Flux Balance Research Articles

Optimizing Metabolite Production with Neighborhood-Based Binary Quantum-Behaved Particle Swarm Optimization and Flux Balance Analysis.

Metabolic engineering is a rapidly evolving field that involves optimizing microbial cell factories to overproduce various industrial products. To achieve this, several tools, leveraging constraint-based stoichiometric models and metaheuristic algorithms like particle swarm optimization (PSO), have been developed. However, PSO can potentially get trapped in local optima. Quantum-behaved PSO (QPSO) overcomes this limitation, and our study further enhances its binary version (BQPSO) with a neighborhood topology, leading to the advanced neighborhood-based BQPSO (NBQPSO). Combined with flux balance analysis (FBA), this forms an innovative approach, NBQPSO-FBA, for identifying optimal knockout strategies to maximize the desired metabolite production. Additionally, we introduced a novel encoding strategy suitable for large-scale genome-scale metabolic models (GSMMs). Evaluated on four E. coli GSMMs (iJR904, iAF1260, iJO1366, and iML1515), NBQPSO-FBA matches or surpasses established bi-level linear programming (LP) and heuristic methods in metabolite production optimization. Notably, it achieved 90.69% realization of the theoretical maximum in acetate production and demonstrated comparable performance with leading algorithms in lactate production. The efficiency of NBQPSO-FBA, which requires fewer knockouts, makes it a practical and effective tool for optimizing microbial cell factories. This addresses the rising demand for microbial products across various industries.

An Unstructured Mesh Reaction-Drift-Diffusion Master Equation with Reversible Reactions.

We develop a convergent reaction-drift-diffusion master equation (CRDDME) to facilitate the study of reaction processes in which spatial transport is influenced by drift due to one-body potential fields within general domain geometries. The generalized CRDDME is obtained through two steps. We first derive an unstructured grid jump process approximation for reversible diffusions, enabling the simulation of drift-diffusion processes where the drift arises due to a conservative field that biases particle motion. Leveraging the Edge-Averaged Finite Element method, our approach preserves detailed balance of drift-diffusion fluxes at equilibrium, and preserves an equilibrium Gibbs-Boltzmann distribution for particles undergoing drift-diffusion on the unstructured mesh. We next formulate a spatially-continuous volume reactivity particle-based reaction-drift-diffusion model for reversible reactions of the form . A finite volume discretization is used to generate jump process approximations to reaction terms in this model. The discretization is developed to ensure the combined reaction-drift-diffusion jump process approximation is consistent with detailed balance of reaction fluxes holding at equilibrium, along with supporting a discrete version of the continuous equilibrium state. The new CRDDME model represents a continuous-time discrete-space jump process approximation to the underlying volume reactivity model. We demonstrate the convergence and accuracy of the new CRDDME through a number of numerical examples, and illustrate its use on an idealized model for membrane protein receptor dynamics in T cell signaling.

Heterologous Biosynthesis of Taxifolin in Yarrowia lipolytica: Metabolic Engineering and Genome-Scale Metabolic Modeling.

Taxifolin, also known as dihydroquercetin (DHQ), is a flavonoid recognized for its potent antioxidant properties and a wide range of biological activities, including anti-tumor, antiviral, and immunomodulatory effects. Conventional extraction and chemical synthesis methods for taxifolin are often limited by low yields and associated environmental concerns. In this study, we investigated the heterologous biosynthesis of taxifolin in Yarrowia lipolytica through a combination of metabolic engineering and genome-scale metabolic modeling (GSM), complemented by flux balance analysis (FBA). We engineered Yarrowia lipolytica by introducing key biosynthetic genes and successfully synthesized taxifolin using naringenin (NAR) as a substrate, chosen for its low cost. Fermentation experiments demonstrated an optimal taxifolin yield of 10% at a substrate concentration of 200mg/L naringenin, with a maximum yield of 26.4mg/L taxifolin at 1g/L naringenin. To further enhance production, we applied a marker-free Cre-loxP-based gene integration method, allowing stable genomic integration of key genes, which increased taxifolin yield to 34.9mg/L at 1g/L naringenin. Additionally, intermediate metabolites eriodictyol (ERI) and dihydrokaempferol (DHK) accumulated to concentrations of 89.2mg/L and 21.7mg/L, respectively. Furthermore, we integrated metabolic data into a GSM and applied FBA to optimize the taxifolin biosynthetic pathway. Through Pareto frontier analysis, sensitivity analysis, flux variability analysis, and single gene deletion simulations, we identified key genetic modifications that significantly enhanced taxifolin yield. Overexpression of GND1 and IDP2 increased yields by 94% and 155%, respectively, while knockout of LIP2 led to a 46% increase. Using tri-baffled shake flasks to improve oxygen supply resulted in a 120% yield increase, whereas YPG medium decreased yield by 59%, validating our model's accuracy. To ensure stable and efficient gene expression, we integrated multi-copy constructs into the ribosomal DNA (rDNA) locus of Yarrowia lipolytica, doubling taxifolin production. These results demonstrate the effectiveness of GSM and FBA in addressing bottlenecks in microbial taxifolin biosynthesis and provide a basis for future optimization and large-scale production.

Syntrophic relationship among microbial communities enhance methane production during temperature transition from mesophilic to thermotolerant conditions

Hydrogenotrophic methanogenesis, in a syntrophic relationship with fermentative and acetogenic bacteria, is an essential key for maximizing biogas production and reducing CO2 emissions. However, its impact on CH4 production under varying temperatures is not widely understood. This study investigates the effects of incremental temperature transition from 37 °C to 45 °C at 2 °C intervals, on anaerobic digestion (AD) using digested sewage sludge with low glucose addition. Results showed CH4 production levels of 298 and 309 ml CH4/g CODfed at 37 °C and 39 °C, respectively, and exhibited a decrease at 41 °C. A temperature increase to 43 °C restored CH4 production to 260 ml CH4/g CODfed, with lower CO2 output. At 45 °C, the CH4 - CO2 production gap widened, indicating enhanced methanogenic activity due to prolonged high temperatures. Methanothrix was the predominant methanogen, followed by Methanobacterium whose activity increased at 43 °C and 45 °C alongside the upregulation of key hydrogenotrophic pathway enzymes (fwd, ftr, mch). The increasing abundance of fermentative bacteria at 43 °C and 45 °C may upregulates hydrogenotrophic methanogenesis. Flux balance analysis revealed the role of Acetomicrobium and Clostridia sp. strains that supplied the most acetate, providing approximately 7 – 600 mmol/gDW/h to the microbial communities. These findings highlight the adaptability of microbial communities and possible syntrophic relationships in response to temperature changes, demonstrating the resilience of AD processes under varying environmental conditions.

Flux balance analysis and peptide mapping elucidate the impact of bioreactor pH on Chinese hamster ovary (CHO) cell metabolism and N-linked glycosylation in the fab and Fc regions of the produced IgG

Culture conditions have a profound impact on therapeutic protein production and glycosylation, a critical therapeutic-quality attribute, especially for monoclonal antibodies (mAbs). While the critical culture parameter of pH has been known since the early 1990s to affect protein glycosylation and production, detailed glycan and metabolic characterization and mechanistic understanding are critically lacking. Here, Chinese Hamster Ovary (CHO) cells were grown in bioreactors at pH 6.75, 7, and 7.25 (± 0.03) to examine how pH affects cell metabolism and site-specific N-linked glycosylation of the produced broadly neutralizing anti-HIV IgG1 mAb. VRC01 has N-linked glycosylation sites in both the Fc region and the Fab region, a situation not previously examined with respect to mAb glycosylation as affected by culture conditions. Using parsimonious Flux Balance Analysis (pFBA) and Flux Variability Analysis (FVA), we dissect and quantitate the impact of pH on cell growth, glucose/lactate metabolism, accumulation of the toxic metabolite ammonia, IgG production rates, and nonessential amino acid metabolism. pFBA revealed that beyond the established mechanism of glutamine conversion to glutamate, ammonia is also produced by the reaction converting serine to pyruvate, especially in the later phases of culture. pFBA also provided insights into the switch from ammonia production to consumption, notably due to depletion of glutamine, and consumption of glutamate and aspartate. We document that culture duration and pH alter the complex bimodal patterns (production/uptake) of several essential and non-essential amino acids. Site-specific N-linked glycan analysis using glycopeptide mapping demonstrated that pH significantly affects the glycosylation profiles of the two IgG1 sites. Fc region glycans were completely fucosylated but did not contain any sialylation. The Fab region glycans were not completely fucosylated but contained sialylated glycans. Bioreactor pH affected both the fucosylation and sialylation indexes in the Fab region and the galactosylation index of the Fc region. However, fucosylation in the Fc region was unaffected thus demonstrating that the effect of pH on site-specific N-linked glycosylation is complex.

Disentangling the microbial genomic traits associated with aromatic hydrocarbon degradation in a jet fuel-contaminated aquifer.

Spills of petroleum or its derivatives in the environment lead to an enrichment of microorganisms able to degrade such compounds. The interactions taking place in such microbial communities are complex and poorly understood, since they depend on multiple factors, including diversity and metabolic potential of the microorganisms and a broad range of fluctuating environmental conditions. In our previous study, a complete characterization, based on high-throughput sequencing, was performed in a jet-fuel plume using soil samples and in in-situ microcosms amended with hydrocarbons and exposed for 120days. Herein, we propose a metabolic model to describe the monoaromatic hydrocarbon degradation process that takes place in such jet-fuel-contaminated sites, by combining genome-centered analysis, functional predictions, and flux balance analysis (FBA). In total, twenty high/medium quality MAGs were recovered; three of them assigned to anaerobic bacteria (Thermincolales, Geobacter and Pelotomaculaceace) and one affiliated to the aerobic bacterium Acinetobacter radioresistens, potentially the main players of hydrocarbon degradation in jet-fuel plumes. Taxonomic assignment of the genes indicated that a putative new species of Geobacteria has the potential for anaerobic degradation pathway, while the Pelotomaculaceae and Thermincolales members probably act via syntrophy oxidizing acetate and hydrogen (fermentation products of oil degradation) via sulfate and/or nitrate reduction.

Strategy for Optimizing Vitamin B12 Production in Pseudomonas putida KT2440 Using Metabolic Modeling.

Background/Objectives: Vitamin B12 is very important for human health, as it is a cofactor for enzymatic activities and plays various roles in human physiology. It is highly valued in the pharmaceutical, food, and additive production industries. Some of the bacteria currently used for the vitamin production are difficult to modify with gene-editing tools and may have slow growth. We propose the use of the bacteria Pseudomonas putida KT2440 for the production of vitamin B12 because it has a robust chassis for genetic modifications. The present wok evaluates P. putida KT2440 as a host for vitamin B12 production and explore potential gene-editing optimization strategies. Methods: We curated and modified a genome-scale metabolic model of Pseudomonas putida KT2440 and evaluated different strategies to optimize vitamin B12 production using the knockin and OptGene algorithms from the COBRA Toolbox. Furthermore, we examined the presence of riboswitches as cis-regulatory elements and calculated theoretical biomass growth yields and vitamin B12 production using a flux balance analysis (FBA). Results: According to the flux balance analysis of P. putida KT2440 under culture conditions, the biomass production values could reach 1.802 gDW-1·h1·L-1, and vitamin B12 production could reach 0.359 µmol·gDW-1·h-1·L-1. The theoretical vitamin B12 synthesis rate calculated using P. putida KT2040 with two additional reactions was 14 times higher than that calculated using the control, Pseudomonas denitrificans, which has been used for the industrial production of this vitamin. Conclusions: We propose that, with the addition of aminopropanol linker genes and the modification of riboswitches, P. putida KT2440 may become a suitable host for the industrial production of vitamin B12.

Metabolic Flux Analysis of Xanthomonas oryzae Treated with Bismerthiazol Revealed Glutathione Oxidoreductase in Glutathione Metabolism Serves as an Effective Target.

Bacterial blight (BB) of rice caused by Xanthomonas oryzae pathovar oryzae (Xoo) is a serious global rice disease. Due to increasing bactericide resistance, developing new inhibitors is urgent. Drug repositioning offers a potential strategy to address this issue. In this study, we integrated transcriptional data into a genome-scale metabolic model (GSMM) to screen novel anti-Xoo targets. Two RNA-seq datasets (before and after bismerthiazol treatment) were used to constrain the GSMM and simulate metabolic processes. Metabolic fluxes were calculated using parsimonious flux balance analysis (pFBA) identifying reactions with significant changes for target screening. Glutathione oxidoreductase (GSR) was selected as a potential anti-Xoo target and validated through antibacterial experiments. Virtual screening based on the target identified DB12411 as a lead compound with the potential for new antibacterial agents. This approach demonstrates that integrating metabolic networks and transcriptional data can aid in both understanding antibacterial mechanisms and discovering novel drug targets.

Dynamic metabolic modeling of ATP allocation during viral infection.

Viral pathogens, like SARS-CoV-2, hijack the host's macromolecular production machinery, imposing an energetic burden that is distributed across cellular metabolism. To explore the dynamic metabolic tension between the host's survival and viral replication, we developed a computational framework that uses genome-scale models to perform dynamic Flux Balance Analysis of human cell metabolism during virus infections. Relative to previous models, our framework addresses the physiology of viral infections of non-proliferating host cells through two new features. First, by incorporating the lipid content of SARS-CoV-2 biomass, we discovered activation of previously overlooked pathways giving rise to new predictions of possible drug targets. Furthermore, we introduce a dynamic model that simulates the partitioning of resources between the virus and the host cell, capturing the extent to which the competition depletes the human cells from essential ATP. By incorporating viral dynamics into our COMETS framework for spatio-temporal modeling of metabolism, we provide a mechanistic, dynamic and generalizable starting point for bridging systems biology modeling with viral pathogenesis. This framework could be extended to broadly incorporate phage dynamics in microbial systems and ecosystems.

Conditional flux balance analysis toolbox for python: application to research metabolism in cyclic environments.

We present py_cFBA, a Python-based toolbox for conditional flux balance analysis (cFBA). Our toolbox allows for an easy implementation of cFBA models using a well-documented and modular approach and supports the generation of Systems Biology Markup Language models. The toolbox is designed to be user-friendly, versatile, and freely available to non-commercial users, serving as a valuable resource for researchers predicting metabolic behaviour with resource allocation in dynamic-cyclic environments. Extensive documentation, installation steps, tutorials, and examples are available at https://tp-watson-python-cfba.readthedocs.io/en/. The py_cFBA python package is available at https://pypi.org/project/py-cfba/.

Top-down and bottom-up effects on visual perception during data-based scientific reasoning in the context of population dynamics

ABSTRACT This study investigates the role of visual perception in data-based scientific reasoning within population dynamics. Visual perception involves representational characteristics of stimuli and context-based conceptions, influenced by top-down (contextual assumptions) and bottom-up (data representation) effects. In population dynamics, top-down effects relate to assumptions like natural equilibrium, while bottom-up effects concern how data are represented, typically through line graphs. We assessed pre-service biology teachers’ (N = 20) initial conceptions on population dynamics, categorizing them as either the Balance of Nature (BoN) or Flux of Nature (FoN) metaphors. Participants viewed line graphs depicting population dynamics while eye-tracking technology recorded their visual focus and think-aloud protocols provided verbal data. ANOVA results showed significant differences in eye-tracking metrics between AOI groups of BoN and FoN graphs, but no significant effects between initial conceptions and eye-tracking measures. Heat map analyses revealed that participants focused on visually prominent graph features, especially when interpretation required conceptual knowledge and pattern identification.

MitoMAMMAL: a genome scale model of mammalian mitochondria predicts cardiac and BAT metabolism

Abstract Motivation Mitochondria perform several essential functions in order to maintain cellular homeostasis and mitochondrial metabolism is inherently flexible to allow correct function in a wide range of tissues. Dysregulated mitochondrial metabolism can therefore affect different tissues in different ways which presents experimental challenges in understanding the pathology of mitochondrial diseases. System-level metabolic modelling is therefore useful in gaining in-depth insights into tissue-specific mitochondrial metabolism, yet despite the mouse being a common model organism used in research, there is currently no mouse specific mitochondrial metabolic model available. Results In this work, building upon the similarity between human and mouse mitochondrial metabolism, we have created mitoMammal, a genome-scale metabolic model that contains human and mouse specific gene-product reaction rules. MitoMammal is therefore able to model mouse and human mitochondrial metabolism. To demonstrate this feature, using an adapted E-Flux algorithm, we first integrated proteomic data extracted from mitochondria of isolated mouse cardiomyocytes and mouse brown adipocyte tissue. We then integrated transcriptomic data from in vitro differentiated human brown adipose cells and modelled the context specific metabolism using flux balance analysis. In all three simulations, mitoMammal made mostly accurate, and some novel predictions relating to energy metabolism in the context of cardiomyocytes and brown adipocytes. This demonstrates its usefulness in research relating to cardiac disease and diabetes in both mouse and human contexts. Availability and implementation MitoMammal is formatted in SBML3. The code required for constraint-based modelling used in this work is implemented in Python 3 and is available as a Jupyter Notebook. The mitoMammal metabolic model, along with Jupyter notebooks and data used in this work are available at: https://gitlab.com/habermann_lab/mitomammal. Supplementary Information Supplementary data are available at Bioinformatics Advances online.

Combined Surface Flux Transport and Helioseismic Far-Side Active Region Model (FARM)

Maps of the magnetic field at the Sun’s surface are commonly used as boundary conditions in space-weather modeling. However, continuous observations are only available from the Earth-facing part of the Sun’s surface. One commonly used approach to mitigate the lack of far-side information is to apply a surface flux transport (SFT) model to model the evolution of the magnetic field as the Sun rotates. Helioseismology can image active regions on the far side using acoustic oscillations and hence has the potential to improve the modeled surface magnetic field. In this study, we propose a novel approach for estimating magnetic fields of active regions on the Sun’s far side based on seismic measurements and then include them into an SFT model. To calibrate the conversion from helioseismic signal to magnetic field, we apply our SFT model to line-of-sight magnetograms from Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO) to obtain reference maps of global magnetic fields (including the far side). The resulting magnetic maps are compared with helioseismic phase maps on the Sun’s far side computed using helioseismic holography. The spatial structure of the magnetic field within an active region is reflected in the spatial structure of the helioseismic phase shifts. We assign polarities to the unipolar magnetic-field concentrations based upon Hale’s law and require approximate flux balance between the two polarities. From 2010 to 2024, we modeled 859 active regions, with an average total unsigned flux of 7.84⋅1021\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$7.84 \\cdot 10^{21}$\\end{document} Mx and an average area of 4.48⋅1010\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$4.48 \\cdot 10^{10}$\\end{document} km2. Approximately 4.2%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$4.2\\%$\\end{document} of the active regions were found to have an anti-Hale configuration, which we manually corrected. Including these far-side active regions resulted in an average increase of 1.2%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$1.2\\%$\\end{document} (up to 25.3%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$25.3\\%$\\end{document}) in the total unsigned magnetogram flux. Comparisons between modeled open-field areas and EUV observations reveal a substantial improvement in agreement when far-side active regions are included. This proof of concept study demonstrates the potential of the “combined surface flux transport and helioseismic Far-side Active Region Model” (FARM) to improve space-weather modeling.

Substitute Yeast Extract While Maintaining Performance: Showcase Amorpha-4,11-Diene Production.

Yeast extract (YE) is a complex nutritional source associated with high performance on microbial production processes. However, its inherent compositional variability challenges its scalability. While prior efforts have focused on growth-associated products, the dynamics of growth-uncoupled production, which leads to higher production rates and conversion yields, still need to be explored. This production scenario is common in large-scale applications. This study presents a systematic approach to replace YE for the production of the terpene amorpha-4,11-diene in Escherichia coli. Sequential processing was successfully applied to identify glutamic acid, alanine, leucine, valine, isoleucine and glycine as the key amino acids (AAs) under slow-growth conditions. Thoroughly applying biomass retention as part of sequential processing increased production capacity by 45% using these AAs instead of YE. Further studies, including flux balance analyses, targeted pyruvate as the common AA precursor. The optimized fed-batch process feeding pyruvate with 0.09 gPyr h-1 enhanced amorpha-4,11-diene production by 37%, although adding only 1% carbon via pyruvate. Flux balance analysis revealed the criteria for optimum pyruvate feeding, for example, to prevent succinate secretion and maintain the NADH/NAD+ balance. These findings illustrate the interplay between media composition and metabolic activity and provide a successful guideline for identifying lean, best-performing media for industrial applications.

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.​.

Enhancing soil water stability and retention through plastic mulching under atypical climatic conditions on the Chinese loess plateau

Mulching is an agricultural practice that is extensively implemented worldwide to conserve water in soil to enhance agricultural production，and especially in the temperate continental monsoon climate regions. However, the mechanism controlling soil moisture evaporation, infiltration, and retention by mulching is unclear. We assess the impact of various mulching regimes on the soil–water equilibrium in the root zone of corn fields under atypical climate conditions(Excessive Precipitation) from 2020–2021 in five treatments: (1) ridges mulched with plastic film and furrows without mulching (RF), (2) conventional flat planting with full plastic mulching (FPM), (3) conventional flat planting with straw mulching (SM), (4) conventional flat planting with partial plastic mulching (PPM), and (5) conventional (control) flat planting with no mulching (CK). The HYDRUS-2D model was calibrated and validated using experimental data, to assess soil water content, water flux, and soil water balance within a two-dimensional soil profile. This model accurately replicated the root zone within the soil profile under all mulching scenarios, with numerical simulation outcomes closely aligning with observed measurements. Average R² values for FPM, PPM, RF, SM, and CK scenarios were 0.76, 0.75, 0.86, 0.85, and 0.77, respectively. During the 2020 and 2021 growing seasons, characterized by increased rainfall, plastic-covered treatments (FPM, PPM, RF) more efficiently reduced soil evaporation and enhanced soil-water retention. The combined soil drainage and storage changes for FPM, PPM, RF, and SM treatments exceeded those of CK by an average of 114.57, 64.93, 77.38, and 6.74 mm, respectively. FPM, PPM, and RF treatments had substantial water-retention capabilities during years of atypical climate. Notably, FPM ensured adequate water supply, facilitated deep soil water replenishment, and more effectively maintained soil water stability and retention. This underscores the pivotal regulatory function of mulching in mitigating the impacts of consecutive years of unusual climatic conditions.

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.

Evaluating variation of respiration:photosynthesis ratio in sagebrush species: Implications for carbon flux modeling

AbstractPlant respiration and photosynthesis are the two main processes influencing carbon (C) flux balance at leaf‐to‐ecosystem scales. The ratio of respiration to photosynthesis (R:A) or carbon use efficiency (CUE) is considered an important trait for determining global carbon storage in the near future. One school of thought assumes that R:A is constant in terrestrial productivity models, irrespective of biomass, climate, and species. Others believe it is variable, although within a limited range. Semiarid systems dominated by woody vegetation, such as sagebrush steppe, have been recognized as potentially important C sinks on regional to global scales in the context of future climate scenarios. Therefore, there is a critical need to study R:A over different organizational scales (i.e., at the leaf, whole plant, and ecosystem scales) to use this approach for future C flux predictions under climate change scenarios. The objective of this study was to compare leaf‐, shrub‐, and ecosystem‐scale R:A among three sagebrush (Artemisia spp.) communities, and to determine how R:A varies throughout the growing season (i.e., early, mid‐, and late summer) among these communities. We measured photosynthesis and respiration monthly in three sagebrush communities spanning a 685‐m elevation gradient at the Reynolds Creek Experimental Watershed and Critical Zone Observatory in southwestern Idaho. Consistent with our expectations, we found large seasonal variations in R and A at all scales, but with differences in A among the three sagebrush communities significant only at the leaf scale. The R:A ratio was not significantly different among the three species at all organizational scales. However, the R:A ratio did vary among months at the leaf level and there was a statistical interaction between species and month at both leaf and shrub levels. Our study indicates that the R:A ratio is generally conservative, although not tightly constrained (range: 0.12–0.77) among three sagebrush species. Therefore, approaches that assume conservative R:A ratios in terrestrial productivity models need to be considered carefully to evaluate the impact of projected climatic changes on future C cycling in shrub‐dominated rangeland ecosystems.