A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism

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.

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.

Cell factory design with advanced metabolic modelling empowered by artificial intelligence

Advances in synthetic biology and artificial intelligence (AI) have provided new opportunities for modern biotechnology. High-performance cell factories, the backbone of industrial biotechnology, are ultimately responsible for determining whether a bio-based product succeeds or fails in the fierce competition with petroleum-based products. To date, one of the greatest challenges in synthetic biology is the creation of high-performance cell factories in a consistent and efficient manner. As so-called white-box models, numerous metabolic network models have been developed and used in computational strain design. Moreover, great progress has been made in AI-powered strain engineering in recent years. Both approaches have advantages and disadvantages. Therefore, the deep integration of AI with metabolic models is crucial for the construction of superior cell factories with higher titres, yields and production rates. The detailed applications of the latest advanced metabolic models and AI in computational strain design are summarized in this review. Additionally, approaches for the deep integration of AI and metabolic models are discussed. It is anticipated that advanced mechanistic metabolic models powered by AI will pave the way for the efficient construction of powerful industrial chassis strains in the coming years.

Strategies in engineering sustainable biochemical synthesis through microbial systems

Growing environmental concerns and the urgency to address climate change have increased demand for the development of sustainable alternatives to fossil-derived fuels and chemicals. Microbial systems, possessing inherent biosynthetic capabilities, present a promising approach for achieving this goal. This review discusses the coupling of systems and synthetic biology to enable the elucidation and manipulation of microbial phenotypes for the production of chemicals that can substitute for petroleum-derived counterparts and contribute to advancing green biotechnology. The integration of artificial intelligence with metabolic engineering to facilitate precise and data-driven design of biosynthetic pathways is also discussed, along with the identification of current limitations and proposition of strategies for optimizing biosystems, thereby propelling the field of chemical biology towards sustainable chemical production.

Engineering of Saccharomyces cerevisiae for enhanced metabolic robustness and L-lactic acid production from lignocellulosic biomass

Metabolic engineering for high productivity and increased robustness is needed to enable sustainable biomanufacturing of lactic acid from lignocellulosic biomass. Lactic acid is an important commodity chemical used for instance as a monomer for production of polylactic acid, a biodegradable polymer. Here, rational and model-based optimization was used to engineer a diploid, xylose fermenting Saccharomyces cerevisiae strain to produce L-lactic acid. The metabolic flux was steered towards lactic acid through the introduction of multiple lactate dehydrogenase encoding genes while deleting ERF2, GPD1, and CYB2. A production of 93 g/L of lactic acid with a yield of 0.84 g/g was achieved using xylose as the carbon source. To increase xylose utilization and reduce acetic acid synthesis, PHO13 and ALD6 were also deleted from the strain. Finally, CDC19 encoding a pyruvate kinase was overexpressed, resulting in a yield of 0.75 g lactic acid/g sugars consumed, when the substrate used was a synthetic lignocellulosic hydrolysate medium, containing hexoses, pentoses and inhibitors such as acetate and furfural. Notably, modeling also provided leads for understanding the influence of oxygen in lactic acid production. High lactic acid production from xylose, at oxygen-limitation could be explained by a reduced flux through the oxidative phosphorylation pathway. On the contrast, higher oxygen levels were beneficial for lactic acid production with the synthetic hydrolysate medium, likely as higher ATP concentrations are needed for tolerating the inhibitors therein. The work highlights the potential of S. cerevisiae for industrial production of lactic acid from lignocellulosic biomass.

Development of a genome-scale metabolic model for the lager hybrid yeast S. pastorianus to understand the evolution of metabolic pathways in industrial settings

In silico tools such as genome-scale metabolic models have shown to be powerful for metabolic engineering of microorganisms. Saccharomyces pastorianus is a complex aneuploid hybrid between the mesophilic Saccharomyces cerevisiae and the cold-tolerant Saccharomyces eubayanus. This species is of biotechnological importance because it is the primary yeast used in lager beer fermentation and is also a key model for studying the evolution of hybrid genomes, including expression pattern of ortholog genes, composition of protein complexes, and phenotypic plasticity. Here, we created the iSP_1513 GSMM for S. pastorianus CBS1513 to allow top-down computational approaches to predict the evolution of metabolic pathways and to aid strain optimization in production processes. The iSP_1513 comprises 4,062 reactions, 1,808 alleles, and 2,747 metabolites, and takes into account the functional redundancy in the gene-protein-reaction rule caused by the presence of orthologous genes. Moreover, a universal algorithm to constrain GSMM reactions using transcriptome data was developed as a python library and enabled the integration of temperature as parameter. Essentiality data sets, growth data on various carbohydrates and volatile metabolites secretion were used to validate the model and showed the potential of media engineering to improve specific flavor compounds. The iSP_1513 also highlighted the different contributions of the parental sub-genomes to the oxidative and non-oxidative parts of the pentose phosphate pathway. Overall, the iSP_1513 GSMM represent an important step toward understanding the metabolic capabilities, evolutionary trajectories, and adaptation potential of S. pastorianus in different industrial settings.

IMPORTANCE

Genome-scale metabolic models (GSMM) have been successfully applied to predict cellular behavior and design cell factories in several model organisms, but no models to date are currently available for hybrid species due to their more complex genetics and general lack of molecular data. In this study, we generated a bespoke GSMM, iSP_1513, for this industrial aneuploid hybrid Saccharomyces pastorianus, which takes into account the aneuploidy and functional redundancy from orthologous parental alleles. This model will (i) help understand the metabolic capabilities and adaptive potential of S. pastorianus (domestication processes), (ii) aid top-down predictions for strain development (industrial biotechnology), and (iii) allow predictions of evolutionary trajectories of metabolic pathways in aneuploid hybrids (evolutionary genetics).

Omics data analysis reveals the system-level constraint on cellular amino acid composition

Proteins play a pivotal role in coordinating the functions of organisms, essentially governing their traits, as the dynamic arrangement of diverse amino acids leads to a multitude of folded configurations within peptide chains. Despite dynamic changes in amino acid composition of an individual protein (referred to as AAP) and great variance in protein expression levels under different conditions, our study, utilizing transcriptomics data from four model organisms uncovers surprising stability in the overall amino acid composition of the total cellular proteins (referred to as AACell). Although this value may vary between different species, we observed no significant differences among distinct strains of the same species. This indicates that organisms enforce system-level constraints to maintain a consistent AACell, even amid fluctuations in AAP and protein expression. Further exploration of this phenomenon promises insights into the intricate mechanisms orchestrating cellular protein expression and adaptation to varying environmental challenges.

Emergence of Orchestrated and Dynamic Metabolism of Saccharomyces cerevisiae

Microbial metabolism is a fundamental cellular process that involves many biochemical events and is distinguished by its emergent properties. While the molecular details of individual reactions have been increasingly elucidated, it is not well understood how these reactions are quantitatively orchestrated to produce collective cellular behaviors. Here we developed a coarse-grained, systems, and dynamic mathematical framework, which integrates metabolic reactions with signal transduction and gene regulation to dissect the emergent metabolic traits of Saccharomyces cerevisiae. Our framework mechanistically captures a set of characteristic cellular behaviors, including the Crabtree effect, diauxic shift, diauxic lag time, and differential growth under nutrient-altered environments. It also allows modular expansion for zooming in on specific pathways for detailed metabolic profiles. This study provides a systems mathematical framework for yeast metabolic behaviors, providing insights into yeast physiology and metabolic engineering.

Genome-scale metabolic models in translational medicine: the current status and potential of machine learning in improving the effectiveness of the models

The genome-scale metabolic model (GEM) has emerged as one of the leading modeling approaches for systems-level metabolic studies and has been widely explored for a broad range of organisms and applications. Owing to the development of genome sequencing technologies and available biochemical data, it is possible to reconstruct GEMs for model and non-model microorganisms as well as for multicellular organisms such as humans and animal models. GEMs will evolve in parallel with the availability of biological data, new mathematical modeling techniques and the development of automated GEM reconstruction tools. The use of high-quality, context-specific GEMs, a subset of the original GEM in which inactive reactions are removed while maintaining metabolic functions in the extracted model, for model organisms along with machine learning (ML) techniques could increase their applications and effectiveness in translational research in the near future. Here, we briefly review the current state of GEMs, discuss the potential contributions of ML approaches for more efficient and frequent application of these models in translational research, and explore the extension of GEMs to integrative cellular models.

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.

Genome-scale model development and genomic sequencing of the oleaginous clade Lipomyces

The Lipomyces clade contains oleaginous yeast species with advantageous metabolic features for biochemical and biofuel production. Limited knowledge about the metabolic networks of the species and limited tools for genetic engineering have led to a relatively small amount of research on the microbes. Here, a genome-scale metabolic model (GSM) of Lipomyces starkeyi NRRL Y-11557 was built using orthologous protein mappings to model yeast species. Phenotypic growth assays were used to validate the GSM (66% accuracy) and indicated that NRRL Y-11557 utilized diverse carbohydrates but had more limited catabolism of organic acids. The final GSM contained 2,193 reactions, 1,909 metabolites, and 996 genes and was thus named iLst996. The model contained 96 of the annotated carbohydrate-active enzymes. iLst996 predicted a flux distribution in line with oleaginous yeast measurements and was utilized to predict theoretical lipid yields. Twenty-five other yeasts in the Lipomyces clade were then genome sequenced and annotated. Sixteen of the Lipomyces species had orthologs for more than 97% of the iLst996 genes, demonstrating the usefulness of iLst996 as a broad GSM for Lipomyces metabolism. Pathways that diverged from iLst996 mainly revolved around alternate carbon metabolism, with ortholog groups excluding NRRL Y-11557 annotated to be involved in transport, glycerolipid, and starch metabolism, among others. Overall, this study provides a useful modeling tool and data for analyzing and understanding Lipomyces species metabolism and will assist further engineering efforts in Lipomyces.

Convenient synthesis and delivery of a megabase-scale designer accessory chromosome empower biosynthetic capacity

Synthetic biology confers new functions to hosts by introducing exogenous genetic elements, yet rebuilding complex traits that are based on large-scale genetic information remains challenging. Here, we developed a CRISPR/Cas9-mediated haploidization method that bypasses the natural process of meiosis. Based on the programmed haploidization in yeast, we further developed an easy-to-use method designated HAnDy (Haploidization-based DNA Assembly and Delivery in yeast) that enables efficient assembly and delivery of large DNA, with no need for any fussy in vitro manipulations. Using HAnDy, a de novo designed 1.024 Mb synthetic accessory chromosome (synAC) encoding 542 exogenous genes was parallelly assembled and then directly transferred to six phylogenetically diverse yeasts. The synAC significantly promotes hosts' adaptations and increases the scope of the metabolic network, which allows the emergence of valuable compounds. Our approach should facilitate the assembly and delivery of large-scale DNA for expanding and deciphering complex biological functions.

The metabolic domestication syndrome of budding yeast

Cellular metabolism evolves through changes in the structure and quantitative states of metabolic networks. Here, we explore the evolutionary dynamics of metabolic states by focusing on the collection of metabolite levels, the metabolome, which captures key aspects of cellular physiology. Using a phylogenetic framework, we profiled metabolites in 27 populations of nine budding yeast species, providing a graduated view of metabolic variation across multiple evolutionary time scales. Metabolite levels evolve more rapidly and independently of changes in the metabolic network's structure, providing complementary information to enzyme repertoire. Although metabolome variation accumulates mainly gradually over time, it is profoundly affected by domestication. We found pervasive signatures of convergent evolution in the metabolomes of independently domesticated clades of Saccharomyces cerevisiae. Such recurring metabolite differences between wild and domesticated populations affect a substantial part of the metabolome, including rewiring of the TCA cycle and several amino acids that influence aroma production, likely reflecting adaptation to human niches. Overall, our work reveals previously unrecognized diversity in central metabolism and the pervasive influence of human-driven selection on metabolite levels in yeasts.

Mitochondrial ATP generation is more proteome efficient than glycolysis

Metabolic efficiency profoundly influences organismal fitness. Nonphotosynthetic organisms, from yeast to mammals, derive usable energy primarily through glycolysis and respiration. Although respiration is more energy efficient, some cells favor glycolysis even when oxygen is available (aerobic glycolysis, Warburg effect). A leading explanation is that glycolysis is more efficient in terms of ATP production per unit mass of protein (that is, faster). Through quantitative flux analysis and proteomics, we find, however, that mitochondrial respiration is actually more proteome efficient than aerobic glycolysis. This is shown across yeast strains, T cells, cancer cells, and tissues and tumors in vivo. Instead of aerobic glycolysis being valuable for fast ATP production, it correlates with high glycolytic protein expression, which promotes hypoxic growth. Aerobic glycolytic yeasts do not excel at aerobic growth but outgrow respiratory cells during oxygen limitation. We accordingly propose that aerobic glycolysis emerges from cells maintaining a proteome conducive to both aerobic and hypoxic growth.

Towards a hybrid model-driven platform based on flux balance analysis and a machine learning pipeline for biosystem design

Metabolic modeling and machine learning (ML) are crucial components of the evolving next-generation tools in systems and synthetic biology, aiming to unravel the intricate relationship between genotype, phenotype, and the environment. Nonetheless, the comprehensive exploration of integrating these two frameworks, and fully harnessing the potential of fluxomic data, remains an unexplored territory. In this study, we present, rigorously evaluate, and compare ML-based techniques for data integration. The hybrid model revealed that the overexpression of six target genes and the knockout of seven target genes contribute to enhanced ethanol production. Specifically, we investigated the influence of succinate dehydrogenase (SDH) on ethanol biosynthesis in Saccharomyces cerevisiae through shake flask experiments. The findings indicate a noticeable increase in ethanol yield, ranging from 6 % to 10 %, in SDH subunit gene knockout strains compared to the wild-type strain. Moreover, in pursuit of a high-yielding strain for ethanol production, dual-gene deletion experiments were conducted targeting glycerol-3-phosphate dehydrogenase (GPD) and SDH. The results unequivocally demonstrate significant enhancements in ethanol production for the engineered strains Δsdh4Δgpd1, Δsdh5Δgpd1, Δsdh6Δgpd1, Δsdh4Δgpd2, Δsdh5Δgpd2, and Δsdh6Δgpd2, with improvements of 21.6 %, 27.9 %, and 22.7 %, respectively. Overall, the results highlighted that integrating mechanistic flux features substantially improves the prediction of gene knockout strains not accounted for in metabolic reconstructions. In addition, the finding in this study delivers valuable tools for comprehending and manipulating intricate phenotypes, thereby enhancing prediction accuracy and facilitating deeper insights into mechanistic aspects within the field of synthetic biology.

Prediction and integration of metabolite-protein interactions with genome-scale metabolic models

Metabolites, as small molecules, can act not only as substrates to enzymes, but also as effectors of activity of proteins with different functions, thereby affecting various cellular processes. While several experimental techniques have started to catalogue the metabolite-protein interactions (MPIs) present in different cellular contexts, characterizing the functional relevance of MPIs remains a challenging problem. Computational approaches from the constrained-based modeling framework allow for predicting MPIs and integrating their effects in the in silico analysis of metabolic and physiological phenotypes, like cell growth. Here, we provide a classification of all existing constraint-based approaches that predict and integrate MPIs using genome-scale metabolic networks as input. In addition, we benchmark the performance of the approaches to predict MPIs in a comparative study using different features extracted from the model structure and predicted metabolic phenotypes with the state-of-the-art metabolic networks of Escherichia coli and Saccharomyces cerevisiae. Lastly, we provide an outlook for future, feasible directions to expand the consideration of MPIs in constraint-based modeling approaches with wide biotechnological applications.

Increased CO2 fixation enables high carbon-yield production of 3-hydroxypropionic acid in yeast

CO2 fixation plays a key role to make biobased production cost competitive. Here, we use 3-hydroxypropionic acid (3-HP) to showcase how CO2 fixation enables approaching theoretical-yield production. Using genome-scale metabolic models to calculate the production envelope, we demonstrate that the provision of bicarbonate, formed from CO2, restricts previous attempts for high yield production of 3-HP. We thus develop multiple strategies for bicarbonate uptake, including the identification of Sul1 as a potential bicarbonate transporter, domain swapping of malonyl-CoA reductase, identification of Esbp6 as a potential 3-HP exporter, and deletion of Uga1 to prevent 3-HP degradation. The combined rational engineering increases 3-HP production from 0.14 g/L to 11.25 g/L in shake flask using 20 g/L glucose, approaching the maximum theoretical yield with concurrent biomass formation. The engineered yeast forms the basis for commercialization of bio-acrylic acid, while our CO2 fixation strategies pave the way for CO2 being used as the sole carbon source.

Interpreting protein abundance in Saccharomyces cerevisiae through relational learning

MOTIVATION

Proteomic profiles reflect the functional readout of the physiological state of an organism. An increased understanding of what controls and defines protein abundances is of high scientific interest. Saccharomyces cerevisiae is a well-studied model organism, and there is a large amount of structured knowledge on yeast systems biology in databases such as the Saccharomyces Genome Database, and highly curated genome-scale metabolic models like Yeast8. These datasets, the result of decades of experiments, are abundant in information, and adhere to semantically meaningful ontologies.

RESULTS

By representing this knowledge in an expressive Datalog database we generated data descriptors using relational learning that, when combined with supervised machine learning, enables us to predict protein abundances in an explainable manner. We learnt predictive relationships between protein abundances, function and phenotype; such as α-amino acid accumulations and deviations in chronological lifespan. We further demonstrate the power of this methodology on the proteins His4 and Ilv2, connecting qualitative biological concepts to quantified abundances.

AVAILABILITY AND IMPLEMENTATION

All data and processing scripts are available at the following Github repository: https://github.com/DanielBrunnsaker/ProtPredict.

Applications of synthetic yeast consortia for the production of native and non-native chemicals

The application of microbial consortia is a new approach in synthetic biology. Synthetic yeast consortia, simple or complex synthetic mixed cultures, have been used for the production of various metabolites. Cooperation between the members of a consortium and cross-feeding can be applied to create stable microbial communication. These consortia can: consume a variety of substrates, perform more complex functions, produce metabolites in high titer, rate, and yield (TRY), and show higher stability during industrial fermentations. Due to the new research context of synthetic consortia, few yeasts were used to build these consortia, including Saccharomyces cerevisiae, Pichia pastoris, and Yarrowia lipolytica. Here, application of the yeasts for design of synthetic microbial consortia and their advantages and bottlenecks for effective and robust production of valuable metabolites from bioresource, including: cellulose, xylose, glycerol and so on, have been reviewed. Key trends and challenges are also discussed for the future development of synthetic yeast consortia.

FastKnock: an efficient next-generation approach to identify all knockout strategies for strain optimization

Overproduction of desired native or nonnative biochemical(s) in (micro)organisms can be achieved through metabolic engineering. Appropriate rewiring of cell metabolism is performed by making rational changes such as insertion, up-/down-regulation and knockout of genes and consequently metabolic reactions. Finding appropriate targets (including proper sets of reactions to be knocked out) for metabolic engineering to design optimal production strains has been the goal of a number of computational algorithms. We developed FastKnock, an efficient next-generation algorithm for identifying all possible knockout strategies (with a predefined maximum number of reaction deletions) for the growth-coupled overproduction of biochemical(s) of interest. We achieve this by developing a special depth-first traversal algorithm that allows us to prune the search space significantly. This leads to a drastic reduction in execution time. We evaluate the performance of the FastKnock algorithm using various Escherichia coli genome-scale metabolic models in different conditions (minimal and rich mediums) for the overproduction of a number of desired metabolites. FastKnock efficiently prunes the search space to less than 0.2% for quadruple- and 0.02% for quintuple-reaction knockouts. Compared to the classic approaches such as OptKnock and the state-of-the-art techniques such as MCSEnumerator methods, FastKnock found many more beneficial and important practical solutions. The availability of all the solutions provides the opportunity to further characterize, rank and select the most appropriate intervention strategy based on any desired evaluation index. Our implementation of the FastKnock method in Python is publicly available at https://github.com/leilahsn/FastKnock .

Data integration strategies for whole-cell modeling

Data makes the world go round-and high quality data is a prerequisite for precise models, especially for whole-cell models (WCM). Data for WCM must be reusable, contain information about the exact experimental background, and should-in its entirety-cover all relevant processes in the cell. Here, we review basic requirements to data for WCM and strategies how to combine them. As a species-specific resource, we introduce the Yeast Cell Model Data Base (YCMDB) to illustrate requirements and solutions. We discuss recent standards for data as well as for computational models including the modeling process as data to be reported. We outline strategies for constructions of WCM despite their inherent complexity.

ICON-GEMs: integration of co-expression network in genome-scale metabolic models, shedding light through systems biology

BACKGROUND

Flux Balance Analysis (FBA) is a key metabolic modeling method used to simulate cellular metabolism under steady-state conditions. Its simplicity and versatility have led to various strategies incorporating transcriptomic and proteomic data into FBA, successfully predicting flux distribution and phenotypic results. However, despite these advances, the untapped potential lies in leveraging gene-related connections like co-expression patterns for valuable insights.

RESULTS

To fill this gap, we introduce ICON-GEMs, an innovative constraint-based model to incorporate gene co-expression network into the FBA model, facilitating more precise determination of flux distributions and functional pathways. In this study, transcriptomic data from both Escherichia coli and Saccharomyces cerevisiae were integrated into their respective genome-scale metabolic models. A comprehensive gene co-expression network was constructed as a global view of metabolic mechanism of the cell. By leveraging quadratic programming, we maximized the alignment between pairs of reaction fluxes and the correlation of their corresponding genes in the co-expression network. The outcomes notably demonstrated that ICON-GEMs outperformed existing methodologies in predictive accuracy. Flux variabilities over subsystems and functional modules also demonstrate promising results. Furthermore, a comparison involving different types of biological networks, including protein-protein interactions and random networks, reveals insights into the utilization of the co-expression network in genome-scale metabolic engineering.

CONCLUSION

ICON-GEMs introduce an innovative constrained model capable of simultaneous integration of gene co-expression networks, ready for board application across diverse transcriptomic data sets and multiple organisms. It is freely available as open-source at https://github.com/ThummaratPaklao/ICOM-GEMs.git .

Improving Gibberellin GA3 Production with the Construction of a Genome-Scale Metabolic Model of Fusarium fujikuroi

Liquid fermentation is the primary method for GA3 production usingFusarium fujikuroi. However, production capacity is limited due to unknown metabolic pathways. To address this, we constructed a genome-scale metabolic model (iCY1235) with 1753 reactions, 1979 metabolites, and 1235 genes to understand the GA3 regulation mechanisms. The model was validated by analyzing growth rates under different glucose uptake rates and identifying essential genes. We used the model to optimize fermentation conditions, including carbon sources and dissolved oxygen. Through the OptForce algorithm, we identified 20 reactions as targets. Overexpressing FFUJ_02053 and FFUJ_14337 resulted in a 37.5 and 75% increase in GA3 titers, respectively. These targets enhance carbon flux toward GA3 production. Our model holds promise for guiding the metabolic engineering of F. fujikuroi to achieve targeted overproduction. In summary, our study utilizes the iCY1235 model to understand GA3 regulation, optimize fermentation conditions, and identify specific targets for enhancing GA3 production through metabolic engineering.

Improving pathway prediction accuracy of constraints-based metabolic network models by treating enzymes as microcompartments

Metabolic network models have become increasingly precise and accurate as the most widespread and practical digital representations of living cells. The prediction functions were significantly expanded by integrating cellular resources and abiotic constraints in recent years. However, if unreasonable modeling methods were adopted due to a lack of consideration of biological knowledge, the conflicts between stoichiometric and other constraints, such as thermodynamic feasibility and enzyme resource availability, would lead to distorted predictions. In this work, we investigated a prediction anomaly of EcoETM, a constraints-based metabolic network model, and introduced the idea of enzyme compartmentalization into the analysis process. Through rational combination of reactions, we avoid the false prediction of pathway feasibility caused by the unrealistic assumption of free intermediate metabolites. This allowed us to correct the pathway structures of l-serine and l-tryptophan. A specific analysis explains the application method of the EcoETM-like model and demonstrates its potential and value in correcting the prediction results in pathway structure by resolving the conflict between different constraints and incorporating the evolved roles of enzymes as reaction compartments. Notably, this work also reveals the trade-off between product yield and thermodynamic feasibility. Our work is of great value for the structural improvement of constraints-based models.

Advances in genome-scale metabolic models of industrially important fungi

Many fungal species have been used industrially for production of biofuels and bioproducts. Developing strains with better performance in biomanufacturing contexts requires a systematic understanding of cellular metabolism. Genome-scale metabolic models (GEMs) offer a comprehensive view of interconnected pathways and a mathematical framework for downstream analysis. Recently, GEMs have been developed or updated for several industrially important fungi. Some of them incorporate enzyme constraints, enabling improved predictions of cell states and proteome allocation. Here, we provide an overview of these newly developed GEMs and computational methods that facilitate construction of enzyme-constrained GEMs and utilize flux predictions from GEMs. Furthermore, we highlight the pivotal roles of these GEMs in iterative design-build-test-learn cycles, ultimately advancing the field of fungal biomanufacturing.

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.

Improved production of Taxol® precursors in S. cerevisiae using combinatorial in silico design and metabolic engineering

BACKGROUND

Integrated metabolic engineering approaches that combine system and synthetic biology tools enable the efficient design of microbial cell factories for synthesizing high-value products. In this study, we utilized in silico design algorithms on the yeast genome-scale model to predict genomic modifications that could enhance the production of early-step Taxol® in engineered Saccharomyces cerevisiae cells.

RESULTS

Using constraint-based reconstruction and analysis (COBRA) methods, we narrowed down the solution set of genomic modification candidates. We screened 17 genomic modifications, including nine gene deletions and eight gene overexpressions, through wet-lab studies to determine their impact on taxadiene production, the first metabolite in the Taxol® biosynthetic pathway. Under different cultivation conditions, most single genomic modifications resulted in increased taxadiene production. The strain named KM32, which contained four overexpressed genes (ILV2, TRR1, ADE13, and ECM31) involved in branched-chain amino acid biosynthesis, the thioredoxin system, de novo purine synthesis, and the pantothenate pathway, respectively, exhibited the best performance. KM32 achieved a 50% increase in taxadiene production, reaching 215 mg/L. Furthermore, KM32 produced the highest reported yields of taxa-4(20),11-dien-5α-ol (T5α-ol) at 43.65 mg/L and taxa-4(20),11-dien-5-α-yl acetate (T5αAc) at 26.2 mg/L among early-step Taxol® metabolites in S. cerevisiae.

CONCLUSIONS

This study highlights the effectiveness of computational and integrated approaches in identifying promising genomic modifications that can enhance the performance of yeast cell factories. By employing in silico design algorithms and wet-lab screening, we successfully improved taxadiene production in engineered S. cerevisiae strains. The best-performing strain, KM32, achieved substantial increases in taxadiene as well as production of T5α-ol and T5αAc. These findings emphasize the importance of using systematic and integrated strategies to develop efficient yeast cell factories, providing potential implications for the industrial production of high-value isoprenoids like Taxol®.

An explainability framework for deep learning on chemical reactions exemplified by enzyme-catalysed reaction classification

Assigning or proposing a catalysing enzyme given a chemical or biochemical reaction is of great interest to life sciences and chemistry alike. The exploration and design of metabolic pathways and the challenge of finding more sustainable enzyme-catalysed alternatives to traditional organic reactions are just two examples of tasks that require an association between reaction and enzyme. However, given the lack of large and balanced annotated data sets of enzyme-catalysed reactions, assigning an enzyme to a reaction still relies on expert-curated rules and databases. Here, we present a data-driven explainable human-in-the-loop machine learning approach to support and ultimately automate the association of a catalysing enzyme with a given biochemical reaction. In addition, the proposed method is capable of predicting enzymes as candidate catalysts for organic reactions amendable to biocatalysis. Finally, the introduced explainability and visualisation methods can easily be generalised to support other machine-learning approaches involving chemical and biochemical reactions.

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.

Modeling Red Blood Cell Metabolism in the Omics Era

Red blood cells (RBCs) are abundant (more than 80% of the total cells in the human body), yet relatively simple, as they lack nuclei and organelles, including mitochondria. Since the earliest days of biochemistry, the accessibility of blood and RBCs made them an ideal matrix for the characterization of metabolism. Because of this, investigations into RBC metabolism are of extreme relevance for research and diagnostic purposes in scientific and clinical endeavors. The relative simplicity of RBCs has made them an eligible model for the development of reconstruction maps of eukaryotic cell metabolism since the early days of systems biology. Computational models hold the potential to deepen knowledge of RBC metabolism, but also and foremost to predict in silico RBC metabolic behaviors in response to environmental stimuli. Here, we review now classic concepts on RBC metabolism, prior work in systems biology of unicellular organisms, and how this work paved the way for the development of reconstruction models of RBC metabolism. Translationally, we discuss how the fields of metabolomics and systems biology have generated evidence to advance our understanding of the RBC storage lesion, a process of decline in storage quality that impacts over a hundred million blood units transfused every year.

Accurate prediction of in vivo protein abundances by coupling constraint-based modelling and machine learning

Quantification of how different environmental cues affect protein allocation can provide important insights for understanding cell physiology. While absolute quantification of proteins can be obtained by resource-intensive mass-spectrometry-based technologies, prediction of protein abundances offers another way to obtain insights into protein allocation. Here we present CAMEL, a framework that couples constraint-based modelling with machine learning to predict protein abundance for any environmental condition. This is achieved by building machine learning models that leverage static features, derived from protein sequences, and condition-dependent features predicted from protein-constrained metabolic models. Our findings demonstrate that CAMEL results in excellent prediction of protein allocation in E. coli (average Pearson correlation of at least 0.9), and moderate performance in S. cerevisiae (average Pearson correlation of at least 0.5). Therefore, CAMEL outperformed contending approaches without using molecular read-outs from unseen conditions and provides a valuable tool for using protein allocation in biotechnological applications.

An integrated systems biology approach reveals differences in formate metabolism in the genus Methanothermobacter

Methanogenesis allows methanogenic archaea to generate cellular energy for their growth while producing methane. Thermophilic hydrogenotrophic species of the genus Methanothermobacter have been recognized as robust biocatalysts for a circular carbon economy and are already applied in power-to-gas technology with biomethanation, which is a platform to store renewable energy and utilize captured carbon dioxide. Here, we generated curated genome-scale metabolic reconstructions for three Methanothermobacter strains and investigated differences in the growth performance of these same strains in chemostat bioreactor experiments with hydrogen and carbon dioxide or formate as substrates. Using an integrated systems biology approach, we identified differences in formate anabolism between the strains and revealed that formate anabolism influences the diversion of carbon between biomass and methane. This finding, together with the omics datasets and the metabolic models we generated, can be implemented for biotechnological applications of Methanothermobacter in power-to-gas technology, and as a perspective, for value-added chemical production.

MVML-MPI: Multi-View Multi-Label Learning for Metabolic Pathway Inference

Development of robust and effective strategies for synthesizing new compounds, drug targeting and constructing GEnome-scale Metabolic models (GEMs) requires a deep understanding of the underlying biological processes. A critical step in achieving this goal is accurately identifying the categories of pathways in which a compound participated. However, current machine learning-based methods often overlook the multifaceted nature of compounds, resulting in inaccurate pathway predictions. Therefore, we present a novel framework on Multi-View Multi-Label Learning for Metabolic Pathway Inference, hereby named MVML-MPI. First, MVML-MPI learns the distinct compound representations in parallel with corresponding compound encoders to fully extract features. Subsequently, we propose an attention-based mechanism that offers a fusion module to complement these multi-view representations. As a result, MVML-MPI accurately represents and effectively captures the complex relationship between compounds and metabolic pathways and distinguishes itself from current machine learning-based methods. In experiments conducted on the Kyoto Encyclopedia of Genes and Genomes pathways dataset, MVML-MPI outperformed state-of-the-art methods, demonstrating the superiority of MVML-MPI and its potential to utilize the field of metabolic pathway design, which can aid in optimizing drug-like compounds and facilitating the development of GEMs. The code and data underlying this article are freely available at https://github.com/guofei-tju/MVML-MPI. Contact:  jtang@cse.sc.edu, guofei@csu.edu.com or wuxi_dyj@csj.uestc.edu.cn.

Pichia pastoris growth-coupled heme biosynthesis analysis using metabolic modelling

Soy leghemoglobin is one of the most important and key ingredients in plant-based meat substitutes that can imitate the colour and flavour of the meat. To improve the high-yield production of leghemoglobin protein and its main component-heme in the yeast Pichia pastoris, glycerol and methanol cultivation conditions were studied. Additionally, in-silico metabolic modelling analysis of growth-coupled enzyme quantity, suggests metabolic gene up/down-regulation strategies for heme production. First, cultivations and metabolic modelling analysis of P. pastoris were performed on glycerol and methanol in different growth media. Glycerol cultivation uptake and production rates can be increased by 50% according to metabolic modelling results, but methanol cultivation-is near the theoretical maximum. Growth-coupled metabolic optimisation results revealed the best feasible upregulation (33 reactions) (1.47% of total reactions) and 66 downregulation/deletion (2.98% of total) reaction suggestions. Finally, we describe reaction regulation suggestions with the highest potential to increase heme production yields.

The effective deficiency of biochemical networks

The deficiency of a (bio)chemical reaction network can be conceptually interpreted as a measure of its ability to support exotic dynamical behavior and/or multistationarity. The classical definition of deficiency relates to the capacity of a network to permit variations of the complex formation rate vector at steady state, irrespective of the network kinetics. However, the deficiency is by definition completely insensitive to the fine details of the directionality of reactions as well as bounds on reaction fluxes. While the classical definition of deficiency can be readily applied in the analysis of unconstrained, weakly reversible networks, it only provides an upper bound in the cases where relevant constraints on reaction fluxes are imposed. Here we propose the concept of effective deficiency, which provides a more accurate assessment of the network's capacity to permit steady state variations at the complex level for constrained networks of any reversibility patterns. The effective deficiency relies on the concept of nonstoichiometric balanced complexes, which we have already shown to be present in real-world biochemical networks operating under flux constraints. Our results demonstrate that the effective deficiency of real-world biochemical networks is smaller than the classical deficiency, indicating the effects of reaction directionality and flux bounds on the variation of the complex formation rate vector at steady state.

Proteomics and constraint-based modelling reveal enzyme kinetic properties of Chlamydomonas reinhardtii on a genome scale

Metabolic engineering of microalgae offers a promising solution for sustainable biofuel production, and rational design of engineering strategies can be improved by employing metabolic models that integrate enzyme turnover numbers. However, the coverage of turnover numbers for Chlamydomonas reinhardtii, a model eukaryotic microalga accessible to metabolic engineering, is 17-fold smaller compared to the heterotrophic cell factory Saccharomyces cerevisiae. Here we generate quantitative protein abundance data of Chlamydomonas covering 2337 to 3708 proteins in various growth conditions to estimate in vivo maximum apparent turnover numbers. Using constrained-based modeling we provide proxies for in vivo turnover numbers of 568 reactions, representing a 10-fold increase over the in vitro data for Chlamydomonas. Integration of the in vivo estimates instead of in vitro values in a metabolic model of Chlamydomonas improved the accuracy of enzyme usage predictions. Our results help in extending the knowledge on uncharacterized enzymes and improve biotechnological applications of Chlamydomonas.

Mitigating biomass composition uncertainties in flux balance analysis using ensemble representations

The biomass equation is a critical component in genome-scale metabolic models (GEMs): it is used as the de facto objective function in flux balance analysis (FBA). This equation accounts for the quantities of all known biomass precursors that are required for cell growth based on the macromolecular and monomer compositions measured at certain conditions. However, it is often reported that the macromolecular composition of cells could change across different environmental conditions and thus the use of the same single biomass equation in FBA, under multiple conditions, is questionable. Herein, we first investigated the qualitative and quantitative variations of macromolecular compositions of three representative host organisms, Escherichia coli, Saccharomyces cerevisiae and Cricetulus griseus, across different environmental/genetic variations. While macromolecular building blocks such as RNA, protein, and lipid composition vary notably, changes in fundamental biomass monomer units such as nucleotides and amino acids are not appreciable. We also observed that flux predictions through FBA is quite sensitive to macromolecular compositions but not the monomer compositions. Based on these observations, we propose ensemble representations of biomass equation in FBA to account for the natural variation of cellular constituents. Such ensemble representations of biomass better predicted the flux through anabolic reactions as it allows for the flexibility in the biosynthetic demands of the cells. The current study clearly highlights that certain component of the biomass equation indeed vary across different conditions, and the ensemble representation of biomass equation in FBA by accounting for such natural variations could avoid inaccuracies that may arise from in silico simulations.

Genome-scale metabolic modeling of Aspergillus fumigatus strains reveals growth dependencies on the lung microbiome

Aspergillus fumigatus, an opportunistic human pathogen, frequently infects the lungs of people with cystic fibrosis and is one of the most common causes of infectious-disease death in immunocompromised patients. Here, we construct 252 strain-specific, genome-scale metabolic models of this important fungal pathogen to study and better understand the metabolic component of its pathogenic versatility. The models show that 23.1% of A. fumigatus metabolic reactions are not conserved across strains and are mainly associated with amino acid, nucleotide, and nitrogen metabolism. Profiles of non-conserved reactions and growth-supporting reaction fluxes are sufficient to differentiate strains, for example by environmental or clinical origin. In addition, shotgun metagenomics analysis of sputum from 40 cystic fibrosis patients (15 females, 25 males) before and after diagnosis with an A. fumigatus colonization suggests that the fungus shapes the lung microbiome towards a more beneficial fungal growth environment associated with aromatic amino acid availability and the shikimate pathway. Our findings are starting points for the development of drugs or microbiome intervention strategies targeting fungal metabolic needs for survival and colonization in the non-native environment of the human lung.

Inferring and comparing metabolism across heterogeneous sets of annotated genomes using AuCoMe

Comparative analysis of genome-scale metabolic networks (GSMNs) may yield important information on the biology, evolution, and adaptation of species. However, it is impeded by the high heterogeneity of the quality and completeness of structural and functional genome annotations, which may bias the results of such comparisons. To address this issue, we developed AuCoMe, a pipeline to automatically reconstruct homogeneous GSMNs from a heterogeneous set of annotated genomes without discarding available manual annotations. We tested AuCoMe with three data sets, one bacterial, one fungal, and one algal, and showed that it successfully reduces technical biases while capturing the metabolic specificities of each organism. Our results also point out shared and divergent metabolic traits among evolutionarily distant algae, underlining the potential of AuCoMe to accelerate the broad exploration of metabolic evolution across the tree of life.

Prediction of metabolite-protein interactions based on integration of machine learning and constraint-based modeling

Motivation

Metabolite-protein interactions play an important role in regulating protein functions and metabolism. Yet, predictions of metabolite-protein interactions using genome-scale metabolic networks are lacking. Here, we fill this gap by presenting a computational framework, termed SARTRE, that employs features corresponding to shadow prices determined in the context of flux variability analysis to predict metabolite-protein interactions using supervised machine learning.

Results

By using gold standards for metabolite-protein interactomes and well-curated genome-scale metabolic models of Escherichia coli and Saccharomyces cerevisiae, we found that the implementation of SARTRE with random forest classifiers accurately predicts metabolite-protein interactions, supported by an average area under the receiver operating curve of 0.86 and 0.85, respectively. Ranking of features based on their importance for classification demonstrated the key role of shadow prices in predicting metabolite-protein interactions. The quality of predictions is further supported by the excellent agreement of the organism-specific classifiers on unseen interactions shared between the two model organisms. Further, predictions from SARTRE are highly competitive against those obtained from a recent deep-learning approach relying on a variety of protein and metabolite features. Together, these findings show that features extracted from constraint-based analyses of metabolic networks pave the way for understanding the functional roles of the interactions between proteins and small molecules.

Availability and implementation

https://github.com/fayazsoleymani/SARTRE.

Sensitivity analysis and adaptive mutation strategy differential evolution algorithm for optimizing enzymes' turnover numbers in metabolic models

Genome-scale metabolic network model (GSMM) based on enzyme constraints greatly improves general metabolic models. The turnover number ( k cat ${k}_{\mathrm{cat}}$ ) of enzymes is used as a parameter to limit the reaction when extending GSMM. Therefore, turnover number plays a crucial role in the prediction accuracy of cell metabolism. In this work, we proposed an enzyme-constrained GSMM parameter optimization method. First, sensitivity analysis of the parameters was carried out to select the parameters with the greatest influence on predicting the specific growth rate. Then, differential evolution (DE) algorithm with adaptive mutation strategy was adopted to optimize the parameters. This algorithm can dynamically select five different mutation strategies. Finally, the specific growth rate prediction, flux variability, and phase plane of the optimized model were analyzed to further evaluate the model. The enzyme-constrained GSMM of Saccharomyces cerevisiae, ecYeast8.3.4, was optimized. Results of the sensitivity analysis showed that the optimization variables can be divided into three groups based on sensitivity: most sensitive (149 k cat ${k}_{\mathrm{cat}}$ c), highly sensitive (1759 k cat ${k}_{\mathrm{cat}}$ ), and nonsensitive (2502 k cat ${k}_{\mathrm{cat}}$ ) groups. Six optimization strategies were developed based on the results of the sensitivity analysis. The results showed that the DE with adaptive mutation strategy can indeed improve the model by optimizing highly sensitive parameters. Retaining all parameters and optimizing the highly sensitive parameters are the recommended optimization strategy.

FastKnock: An efficient next-generation approach to identify all knockout strategies for strain optimization

Overproduction of desired native or nonnative biochemical(s) in (micro)organisms can be achieved through metabolic engineering. Appropriate rewiring of cell metabolism is performed making rational changes such as insertion, up-/down-regulation and knockout of genes and consequently metabolic reactions. Finding appropriate targets (including proper sets of reactions to be knocked out) for metabolic engineering to design optimal production strains has been the goal of a number of computational algorithms. We developed FastKnock, an efficient next-generation algorithm for identifying all possible knockout strategies for the growth-coupled overproduction of biochemical(s) of interest. We achieve this by developing a special depth-first traversal algorithm that allows us to prune the search space significantly. This leads to a drastic reduction in execution time. We evaluate the performance of the FastKnock algorithm using three Escherichia coli genome-scale metabolic models in different conditions (minimal and rich mediums) for the overproduction of a number of desired metabolites. FastKnock efficiently prunes the search space to less than 0.2% for quadruple and 0.02% for quintuple-reaction knockouts. Compared to the classic approaches such as OptKnock and the state-of-the-art techniques such as MCSEnumerator methods, FastKnock found many more useful and important practical solutions. The availability of all the solutions provides the opportunity to further characterize and select the most appropriate intervention strategy based on any desired evaluation index. Our implementation of the FastKnock method in Python is publicly available at https://github.com/leilahsn/FastKnock.

From genotype to phenotype: computational approaches for inferring microbial traits relevant to the food industry

When selecting microbial strains for the production of fermented foods, various microbial phenotypes need to be taken into account to achieve target product characteristics, such as biosafety, flavor, texture, and health-promoting effects. Through continuous advances in sequencing technologies, microbial whole-genome sequences of increasing quality can now be obtained both cheaper and faster, which increases the relevance of genome-based characterization of microbial phenotypes. Prediction of microbial phenotypes from genome sequences makes it possible to quickly screen large strain collections in silico to identify candidates with desirable traits. Several microbial phenotypes relevant to the production of fermented foods can be predicted using knowledge-based approaches, leveraging our existing understanding of the genetic and molecular mechanisms underlying those phenotypes. In the absence of this knowledge, data-driven approaches can be applied to estimate genotype-phenotype relationships based on large experimental datasets. Here, we review computational methods that implement knowledge- and data-driven approaches for phenotype prediction, as well as methods that combine elements from both approaches. Furthermore, we provide examples of how these methods have been applied in industrial biotechnology, with special focus on the fermented food industry.

Gemini: memory-efficient integration of hundreds of gene networks with high-order pooling

MOTIVATION

The exponential growth of genomic sequencing data has created ever-expanding repositories of gene networks. Unsupervised network integration methods are critical to learn informative representations for each gene, which are later used as features for downstream applications. However, these network integration methods must be scalable to account for the increasing number of networks and robust to an uneven distribution of network types within hundreds of gene networks.

RESULTS

To address these needs, we present Gemini, a novel network integration method that uses memory-efficient high-order pooling to represent and weight each network according to its uniqueness. Gemini then mitigates the uneven network distribution through mixing up existing networks to create many new networks. We find that Gemini leads to more than a 10% improvement in F1 score, 15% improvement in micro-AUPRC, and 63% improvement in macro-AUPRC for human protein function prediction by integrating hundreds of networks from BioGRID, and that Gemini's performance significantly improves when more networks are added to the input network collection, while Mashup and BIONIC embeddings' performance deteriorates. Gemini thereby enables memory-efficient and informative network integration for large gene networks and can be used to massively integrate and analyze networks in other domains.

AVAILABILITY AND IMPLEMENTATION

Gemini can be accessed at: https://github.com/MinxZ/Gemini.

A High-Quality Genome-Scale Model for Rhodococcus opacus Metabolism

Rhodococcus opacus is a bacterium that has a high tolerance to aromatic compounds and can produce significant amounts of triacylglycerol (TAG). Here, we present iGR1773, the first genome-scale model (GSM) of R. opacus PD630 metabolism based on its genomic sequence and associated data. The model includes 1773 genes, 3025 reactions, and 1956 metabolites, was developed in a reproducible manner using CarveMe, and was evaluated through Metabolic Model tests (MEMOTE). We combine the model with two Constraint-Based Reconstruction and Analysis (COBRA) methods that use transcriptomics data to predict growth rates and fluxes: E-Flux2 and SPOT (Simplified Pearson Correlation with Transcriptomic data). Growth rates are best predicted by E-Flux2. Flux profiles are more accurately predicted by E-Flux2 than flux balance analysis (FBA) and parsimonious FBA (pFBA), when compared to 44 central carbon fluxes measured by 13C-Metabolic Flux Analysis (13C-MFA). Under glucose-fed conditions, E-Flux2 presents an R2 value of 0.54, while predictions based on pFBA had an inferior R2 of 0.28. We attribute this improved performance to the extra activity information provided by the transcriptomics data. For phenol-fed metabolism, in which the substrate first enters the TCA cycle, E-Flux2's flux predictions display a high R2 of 0.96 while pFBA showed an R2 of 0.93. We also show that glucose metabolism and phenol metabolism function with similar relative ATP maintenance costs. These findings demonstrate that iGR1773 can help the metabolic engineering community predict aromatic substrate utilization patterns and perform computational strain design.

Direct Utilization of Peroxisomal Acetyl-CoA for the Synthesis of Polyketide Compounds in Saccharomyces cerevisiae

Polyketides are a class of natural products with many applications but are mainly appealing as pharmaceuticals. Heterologous production of polyketides in the yeast Saccharomyces cerevisiae has been widely explored because of the many merits of this model eukaryotic microorganism. Although acetyl-CoA and malonyl-CoA, the precursors for polyketide synthesis, are distributed in several yeast subcellular organelles, only cytosolic synthesis of polyketides has been pursued in previous studies. In this study, we investigate polyketide synthesis by directly using acetyl-CoA in the peroxisomes of yeast strain CEN.PK2-1D. We first demonstrate that the polyketide flaviolin can be synthesized in this organelle upon peroxisomal colocalization of native acetyl-CoA carboxylase and 1,3,6,8-tetrahydroxynaphthalene synthase (a type III polyketide synthase). Next, using the synthesis of the polyketide triacetic acid lactone as an example, we show that (1) a new peroxisome targeting sequence, pPTS1, is more effective than the previously reported ePTS1 for peroxisomal polyketide synthesis; (2) engineering peroxisome proliferation is effective to boost polyketide production; and (3) peroxisomes provide an additional acetyl-CoA reservoir and extra space to accommodate enzymes so that utilizing the peroxisomal pathway plus the cytosolic pathway produces more polyketide than the cytosolic pathway alone. This research lays the groundwork for more efficient heterologous polyketide biosynthesis using acetyl-CoA pools in subcellular organelles.

Metabolic Modeling of Hermetia illucens Larvae Resource Allocation for High-Value Fatty Acid Production

All plant and animal kingdom organisms use highly connected biochemical networks to facilitate sustaining, proliferation, and growth functions. While the biochemical network details are well known, the understanding of the intense regulation principles is still limited. We chose to investigate the Hermetia illucens fly at the larval stage because this stage is a crucial period for the successful accumulation and allocation of resources for the subsequent organism's developmental stages. We combined iterative wet lab experiments and innovative metabolic modeling design approaches to simulate and explain the H. illucens larval stage resource allocation processes and biotechnology potential. We performed time-based growth and high-value chemical compound accumulation wet lab chemical analysis experiments on larvae and the Gainesville diet composition. We built and validated the first H. illucens medium-size, stoichiometric metabolic model to predict the effects of diet-based alterations on fatty acid allocation potential. Using optimization methods such as flux balance and flux variability analysis on the novel insect metabolic model, we predicted that doubled essential amino acid consumption increased the growth rate by 32%, but pure glucose consumption had no positive impact on growth. In the case of doubled pure valine consumption, the model predicted a 2% higher growth rate. In this study, we describe a new framework for researching the impact of dietary alterations on the metabolism of multi-cellular organisms at different developmental stages for improved, sustainable, and directed high-value chemicals.

Evaluating proteome allocation of Saccharomyces cerevisiae phenotypes with resource balance analysis

Saccharomyces cerevisiae is an important model organism and a workhorse in bioproduction. Here, we reconstructed a compact and tractable genome-scale resource balance analysis (RBA) model (i.e., named scRBA) to analyze metabolic fluxes and proteome allocation in a computationally efficient manner. Resource capacity models such as scRBA provide the quantitative means to identify bottlenecks in biosynthetic pathways due to enzyme, compartment size, and/or ribosome availability limitations. ATP maintenance rate and in vivo apparent turnover numbers (k_app) were regressed from metabolic flux and protein concentration data to capture observed physiological growth yield and proteome efficiency and allocation, respectively. Estimated parameter values were found to vary with oxygen and nutrient availability. Overall, this work (i) provides condition-specific model parameters to recapitulate phenotypes corresponding to different extracellular environments, (ii) alludes to the enhancing effect of substrate channeling and post-translational activation on in vivo enzyme efficiency in glycolysis and electron transport chain, and (iii) reveals that the Crabtree effect is underpinned by specific limitations in mitochondrial proteome capacity and secondarily ribosome availability rather than overall proteome capacity.