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  • Review Article
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Systems-biology approaches for predicting genomic evolution

Nature Reviews Genetics volume 12, pages 591–602 (2011)Cite this article

Subjects

Key Points

  • Although long-term microbial evolutionary experiments have shown numerous examples of parallel phenotypic and genetic evolution, it is unclear how predictable evolution is at the level of genomes and molecular networks.

  • Predicting evolutionary events is a formidable task, not least because it requires a detailed knowledge of the range of available mutations and their phenotypic effects. This issue can be best addressed by synthesizing evolutionary theory, systems biology and molecular data

  • Recent genome-scale microbial metabolic networks are good starting points for such a synthesis. These models allow the analysis of large cellular subsystems that have clear links to changes in environmental conditions. The predictive power of these models is coupled with mechanistic insights and wide usage.

  • Models can calculate evolutionarily relevant variables that are difficult to estimate experimentally on a large-scale or across environmental conditions. In particular, they can infer epistatic interactions and mutational effects.

  • Models provide mechanistic insights into complex evolutionary phenomena from the causes of gene dispensability and the adaptation of global transcriptional programs to the evolution of minimized genomes.

  • By integrating systems models with comparative genomics, it is becoming possible to explain general trends in genome evolution. For example, these approaches reinvestigate how and why networks become more complex and why genes evolve at different rates or duplicate.

  • These models also hold the promise of transforming evolutionary biology into a more predictive discipline. They can make specific and reliable predictions on the outcome of metabolic evolution, both in short-term laboratory evolution and on macroevolutionary time scales.

  • Existing models have several limitations, however, and they are available for only a handful of species. Moreover, advances in computational systems biology should be more tightly integrated with technological advances in the fields of experimental evolution, genome engineering and automated model reconstruction.

Abstract

Is evolution predictable at the molecular level? The ambitious goal to answer this question requires an understanding of the mutational effects that govern the complex relationship between genotype and phenotype. In practice, it involves integrating systems-biology modelling, microbial laboratory evolution experiments and large-scale mutational analyses — a feat that is made possible by the recent availability of the necessary computational tools and experimental techniques. This Review investigates recent progresses in mapping evolutionary trajectories and discusses the degree to which these predictions are realistic.

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Figure 1: Model and data: mutational effects, epistasis and gene content evolution.
Figure 2: Systems-biology approaches to studying evolutionary trajectories.

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Acknowledgements

We wish to thank the anonymous reviewers and S. G. Oliver for their valuable comments on the manuscript. This work was supported by grants from the European Research Council (C.P.), the Wellcome Trust (C.P.), the 'Lendület Program' of the Hungarian Academy of Sciences (B.P.), the International Human Frontier Science Program Organization and the Hungarian Scientific Research Fund (B.P and C.P). R.N. is supported by The Netherlands Genomics Initiative (NGI – Horizon grant) and The Netherlands Organisation for Scientific Research (NWO - VENI Grant).

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Authors and Affiliations

  1. Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Center, Temesvári krt. 62, Szeged, H-6726, Hungary

    Balázs Papp & Csaba Pál

  2. Cambridge Systems Biology Centre and Department of Genetics, University of Cambridge, Cambridge, CB2 3EH, UK

    Balázs Papp

  3. Departments of Bioinformatics (CMBI) and Systems Biology (CSBB), Nijmegen Centre for Molecular Life Sciences, Radboud University Nijmegen Medical Centre, P.O. BOX 9101, Nijmegen, 6500, HB, The Netherlands

    Richard A. Notebaart

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FURTHER INFORMATION

Balázs Papp and Csaba Pál's homepage

BiGG, a database of large-scale metabolic reconstructions

BRENDA, the enzyme information system

Kyoto Encyclopedia of Genes and Genomes (KEGG)

Nature Reviews Genetics series on Modelling

The openCOBRA Project, a Matlab toolbox for constraint-based modelling

Glossary

Epistatic interaction

Non-independent effect of mutations on a phenotype. Epistasis is negative when a genotype with two mutations has a lower phenotype value or positive when it has a higher value than would be expected from the product of the single mutant values.

Graph-theoretical approaches

The study of graphs. A graph provides an abstract representation of a biological or physical system in which components are represented by nodes that are connected to each other by edges (links).

Robustness

Mutational robustness describes the resilience of phenotypes to genetic perturbations.

Gene dispensability

A measure that is inversely related to the overall importance of a gene. It is usually approximated by the fitness of the corresponding gene-knockout strain under laboratory conditions.

Metabolic fluxes

Turnover rate of substrates through metabolic reactions or pathways.

Flux balance analysis

(FBA). A mathematical approach for analysing the behaviour of large-scale metabolic networks. It does not require knowledge of metabolite concentration or enzyme kinetic details.

Synthetic lethal interaction

A form of epistasis between two genes in which the double mutant shows a no-growth phenotype that is not exhibited by either single mutant.

Trimodal

Trimodality is a statistical term for a distribution that has three modes.

Pleiotropy

The phenomenon of one mutation affecting multiple traits.

(Nearly) neutral mutations

A neutral mutation is one that has no fitness effect. A mutation is 'nearly' neutral when its fitness effect is too small to be governed by selection, and hence its fate is determined largely by genetic drift.

Adaptive landscapes

Visualizations of the relationship between genotype and fitness. The plane of the landscape contains all possible genotypes in such a way that similar genotypes are located close to each other on the plane and the height of the landscape reflects the fitness of the corresponding genotype.

Trade-off

Two traits are in a trade-off relationship when an increase in fitness owing to a change in one trait is opposed by a decrease in fitness owing to a concomitant change in the second trait.

Historical contingency

This term describes the situation in which future evolutionary alternatives of a population depend on its prior history.

Cross-feeding

This describes the situation in which one species or strain degrades a primary resource and secretes a chemical compound that is used as a substrate by another species or strain.

Multiplex automated genome engineering

An automated and efficient experimental technique to simultaneously modify many targeted genomic locations.

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Papp, B., Notebaart, R. & Pál, C. Systems-biology approaches for predicting genomic evolution. Nat Rev Genet 12, 591–602 (2011). https://doi.org/10.1038/nrg3033

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