Predictive biology: modelling, understanding and harnessing microbial complexity

Abstract

Predictive biology, particularly for microorganisms, is the next great chapter in synthetic and systems biology.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 multispecies 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 datasets become available, their potential utility in transforming theory into practice remains firmly rooted in the underlying quantitative principles that govern biological systems. This review discusses key areas of predictive biology that are of growing interest to microbiology, challenges associated with the innate complexity of microorganisms, and the value of quantitative methods in making microbiology more predictable.

Digital Insights Into Nucleotide Metabolism and Antibiotic Treatment Failure

“…In recent years several new experimental and digital technologies have emerged with promise to increase clinical microbiology laboratory throughput and enhance clinical management of bacterial infections (3)(4)(5). Moreover, advances in prokaryotic systems biology (6,7) and interpretable machine learning (8) are for the first time accelerating discovery of mechanisms underlying antibiotic efficacy (9,10).…”

Spurious regulatory connections dictate the expression-fitness landscape of translation termination factors

“…Formulating predictive models connecting genetic information to phenotypes constitutes an overarching goal in genomics and systems biology (Lopatkin and Collins, 2020; Ostrov et al, 2019; Shendure et al, 2019). In single-celled microbes, the relationship between genotype and phenotype can be conceptually decomposed into two distinct maps; the first relating genome sequence to gene expression, and the second, connecting gene expression to whole-cell properties such as proliferation.…”

Yeast optimizes metal utilization based on metabolic network and enzyme kinetics

“…Predicting cellular behavior with quantitative models is of particular interest (4), which could hopefully aid in uncovering the mechanisms and driving forces by which cells adapt to perturbations (e.g., reduced availability of metal ions). Genomescale metabolic models (GEMs) together with constraint-based approaches enable predicting the optimal state of cells subject to external and internal constraints based on optimization principles (5,6).…”