Flux Balance Analysis Research Articles

Effect of microbial interactions on performance of community metabolic modeling algorithms: flux balance analysis (FBA), community FBA (cFBA) and SteadyCom.

To explore the impact of microbial interactions on outcomes from three prevalent algorithms (Flux Balance Analysis (FBA), community FBA (cFBA), and SteadyCom) analyzing microbial community metabolic networks, five toy community models representing common microbial interactions were designed. These include commensalism, mutualism, competition, mutualism-competition, and commensalism-competition. Various scenarios, considering different biomass yields and substrate constraints, were examined for each type. In commensal communities, all algorithms consistently produced similar results. However, changes in biomass yields and substrate constraints led to variable abundances (0.33-0.8) and community growth rates (2-5 1/h) within a broad range. For competitive communities, all algorithms predicted growth of fastest-growing member. To comply with the natural coexistence of members, suboptimal solutions over optimal point are recommended. FBA faced challenges in modeling mutualism, consistently predicting growth of only one member. Although cFBA and SteadyCom resulted in a lower community growth rate, coexistence of both members were satisfied. In toy models with dual interactions, more realistic outcomes were achieved contrary to purely competitive model as the dependency fosters the coexistence which was missing in the competitive only scenarios. These findings emphasize the importance of algorithm choice based on specific microbial interaction types for reliable community behavior predictions.​.

Investigation of Bottleneck Enzyme Through Flux Balance Analysis to Improve Glycolic Acid Production in Escherichia coli.

Amid rising environmental concerns, attempts have been made to produce glycolic acid (GA) using microbial processes with renewable carbon resources instead of using chemicals. The Dahms pathway for GA production uses xylose as a substrate and consists of relatively simple enzymatic steps. However, employing it leads to a decrease in cell growth and GA productivity. Systematically identifying and addressing metabolic bottlenecks in the Dahms pathway are essential for efficient glycolic acid (GA) production have not yet been performed. Through metabolic flux balance analysis, we found that insufficient aldehyde dehydrogenase (AldA) activity lowers GA production and negatively affects cell growth due to reduced energy production. Thus, we discovered a novel AldA isolated from Buttiauxella agrestis (BaAldA) demonstrated a 1.69-fold lower KM and a 1.49-fold higher turnover rate (kcat/KM) than AldA from Escherichia coli (EcAldA). GA production in E. coli harboring BaAldA was 1.59 times higher than in the original strain. Fed-batch fermentation of E. coli harboring BaAldA produced 22.70g/L GA with a yield of 0.497g/gxylose (98.2% of the theoretical maximum yield in the Dahms pathway), showing a higher final yield for GA than previously reported in E. coli. Our novel BaAldA enzyme shows great potential for the production of GA using microorganisms or enzymes. Furthermore, our approach to identifying metabolic bottlenecks using flux balance analysis could be utilized to enhance the microbial production of various desirable products in future studies.

Metabolic modelling as a powerful tool to identify critical components of Pneumocystis growth medium.

Establishing suitable in vitro culture conditions for microorganisms is crucial for dissecting their biology and empowering potential applications. However, a significant number of bacterial and fungal species, including Pneumocystis jirovecii, remain unculturable, hampering research efforts. P. jirovecii is a deadly pathogen of humans that causes life-threatening pneumonia in immunocompromised individuals and transplant patients. Despite the major impact of Pneumocystis on human health, limited progress has been made in dissecting the pathobiology of this fungus. This is largely due to the fact that its experimental dissection has been constrained by the inability to culture the organism in vitro. We present a comprehensive in silico genome-scale metabolic model of Pneumocystis growth and metabolism, to identify metabolic requirements and imbalances that hinder growth in vitro. We utilise recently published genome data and available information in the literature as well as bioinformatics and software tools to develop and validate the model. In addition, we employ relaxed Flux Balance Analysis and Reinforcement Learning approaches to make predictions regarding metabolic fluxes and to identify critical components of the Pneumocystis growth medium. Our findings offer insights into the biology of Pneumocystis and provide a novel strategy to overcome the longstanding challenge of culturing this pathogen in vitro.

Defining Golden Batches in Biomanufacturing Processes From Internal Metabolic Activity to Detect Process Changes That May Affect Product Quality.

Cellular metabolism plays a role in the observed variability of a drug substance's Critical Quality Attributes (CQAs) made by biomanufacturing processes. Therefore, here we describe a new approach for monitoring biomanufacturing processes that measures a set of metabolic reaction rates (named Critical Metabolic Parameters (CMP) in addition to the macroscopic process conditions currently being used as Critical Process Parameters (CPP) for biomanufacturing. Constraint-based systems biology models like Flux Balance Analysis (FBA) are used to estimate metabolic reaction rates, and metabolic rates are used as inputs for multivariate Batch Evolution Models (BEM). Metabolic activity was reproducible among batches and could be monitored to detect a deliberately induced macroscopic process shift (i.e., temperature change). The CMP approach has the potential to enable "golden batches" in biomanufacturing processes to be defined from the internal metabolic activity and to aid in detecting process changes that may impact the quality of the product. Overall, the data suggested that monitoring of metabolic activity has promise for biomanufacturing process control.

INAP 2.0: Harnessing metabolic complementarity in microbial network analysis.

With the widespread adoption of metagenomic sequencing, new perspectives have emerged for studying microbial ecological networks, yielding metabolic evidence of interspecies interactions that traditional co-occurrence networks cannot infer. This protocol introduces the integrated Network Analysis Pipeline 2.0 (iNAP 2.0), which features an innovative metabolic complementarity network for microbial studies from metagenomics sequencing data. iNAP 2.0 sets up a four-module process for metabolic interaction analysis, namely: (I) Prepare genome-scale metabolic models; (II) Infer pairwise interactions of genome-scale metabolic models; (III) Construct metabolic interaction networks; and (IV) Analyze metabolic interaction networks. Starting from metagenome-assembled or complete genomes, iNAP 2.0 offers a variety of methods to quantify the potential and trends of metabolic complementarity between models, including the PhyloMint pipeline based on phylogenetic distance-adjusted metabolic complementarity, the SMETANA (species metabolic interaction analysis) approach based on cross-feeding substrate exchange prediction, and metabolic distance calculation based on parsimonious flux balance analysis (pFBA). Notably, iNAP 2.0 integrates the random matrix theory (RMT) approach to find the suitable threshold for metabolic interaction network construction. Finally, the metabolic interaction networks can proceed to analysis using topological feature analysis such as hub node determination. In addition, a key feature of iNAP 2.0 is the identification of potentially transferable metabolites between species, presented as intermediate nodes that connect microbial nodes in the metabolic complementarity network. To illustrate these new features, we use a set of metagenome-assembled genomes as an example to comprehensively document the usage of the tools. iNAP 2.0 is available at https://inap.denglab.org.cn for all users to register and use for free.

Genome-Scale Metabolic Model Reconstruction and Investigation into the Fluxome of the Fast-Growing Cyanobacterium Synechococcus sp. PCC 11901.

The ability to convert atmospheric CO2 and light into biomass and value-added chemicals makes cyanobacteria a promising resource microbial host for biotechnological applications. A newly discovered fastest-growing cyanobacterial strain, Synechococcus sp. PCC 11901, has been reported to have the highest biomass accumulation rate, making it a preferred target host for producing renewable fuels, value-added biochemicals, and natural products. System-level knowledge of an organism is imperative to understand the metabolic potential of the strain, which can be attained by developing genome-scale metabolic models (GEMs). We present the first genome-scale metabolic model of Synechococcus sp. PCC 11901 (iRS840), which contains 840 genes, 1001 reactions, and 944 metabolites. The model has been optimized and validated under different trophic modes, i.e., autotrophic and mixotrophic, by conducting an in vivo growth experiment. The robustness of the metabolic network was evaluated by changing the biomass coefficient of the model, which showed a higher sensitivity toward pigments under the photoautotrophic condition, whereas under the heterotrophic condition, amino acids were found to be more influential. Furthermore, it was discovered that PCC 11901 synthesizes succinyl-CoA via succinic semialdehyde due to its imperfect TCA cycle. Subsequent flux balance analysis (FBA) revealed a quantum yield of 0.16 in silico, which is higher compared to that of PCC 6803. Under mixotrophic conditions (with glycerol and carbon dioxide), the flux through the Calvin cycle increased compared to autotrophic conditions. This model will be useful for gaining insights into the metabolic potential of PCC 11901 and developing effective metabolic engineering strategies for product development.

Measuring and understanding information storage and transfer in a simulated human gut microbiome.

Considering biological systems as information processing entities and analyzing their organizational structure via information-theoretic measures has become an established approach in life sciences. We transfer this framework to a field of broad general interest, the human gut microbiome. We use BacArena, a software combining agent-based modelling and flux-balance analysis, to simulate a simplified human intestinal microbiome (SIHUMI). In a first step, we derive information theoretic measures from the simulated abundance data, and, in a second step, relate them to the metabolic processes underlying the abundance data. Our study provides further evidence on the role of active information storage as an indicator of unexpected structural change in the observed system. Besides, we show that information transfer reflects coherent behavior in the microbial community, both as a reaction to environmental changes and as a result of direct effective interaction. In this sense, purely abundance-based information theoretic measures can provide meaningful insight on metabolic interactions within bacterial communities. Furthermore, we shed light on the important however little noticed technical aspect of distinguishing immediate and delayed effects in the interpretation of local information theoretical measures.

Metabolic Flux Modeling in Marine Ecosystems.

Ocean metabolism constitutes a complex, multiscale ensemble of biochemical reaction networks harbored within and between the boundaries of a myriad of organisms. Gaining a quantitative understanding of how these networks operate requires mathematical tools capable of solving in silico the resource allocation problem each cell faces in real life. Toward this goal, stoichiometric modeling of metabolism, such as flux balance analysis, has emerged as a powerful computational tool for unraveling the intricacies of metabolic processes in microbes, microbial communities, and multicellular organisms. Here, we provide an overview of this approach and its applications, future prospects, and practical considerations in the context of marine sciences. We explore how flux balance analysis has been employed to study marine organisms, help elucidate nutrient cycling, and predict metabolic capabilities within diverse marine environments, and highlight future prospects for this field in advancing our knowledge of marine ecosystems and their sustainability.

Constraint-based modelling predicts metabolic signatures of low and high-grade serous ovarian cancer

Ovarian cancer is an aggressive, heterogeneous disease, burdened with late diagnosis and resistance to chemotherapy. Clinical features of ovarian cancer could be explained by investigating its metabolism, and how the regulation of specific pathways links to individual phenotypes. Ovarian cancer is of particular interest for metabolic research due to its heterogeneous nature, with five distinct subtypes having been identified, each of which may display a unique metabolic signature. To elucidate metabolic differences, constraint-based modelling (CBM) represents a powerful technology, inviting the integration of ‘omics’ data, such as transcriptomics. However, many CBM methods have not prioritised accurate growth rate predictions, and there are very few ovarian cancer genome-scale studies. Here, a novel method for CBM has been developed, employing the genome-scale model Human1 and flux balance analysis, enabling the integration of in vitro growth rates, transcriptomics data and media conditions to predict the metabolic behaviour of cells. Using low- and high-grade ovarian cancer, subtype-specific metabolic differences have been predicted, which have been supported by publicly available CRISPR-Cas9 data from the Cancer Cell Line Encyclopaedia and an extensive literature review. Metabolic drivers of aggressive, invasive phenotypes, as well as pathways responsible for increased chemoresistance in low-grade cell lines have been suggested. Experimental gene dependency data has been used to validate areas of the pentose phosphate pathway as essential for low-grade cellular growth, highlighting potential vulnerabilities for this ovarian cancer subtype.

Integrating Spectral Sensing and Systems Biology for Precision Viticulture: Effects of Shade Nets on Grapevine Leaves

This study investigates how grapevines (Vitis vinifera L.) respond to shading induced by artificial nets, focusing on physiological and metabolic changes. Through a multidisciplinary approach, grapevines’ adaptations to shading are presented via biochemical analyses and hyperspectral data that are then combined with systems biology techniques. In the study, conducted in a ‘Moscatel Galego Branco’ vineyard in Portugal’s Douro Wine Region during post-veraison, shading was applied and predawn leaf water potential (Ψpd) was then measured to assess water stress. Biochemical analyses and hyperspectral data were integrated to explore adaptations to shading, revealing higher chlorophyll levels (chlorophyll a-b 117.39% higher) and increased Reactive Oxygen Species (ROS) levels in unshaded vines (52.10% higher). Using a self-learning artificial intelligence algorithm (SL-AI), simulations highlighted ROS’s role in stress response and accurately predicted chlorophyll a (R2: 0.92, MAPE: 24.39%), chlorophyll b (R2: 0.96, MAPE: 17.61%), and ROS levels (R2: 0.76, MAPE: 52.17%). In silico simulations employing flux balance analysis (FBA) elucidated distinct metabolic phenotypes between shaded and unshaded vines across cellular compartments. Integrating these findings provides a systems biology approach for understanding grapevine responses to environmental stressors. The leveraging of advanced omics technologies and precise metabolic models holds immense potential for untangling grapevine metabolism and optimizing viticultural practices for enhanced productivity and quality.

Predictive Functional Profiling Reveals Putative Metabolic Capacities of Bacterial Communities in Drinking Water Resources and Distribution Supply in Mega Manila, Philippines

Assessing bacterial communities across water resources is crucial for understanding ecological dynamics and improving water quality management. This study examines the functional profiles of bacterial communities in drinking water resources in Mega Manila, Philippines, including Laguna Lake tributaries, pre-treatment plant sites, groundwater sources, and post-treatment plant sites. Using eDNA sequencing, flux balance analysis, and taxonomy-to-phenotype mapping, we identified metabolic pathways involved in nutrient metabolism, pollutant degradation, antibio- tic synthesis, and nutrient cycling. Despite site variations, there are shared metabolic pathways, suggesting the influence of common ecological factors. Site-specific differences in pathways like ascorbate, aldarate, and phenylalanine metabolism indicate localized environmental adaptations. Antibiotic synthesis pathways, such as streptomycin and polyketide sugar unit biosynthesis, were detected across sites. Bacterial communities in raw and pre-treatment water showed potential for pollutant degradation such as for endocrine-disrupting chemicals. High levels of ammonia-oxidizing and sulfate-reducing bacteria in pre- and post-treatment water suggest active nitrogen removal and pH neutralization, indicating a need to reassess existing water treatment approaches. This study underscores the adaptability of bacterial communities to environmental factors, as well as the importance of considering their functional profiles in assessing drinking water quality resources in urban areas.

Metabolic modelling identifies mitochondrial Pi uptake and pyruvate efflux as key aspects of daytime metabolism and proton homeostasis in crassulacean acid metabolism leaves.

Crassulacean acid metabolism (CAM) leaves are characterized by nocturnal acidification and diurnal deacidification processes related with the timed actions of phosphoenolpyruvate carboxylase and Rubisco, respectively. How CAM leaves manage cytosolic proton homeostasis, particularly when facing massive diurnal proton effluxes from the vacuole, remains unclear. A 12-phase flux balance analysis (FBA) model was constructed for a mature malic enzyme-type CAM mesophyll cell in order to predict diel kinetics of intracellular proton fluxes. The charge- and proton-balanced FBA model identified the mitochondrial phosphate carrier (PiC, Pi/H+ symport), which provides Pi to the matrix to sustain ATP biosynthesis, as a major consumer of cytosolic protons during daytime (> 50%). The delivery of Pi to the mitochondrion, co-transported with protons, is required for oxidative phosphorylation and allows sufficient ATP to be synthesized to meet the high energy demand during CAM Phase III. Additionally, the model predicts that mitochondrial pyruvate originating from decarboxylation of malate is exclusively exported to the cytosol, probably via a pyruvate channel mechanism, to fuel gluconeogenesis. In this biochemical cycle, glyceraldehyde 3-phosphate dehydrogenase (GAPDH) acts as another important cytosolic proton consumer. Overall, our findings emphasize the importance of mitochondria in CAM and uncover a hitherto unappreciated role in metabolic proton homeostasis.

A quantitative description of light-limited cyanobacterial growth using flux balance analysis.

The metabolism of phototrophic cyanobacteria is an integral part of global biogeochemical cycles, and the capability of cyanobacteria to assimilate atmospheric CO2 into organic carbon has manifold potential applications for a sustainable biotechnology. To elucidate the properties of cyanobacterial metabolism and growth, computational reconstructions of genome-scale metabolic networks play an increasingly important role. Here, we present an updated reconstruction of the metabolic network of the cyanobacterium Synechocystis sp. PCC 6803 and its quantitative evaluation using flux balance analysis (FBA). To overcome limitations of conventional FBA, and to allow for the integration of experimental analyses, we develop a novel approach to describe light absorption and light utilization within the framework of FBA. Our approach incorporates photoinhibition and a variable quantum yield into the constraint-based description of light-limited phototrophic growth. We show that the resulting model is capable of predicting quantitative properties of cyanobacterial growth, including photosynthetic oxygen evolution and the ATP/NADPH ratio required for growth and cellular maintenance. Our approach retains the computational and conceptual simplicity of FBA and is readily applicable to other phototrophic microorganisms.

Design, construction and optimization of formaldehyde growth biosensors with broad application in biotechnology.

Formaldehyde is a key metabolite in natural and synthetic one-carbon metabolism. To facilitate the engineering of formaldehyde-producing enzymes, the development of sensitive, user-friendly, and cost-effective detection methods is required. In this study, we engineered Escherichia coli to serve as a cellular biosensor capable of detecting a broad range of formaldehyde concentrations. Using both natural and promiscuous formaldehyde assimilation enzymes, we designed three distinct E. coli growth biosensor strains that depend on formaldehyde for cell growth. These strains were engineered to be auxotrophic for one or several essential metabolites that could be produced through formaldehyde assimilation. The respective assimilating enzyme was expressed from the genome to compensate the auxotrophy in the presence of formaldehyde. We first predicted the formaldehyde dependency of the biosensors by flux balance analysis and then analysed it experimentally. Subsequent to strain engineering, we enhanced the formaldehyde sensitivity of two biosensors either through adaptive laboratory evolution or modifications at metabolic branch points. The final set of biosensors demonstrated the ability to detect formaldehyde concentrations ranging approximately from 30 μM to 13 mM. We demonstrated the application of the biosensors by assaying the invivo activity of different methanol dehydrogenases in the most sensitive strain. The fully genomic nature of the biosensors allows them to be deployed as "plug-and-play" devices for high-throughput screenings of extensive enzyme libraries. The formaldehyde growth biosensors developed in this study hold significant promise for advancing the field of enzyme engineering, thereby supporting the establishment of a sustainable one-carbon bioeconomy.

Current State, Challenges, and Opportunities in Genome-Scale Resource Allocation Models: A Mathematical Perspective.

Stoichiometric genome-scale metabolic models (generally abbreviated GSM, GSMM, or GEM) have had many applications in exploring phenotypes and guiding metabolic engineering interventions. Nevertheless, these models and predictions thereof can become limited as they do not directly account for protein cost, enzyme kinetics, and cell surface or volume proteome limitations. Lack of such mechanistic detail could lead to overly optimistic predictions and engineered strains. Initial efforts to correct these deficiencies were by the application of precursor tools for GSMs, such as flux balance analysis with molecular crowding. In the past decade, several frameworks have been introduced to incorporate proteome-related limitations using a genome-scale stoichiometric model as the reconstruction basis, which herein are called resource allocation models (RAMs). This review provides a broad overview of representative or commonly used existing RAM frameworks. This review discusses increasingly complex models, beginning with stoichiometric models to precursor to RAM frameworks to existing RAM frameworks. RAM frameworks are broadly divided into two categories: coarse-grained and fine-grained, with different strengths and challenges. Discussion includes pinpointing their utility, data needs, highlighting framework strengths and limitations, and appropriateness to various research endeavors, largely through contrasting their mathematical frameworks. Finally, promising future applications of RAMs are discussed.

Simultaneous design of fermentation and microbe

AbstractConstraint‐based optimization of microbial strains and model‐based bioprocess design have been used extensively to enhance yields in biotechnological processes. However, strain and process optimization are usually carried out in sequential steps, causing underperformance of the biotechnological process when scaling up to industrial fermentation conditions. Herein, we propose the optimization formulation SimulKnock that combines the optimization of a fermentation process with metabolic network design in a bilevel optimization program. The upper level maximizes space‐time yield and includes mass balances of a continuous fermentation, while the lower level is based on flux balance analysis. SimulKnock predicts optimal gene deletions and finds the optimal trade‐off between growth rate and product yield. Results of a case study with a genome‐scale metabolic model of Escherichia coli indicate higher space‐time yields than a sequential approach using OptKnock for almost all target products considered. By leveraging SimulKnock, we reduce the gap between strain and process optimization.

Biosynthesis of polyhydroxybutyrate from methane and carbon dioxide using type II methanotrophs

Methane (CH4) and carbon dioxide (CO2) are the dominant greenhouse gases (GHGs) that are increasing at an alarming rate. Methanotrophs have emerged as potential CH4 and CO2 biorefineries. This study demonstrated the synchronous incorporation of CH4 and CO2 into polyhydroxybutyrate (PHB) for the first time using 13C-labeling experiments in methanotrophs. By supplying substantial amounts of CO2, PHB content was enhanced in all investigated type II methanotrophic strains by 140 %, 146 %, and 162 %. The highest content of PHB from CH4 and CO2 in flask-scale cultivation reached 38 % dry cell weight in Methylocystis sp. MJC1, in which carbon percentage in PHB from CO2 was 45 %. Flux balance analysis predicted the critical roles of crotonyl-CoA carboxylase/reductase and phosphoenolpyruvate carboxylase in CO2 recycling. This study provided proof of the conversion of GHGs into a valuable and practical product using methanotrophic bacteria, contributing to addressing GHG emissions.

Abstract PS03-02: Single-cell metabolic profiling uncovers new precision immunotherapy strategy in triple-negative breast cancer

Abstract Background: Immunotherapy has emerged as a novel cornerstone in the treatment of triple-negative breast cancer (TNBC), while its benefits have been observed in only a limited subset of patients. The intricate metabolic interplay between cancer cells and microenvironmental cells remodels the tumor microenvironment and exerts a significant influence on the responses to immunotherapy. However, there is still a lack of clarity regarding the strategies to target this metabolic crosstalk in order to predict efficacy and enhance sensitivity for immunotherapy. Methods: We conducted single-cell RNA sequencing (scRNA-seq) analysis on a cohort of 27 patients with TNBC receiving either immunotherapy combination regimen or chemotherapy alone. Building upon this foundation, we embarked on a comprehensive investigation of the single-cell metabolic landscape from three distinct perspectives: metabolic genes, metabolic pathways, and metabolic flux, represented by the flux balance analysis (FBA) algorithm Compass. Furthermore, we identified crucial cell subgroups that exhibited a strong correlation with immunotherapy efficacy and elucidated their metabolic characteristics to postulate potential metabolic crosstalk. Then, we employed the in vitro, in vivo, and patient-derived models to unravel the underlying mechanisms of metabolic crosstalk and proposed the translational strategies. Results: Utilizing newly-developed single-cell FBA method, we illustrated that different cell types have distinct metabolic features. Specifically, tumor cells and macrophages had more active metabolic reprogramming and higher metabolic heterogeneity. Furthermore, we illustrated that a subset of tumor cells having active antioxidant metabolism (featured by the upregulation of GSTP1) and macrophage subset depending on glutamine metabolism (characterized by CCL3+) were negatively and positively predictive of immunotherapy responses, respectively. Mechanistically, GSTP1-mediated excessive consumption of glutamine by tumor cells competitively restrained the intake of glutamine into CCL3+ macrophages to promote their ferroptosis and reduce their infiltration. Clinically, we demonstrated that targeting the GSTP1, which is specifically expressed in tumor cells, remodeled immune-metabolic microenvironment and enhanced immunotherapy efficacy. Furthermore, we established the utility of plasma CCL3 abundance, GSTP1 protein abundance, and CCL3 protein abundance, as robust predictors of response to immunotherapy, in several independent immunotherapy cohorts. Conclusion: Our study presented a comprehensive and multidimensional analysis of the single-cell metabolic landscape. Identifying the crucial metabolic crosstalk remodeling the immune microenvironment, our promising finding highlights the potential significance of modulating cell-cell metabolic interactions in sensitizing TNBC to immunotherapy, providing valuable insights for future therapeutic interventions. Citation Format: Ying Xu, Yi Xiao, Han Wang, Zhimin Shao, Yi-Zhou Jiang. Single-cell metabolic profiling uncovers new precision immunotherapy strategy in triple-negative breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS03-02.

Convex Representation of Metabolic Networks with Michaelis–Menten Kinetics

Polyhedral models of metabolic networks are computationally tractable and can predict some cellular functions. A longstanding challenge is incorporating metabolites without losing tractability. In this paper, we do so using a new second-order cone representation of the Michaelis–Menten kinetics. The resulting model consists of linear stoichiometric constraints alongside second-order cone constraints that couple the reaction fluxes to metabolite concentrations. We formulate several new problems around this model: conic flux balance analysis, which augments flux balance analysis with metabolite concentrations; dynamic conic flux balance analysis; and finding minimal cut sets of networks with both reactions and metabolites. Solving these problems yields information about both fluxes and metabolite concentrations. They are second-order cone or mixed-integer second-order cone programs, which, while not as tractable as their linear counterparts, can nonetheless be solved at practical scales using existing software.

Decipher syntrophies within C2-C4 organic acids-degrading anaerobic microbiomes: A multi-omic exploration

In methanogenic ecosystems, carboxylic acid degradation is a crucial process facilitated by specialized bacteria working in syntrophy with methanogens. This study explores the transcriptomic responses in 178 metagenome-assembled genomes upon changes in feeding composition, specifically lactate, butyrate, propionate, and acetate, in four lab-scale bioreactors. Methanothrix soehngenii and Methanoculleus sp. emerged as key biomethanation contributors. Through metatranscriptomics, machine learning-based functional annotation, and flux balance analysis the underlying microbial interactions and dynamics were deciphered. Syntrophomonadaceae and Mesotoga species maintained mutualistic metabolite exchanges with hydrogenotrophic methanogens, degrading primarily butyrate and lactate, respectively. Acetate was mainly consumed by Smithellaceae sp., in competition with M. soehngenii in all reactors. Furthermore, the acetoclastic archaeon exhibited a previously undocumented capability to metabolize lactate, thereby confirming the prevalence of acetotrophic pathway. Transcriptomic profiles revealed an additional layer of complexity, where propionate dismutation and beta-oxidation pathways are interconnected by the exchanges of butyrate between putative syntrophs, including Syntrophomonadaceae and Smithellaceae species. Integrating multi-omics data with genome-scale metabolic modeling enabled the accurate reconstruction of dynamics within the controlled ecosystem. This composite novel approach was applied to the AD system to unveil the intricate relationships operating within the microbiome to promote thriving. Results elucidated the still poorly explored organization of the so-called microbial dark matter.