Predictive biology: modelling, understanding and harnessing microbial complexity

Abstract

Predictive biology is the next great chapter in synthetic and systems biology, particularly for microorganisms. Tasks that once seemed infeasible are increasingly being realized such as designing and implementing intricate synthetic gene circuits that perform complex sensing and actuation functions, and assembling multi-species bacterial communities with specific, predefined compositions. These achievements have been made possible by the integration of diverse expertise across biology, physics and engineering, resulting in an emerging, quantitative understanding of biological design. As ever-expanding multi-omic data sets become available, their potential utility in transforming theory into practice remains firmly rooted in the underlying quantitative principles that govern biological systems. In this Review, we discuss key areas of predictive biology that are of growing interest to microbiology, the challenges associated with the innate complexity of microorganisms and the value of quantitative methods in making microbiology more predictable.

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Fig. 1: Factors that contribute to growth dynamics.
Fig. 2: A next-generation paradigm of predictive biology.

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Acknowledgements

The authors thank Lingchong You for his feedback on the manuscript. This work was supported by the Defence Threat Reduction Agency (HDTRA1-15-1-0051), the Broad Institute of MIT and Harvard, and a generous gift from Anita and Josh Bekenstein.

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A.J.L. researched data for the manuscript. A.J.L. and J.J.C. outlined the manuscript. A.J.L. wrote the manuscript. A.J.L. and J.J.C. reviewed and edited the manuscript before submission.

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Correspondence to James J. Collins.

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Glossary

Genetic toggle switch

A synthetic gene circuit consisting of two mutually inhibitory repressor genes, their associated promoters and a reporter gene; bistable feedback results in the circuit stably assuming one of two states (that is, toggling between reporter gene ON and OFF) in response to the transient application of exogenous inducers.

Bistable

A system that can exhibit two distinct stable states.

Repressilator

A synthetic gene circuit consisting of three repressor genes, arranged such that gene A represses gene B, gene B represses gene C, and gene C represses gene A; this architecture enables the circuit to produce oscillatory outputs (for example, a reporter gene will cycle between ON and OFF states).

Discrete dynamical models

Changes in the state variable(s) occur at particular time points and remain fixed in between time points.

Continuous dynamical models

Changes in the state variable(s) occur uninterrupted (for example, continuously) over an arbitrary time window.

Ordinary differential equations

(ODEs). A set of equations describing the relationship between the derivative of one or more dependent variables with respect to one independent variable.

Deterministic

A type of process whereby the future state of a system involves no randomness and depends entirely on its initial state along with the parameters that govern its change.

Stochastic

A type of process whereby the future state of a system involves a certain degree of randomness.

Dynamical models

Mathematical models that describe the change in one or more variables of interest over time.

Deep learning

A class of artificial intelligence frameworks that are able to learn in an unsupervised manner, typically using structured layers of artificial neural networks.

Quorum sensing

(QS). A control strategy wherein individual cells secrete a signal molecule into their environment; the collective concentration serves as a proxy for local cell density and enables cells to trigger downstream gene expression based on population size.

Crabtree effect

The phenomenon wherein yeast produce ethanol rather than biomass under high-glucose aerobic conditions.

Chemostat

A type of bioreactor that uses inflow and outflow of new or spent media to continuously culture microorganisms at a specified growth rate under chemically defined conditions.

Support vector machine

A trained machine-learning methodology that uses classification algorithms to separate data into multiple groupings.

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Lopatkin, A.J., Collins, J.J. Predictive biology: modelling, understanding and harnessing microbial complexity. Nat Rev Microbiol (2020). https://doi.org/10.1038/s41579-020-0372-5

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