Dynamic flux balance analysis of diauxic growth in Escherichia coli

Deciphering and designing microbial communities by genome-scale metabolic modelling

Microbial communities are shaped by the complex interactions among organisms and the environment. Genome-scale metabolic models (GEMs) can provide deeper insights into the complexity and ecological properties of various microbial communities, revealing their intricate interactions. Many researchers have modified GEMs for the microbial communities based on specific needs. Thus, GEMs need to be comprehensively summarized to better understand the trends in their development. In this review, we summarized the key developments in deciphering and designing microbial communities using different GEMs. A timeline of selected highlights in GEMs indicated that this area is evolving from the single-strain level to the microbial community level. Then, we outlined a framework for constructing GEMs of microbial communities. We also summarized the models and resources of static and dynamic community-level GEMs. We focused on the role of external environmental and intracellular resources in shaping the assembly of microbial communities. Finally, we discussed the key challenges and future directions of GEMs, focusing on the integration of GEMs with quorum sensing mechanisms, microbial ecology interactions, machine learning algorithms, and automatic modeling, all of which contribute to consortia-based applications in different fields.

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.

Microalgae growth-promoting bacteria for cultivation strategies: Recent updates and progress

Microalgae growth-promoting bacteria (MGPB), both actinobacteria and non-actinobacteria, have received considerable attention recently because of their potential to develop microalgae-bacteria co-culture strategies for improved efficiency and sustainability of the water-energy-environment nexus. Owing to their diverse metabolic pathways and ability to adapt to diverse conditions, microalgal-MGPB co-cultures could be promising biological systems under uncertain environmental and nutrient conditions. This review proposes the recent updates and progress on MGPB for microalgae cultivation through co-culture strategies. Firstly, potential MGPB strains for microalgae cultivation are introduced. Following, microalgal-MGPB interaction mechanisms and applications of their co-cultures for biomass production and wastewater treatment are reviewed. Moreover, state-of-the-art studies on synthetic biology and metabolic network analysis, along with the challenges and prospects of opting these approaches for microalgal-MGPB co-cultures are presented. It is anticipated that these strategies may significantly improve the sustainability of microalgal-MGPB co-cultures for wastewater treatment, biomass valorization, and bioproducts synthesis in a circular bioeconomy paradigm.

Machine learning for the advancement of genome-scale metabolic modeling

Constraint-based modeling (CBM) has evolved as the core systems biology tool to map the interrelations between genotype, phenotype, and external environment. The recent advancement of high-throughput experimental approaches and multi-omics strategies has generated a plethora of new and precise information from wide-ranging biological domains. On the other hand, the continuously growing field of machine learning (ML) and its specialized branch of deep learning (DL) provide essential computational architectures for decoding complex and heterogeneous biological data. In recent years, both multi-omics and ML have assisted in the escalation of CBM. Condition-specific omics data, such as transcriptomics and proteomics, helped contextualize the model prediction while analyzing a particular phenotypic signature. At the same time, the advanced ML tools have eased the model reconstruction and analysis to increase the accuracy and prediction power. However, the development of these multi-disciplinary methodological frameworks mainly occurs independently, which limits the concatenation of biological knowledge from different domains. Hence, we have reviewed the potential of integrating multi-disciplinary tools and strategies from various fields, such as synthetic biology, CBM, omics, and ML, to explore the biochemical phenomenon beyond the conventional biological dogma. How the integrative knowledge of these intersected domains has improved bioengineering and biomedical applications has also been highlighted. We categorically explained the conventional genome-scale metabolic model (GEM) reconstruction tools and their improvement strategies through ML paradigms. Further, the crucial role of ML and DL in omics data restructuring for GEM development has also been briefly discussed. Finally, the case-study-based assessment of the state-of-the-art method for improving biomedical and metabolic engineering strategies has been elaborated. Therefore, this review demonstrates how integrating experimental and in silico strategies can help map the ever-expanding knowledge of biological systems driven by condition-specific cellular information. This multiview approach will elevate the application of ML-based CBM in the biomedical and bioengineering fields for the betterment of society and the environment.

Insights into yeast response to chemotherapeutic agent through time series genome-scale metabolic models

Organism-specific genome-scale metabolic models (GSMMs) can unveil molecular mechanisms within cells and are commonly used in diverse applications, from synthetic biology, biotechnology, and systems biology to metabolic engineering. There are limited studies incorporating time-series transcriptomics in GSMM simulations. Yeast is an easy-to-manipulate model organism for tumor research. Here, a novel approach (TS-GSMM) was proposed to integrate time-series transcriptomics with GSMMs to narrow down the feasible solution space of all possible flux distributions and attain time-series flux samples. The flux samples were clustered using machine learning techniques, and the clusters' functional analysis was performed using reaction set enrichment analysis. A time series transcriptomics response of Yeast cells to a chemotherapeutic reagent-doxorubicin-was mapped onto a Yeast GSMM. Eleven flux clusters were obtained with our approach, and pathway dynamics were displayed. Induction of fluxes related to bicarbonate formation and transport, ergosterol and spermidine transport, and ATP production were captured. Integrating time-series transcriptomics data with GSMMs is a promising approach to reveal pathway dynamics without any kinetic modeling and detects pathways that cannot be identified through transcriptomics-only analysis. The codes are available at https://github.com/karabekmez/TS-GSMM.

Assessing structural uncertainty of biochemical regulatory networks in metabolic pathways under varying data quality

Ordinary differential equation (ODE) models are powerful tools for studying the dynamics of metabolic pathways. However, key challenges lie in constructing ODE models for metabolic pathways, specifically in our limited knowledge about which metabolite levels control which reaction rates. Identification of these regulatory networks is further complicated by the limited availability of relevant data. Here, we assess the conditions under which it is feasible to accurately identify regulatory networks in metabolic pathways by computationally fitting candidate network models with biochemical systems theory (BST) kinetics to data of varying quality. We use network motifs commonly found in metabolic pathways as a simplified testbed. Key features correlated with the level of difficulty in identifying the correct regulatory network were identified, highlighting the impact of sampling rate, data noise, and data incompleteness on structural uncertainty. We found that for a simple branched network motif with an equal number of metabolites and fluxes, identification of the correct regulatory network can be largely achieved and is robust to missing one of the metabolite profiles. However, with a bi-substrate bi-product reaction or more fluxes than metabolites in the network motif, the identification becomes more challenging. Stronger regulatory interactions and higher metabolite concentrations were found to be correlated with less structural uncertainty. These results could aid efforts to predict whether the true metabolic regulatory network can be computationally identified for a given stoichiometric network topology and dataset quality, thus helping to identify optimal measures to mitigate such identifiability issues in kinetic model development.

Prelude to a Compositional Systems Biology

Composition is a powerful principle for systems biology, focused on the interfaces, interconnections, and orchestration of distributed processes. Whereas most systems biology models focus on the structure or dynamics of specific subsystems in controlled conditions, compositional systems biology aims to connect such models into integrative multiscale simulations. This emphasizes the space between models-a compositional perspective asks what variables should be exposed through a submodel's interface? How do coupled models connect and translate across scales? How can we connect domain-specific models across biological and physical research areas to drive the synthesis of new knowledge? What is required of software that integrates diverse datasets and submodels into unified multiscale simulations? How can the resulting integrative models be accessed, flexibly recombined into new forms, and iteratively refined by a community of researchers? This essay offers a high-level overview of the key components for compositional systems biology, including: 1) a conceptual framework and corresponding graphical framework to represent interfaces, composition patterns, and orchestration patterns; 2) standardized composition schemas that offer consistent formats for composable data types and models, fostering robust infrastructure for a registry of simulation modules that can be flexibly assembled; 3) a foundational set of biological templates-schemas for cellular and molecular interfaces, which can be filled with detailed submodels and datasets, and are designed to integrate knowledge that sheds light on the molecular emergence of cells; and 4) scientific collaboration facilitated by user-friendly interfaces for connecting researchers with datasets and models, and which allows a community of researchers to effectively build integrative multiscale models of cellular systems.

Advancing environmental biotechnology with microbial community modeling rooted in functional 'omics

Emerging biotechnologies that solve pressing environmental and climate emergencies will require harnessing the vast functional diversity of the underlying microbiomes driving such engineered processes. Modeling is a critical aspect of process engineering that informs system design as well as aids diagnostic optimization of performance. 'Conventional' bioprocess models assume homogenous biomass within functional guilds and thus fail to predict emergent properties of diverse microbial physiologies, such as product specificity and community interactions. Yet, recent advances in functional 'omics-based approaches can provide a 'lens' through which we can probe and measure in situ ecophysiologies of environmental microbiomes. Here, we overview microbial community modeling approaches that incorporate functional 'omics data, which we posit can advance our ability to design and control new environmental biotechnologies going forward.

Self-consistent signal transduction analysis for modeling context-specific signaling cascades and perturbations

Biological signal transduction networks are central to information processing and regulation of gene expression across all domains of life. Dysregulation is known to cause a wide array of diseases, including cancers. Here I introduce self-consistent signal transduction analysis, which utilizes genome-scale -omics data (specifically transcriptomics and/or proteomics) in order to predict the flow of information through these networks in an individualized manner. I apply the method to the study of endocrine therapy in breast cancer patients, and show that drugs that inhibit estrogen receptor α elicit a wide array of antitumoral effects, and that their most clinically-impactful ones are through the modulation of proliferative signals that control the genes GREB1, HK1, AKT1, MAPK1, AKT2, and NQO1. This method offers researchers a valuable tool in understanding how and why dysregulation occurs, and how perturbations to the network (such as targeted therapies) effect the network itself, and ultimately patient outcomes.

Emerging methods for genome-scale metabolic modeling of microbial communities

Genome-scale metabolic models (GEMs) are consolidating as platforms for studying mixed microbial populations, by combining biological data and knowledge with mathematical rigor. However, deploying these models to answer research questions can be challenging due to the increasing number of available computational tools, the lack of universal standards, and their inherent limitations. Here, we present a comprehensive overview of foundational concepts for building and evaluating genome-scale models of microbial communities. We then compare tools in terms of requirements, capabilities, and applications. Next, we highlight the current pitfalls and open challenges to consider when adopting existing tools and developing new ones. Our compendium can be relevant for the expanding community of modelers, both at the entry and experienced levels.

Engineering Gut Symbionts: A Way to Promote Bee Growth?

Bees play a crucial role as pollinators, contributing significantly to ecosystems. However, the honeybee population faces challenges such as global warming, pesticide use, and pathogenic microorganisms. Promoting bee growth using several approaches is therefore crucial for maintaining their roles. To this end, the bacterial microbiota is well-known for its native role in supporting bee growth in several respects. Maximizing the capabilities of these microorganisms holds the theoretical potential to promote the growth of bees. Recent advancements have made it feasible to achieve this enhancement through the application of genetic engineering. In this review, we present the roles of gut symbionts in promoting bee growth and collectively summarize the engineering approaches that would be needed for future applications. Particularly, as the engineering of bee gut symbionts has not been advanced, the dominant gut symbiotic bacteria Snodgrassella alvi and Gilliamella apicola are the main focus of the paper, along with other dominant species. Moreover, we propose engineering strategies that will allow for the improvement in bee growth with listed gene targets for modification to further encourage the use of engineered gut symbionts to promote bee growth.

Revealing the dynamics and mechanisms of bacterial interactions in cheese production with metabolic modelling

Cheese taste and flavour properties result from complex metabolic processes occurring in microbial communities. A deeper understanding of such mechanisms makes it possible to improve both industrial production processes and end-product quality through the design of microbial consortia. In this work, we caracterise the metabolism of a three-species community consisting of Lactococcus lactis, Lactobacillus plantarum and Propionibacterium freudenreichii during a seven-week cheese production process. Using genome-scale metabolic models and omics data integration, we modeled and calibrated individual dynamics using monoculture experiments, and coupled these models to capture the metabolism of the community. This model accurately predicts the dynamics of the community, enlightening the contribution of each microbial species to organoleptic compound production. Further metabolic exploration revealed additional possible interactions between the bacterial species. This work provides a methodological framework for the prediction of community-wide metabolism and highlights the added value of dynamic metabolic modeling for the comprehension of fermented food processes.

Genome-scale metabolic network models for industrial microorganisms metabolic engineering: Current advances and future prospects

The construction of high-performance microbial cell factories (MCFs) is the centerpiece of biomanufacturing. However, the complex metabolic regulatory network of microorganisms poses great challenges for the efficient design and construction of MCFs. The genome-scale metabolic network models (GSMs) can systematically simulate the metabolic regulation process of microorganisms in silico, providing effective guidance for the rapid design and construction of MCFs. In this review, we summarized the development status of 16 important industrial microbial GSMs, and further outline the technologies or methods that continuously promote high-quality GSMs construction from five aspects: I) Databases and modeling tools facilitate GSMs reconstruction; II) evolving gap-filling technologies; III) constraint-based model reconstruction; IV) advances in algorithms; and V) developed visualization tools. In addition, we also summarized the applications of GSMs in guiding metabolic engineering from four aspects: I) exploring and explaining metabolic features; II) predicting the effects of genetic perturbations on metabolism; III) predicting the optimal phenotype; IV) guiding cell factories construction in practical experiment. Finally, we discussed the development of GSMs, aiming to provide a reference for efficiently reconstructing GSMs and guiding metabolic engineering.

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.

Dynamic flux balance analysis of high cell density fed-batch culture of Escherichia coli BL21 (DE3) with mass spectrometry-based spent media analysis

Dynamic flux balance analysis (FBA) allows estimation of intracellular reaction rates using organism-specific genome-scale metabolic models (GSMM) and by assuming instantaneous pseudo-steady states for processes that are inherently dynamic. This technique is well-suited for industrial bioprocesses employing complex media characterized by a hierarchy of substrate uptake and product secretion. However, knowledge of exchange rates of many components of the media would be required to obtain meaningful results. Here, we performed spent media analysis using mass spectrometry coupled with liquid and gas chromatography for a fed-batch, high-cell density cultivation of Escherichia coli BL21(DE3) expressing a recombinant protein. Time course measurements thus obtained for 246 metabolites were converted to instantaneous exchange rates. These were then used as constraints for dynamic FBA using a previously reported GSMM, thus providing insights into how the flux map evolves through the process. Changes in tri-carboxylic acid cycle fluxes correlated with the increased demand for energy during recombinant protein production. The results show how amino acids act as hubs for the synthesis of other cellular metabolites. Our results provide a deeper understanding of an industrial bioprocess and will have implications in further optimizing the process.

Spatial transcriptome-guided multi-scale framework connects P. aeruginosa metabolic states to oxidative stress biofilm microenvironment

With the generation of spatially resolved transcriptomics of microbial biofilms, computational tools can be used to integrate this data to elucidate the multi-scale mechanisms controlling heterogeneous biofilm metabolism. This work presents a Multi-scale model of Metabolism In Cellular Systems (MiMICS) which is a computational framework that couples a genome-scale metabolic network reconstruction (GENRE) with Hybrid Automata Library (HAL), an existing agent-based model and reaction-diffusion model platform. A key feature of MiMICS is the ability to incorporate multiple -omics-guided metabolic models, which can represent unique metabolic states that yield different metabolic parameter values passed to the extracellular models. We used MiMICS to simulate Pseudomonas aeruginosa regulation of denitrification and oxidative stress metabolism in hypoxic and nitric oxide (NO) biofilm microenvironments. Integration of P. aeruginosa PA14 biofilm spatial transcriptomic data into a P. aeruginosa PA14 GENRE generated four PA14 metabolic model states that were input into MiMICS. Characteristic of aerobic, denitrification, and oxidative stress metabolism, the four metabolic model states predicted different oxygen, nitrate, and NO exchange fluxes that were passed as inputs to update the agent's local metabolite concentrations in the extracellular reaction-diffusion model. Individual bacterial agents chose a PA14 metabolic model state based on a combination of stochastic rules, and agents sensing local oxygen and NO. Transcriptome-guided MiMICS predictions suggested microscale denitrification and oxidative stress metabolic heterogeneity emerged due to local variability in the NO biofilm microenvironment. MiMICS accurately predicted the biofilm's spatial relationships between denitrification, oxidative stress, and central carbon metabolism. As simulated cells responded to extracellular NO, MiMICS revealed dynamics of cell populations heterogeneously upregulating reactions in the denitrification pathway, which may function to maintain NO levels within non-toxic ranges. We demonstrated that MiMICS is a valuable computational tool to incorporate multiple -omics-guided metabolic models to mechanistically map heterogeneous microbial metabolic states to the biofilm microenvironment.

2'-Fucosyllactose helps butyrate producers outgrow competitors in infant gut microbiota simulations

A reduced capacity for butyrate production by the early infant gut microbiota is associated with negative health effects, such as inflammation and the development of allergies. Here, we develop new hypotheses on the effect of the prebiotic galacto-oligosaccharides (GOS) or 2'-fucosyllactose (2'-FL) on butyrate production by the infant gut microbiota using a multiscale, spatiotemporal mathematical model of the infant gut. The model simulates a community of cross-feeding gut bacteria in metabolic detail. It represents the community as a grid of bacterial populations that exchange metabolites, using 20 different subspecies-specific metabolic networks taken from the AGORA database. The simulations predict that both GOS and 2'-FL promote the growth of Bifidobacterium, whereas butyrate producing bacteria are only consistently abundant in the presence of propane-1,2-diol, a product of 2'-FL metabolism. In absence of prebiotics or in presence of only GOS, however, Bacteroides vulgatus and Cutibacterium acnes outcompete butyrate producers by consuming intermediate metabolites.

Protein constraints in genome-scale metabolic models: Data integration, parameter estimation, and prediction of metabolic phenotypes

Genome-scale metabolic models provide a valuable resource to study metabolism and cell physiology. These models are employed with approaches from the constraint-based modeling framework to predict metabolic and physiological phenotypes. The prediction performance of genome-scale metabolic models can be improved by including protein constraints. The resulting protein-constrained models consider data on turnover numbers (kcat ) and facilitate the integration of protein abundances. In this systematic review, we present and discuss the current state-of-the-art regarding the estimation of kinetic parameters used in protein-constrained models. We also highlight how data-driven and constraint-based approaches can aid the estimation of turnover numbers and their usage in improving predictions of cellular phenotypes. Finally, we identify standing challenges in protein-constrained metabolic models and provide a perspective regarding future approaches to improve the predictive performance.

COSMIC-dFBA: A novel multi-scale hybrid framework for bioprocess modeling

Metabolism governs cell performance in biomanufacturing, as it fuels growth and productivity. However, even in well-controlled culture systems, metabolism is dynamic, with shifting objectives and resources, thus limiting the predictive capability of mechanistic models for process design and optimization. Here, we present Cellular Objectives and State Modulation In bioreaCtors (COSMIC)-dFBA, a hybrid multi-scale modeling paradigm that accurately predicts cell density, antibody titer, and bioreactor metabolite concentration profiles. Using machine-learning, COSMIC-dFBA decomposes the instantaneous metabolite uptake and secretion rates in a bioreactor into weighted contributions from each cell state (growth or antibody-producing state) and integrates these with a genome-scale metabolic model. A major strength of COSMIC-dFBA is that it can be parameterized with only metabolite concentrations from spent media, although constraining the metabolic model with other omics data can further improve its capabilities. Using COSMIC-dFBA, we can predict the final cell density and antibody titer to within 10% of the measured data, and compared to a standard dFBA model, we found the framework showed a 90% and 72% improvement in cell density and antibody titer prediction, respectively. Thus, we demonstrate our hybrid modeling framework effectively captures cellular metabolism and expands the applicability of dFBA to model the dynamic conditions in a bioreactor.

Cross-regulation between proteome reallocation and metabolic flux redistribution governs bacterial growth transition kinetics

Bacteria need to adjust their metabolism and protein synthesis simultaneously to adapt to changing nutrient conditions. It's still a grand challenge to predict how cells coordinate such adaptation due to the cross-regulation between the metabolic fluxes and the protein synthesis. Here we developed a dynamic Constrained Allocation Flux Balance Analysis method (dCAFBA), which integrates flux-controlled proteome allocation and protein limited flux balance analysis. This framework can predict the redistribution dynamics of metabolic fluxes without requiring detailed enzyme parameters. We reveal that during nutrient up-shifts, the calculated metabolic fluxes change in agreement with experimental measurements of enzyme protein dynamics. During nutrient down-shifts, we uncover a switch of metabolic bottleneck from carbon uptake proteins to metabolic enzymes, which disrupts the coordination between metabolic flux and their enzyme abundance. Our method provides a quantitative framework to investigate cellular metabolism under varying environments and reveals insights into bacterial adaptation strategies.

Development and applications of genome-scale metabolic network models

The genome-scale metabolic network model is an effective tool for characterizing the gene-protein-response relationship in the entire metabolic pathway of an organism. By combining various algorithms, the genome-scale metabolic network model can effectively simulate the influence of a specific environment on the physiological state of cells, optimize the culture conditions of strains, and predict the targets of genetic modification to achieve targeted modification of strains. In this review, we summarize the whole process of model building, sort out the various tools that may be involved in the model building process, and explain the role of various algorithms in model analysis. In addition, we also summarized the application of GSMM in network characteristics, cell phenotypes, metabolic engineering, etc. Finally, we discuss the current challenges facing GSMM.

Recent advances in the characterization of essential genes and development of a database of essential genes

Over the past few decades, there has been a significant interest in the study of essential genes, which are crucial for the survival of an organism under specific environmental conditions and thus have practical applications in the fields of synthetic biology and medicine. An increasing amount of experimental data on essential genes has been obtained with the continuous development of technological methods. Meanwhile, various computational prediction methods, related databases and web servers have emerged accordingly. To facilitate the study of essential genes, we have established a database of essential genes (DEG), which has become popular with continuous updates to facilitate essential gene feature analysis and prediction, drug and vaccine development, as well as artificial genome design and construction. In this article, we summarized the studies of essential genes, overviewed the relevant databases, and discussed their practical applications. Furthermore, we provided an overview of the main applications of DEG and conducted comprehensive analyses based on its latest version. However, it should be noted that the essential gene is a dynamic concept instead of a binary one, which presents both opportunities and challenges for their future development.

Review on computer-assisted biosynthetic capacities elucidation to assess metabolic interactions and communication within microbial communities

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

Predicting microbial interactions with approaches based on flux balance analysis: an evaluation

BACKGROUND

Given a genome-scale metabolic model (GEM) of a microorganism and criteria for optimization, flux balance analysis (FBA) predicts the optimal growth rate and its corresponding flux distribution for a specific medium. FBA has been extended to microbial consortia and thus can be used to predict interactions by comparing in-silico growth rates for co- and monocultures. Although FBA-based methods for microbial interaction prediction are becoming popular, a systematic evaluation of their accuracy has not yet been performed.

RESULTS

Here, we evaluate the accuracy of FBA-based predictions of human and mouse gut bacterial interactions using growth data from the literature. For this, we collected 26 GEMs from the semi-curated AGORA database as well as four previously published curated GEMs. We tested the accuracy of three tools (COMETS, Microbiome Modeling Toolbox and MICOM) by comparing growth rates predicted in mono- and co-culture to growth rates extracted from the literature and also investigated the impact of different tool settings and media. We found that except for curated GEMs, predicted growth rates and their ratios (i.e. interaction strengths) do not correlate with growth rates and interaction strengths obtained from in vitro data.

CONCLUSIONS

Prediction of growth rates with FBA using semi-curated GEMs is currently not sufficiently accurate to predict interaction strengths reliably.

Genome-scale metabolic network model and phenome of solvent-tolerant Pseudomonas putida S12

BACKGROUND

Pseudomonas putida S12 is a gram-negative bacterium renowned for its high tolerance to organic solvents and metabolic versatility, making it attractive for various applications, including bioremediation and the production of aromatic compounds, bioplastics, biofuels, and value-added compounds. However, a metabolic model of S12 has yet to be developed.

RESULTS

In this study, we present a comprehensive and highly curated genome-scale metabolic network model of S12 (iSH1474), containing 1,474 genes, 1,436 unique metabolites, and 2,938 metabolic reactions. The model was constructed by leveraging existing metabolic models and conducting comparative analyses of genomes and phenomes. Approximately 2,000 different phenotypes were measured for S12 and its closely related KT2440 strain under various nutritional and environmental conditions. These phenotypic data, combined with the reported experimental data, were used to refine and validate the reconstruction. Model predictions quantitatively agreed well with in vivo flux measurements and the batch cultivation of S12, which demonstrated that iSH1474 accurately represents the metabolic capabilities of S12. Furthermore, the model was simulated to investigate the maximum theoretical metabolic capacity of S12 growing on toxic organic solvents.

CONCLUSIONS

iSH1474 represents a significant advancement in our understanding of the cellular metabolism of P. putida S12. The combined results of metabolic simulation and comparative genome and phenome analyses identified the genetic and metabolic determinants of the characteristic phenotypes of S12. This study could accelerate the development of this versatile organism as an efficient cell factory for various biotechnological applications.

Leveraging genome-scale metabolic models to understand aerobic methanotrophs

Genome-scale metabolic models (GEMs) are valuable tools serving systems biology and metabolic engineering. However, GEMs are still an underestimated tool in informing microbial ecology. Since their first application for aerobic gammaproteobacterial methane oxidizers less than a decade ago, GEMs have substantially increased our understanding of the metabolism of methanotrophs, a microbial guild of high relevance for the natural and biotechnological mitigation of methane efflux to the atmosphere. Particularly, GEMs helped to elucidate critical metabolic and regulatory pathways of several methanotrophic strains, predicted microbial responses to environmental perturbations, and were used to model metabolic interactions in cocultures. Here, we conducted a systematic review of GEMs exploring aerobic methanotrophy, summarizing recent advances, pointing out weaknesses, and drawing out probable future uses of GEMs to improve our understanding of the ecology of methane oxidizers. We also focus on their potential to unravel causes and consequences when studying interactions of methane-oxidizing bacteria with other methanotrophs or members of microbial communities in general. This review aims to bridge the gap between applied sciences and microbial ecology research on methane oxidizers as model organisms and to provide an outlook for future studies.

Microbial interactions from a new perspective: reinforcement learning reveals new insights into microbiome evolution

MOTIVATION

Microbes are essential part of all ecosystems, influencing material flow and shaping their surroundings. Metabolic modeling has been a useful tool and provided tremendous insights into microbial community metabolism. However, current methods based on flux balance analysis (FBA) usually fail to predict metabolic and regulatory strategies that lead to long-term survival and stability especially in heterogenous communities.

RESULTS

Here, we introduce a novel reinforcement learning algorithm, Self-Playing Microbes in Dynamic FBA, which treats microbial metabolism as a decision-making process, allowing individual microbial agents to evolve by learning and adapting metabolic strategies for enhanced long-term fitness. This algorithm predicts what microbial flux regulation policies will stabilize in the dynamic ecosystem of interest in the presence of other microbes with minimal reliance on predefined strategies. Throughout this article, we present several scenarios wherein our algorithm outperforms existing methods in reproducing outcomes, and we explore the biological significance of these predictions.

AVAILABILITY AND IMPLEMENTATION

The source code for this article is available at: https://github.com/chan-csu/SPAM-DFBA.

Community-scale models of microbiomes: Articulating metabolic modelling and metagenome sequencing

Building models is essential for understanding the functions and dynamics of microbial communities. Metabolic models built on genome-scale metabolic network reconstructions (GENREs) are especially relevant as a means to decipher the complex interactions occurring among species. Model reconstruction increasingly relies on metagenomics, which permits direct characterisation of naturally occurring communities that may contain organisms that cannot be isolated or cultured. In this review, we provide an overview of the field of metabolic modelling and its increasing reliance on and synergy with metagenomics and bioinformatics. We survey the means of assigning functions and reconstructing metabolic networks from (meta-)genomes, and present the variety and mathematical fundamentals of metabolic models that foster the understanding of microbial dynamics. We emphasise the characterisation of interactions and the scaling of model construction to large communities, two important bottlenecks in the applicability of these models. We give an overview of the current state of the art in metagenome sequencing and bioinformatics analysis, focusing on the reconstruction of genomes in microbial communities. Metagenomics benefits tremendously from third-generation sequencing, and we discuss the opportunities of long-read sequencing, strain-level characterisation and eukaryotic metagenomics. We aim at providing algorithmic and mathematical support, together with tool and application resources, that permit bridging the gap between metagenomics and metabolic modelling.

Dynamic flux balance analysis of 1,3-propanediol production by clostridium butyricum fermentation

To study the relationship between the yield of 1,3-propanediol (1,3-PDO) and the flux change of the Clostridium butyricum metabolic pathway, an optimized calculation method based on dynamic flux balance analysis was used by combining genome-scale flux balance analysis with a kinetic model. A more comprehensive and extensive metabolic pathway was obtained by optimization calculations. The primary extended branches include: the dihydroxyacetone node, which enters the pentose phosphate pathway; the α-oxoglutarate node, which has synthetic metabolic pathways for glutamic acid and amino acids; and the serine and homocysteine nodes, which produce cystathionine before homocysteine enters the methionine cycle pathway. According to the expanded metabolic network, the flux distribution of key nodes in the metabolic pathway and the relationship between the flux distribution ratio of nodes and the yield of 1,3-PDO were analyzed. At the dihydroxyacetone node, the flux of dihydroxyacetone converted to dihydroxyacetone phosphate was positively correlated with the yield of 1,3-PDO. As an important intermediate product, the flux change in the metabolic pathway of α-oxoglutarate reacting with amino acids to produce glutamic acid is positively correlated with the yield. When pyruvate was used as the central node to convert into lactic acid and α-oxoglutarate, the proportion of branch flux was negatively correlated with the yield of 1,3-PDO. These studies provide a theoretical basis for the optimization and further study of the metabolic pathway of C. butyricum.

Review: Towards the next-generation models of the rumen microbiome for enhancing predictive power and guiding sustainable production strategies

The rumen ecosystem harbours a galaxy of microbes working in syntrophy to carry out a metabolic cascade of hydrolytic and fermentative reactions. This fermentation process allows ruminants to harvest nutrients from a wide range of feedstuff otherwise inaccessible to the host. The interconnection between the ruminant and its rumen microbiota shapes key animal phenotypes such as feed efficiency and methane emissions and suggests the potential of reducing methane emissions and enhancing feed conversion into animal products by manipulating the rumen microbiota. Whilst significant technological progress in omics techniques has increased our knowledge of the rumen microbiota and its genome (microbiome), translating omics knowledge into effective microbial manipulation strategies remains a great challenge. This challenge can be addressed by modelling approaches integrating causality principles and thus going beyond current correlation-based approaches applied to analyse rumen microbial genomic data. However, existing rumen models are not yet adapted to capitalise on microbial genomic information. This gap between the rumen microbiota available omics data and the way microbial metabolism is represented in the existing rumen models needs to be filled to enhance rumen understanding and produce better predictive models with capabilities for guiding nutritional strategies. To fill this gap, the integration of computational biology tools and mathematical modelling frameworks is needed to translate the information of the metabolic potential of the rumen microbes (inferred from their genomes) into a mathematical object. In this paper, we aim to discuss the potential use of two modelling approaches for the integration of microbial genomic information into dynamic models. The first modelling approach explores the theory of state observers to integrate microbial time series data into rumen fermentation models. The second approach is based on the genome-scale network reconstructions of rumen microbes. For a given microorganism, the network reconstruction produces a stoichiometry matrix of the metabolism. This matrix is the core of the so-called genome-scale metabolic models which can be exploited by a plethora of methods comprised within the constraint-based reconstruction and analysis approaches. We will discuss how these methods can be used to produce the next-generation models of the rumen microbiome.

Technologies for whole-cell modeling: Genome-wide reconstruction of a cell in silico

With advances in high-throughput, large-scale in vivo measurement and genome modification techniques at the single-nucleotide level, there is an increasing demand for the development of new technologies for the flexible design and control of cellular systems. Computer-aided design is a powerful tool to design new cells. Whole-cell modeling aims to integrate various cellular subsystems, determine their interactions and cooperative mechanisms, and predict comprehensive cellular behaviors by computational simulations on a genome-wide scale. It has been applied to prokaryotes, yeasts, and higher eukaryotic cells, and utilized in a wide range of applications, including production of valuable substances, drug discovery, and controlled differentiation. Whole-cell modeling, consisting of several thousand elements with diverse scales and properties, requires innovative model construction, simulation, and analysis techniques. Furthermore, whole-cell modeling has been extended to multiple scales, including high-resolution modeling at the single-nucleotide and single-amino acid levels and multicellular modeling of tissues and organs. This review presents an overview of the current state of whole-cell modeling, discusses the novel computational and experimental technologies driving it, and introduces further developments toward multihierarchical modeling on a whole-genome scale.

Sulfate limitation increases specific plasmid DNA yield and productivity in E. coli fed-batch processes

Plasmid DNA (pDNA) is a key biotechnological product whose importance became apparent in the last years due to its role as a raw material in the messenger ribonucleic acid (mRNA) vaccine manufacturing process. In pharmaceutical production processes, cells need to grow in the defined medium in order to guarantee the highest standards of quality and repeatability. However, often these requirements result in low product titer, productivity, and yield. In this study, we used constraint-based metabolic modeling to optimize the average volumetric productivity of pDNA production in a fed-batch process. We identified a set of 13 nutrients in the growth medium that are essential for cell growth but not for pDNA replication. When these nutrients are depleted in the medium, cell growth is stalled and pDNA production is increased, raising the specific and volumetric yield and productivity. To exploit this effect we designed a three-stage process (1. batch, 2. fed-batch with cell growth, 3. fed-batch without cell growth). The transition between stage 2 and 3 is induced by sulfate starvation. Its onset can be easily controlled via the initial concentration of sulfate in the medium. We validated the decoupling behavior of sulfate and assessed pDNA quality attributes (supercoiled pDNA content) in E. coli with lab-scale bioreactor cultivations. The results showed an increase in supercoiled pDNA to biomass yield by 33% and an increase of supercoiled pDNA volumetric productivity by 13 % upon limitation of sulfate. In conclusion, even for routinely manufactured biotechnological products such as pDNA, simple changes in the growth medium can significantly improve the yield and quality.

Applications of genome-scale metabolic models to investigate microbial metabolic adaptations in response to genetic or environmental perturbations

Environmental perturbations are encountered by microorganisms regularly and will require metabolic adaptations to ensure an organism can survive in the newly presenting conditions. In order to study the mechanisms of metabolic adaptation in such conditions, various experimental and computational approaches have been used. Genome-scale metabolic models (GEMs) are one of the most powerful approaches to study metabolism, providing a platform to study the systems level adaptations of an organism to different environments which could otherwise be infeasible experimentally. In this review, we are describing the application of GEMs in understanding how microbes reprogram their metabolic system as a result of environmental variation. In particular, we provide the details of metabolic model reconstruction approaches, various algorithms and tools for model simulation, consequences of genetic perturbations, integration of '-omics' datasets for creating context-specific models and their application in studying metabolic adaptation due to the change in environmental conditions.

Genome-scale metabolic models reveal determinants of phenotypic differences in non-Saccharomyces yeasts

BACKGROUND

Use of alternative non-Saccharomyces yeasts in wine and beer brewing has gained more attention the recent years. This is both due to the desire to obtain a wider variety of flavours in the product and to reduce the final alcohol content. Given the metabolic differences between the yeast species, we wanted to account for some of the differences by using in silico models.

RESULTS

We created and studied genome-scale metabolic models of five different non-Saccharomyces species using an automated processes. These were: Metschnikowia pulcherrima, Lachancea thermotolerans, Hanseniaspora osmophila, Torulaspora delbrueckii and Kluyveromyces lactis. Using the models, we predicted that M. pulcherrima, when compared to the other species, conducts more respiration and thus produces less fermentation products, a finding which agrees with experimental data. Complex I of the electron transport chain was to be present in M. pulcherrima, but absent in the others. The predicted importance of Complex I was diminished when we incorporated constraints on the amount of enzymatic protein, as this shifts the metabolism towards fermentation.

CONCLUSIONS

Our results suggest that Complex I in the electron transport chain is a key differentiator between Metschnikowia pulcherrima and the other yeasts considered. Yet, more annotations and experimental data have the potential to improve model quality in order to increase fidelity and confidence in these results. Further experiments should be conducted to confirm the in vivo effect of Complex I in M. pulcherrima and its respiratory metabolism.

'Social' versus 'asocial' cells-dynamic competition flux balance analysis

In multicellular organisms cells compete for resources or growth factors. If any one cell type wins, the co-existence of diverse cell types disappears. Existing dynamic Flux Balance Analysis (dFBA) does not accommodate changes in cell density caused by competition. Therefore we here develop 'dynamic competition Flux Balance Analysis' (dcFBA). With total biomass synthesis as objective, lower-growth-yield cells were outcompeted even when cells synthesized mutually required nutrients. Signal transduction between cells established co-existence, which suggests that such 'socialness' is required for multicellularity. Whilst mutants with increased specific growth rate did not outgrow the other cell types, loss of social characteristics did enable a mutant to outgrow the other cells. We discuss that 'asocialness' rather than enhanced growth rates, i.e., a reduced sensitivity to regulatory factors rather than enhanced growth rates, may characterize cancer cells and organisms causing ecological blooms. Therapies reinforcing cross-regulation may therefore be more effective than those targeting replication rates.

Trade-offs between the instantaneous growth rate and long-term fitness: Consequences for microbial physiology and predictive computational models

Microbial systems biology has made enormous advances in relating microbial physiology to the underlying biochemistry and molecular biology. By meticulously studying model microorganisms, in particular Escherichia coli and Saccharomyces cerevisiae, increasingly comprehensive computational models predict metabolic fluxes, protein expression, and growth. The modeling rationale is that cells are constrained by a limited pool of resources that they allocate optimally to maximize fitness. As a consequence, the expression of particular proteins is at the expense of others, causing trade-offs between cellular objectives such as instantaneous growth, stress tolerance, and capacity to adapt to new environments. While current computational models are remarkably predictive for E. coli and S. cerevisiae when grown in laboratory environments, this may not hold for other growth conditions and other microorganisms. In this contribution, we therefore discuss the relationship between the instantaneous growth rate, limited resources, and long-term fitness. We discuss uses and limitations of current computational models, in particular for rapidly changing and adverse environments, and propose to classify microbial growth strategies based on Grimes's CSR framework.

A Desmethylphosphinothricin Dipeptide Derivative Effectively Inhibits Escherichia coli and Bacillus subtilis Growth

New antibiotics are unquestionably needed to fight the emergence and spread of multidrug-resistant bacteria. To date, antibiotics targeting bacterial central metabolism have been poorly investigated. By determining the minimal inhibitory concentration (MIC) of desmethylphosphinothricin (Glu-γ-PH), an analogue of glutamate with a phosphinic moiety replacing the γ-carboxyl group, we previously showed its promising antibacterial activity on Escherichia coli. Herein, we synthetized and determined the growth inhibition exerted on E. coli by an L-Leu dipeptide derivative of Glu-γ-PH (L-Leu-D,L-Glu-γ-PH). Furthermore, we compared the growth inhibition obtained with this dipeptide with that exerted by the free amino acid, i.e., Glu-γ-PH, and by their phosphonic and non-desmethylated analogues. All the tested compounds were more effective when assayed in a chemically-defined minimal medium. The dipeptide L-Leu-D,L-Glu-γ-PH had a significantly improved antibacterial activity (2 μg/mL), at a concentration between the non-desmethytaled (0.1 μg/mL) and the phosphonic (80 μg/mL) analogues. Also, in Bacillus subtilis, the dipeptide L-Leu-D,L-Glu-γ-PH displayed an activity comparable to that of the antibiotic amoxicillin. This work highlights the antibacterial relevance of the phosphinic pharmacophore and proposes new avenues for the development of novel antimicrobial drugs containing the phosphinic moiety.

A structured evaluation of genome-scale constraint-based modeling tools for microbial consortia

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

Integrating Omics Data in Genome-Scale Metabolic Modeling: A Methodological Perspective for Precision Medicine

Recent advancements in omics technologies have generated a wealth of biological data. Integrating these data within mathematical models is essential to fully leverage their potential. Genome-scale metabolic models (GEMs) provide a robust framework for studying complex biological systems. GEMs have significantly contributed to our understanding of human metabolism, including the intrinsic relationship between the gut microbiome and the host metabolism. In this review, we highlight the contributions of GEMs and discuss the critical challenges that must be overcome to ensure their reproducibility and enhance their prediction accuracy, particularly in the context of precision medicine. We also explore the role of machine learning in addressing these challenges within GEMs. The integration of omics data with GEMs has the potential to lead to new insights, and to advance our understanding of molecular mechanisms in human health and disease.

A genome-scale dynamic constraint-based modelling (gDCBM) framework predicts growth dynamics, medium composition and intracellular flux distributions in CHO clonal variations

Optimizing mammalian cell growth and bioproduction is a tedious task. However, due to the inherent complexity of eukaryotic cells, heuristic experimental approaches such as, metabolic engineering and bioprocess design, are frequently integrated with mathematical models of cell culture to improve biological process efficiency and find paths for improvement. Constraint-based metabolic models have evolved over the last two decades to be used for dynamic modelling in addition to providing a linear description of steady-state metabolic systems. Formulation and implementation of the underlying optimization problems require special attention to the model's performance and feasibility, lack of defects in the definition of system components, and consideration of optimal alternate solutions, in addition to processing power limitations. Here, the time-resolved dynamics of a genome-scale metabolic network of Chinese hamster ovary (CHO) cell metabolism are shown using a genome-scale dynamic constraint-based modelling framework (gDCBM). The metabolic network was adapted from a reference model of CHO genome-scale metabolic model (GSMM), iCHO_DG44_v1, and dynamic restrictions were imposed to its exchange fluxes based on experimental results. We used this framework for predicting physiological changes in CHO clonal variants. Because of the methodical creation of the components for the flux balance analysis optimization problem and the integration of a switch time, this model can generate sequential predictions of intracellular fluxes during growth and non-growth phases (per hour of culture time) and transparently reveal the shortcomings in such practice. As a result of the differences exploited by various clones, we can understand the relevance of changes in intracellular flux distribution and exometabolomics. The integration of various omics data into the given gDCBM framework, as well as the reductionist analysis of the model, can further help bioprocess optimization.

Assumptions on decision making and environment can yield multiple steady states in microbial community models

BACKGROUND

Microbial community simulations using genome scale metabolic networks (GSMs) are relevant for many application areas, such as the analysis of the human microbiome. Such simulations rely on assumptions about the culturing environment, affecting if the culture may reach a metabolically stationary state with constant microbial concentrations. They also require assumptions on decision making by the microbes: metabolic strategies can be in the interest of individual community members or of the whole community. However, the impact of such common assumptions on community simulation results has not been investigated systematically.

RESULTS

Here, we investigate four combinations of assumptions, elucidate how they are applied in literature, provide novel mathematical formulations for their simulation, and show how the resulting predictions differ qualitatively. Our results stress that different assumption combinations give qualitatively different predictions on microbial coexistence by differential substrate utilization. This fundamental mechanism is critically under explored in the steady state GSM literature with its strong focus on coexistence states due to crossfeeding (division of labor). Furthermore, investigating a realistic synthetic community, where the two involved strains exhibit no growth in isolation, but grow as a community, we predict multiple modes of cooperation, even without an explicit cooperation mechanism.

CONCLUSIONS

Steady state GSM modelling of microbial communities relies both on assumed decision making principles and environmental assumptions. In principle, dynamic flux balance analysis addresses both. In practice, our methods that address the steady state directly may be preferable, especially if the community is expected to display multiple steady states.

A systematic overexpression approach reveals native targets to increase squalene production in Synechocystis sp. PCC 6803

Cyanobacteria are a promising platform for the production of the triterpene squalene (C30), a precursor for all plant and animal sterols, and a highly attractive intermediate towards triterpenoids, a large group of secondary plant metabolites. Synechocystis sp. PCC 6803 natively produces squalene from CO2 through the MEP pathway. Based on the predictions of a constraint-based metabolic model, we took a systematic overexpression approach to quantify native Synechocystis gene's impact on squalene production in a squalene-hopene cyclase gene knock-out strain (Δshc). Our in silico analysis revealed an increased flux through the Calvin-Benson-Bassham cycle in the Δshc mutant compared to the wildtype, including the pentose phosphate pathway, as well as lower glycolysis, while the tricarboxylic acid cycle predicted to be downregulated. Further, all enzymes of the MEP pathway and terpenoid synthesis, as well as enzymes from the central carbon metabolism, Gap2, Tpi and PyrK, were predicted to positively contribute to squalene production upon their overexpression. Each identified target gene was integrated into the genome of Synechocystis Δshc under the control of the rhamnose-inducible promoter Prha. Squalene production was increased in an inducer concentration dependent manner through the overexpression of most predicted genes, which are genes of the MEP pathway, ispH, ispE, and idi, leading to the greatest improvements. Moreover, we were able to overexpress the native squalene synthase gene (sqs) in Synechocystis Δshc, which reached the highest production titer of 13.72 mg l-1 reported for squalene in Synechocystis sp. PCC 6803 so far, thereby providing a promising and sustainable platform for triterpene production.

Global epistasis on fitness landscapes

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

A Holistic Approach from Systems Biology Reveals the Direct Influence of the Quorum-Sensing Phenomenon on Pseudomonas aeruginosa Metabolism to Pyoverdine Biosynthesis

Computational modeling and simulation of biological systems have become valuable tools for understanding and predicting cellular performance and phenotype generation. This work aimed to construct, model, and dynamically simulate the virulence factor pyoverdine (PVD) biosynthesis in Pseudomonas aeruginosa through a systemic approach, considering that the metabolic pathway of PVD synthesis is regulated by the quorum-sensing (QS) phenomenon. The methodology comprised three main stages: (i) Construction, modeling, and validation of the QS gene regulatory network that controls PVD synthesis in P. aeruginosa strain PAO1; (ii) construction, curating, and modeling of the metabolic network of P. aeruginosa using the flux balance analysis (FBA) approach; (iii) integration and modeling of these two networks into an integrative model using the dynamic flux balance analysis (DFBA) approximation, followed, finally, by an in vitro validation of the integrated model for PVD synthesis in P. aeruginosa as a function of QS signaling. The QS gene network, constructed using the standard System Biology Markup Language, comprised 114 chemical species and 103 reactions and was modeled as a deterministic system following the kinetic based on mass action law. This model showed that the higher the bacterial growth, the higher the extracellular concentration of QS signal molecules, thus emulating the natural behavior of P. aeruginosa PAO1. The P. aeruginosa metabolic network model was constructed based on the iMO1056 model, the P. aeruginosa PAO1 strain genomic annotation, and the metabolic pathway of PVD synthesis. The metabolic network model included the PVD synthesis, transport, exchange reactions, and the QS signal molecules. This metabolic network model was curated and then modeled under the FBA approximation, using biomass maximization as the objective function (optimization problem, a term borrowed from the engineering field). Next, chemical reactions shared by both network models were chosen to combine them into an integrative model. To this end, the fluxes of these reactions, obtained from the QS network model, were fixed in the metabolic network model as constraints of the optimization problem using the DFBA approximation. Finally, simulations of the integrative model (CCBM1146, comprising 1123 reactions and 880 metabolites) were run using the DFBA approximation to get (i) the flux profile for each reaction, (ii) the bacterial growth profile, (iii) the biomass profile, and (iv) the concentration profiles of metabolites of interest such as glucose, PVD, and QS signal molecules. The CCBM1146 model showed that the QS phenomenon directly influences the P. aeruginosa metabolism to PVD biosynthesis as a function of the change in QS signal intensity. The CCBM1146 model made it possible to characterize and explain the complex and emergent behavior generated by the interactions between the two networks, which would have been impossible to do by studying each system's individual components or scales separately. This work is the first in silico report of an integrative model comprising the QS gene regulatory network and the metabolic network of P. aeruginosa.

Elucidating dynamic anaerobe metabolism with HRMAS 13C NMR and genome-scale modeling

Anaerobic microbial metabolism drives critical functions within global ecosystems, host-microbiota interactions, and industrial applications, yet remains ill-defined. Here we advance a versatile approach to elaborate cellular metabolism in obligate anaerobes using the pathogen Clostridioides difficile, an amino acid and carbohydrate-fermenting Clostridia. High-resolution magic angle spinning nuclear magnetic resonance (NMR) spectroscopy of C. difficile, grown with fermentable 13C substrates, informed dynamic flux balance analysis (dFBA) of the pathogen's genome-scale metabolism. Analyses identified dynamic recruitment of oxidative and supporting reductive pathways, with integration of high-flux amino acid and glycolytic metabolism at alanine's biosynthesis to support efficient energy generation, nitrogen handling and biomass generation. Model predictions informed an approach leveraging the sensitivity of 13C NMR spectroscopy to simultaneously track cellular carbon and nitrogen flow from [U-13C]glucose and [15N]leucine, confirming the formation of [13C,15N]alanine. Findings identify metabolic strategies used by C. difficile to support its rapid colonization and expansion in gut ecosystems.

Developing a metabolic model-based fed-batch feeding strategy for Pichia pastoris fermentation through fine-tuning of the methanol utilization pathway

Pichia pastoris is a commonly used microbial host for recombinant protein production. It is mostly cultivated in fed-batch mode, in which the environment of the cell is continuously changing. Hence, it is vital to understand the influence of feeding strategy parameters on the intracellular reaction network to fine-tune bioreactor performance. This study used dynamic flux balance analysis (DFBA) integrated with transcriptomics data to simulate the recombinant P. pastoris (Mut^s ) growth during the induction phase for three fed-batch strategies, conducted at constant specific growth rates (μ-stat). The induction phase was split into equal time intervals, and the correlated reactions with protein yield were identified in the three fed-batch strategies using the Pearson correlation coefficient. Subsequently, principal component analysis (PCA) was applied to cluster induction phase time intervals and identify the role of correlated reactions on metabolic differentiation of time intervals. It was found that increasing fluxes through the methanol dissimilation pathway increased protein yield. By adding a methanol assimilation pathway inhibitor (HgCl₂ ) to the shake flask medium growing on glycerol: methanol mixture (10%: 90%, v/v), the protein titre increased by 60%. As per DFBA, the higher the methanol to biomass flux ratio (R_meoh/Δx ), the higher the protein yield. Finally, a novel feeding strategy was developed to increase the amount of R_meoh/Δx compared to the three feeding strategies. The concentration of recombinant human growth hormone (rhGH), used as the model protein, increased by 16% compared to the optimal culture result obtained previously (800 mg L^-1 to 928 mg L^-1 ), while production yield improved by 85% (24.8 mg g_DCW ^-1 to 46 mg g_DCW ^-1 ).

Toward mechanistic modeling and rational engineering of plant respiration

Plant respiration not only provides energy to support all cellular processes, including biomass production, but also plays a major role in the global carbon cycle. Therefore, modulation of plant respiration can be used to both increase the plant yield and mitigate the effects of global climate change. Mechanistic modeling of plant respiration at sufficient biochemical detail can provide key insights for rational engineering of this process. Yet, despite its importance, plant respiration has attracted considerably less modeling effort in comparison to photosynthesis. In this update review, we highlight the advances made in modeling of plant respiration, emphasizing the gradual but important change from phenomenological to models based on first principles. We also provide a detailed account of the existing resources that can contribute to resolving the challenges in modeling plant respiration. These resources point at tangible improvements in the representation of cellular processes that contribute to CO2 evolution and consideration of kinetic properties of underlying enzymes to facilitate mechanistic modeling. The update review emphasizes the need to couple biochemical models of respiration with models of acclimation and adaptation of respiration for their effective usage in guiding breeding efforts and improving terrestrial biosphere models tailored to future climate scenarios.

Synthetic microbial communities (SynComs) of the human gut: design, assembly, and applications

The human gut harbors native microbial communities, forming a highly complex ecosystem. Synthetic microbial communities (SynComs) of the human gut are an assembly of microorganisms isolated from human mucosa or fecal samples. In recent decades, the ever-expanding culturing capacity and affordable sequencing, together with advanced computational modeling, started a ''golden age'' for harnessing the beneficial potential of SynComs to fight gastrointestinal disorders, such as infections and chronic inflammatory bowel diseases. As simplified and completely defined microbiota, SynComs offer a promising reductionist approach to understanding the multispecies and multikingdom interactions in the microbe-host-immune axis. However, there are still many challenges to overcome before we can precisely construct SynComs of designed function and efficacy that allow the translation of scientific findings to patients' treatments. Here, we discussed the strategies used to design, assemble, and test a SynCom, and address the significant challenges, which are of microbiological, engineering, and translational nature, that stand in the way of using SynComs as live bacterial therapeutics.

Evolutionary coexistence in a fluctuating environment by specialization on resource level

Microbial communities in fluctuating environments, such as oceans or the human gut, contain a wealth of diversity. This diversity contributes to the stability of communities and the functions they have in their hosts and ecosystems. To improve stability and increase production of beneficial compounds, we need to understand the underlying mechanisms causing this diversity. When nutrient levels fluctuate over time, one possibly relevant mechanism is coexistence between specialists on low and specialists on high nutrient levels. The relevance of this process is supported by the observations of coexistence in the laboratory, and by simple models, which show that negative frequency dependence of two such specialists can stabilize coexistence. However, as microbial populations are often large and fast growing, they evolve rapidly. Our aim is to determine what happens when species can evolve; whether evolutionary branching can create diversity or whether evolution will destabilize coexistence. We derive an analytical expression of the invasion fitness in fluctuating environments and use adaptive dynamics techniques to find that evolutionarily stable coexistence requires a special type of trade-off between growth at low and high nutrients. We do not find support for the necessary evolutionary trade-off in data available for the bacterium Escherichia coli and the yeast Saccharomyces cerevisiae on glucose. However, this type of data is scarce and might exist for other species or in different conditions. Moreover, we do find evidence for evolutionarily stable coexistence of the two species together. Since we find this coexistence in the scarce data that are available, we predict that specialization on resource level is a relevant mechanism for species diversity in microbial communities in fluctuating environments in natural settings.

Investigating the Unique Ability of Trichodesmium To Fix Carbon and Nitrogen Simultaneously Using MiMoSA

The open ocean is an extremely competitive environment, partially due to the dearth of nutrients. Trichodesmium erythraeum, a marine diazotrophic cyanobacterium, is a keystone species in the ocean due to its ability to fix nitrogen and leak 30 to 50% into the surrounding environment, providing a valuable source of a necessary macronutrient to other species. While there are other diazotrophic cyanobacteria that play an important role in the marine nitrogen cycle, Trichodesmium is unique in its ability to fix both carbon and nitrogen simultaneously during the day without the use of specialized cells called heterocysts to protect nitrogenase from oxygen. Here, we use the advanced modeling framework called multiscale multiobjective systems analysis (MiMoSA) to investigate how Trichodesmium erythraeum can reduce dimolecular nitrogen to ammonium in the presence of oxygen. Our simulations indicate that nitrogenase inhibition is best modeled as Michealis-Menten competitive inhibition and that cells along the filament maintain microaerobia using high flux through Mehler reactions in order to protect nitrogenase from oxygen. We also examined the effect of location on metabolic flux and found that cells at the end of filaments operate in distinctly different metabolic modes than internal cells despite both operating in a photoautotrophic mode. These results give us important insight into how this species is able to operate photosynthesis and nitrogen fixation simultaneously, giving it a distinct advantage over other diazotrophic cyanobacteria because they can harvest light directly to fuel the energy demand of nitrogen fixation. IMPORTANCE Trichodesmium erythraeum is a marine cyanobacterium responsible for approximately half of all biologically fixed nitrogen, making it an integral part of the global nitrogen cycle. Interestingly, unlike other nitrogen-fixing cyanobacteria, Trichodesmium does not use temporal or spatial separation to protect nitrogenase from oxygen poisoning; instead, it operates photosynthesis and nitrogen fixation reactions simultaneously during the day. Unfortunately, the exact mechanism the cells utilize to operate carbon and nitrogen fixation simultaneously is unknown. Here, we use an advanced metabolic modeling framework to investigate and identify the most likely mechanisms Trichodesmium uses to protect nitrogenase from oxygen. The model predicts that cells operate in a microaerobic mode, using both respiratory and Mehler reactions to dramatically reduce intracellular oxygen concentrations.