Predicting Microbiome Metabolism and Interactions through Integrating Multidisciplinary Principles

Schmidt, Caleb M.; Ghadermazi, Parsa; Chan, Siu Hung Joshua

doi:10.1128/msystems.00768-21

Cited by 3 publications

(4 citation statements)

References 26 publications

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“…FBA also can be extended to dynamic simulation of heterogenous microbial systems [25]. There are several inherent limitations in using FBA and DFBA, for example the instantaneous biomass maximization assumption that does not always describe observed microbial interactions [26], [27], [28]. Additionally, parameters like temperature and pH are shown to be critical in determining the performance of AD reactors which are often ignored in FBA-based approaches.…”

Section: Introductionmentioning

confidence: 99%

ADToolbox: Incorporating Metagenomics Data for Improved Prediction of Anaerobic Digestion Dynamics

Ghadermazi,

Rico,

Rimmelman

et al. 2024

Preprint

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Handling the global food waste requires sustainable solutions. Anaerobic digestion (AD) is a common approach for waste remediation and bioenergy production which has been traditionally used mainly for methane production. Shifting the focus from methane to volatile fatty acids (VFAs), such as acetic, propionic, and butyric acids, offers promising alternatives due to their diverse applications and higher added value. Achieving desired AD product distributions requires a comprehensive understanding of factors like temperature, pH, feedstock composition, and the complex microbial dynamics inherent in AD. Various AD modeling approaches exist, from simple equations to complex flux balance analysis (FBA) and machine learning (ML) model. The Anaerobic Digestion Model No. 1 (ADM 1) is a commonly used kinetic model, striking a reasonable balance between parameter requirements and biochemical details involved in the model. Yet, it falls short in capturing specific VFAs like caproic acid and integrating microbial information directly. We present ADToolbox, a Python package for modeling AD metabolism. ADToolbox incorporates metagenomic information into an enhanced ADM model. The model accommodates a more detailed feedstock degradation model and VFA and methanogenesis metabolism. ADToolbox provides a variety of interfaces such as command line interface and an interactive web interface line interface, and a Python API, facilitating large-scale metagenomic analyses and modeling simulations. In this article we indicate that prioritizing the microbial aspect of AD enhances flexibility and predictive power in terms of VFA production accuracy, contributing to sustainable waste management strategies. Explore ADToolbox at https://chan-csu.github.io/ADToolbox for detailed documentation.

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Section: Introductionmentioning

confidence: 99%

ADToolbox: Incorporating Metagenomics Data for Improved Prediction of Anaerobic Digestion Dynamics

Ghadermazi,

Rico,

Rimmelman

et al. 2024

Preprint

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show abstract

“…Therefore, it is important to put the concept of Nash equilibria and evolutionary stability in this context of metabolic interactions [9], [12]. A remedy proposed by Zomorrodi and Segre [33] was to determine Nash equilibria of the systems with several metabolic strategies of interest pre-defined.…”

Section: Introductionmentioning

confidence: 99%

“…Understanding and predicting the dynamics of microbial systems has remained largely unknown despite the enormous growth in multi-omics techniques and it requires a wholistic modeling approach [9]. Mathematical models at different abstraction levels have been developed with the goal of making predictions that can explain the experimentally observed phenotypes [3], [6], [10]- [18].…”

Section: Introductionmentioning

confidence: 99%

“…Previous results showed that DFBA fails to predict any exchange between two different auxotrophs that can experimentally complement each other because during DFBA each model is trying to maximize their instantaneous fitness as ATP is required in biosynthetic pathways that produce amino acids However, since none of such organisms can live without the presence of the other, evolution favors exchanging the required amino acids which is supported by observing growth between auxotrophic E. coli cells on minimal medium [50]. Therefore, it is important to put the concept of Nash equilibria and evolutionary stability in this context of metabolic interactions [9], [12]. A remedy proposed by Zomorrodi and Segre [51] was to determine Nash equilibria of the systems with several metabolic strategies of interest pre-defined.…”

Section: Introductionmentioning

confidence: 99%

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Microbial Interactions from a New Perspective: Reinforcement Learning Reveals New Insights into Microbiome Evolution

Ghadermazi

Chan

2023

Preprint

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Microbes play a vital role in diverse ecosystems, influencing material flow and shaping the dynamics of their surroundings. Understanding the function of microbial life within ecosystems is crucial for tackling modern challenges. Metagenomics studies provide valuable insights into the potential functions of microbial communities but predicting the phenotype of these communities from on their genotype remains a complex endeavor. Trophic interactions between microbes further complicate the prediction of emergent properties in microbiomes. Mathematical modeling, particularly Flux Balance Analysis (FBA) approaches, have been employed to forecast phenotypes and explain experimental findings. However, FBA solutions often lack uniqueness and assume a steady-state condition. While Dynamic Flux Balance Analysis (DFBA) addresses some of these limitations, it still relies on instantaneous biomass maximization assumptions and faces challenges related to non-uniqueness solutions of linear programming problems. In this article, a novel modeling approach is proposed that integrates deep reinforcement learning into DFBA. This framework views microbial metabolism as a decision-making process, where each microbial agent evolves by learning and adapting metabolic strategies to enhance long-term fitness. Reinforcement learning algorithms facilitate the discovery of optimal strategies through iterative trial and error, while considering the consequences of actions within a dynamic context. This approach diverges from traditional FBA assumptions and provides insights into evolutionary stable strategies, requiring minimal reliance on predefined strategies. The proposed method holds promise for elucidating the behavior and mechanisms of microbial systems, including phenomena like quorum sensing and other interactions that can be explained by considering the long-term consequences of metabolic regulation strategies. The modeling algorithm demonstrates success in predicting microbial interactions in simple communities, surpassing the capabilities of existing models, and exhibits potential for scalability when applied to Genome-scale Metabolic Models (GEMs).

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Optimal waste load allocation in river systems based on a new multi-objective cuckoo optimization algorithm

Haghdoost,

Niksokhan,

Zamani

et al. 2023

Environ Sci Pollut Res

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Predicting Microbiome Metabolism and Interactions through Integrating Multidisciplinary Principles

Cited by 3 publications

References 26 publications

ADToolbox: Incorporating Metagenomics Data for Improved Prediction of Anaerobic Digestion Dynamics

ADToolbox: Incorporating Metagenomics Data for Improved Prediction of Anaerobic Digestion Dynamics

Microbial Interactions from a New Perspective: Reinforcement Learning Reveals New Insights into Microbiome Evolution

Optimal waste load allocation in river systems based on a new multi-objective cuckoo optimization algorithm

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