Predictive evolution of metabolic phenotypes using model‐designed environments

Jouhten, Paula; Konstantinidis, D.; Pereira, Filipa; Andrejev, Sergej; Grkovska, Kristina; Castillo, Sandra; Ghiachi, Payam; Beltran, Gemma; Almaas, Eivind; Mas, Albert; Warringer, Jonas; González, Ramón; Morales, Pilar; Patil, Kiran Raosaheb

doi:10.15252/msb.202210980

Cited by 12 publications

(5 citation statements)

References 84 publications

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“…Indeed, since more extensive genome-scale models of secondary metabolism in Streptomyces species were established ( 100 ), this method has been utilized to aid in systems metabolic engineering strategies ( 101 ). Moreover, the approach has been adopted to study predictive evolution of metabolic pathways in order to identify lineages with enhanced metabolite secretion ( 102 ). These studies suggest that, despite the caveats mentioned above, marrying metabolomics with genome-scale modeling of metabolism could prove a major boon in the identification of missing metabolites from cellular metabolic networks.…”

Section: Directly Coupling the Metabolome To The Genomementioning

confidence: 99%

Plant and microbial sciences as key drivers in the development of metabolomics research

Aharoni

Goodacre

Fernie

2023

Proc. Natl. Acad. Sci. U.S.A.

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This year marks the 25th anniversary of the coinage of the term metabolome [S. G. Oliver et al ., Trends Biotech. 16 , 373–378 (1998)]. As the field rapidly advances, it is important to take stock of the progress which has been made to best inform the disciplines future. While a medical-centric perspective on metabolomics has recently been published [M. Giera et al ., Cell Metab. 34 , 21–34 (2022)], this largely ignores the pioneering contributions made by the plant and microbial science communities. In this perspective, we provide a contemporary overview of all fields in which metabolomics is employed with particular emphasis on both methodological and application breakthroughs made in plant and microbial sciences that have shaped this evolving research discipline from the very early days of its establishment. This will not cover all types of metabolomics assays currently employed but will focus mainly on those utilizing mass spectrometry–based measurements since they are currently by far the most prominent. Having established the historical context of metabolomics, we will address the key challenges currently facing metabolomics and offer potential approaches by which these can be faced. Most salient among these is the fact that the vast majority of mass features are as yet not annotated with high confidence; what we may refer to as definitive identification. We discuss the potential of both standard compound libraries and artificial intelligence technologies to address this challenge and the use of natural variance–based approaches such as genome-wide association studies in attempt to assign specific functions to the myriad of structurally similar and complex specialized metabolites. We conclude by stating our contention that as these challenges are epic and that they will need far greater cooperative efforts from biologists, chemists, and computer scientists with an interest in all kingdoms of life than have been made to date. Ultimately, a better linkage of metabolome and genome data will likely also be needed particularly considering the Earth BioGenome Project.

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Section: Directly Coupling the Metabolome To The Genomementioning

confidence: 99%

Plant and microbial sciences as key drivers in the development of metabolomics research

Aharoni

Goodacre

Fernie

2023

Proc. Natl. Acad. Sci. U.S.A.

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“…Instead, these algorithms aim to guide dominant selection or direct adaptive laboratory evolution (ALE), e.g. by predicting how a cell might respond to ALE in the presence of toxic metabolite analogs (Cardoso et al 2018 ) or designing an evolution environment, which selects for adaptations that will positively affect the phenotype of the evolved strain in the desired food application (Jouhten et al 2022 ).…”

Section: Knowledge-driven Approachesmentioning

confidence: 99%

From genotype to phenotype: computational approaches for inferring microbial traits relevant to the food industry

Karlsen

Rau

Sánchez

et al. 2023

FEMS Microbiology Reviews

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When selecting microbial strains for the production of fermented foods, various microbial phenotypes need to be taken into account to achieve target product characteristics, such as biosafety, flavor, texture, and health-promoting effects. Through continuous advances in sequencing technologies, microbial whole-genome sequences of increasing quality can now be obtained both cheaper and faster, which increases the relevance of genome-based characterization of microbial phenotypes. Prediction of microbial phenotypes from genome sequences makes it possible to quickly screen large strain collections in silico to identify candidates with desirable traits. Several microbial phenotypes relevant to the production of fermented foods can be predicted using knowledge-based approaches, leveraging our existing understanding of the genetic and molecular mechanisms underlying those phenotypes. In the absence of this knowledge, data-driven approaches can be applied to estimate genotype-phenotype relationships based on large experimental datasets. Here, we review computational methods that implement knowledge- and data-driven approaches for phenotype prediction, as well as methods that combine elements from both approaches. Furthermore, we provide examples of how these methods have been applied in industrial biotechnology, with special focus on the fermented food industry.

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“…Mathematical models are crucial tools for predicting the responses of genetic circuits to signals and perturbations from the environment and for reliably engineering biological systems. , For example, models that can predict the effect of a gene knockout or overexpression, the response of a cell to a drug or change in carbon source, or the impact of an optogenetic input sequence can allow the design of more ambitious genetic circuits. − Thus, the ability to predict biological time-series data is critical to designing robust genetic circuits and can also provide novel insight into the behaviors of natural systems.…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Networks for Predicting Single-Cell Responses and Probability Landscapes

et al. 2023

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Engineering biology relies on the accurate prediction of cell responses. However, making these predictions is challenging for a variety of reasons, including the stochasticity of biochemical reactions, variability between cells, and incomplete information about underlying biological processes. Machine learning methods, which can model diverse input–output relationships without requiring a priori mechanistic knowledge, are an ideal tool for this task. For example, such approaches can be used to predict gene expression dynamics given time-series data of past expression history. To explore this application, we computationally simulated single-cell responses, incorporating different sources of noise and alternative genetic circuit designs. We showed that deep neural networks trained on these simulated data were able to correctly infer the underlying dynamics of a cell response even in the presence of measurement noise and stochasticity in the biochemical reactions. The training set size and the amount of past data provided as inputs both affected prediction quality, with cascaded genetic circuits that introduce delays requiring more past data. We also tested prediction performance on a bistable auto-activation circuit, finding that our initial method for predicting a single trajectory was fundamentally ill-suited for multimodal dynamics. To address this, we updated the network architecture to predict the entire distribution of future states, showing it could accurately predict bimodal expression distributions. Overall, these methods can be readily applied to the diverse prediction tasks necessary to predict and control a variety of biological circuits, a key aspect of many synthetic biology applications.

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Predictive evolution of metabolic phenotypes using model‐designed environments

Cited by 12 publications

References 84 publications

Plant and microbial sciences as key drivers in the development of metabolomics research

Plant and microbial sciences as key drivers in the development of metabolomics research

From genotype to phenotype: computational approaches for inferring microbial traits relevant to the food industry

Deep Neural Networks for Predicting Single-Cell Responses and Probability Landscapes

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