follow-up experiments, the authors show that it is possible to predict drug target strategies to enhance endogenous ROS production to increase the efficacy of other antibiotics. TCA, tricarboxylic acid cycle.

  4. Expanding predictive scope through integrative modelling.
    Figure 4: Expanding predictive scope through integrative modelling.

    The predictive scope of constraint-based modelling has been extended beyond metabolism either by explicitly accounting for non-metabolic components in the constraint-based modelling approach or by coupling with other modelling frameworks. Metabolites are represented by circles. a | The transcription and translation of the necessary mRNA, proteins and cofactors have been explicitly represented in a constraint-based modelling framework alongside the metabolism of Thermotoga maritima83 (upper panel). This allows simultaneous computation of metabolic fluxes, mRNA transcript expression and proteome levels (lower panel). b | Metabolic models have also been coupled with other modelling frameworks. The probability of metabolic gene activation and repression by transcription factors (TFs) can be computed using a probabilistic transcriptional regulatory network that is based on high-throughput data sets (upper panel). The calculated probabilities are then relayed into the constraints of the metabolic reaction fluxes in the constraint-based model89, which allow prediction of TF-knockout phenotypes (lower panel). c | Structural systems biology can predict biophysical properties of proteins. One study91 calculated the individual activity changes of each metabolic enzyme during temperature shift. The combined effect of all the metabolic enzymes on the cell was computed by integrating the individual enzyme changes into the flux constraints of the Escherichia coli constraint-based model (upper panel), which allowed growth rate to be predicted as a function of temperature (lower panel). Enz, enzyme; NTP, nucleoside 5′-triphosphate; P, probability.


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  1. Department of Bioengineering, University of California, San Diego, 9500 Gilman Dr, La Jolla, California 92093–0412, USA.

    • Aarash Bordbar,
    • Jonathan M. Monk,
    • Zachary A. King &
    • Bernhard O. Palsson

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  • Aarash Bordbar

    Aarash Bordbar is a Ph.D. candidate in Bioengineering at the University of California, San Diego, USA, and holds a B.S. degree in Bioengineering: Biotechnology from the University of California, San Diego. His current research involves developing and applying novel computational methods to the systems biology of human metabolism; he has a particular focus on infection, inflammation, haematology and personalized medicine.

  • Jonathan M. Monk

    Jonathan M. Monk is a graduate student in Nanoengineering at the University of California, San Diego, USA, and holds a B.S. degree in Chemical Engineering from Princeton University, New Jersey, USA. He is currently researching virulence factors of numerous pathogenic Escherichia coli strains by developing genome-scale metabolic models and experimentally validating them using high-throughput screens.

  • Zachary A. King

    Zachary A. King is a graduate student in Bioengineering at the University of California, San Diego, USA, and holds a B.S.E. degree in Biomedical Engineering from the University of Michigan, Ann Arbor, USA. He is developing multiscale modelling tools for Escherichia coli and investigating basic biological constraints that can be used to predict phenotypes of organisms with genetic modifications.

  • Bernhard O. Palsson

    Bernhard O. Palsson is the Galletti Professor of Bioengineering at the University of California, San Diego, USA; a member of the US National Academy of Engineering; and a fellow of the American Association for the Advancement of Science. His research includes developing methods for analysing metabolic dynamics and formulating complete models of selected cells. He has authored 40 US patents, 3 books and 340 peer-reviewed articles, and is the co-founder of several biotechnology companies. He holds a Ph.D. in Chemical Engineering from the University of Wisconsin–Madison, USA, and a B.S. degree in Chemical Engineering from the University of Kansas, Lawrence, USA. Bernhard O. Palsson's homepage.

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