FLYCOP: metabolic modeling-based analysis and engineering microbial communities

Abstract

MotivationSynthetic microbial communities begin to be considered as promising multicellular biocatalysts having a large potential to replace engineered single strains in biotechnology applications, in pharmaceutical, chemical and living architecture sectors. In contrast to single strain engineering, the effective and high-throughput analysis and engineering of microbial consortia face the lack of knowledge, tools and well-defined workflows. This manuscript contributes to fill this important gap with a framework, called FLYCOP (FLexible sYnthetic Consortium OPtimization), which contributes to microbial consortia modeling and engineering, while improving the knowledge about how these communities work. FLYCOP selects the best consortium configuration to optimize a given goal, among multiple and diverse configurations, in a flexible way, taking temporal changes in metabolite concentrations into account.ResultsIn contrast to previous systems optimizing microbial consortia, FLYCOP has novel characteristics to face up to new problems, to represent additional features and to analyze events influencing the consortia behavior. In this manuscript, FLYCOP optimizes a Synechococcus elongatus-Pseudomonas putida consortium to produce the maximum amount of bio-plastic (PHA, polyhydroxyalkanoate), and highlights the influence of metabolites exchange dynamics in a four auxotrophic Escherichia coli consortium with parallel growth. FLYCOP can also provide an explanation about biological evolution driving evolutionary engineering endeavors by describing why and how heterogeneous populations emerge from monoclonal ones.Availability and implementationCode reproducing the study cases described in this manuscript are available on-line: https://github.com/beatrizgj/FLYCOPSupplementary information Supplementary data are available at Bioinformatics online.

Metabolic Modelling Approaches for Describing and Engineering Microbial Communities

“…GEMs are structured representations of a target organism based on existing genetic, biochemical and physiological information. Therefore, GEMs represent the metabolic capabilities of a particular organism and can be used in combination with algorithms such as Flux Balance Analysis (FBA) to predict phenotype from genotype [54][55][56]. An important advantage of metabolic models is their accuracy without requiring kinetic information [56].…”

“…a flexible objective. Therefore, beyond methodological classifications [55], the applications of microbial communities engineering can be also grouped according to their target optimization goal (see Figure 2). In this context, there are a few tools that can be considered as generic, i.e.…”

Dynamic simulations of microbial communities under perturbations: opportunities for microbiome engineering

“…the innate gut microbiome [71]) and developing comprehensive computational tools to engineer synthetic consortia (e.g. [72]).…”

Meta‐Omics‐ and Metabolic Modeling‐Assisted Deciphering of Human Microbiota Metabolism

“…The information acquired from these workflows could then be used to build community‐level metabolic models to provide a mechanistic understanding of the metabolic interactions within the human microbiota. These models could be used to predict the outcome of these interactions through perturbations, and guide the design of the system‐level strategies for modulating human microbiota, which could be achieved through several routes such as fecal microbiota transplantation, employment of engineered or native live bacterial therapeutics and prebiotics, as well as the rational design of a synthetic microbial community . Recently, the integration of meta‐omics and metabolic modeling for this purpose has quickly become a reality .…”

“…These models could be used to predict the outcome of these interactions through perturbations, and guide the design of the system-level strategies for modulating human microbiota, which could be achieved through several routes such as fecal microbiota transplantation, employment of engineered or native live bacterial therapeutics and prebiotics, as well as the rational design of a synthetic microbial community. [94] Recently, the integration of meta-omics and metabolic modeling for this purpose has quickly become a reality. [76,85,95,96] For instance, individual metagenomics data from IBD patients and healthy controls were used to create personalized gut microbial community models consisting of multiple-species AGORA reconstructions to understand the different metabolic interactions between the human host and gut microbiomes in healthy and disease states.…”