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Synthetic neural-like computing in microbial consortia for pattern recognition.

ABSTRACT: Complex biological systems in nature comprise cells that act collectively to solve sophisticated tasks. Synthetic biological systems, in contrast, are designed for specific tasks, following computational principles including logic gates and analog design. Yet such approaches cannot be easily adapted for multiple tasks in biological contexts. Alternatively, artificial neural networks, comprised of flexible interactions for computation, support adaptive designs and are adopted for diverse applications. Here, motivated by the structural similarity between artificial neural networks and cellular networks, we implement neural-like computing in bacteria consortia for recognizing patterns. Specifically, receiver bacteria collectively interact with sender bacteria for decision-making through quorum sensing. Input patterns formed by chemical inducers activate senders to produce signaling molecules at varying levels. These levels, which act as weights, are programmed by tuning the sender promoter strength Furthermore, a gradient descent based algorithm that enables weights optimization was developed. Weights were experimentally examined for recognizing 3 × 3-bit pattern.

SUBMITTER: Li X

PROVIDER: S-EPMC8149857 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature

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Synthetic neural-like computing in microbial consortia for pattern recognition.

Li Ximing X Rizik Luna L Kravchik Valeriia V Khoury Maria M Korin Netanel N Daniel Ramez R

Nature communications 20210525 1

Complex biological systems in nature comprise cells that act collectively to solve sophisticated tasks. Synthetic biological systems, in contrast, are designed for specific tasks, following computational principles including logic gates and analog design. Yet such approaches cannot be easily adapted for multiple tasks in biological contexts. Alternatively, artificial neural networks, comprised of flexible interactions for computation, support adaptive designs and are adopted for diverse applicat ...[more]

PMID: 34035266

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Project description:The metabolic diversity present in microbial communities enables cooperation toward accomplishing more complex tasks than possible by a single organism. Members of a consortium communicate by exchanging metabolites or signals that allow them to coordinate their activity through division of labor. In contrast with monocultures, evidence suggests that microbial consortia self-organize to form spatial patterns, such as observed in biofilms or in soil aggregates, that enable them to respond to gradient, to improve resource interception and to exchange metabolites more effectively. Current biotechnological applications of microorganisms remain rudimentary, often relying on genetically engineered monocultures (e.g., pharmaceuticals) or mixed-cultures of partially known composition (e.g., wastewater treatment), yet the vast potential of "microbial ecological power" observed in most natural environments, remains largely underused. In line with the Unified Microbiome Initiative (UMI) which aims to "discover and advance tools to understand and harness the capabilities of Earth's microbial ecosystems," we propose in this concept paper to capitalize on ecological insights into the spatial and modular design of interlinked microbial consortia that would overcome limitations of natural systems and attempt to optimize the functionality of the members and the performance of the engineered consortium. The topology of the spatial connections linking the various members and the regulated fluxes of media between those modules, while representing a major engineering challenge, would allow the microbial species to interact. The modularity of such spatially linked microbial consortia (SLMC) could facilitate the design of scalable bioprocesses that can be incorporated as parts of a larger biochemical network. By reducing the need for a compatible growth environment for all species simultaneously, SLMC will dramatically expand the range of possible combinations of microorganisms and their potential applications. We briefly review existing tools to engineer such assemblies and optimize potential benefits resulting from the collective activity of their members. Prospective microbial consortia and proposed spatial configurations will be illustrated and preliminary calculations highlighting the advantages of SLMC over co-cultures will be presented, followed by a discussion of challenges and opportunities for moving forward with some designs.

Project description:Currently, the establishment of synthetic microbial consortia with rational strategies has gained extensive attention, becoming one of the important frontiers of synthetic biology. Systems biology can offer insights into the design and construction of synthetic microbial consortia. Taking the high-efficiency production of 2-keto-l-gulonic acid (2-KLG) as an example, we constructed a synthetic microbial consortium "Saccharomyces cerevisiae-Ketogulonigenium vulgare" based on systems biology analysis. In the consortium, K. vulgare was the 2-KLG producing strain, and S. cerevisiae acted as the helper strain. Comparative transcriptomic analysis was performed on an engineered S. cerevisiae (VTC2) and a wild-type S. cerevisiae BY4741. The results showed that the up-regulated genes in VTC2, compared with BY4741, were mainly involved in glycolysis, TCA cycle, purine metabolism, and biosynthesis of amino acids, B vitamins, and antioxidant proteases, all of which play important roles in promoting the growth of K. vulgare. Furthermore, Vitamin C produced by VTC2 could further relieve the oxidative stress in the environment to increase the production of 2-KLG. Therefore, VTC2 would be of great advantage in working with K. vulgare. Thus, the synthetic microbial consortium "VTC2-K. vulgare" was constructed based on transcriptomics analyses, and the accumulation of 2-KLG was increased by 1.49-fold compared with that of mono-cultured K. vulgare, reaching 13.2 ± 0.52 g/L. In addition, the increased production of 2-KLG was accompanied by the up-regulated activities of superoxide dismutase and catalase in the medium and the up-regulated oxidative stress-related genes (sod, cat and gpd) in K. vulgare. The results indicated that the oxidative stress in the synthetic microbial consortium was efficiently reduced. Thus, systems analysis confirmed a favorable symbiotic relationship between microorganisms, providing guidance for further engineering synthetic consortia.

Dataset Information

Synthetic neural-like computing in microbial consortia for pattern recognition.

Publications

Synthetic neural-like computing in microbial consortia for pattern recognition.

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