Review on computer-assisted biosynthetic capacities elucidation to assess metabolic interactions and communication within microbial communities DOI Creative Commons
Mahnoor Zulfiqar, Vinay Singh, Christoph Steinbeck

et al.

Critical Reviews in Microbiology, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 40

Published: Jan. 25, 2024

Microbial communities thrive through interactions and communication, which are challenging to study as most microorganisms not cultivable. To address this challenge, researchers focus on the extracellular space where communication events occur. Exometabolomics interactome analysis provide insights into molecules involved in dynamics of their interactions. Advances sequencing technologies computational methods enable reconstruction taxonomic functional profiles microbial using high-throughput multi-omics data. Network-based approaches, including community flux balance analysis, aim model molecular within between communities. Despite these advances, challenges remain computer-assisted biosynthetic capacities elucidation, requiring continued innovation collaboration among diverse scientists. This review provides current state future directions elucidation studying

Language: Английский

Ecological modelling approaches for predicting emergent properties in microbial communities DOI
Naomi Iris van den Berg, Daniel Machado, Sophia Santos

et al.

Nature Ecology & Evolution, Journal Year: 2022, Volume and Issue: 6(7), P. 855 - 865

Published: May 16, 2022

Language: Английский

Citations

142
Metabolic interaction models recapitulate leaf microbiota ecology DOI
Martin Schäfer, Alan R. Pacheco, Rahel Künzler

et al.

Science, Journal Year: 2023, Volume and Issue: 381(6653)

Published: July 6, 2023

Resource allocation affects the structure of microbiomes, including those associated with living hosts. Understanding degree to which this dependency determines interspecies interactions may advance efforts control host-microbiome relationships. We combined synthetic community experiments computational models predict interaction outcomes between plant-associated bacteria. mapped metabolic capabilities 224 leaf isolates from Arabidopsis thaliana by assessing growth each strain on 45 environmentally relevant carbon sources in vitro. used these data build curated genome-scale for all strains, we simulate >17,500 interactions. The recapitulated observed planta >89% accuracy, highlighting role utilization and contributions niche partitioning cross-feeding assembly microbiomes.

Language: Английский

Citations

79
A study of a diauxic growth experiment using an expanded dynamic flux balance framework DOI Creative Commons
Emil Karlsen,

Marianne Gylseth,

Christian Schulz

et al.

PLoS ONE, Journal Year: 2023, Volume and Issue: 18(1), P. e0280077 - e0280077

Published: Jan. 6, 2023

Flux balance analysis (FBA) remains one of the most used methods for modeling entirety cellular metabolism, and a range applications extensions based on FBA framework have been generated. Dynamic flux (dFBA), expansion into time domain, still has issues regarding accessibility limiting its widespread adoption application, such as lack consistently rigid formalism tools that can be applied without expert knowledge. Recent work combined dFBA with enzyme-constrained (decFBA), which shown to greatly improve accuracy in comparison computational simulations experimental data, but approaches generally do not take account fact altering enzyme composition cell is an instantaneous process. Here, we developed decFBA method explicitly takes change constraints (ecc) account, decFBAecc. The resulting software simple yet flexible using genome-scale metabolic domain full interoperability COBRA Toolbox 3.0. To assess quality predictions decFBAecc, conducted diauxic growth fermentation experiment Escherichia coli BW25113 glucose minimal M9 medium. data dFBA, decFBAecc demonstrates how systematic analyses within fixed constraint-based aid study model parameters. Finally, explaining experimentally observed phenotypes, our importance non-linear dependence exchange fluxes medium metabolite concentrations non-instantaneous composition, effects previously accounted analysis.

Language: Английский

Citations

52
Global epistasis on fitness landscapes DOI Creative Commons
Juan Díaz‐Colunga,

Abigail Skwara,

Karna Gowda

et al.

Philosophical Transactions of the Royal Society B Biological Sciences, Journal Year: 2023, Volume and Issue: 378(1877)

Published: April 2, 2023

Epistatic interactions between mutations add substantial complexity to adaptive landscapes and are often thought of as detrimental our ability predict evolution. Yet, patterns global epistasis, in which the fitness effect a mutation is well-predicted by its genetic background, may actually be help efforts reconstruct infer trajectories. Microscopic mutations, or inherent nonlinearities landscape, cause epistasis emerge. In this brief review, we provide succinct overview recent work about with an emphasis on building intuition why it observed. To end, reconcile simple geometric reasoning mathematical analyses, using these explain different empirical landscape exhibit patterns-ranging from diminishing increasing returns. Finally, highlight open questions research directions. This article part theme issue 'Interdisciplinary approaches predicting evolutionary biology'.

Language: Английский

Citations

44
Emerging multiscale insights on microbial carbon use efficiency in the land carbon cycle DOI Creative Commons
Xianjin He, Elsa Abs, Steven Allison

et al.

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: Sept. 13, 2024

Microbial carbon use efficiency (CUE) affects the fate and storage of in terrestrial ecosystems, but its global importance remains uncertain. Accurately modeling predicting CUE on a scale is challenging due to inconsistencies measurement techniques complex interactions climatic, edaphic, biological factors across scales. The link between microbial soil organic relies stabilization necromass within aggregates or association with minerals, necessitating an integration processes approaches. In this perspective, we propose comprehensive framework that integrates diverse data sources, ranging from genomic information traditional assessments, refine cycle models by incorporating variations CUE, thereby enhancing our understanding contribution cycling.

Language: Английский

Citations

17
Predicting microbial interactions with approaches based on flux balance analysis: an evaluation DOI Creative Commons
Clémence Joseph, Haris Zafeiropoulos, Kristel Bernaerts

et al.

BMC Bioinformatics, Journal Year: 2024, Volume and Issue: 25(1)

Published: Jan. 23, 2024

Abstract Background Given a genome-scale metabolic model (GEM) of microorganism and criteria for optimization, flux balance analysis (FBA) predicts the optimal growth rate its corresponding distribution specific medium. FBA has been extended to microbial consortia thus can be used predict interactions by comparing in-silico rates co- monocultures. Although FBA-based methods interaction prediction are becoming popular, systematic evaluation their accuracy not yet performed. Results Here, we evaluate predictions human mouse gut bacterial using data from literature. For this, collected 26 GEMs semi-curated AGORA database as well four previously published curated GEMs. We tested three tools (COMETS, Microbiome Modeling Toolbox MICOM) predicted in mono- co-culture extracted literature also investigated impact different tool settings media. found that except GEMs, ratios (i.e. strengths) do correlate with strengths obtained vitro data. Conclusions Prediction is currently sufficiently accurate reliably.

Language: Английский

Citations

16
Modelling microbial communities: Harnessing consortia for biotechnological applications DOI Creative Commons

Maziya Ibrahim,

Lavanya Raajaraam, Karthik Raman

et al.

Computational and Structural Biotechnology Journal, Journal Year: 2021, Volume and Issue: 19, P. 3892 - 3907

Published: Jan. 1, 2021

Microbes propagate and thrive in complex communities, there are many benefits to studying engineering microbial communities instead of single strains. Microbial being increasingly leveraged biotechnological applications, as they present significant advantages such the division labour improved substrate utilisation. Nevertheless, also some interesting challenges surmount for design efficient processes. In this review, we discuss key principles interactions, followed by a deep dive into genome-scale metabolic models, focussing on vast repertoire constraint-based modelling methods that enable us characterise understand capabilities communities. Complementary approaches model those based graph theory, briefly discussed. Taken together, these provide rich insights interactions between microbes how influence community productivity. We finally overview allow generate test numerous synthetic compositions, tools methodologies can predict effective genetic interventions further improve productivity With impending advancements high-throughput omics stage is set rapid expansion engineering, with impact

Language: Английский

Citations

59
Bioengineered microbial strains for detoxification of toxic environmental pollutants DOI
Quratulain Maqsood, Aleena Sumrin,

Rafia Waseem

et al.

Environmental Research, Journal Year: 2023, Volume and Issue: 227, P. 115665 - 115665

Published: March 11, 2023

Language: Английский

Citations

41
A structured evaluation of genome-scale constraint-based modeling tools for microbial consortia DOI Creative Commons
William T. Scott, Sara Benito-Vaquerizo, Johannes Zimmermann

et al.

PLoS Computational Biology, Journal Year: 2023, Volume and Issue: 19(8), P. e1011363 - e1011363

Published: Aug. 14, 2023

Harnessing the power of microbial consortia is integral to a diverse range sectors, from healthcare biotechnology environmental remediation. To fully realize this potential, it critical understand mechanisms behind interactions that structure and determine their functions. Constraint-based reconstruction analysis (COBRA) approaches, employing genome-scale metabolic models (GEMs), have emerged as state-of-the-art tool simulate behavior communities constituent genomes. In last decade, many tools been developed use COBRA approaches multi-species consortia, under either steady-state, dynamic, or spatiotemporally varying scenarios. Yet, these not systematically evaluated regarding software quality, most suitable application, predictive power. Hence, uncertain which users should apply system what are urgent directions developers take in future improve existing capacities. This study conducted systematic evaluation COBRA-based for using datasets two-member test cases. First, we performed qualitative assessment 24 published based on list FAIR (Findability, Accessibility, Interoperability, Reusability) features essential quality. Next, quantitatively tested predictions subset 14 against experimental data three different case studies: a) syngas fermentation by C. autoethanogenum kluyveri static tools, b) glucose/xylose with engineered E. coli S. cerevisiae dynamic c) Petri dish enterica incorporating spatiotemporal variation. Our results show performance levels best qualitatively assessed when examining categories tools. The differences mathematical formulation relation were also discussed. Ultimately, provide recommendations refining GEM modeling

Language: Английский

Citations

28
PhysiBoSS 2.0: a sustainable integration of stochastic Boolean and agent-based modelling frameworks DOI Creative Commons
Miguel Ponce-de-León, Arnau Montagud, Vincent Noël

et al.

npj Systems Biology and Applications, Journal Year: 2023, Volume and Issue: 9(1)

Published: Oct. 30, 2023

Abstract In systems biology, mathematical models and simulations play a crucial role in understanding complex biological systems. Different modelling frameworks are employed depending on the nature scales of system under study. For instance, signalling regulatory networks can be simulated using Boolean modelling, whereas multicellular studied agent-based modelling. Herein, we present PhysiBoSS 2.0, hybrid framework that allows simulating within individual cell agents. 2.0 is redesign reimplementation 1.0 was conceived as an add-on expands PhysiCell functionalities by enabling simulation intracellular MaBoSS while keeping decoupled, maintainable model-agnostic design. also set offered to users, including custom specifications, mechanistic submodels substrate internalisation detailed control over parameters. Together with introduce PCTK, Python package developed for handling processing outputs, generating summary plots 3D renders. studying interplay between microenvironment, pathways cellular processes population dynamics, suitable cancer. We show different approaches integrating into multi-scale strategies study drug effects synergies cancer lines validate them experimental data. open-source publicly available GitHub several repositories accompanying interoperable tools.

Language: Английский

Citations

27