The era of big data: Genome-scale modelling meets machine learning

RSEA: A Web Server for Pathway Enrichment Analysis of Metabolic Reaction Sets

Changes in biological pathways provide essential clues about metabolism. Genome-scale metabolic models (GEM) are network-based templates that computationally describe all stoichiometric associations and gene-protein reaction (GPR) relations found in an organism for all its metabolic genes and metabolites. Using reaction stoichiometry as input, GEMs mathematically simulate metabolic reaction fluxes occurring in an organism and predict changes in the metabolic system under the relevant condition. Multiple tools and approaches in the literature can capture fluxes sensitive to a given condition by using GEMs. However, functional enrichment analysis of these reaction lists in a systems biology perspective is not straightforward. Here, we introduce RSEA to annotate given reaction sets to significantly related metabolic pathways: Reaction Set Enrichment Analysis web server tool. RSEA converts given reaction list derived from GEMs into proper reaction identifiers and statistically analyze its enrichment in metabolic pathways. RSEA is designed to provide researchers with a practical and user-friendly platform to explore and interpret sets of reactions in biological pathways and freely available online (https://rseatool.com/).

NEXT-FBA: A hybrid stoichiometric/data-driven approach to improve intracellular flux predictions

Genome-scale metabolic models (GEMs) have been widely utilized to understand cellular metabolism. The application of GEMs has been advanced by computational methods that enable the prediction and analysis of intracellular metabolic states. However, the accuracy and biological relevance of these predictions often suffer from the many degrees of freedom and scarcity of available data to constrain the models adequately. Here, we introduce Neural-net EXtracellular Trained Flux Balance Analysis, (NEXT-FBA), a novel computational methodology that addresses these limitations by utilizing exometabolomic data to derive biologically relevant constraints for intracellular fluxes in GEMs. We achieve this by training artificial neural networks (ANNs) with exometabolomic data from Chinese hamster ovary (CHO) cells and correlating it with 13C-labeled intracellular fluxomic data. By capturing the underlying relationships between exometabolomics and cell metabolism, NEXT-FBA predicts upper and lower bounds for intracellular reaction fluxes to constrain GEMs. We demonstrate the efficacy of NEXT-FBA across several validation experiments, where it outperforms existing methods in predicting intracellular flux distributions that align closely with experimental observations. Furthermore, a case study demonstrates how NEXT-FBA can guide bioprocess optimization by identifying key metabolic shifts and refining flux predictions to yield actionable process and metabolic engineering targets. Overall, NEXT-FBA aims to improve the accuracy and biological relevance of intracellular flux predictions in metabolic modelling, with minimal input data requirements for pre-trained models.

Soil biodiversity and function under global change

Soil organisms represent the most abundant and diverse organisms on the planet and support almost every ecosystem function we know, and thus impact our daily lives. Some of these impacts have been well-documented, such as the role of soil organisms in regulating soil fertility and carbon sequestration; processes that have direct implications for essential ecosystem services including food security and climate change mitigation. Moreover, soil biodiversity also plays a critical role in supporting other aspects from One Health-the combined health of humans, animals, and the environment-to the conservation of historic structures such as monuments. Unfortunately, soil biodiversity is also highly vulnerable to a growing number of stressors associated with global environmental change. Understanding how and when soil biodiversity supports these functions, and how it will adapt to changing environmental conditions, is crucial for conserving soils and maintaining soil processes for future generations. In this Essay, we discuss the fundamental importance of soil biodiversity for supporting multiple ecosystem services and One Health, and further highlight essential knowledge gaps that need to be addressed to conserve soil biodiversity for the next generations.

Recent advances in culture medium design for enhanced production of monoclonal antibodies in CHO cells: A comparative study of machine learning and systems biology approaches

The production of monoclonal antibodies (mAbs) using Chinese Hamster Ovary (CHO) cells has revolutionized the treatment of numerous diseases, solidifying their position as a cornerstone of the biopharmaceutical industry. However, achieving maximum mAb production while upholding strict product quality standards remains a significant hurdle. Optimizing cell culture media emerges as a critical factor in this endeavor, requiring a nuanced understanding of the complex interplay of nutrients, growth factors, and other components that profoundly influence cellular growth, productivity, and product quality. Significant strides have been made in media optimization, including techniques such as media blending, one factor at a time, and statistical design of experiments approaches. The present review provides a comprehensive analysis of the recent advancements in culture media design strategies, focusing on the comparative application of systems biology (SB) and machine learning (ML) approaches. The applications of SB and ML in optimizing CHO cell culture medium and successful examples of their use are summarized. Finally, we highlight the immense potential of integrating SB and ML, emphasizing the development of hybrid models that leverage the strengths of both approaches for robust, efficient, and scalable optimization of mAb production in CHO cells. This review provides a roadmap for researchers and industry professionals to navigate the complex landscape of mAb production optimization, paving the way for developing next-generation CHO cell culture media that drive significant improvements in yield and productivity.

Development and applications of metabolic models in plant multi-omics research

Plant growth and development are characterized by systematic and continuous processes, each involving intricate metabolic coordination mechanisms. Mathematical models are essential tools for investigating plant growth and development, metabolic regulation networks, and growth patterns across different stages. These models offer insights into secondary metabolism patterns in plants and the roles of metabolites. The proliferation of data related to plant genomics, transcriptomics, proteomics, and metabolomics in the last decade has underscored the growing importance of mathematical modeling in this field. This review aims to elucidate the principles and types of metabolic models employed in studying plant secondary metabolism, their strengths, and limitations. Furthermore, the application of mathematical models in various plant systems biology subfields will be discussed. Lastly, the review will outline how mathematical models can be harnessed to address research questions in this context.

A diel multi-tissue genome-scale metabolic model of Vitis vinifera

Vitis vinifera, also known as grapevine, is widely cultivated and commercialized, particularly to produce wine. As wine quality is directly linked to fruit quality, studying grapevine metabolism is important to understand the processes underlying grape composition. Genome-scale metabolic models (GSMMs) have been used for the study of plant metabolism and advances have been made, allowing the integration of omics datasets with GSMMs. On the other hand, Machine learning (ML) has been used to analyze and integrate omics data, and while the combination of ML with GSMMs has shown promising results, it is still scarcely used to study plants. Here, the first GSSM of V. vinifera was reconstructed and validated, comprising 7199 genes, 5399 reactions, and 5141 metabolites across 8 compartments. Tissue-specific models for the stem, leaf, and berry of the Cabernet Sauvignon cultivar were generated from the original model, through the integration of RNA-Seq data. These models have been merged into diel multi-tissue models to study the interactions between tissues at light and dark phases. The potential of combining ML with GSMMs was explored by using ML to analyze the fluxomics data generated by green and mature grape GSMMs and provide insights regarding the metabolism of grapes at different developmental stages. Therefore, the models developed in this work are useful tools to explore different aspects of grapevine metabolism and understand the factors influencing grape quality.

Systems-Level Modeling for CRISPR-Based Metabolic Engineering

The CRISPR-Cas system has enabled the development of sophisticated, multigene metabolic engineering programs through the use of guide RNA-directed activation or repression of target genes. To optimize biosynthetic pathways in microbial systems, we need improved models to inform design and implementation of transcriptional programs. Recent progress has resulted in new modeling approaches for identifying gene targets and predicting the efficacy of guide RNA targeting. Genome-scale and flux balance models have successfully been applied to identify targets for improving biosynthetic production yields using combinatorial CRISPR-interference (CRISPRi) programs. The advent of new approaches for tunable and dynamic CRISPR activation (CRISPRa) promises to further advance these engineering capabilities. Once appropriate targets are identified, guide RNA prediction models can lead to increased efficacy in gene targeting. Developing improved models and incorporating approaches from machine learning may be able to overcome current limitations and greatly expand the capabilities of CRISPR-Cas9 tools for metabolic engineering.

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.

Towards consolidated bioprocessing of biomass and plastic substrates for semi-synthetic production of bio-poly(ethylene furanoate) (PEF) polymer using omics-guided construction of artificial microbial consortia

Poly(ethylene furanoate) (PEF) plastic is a 100% renewable polyester that is currently being pursued for commercialization as the next-generation bio-based plastic. This is in line with growing demand for circular bioeconomy and new plastics economy that is aimed at minimizing plastic waste mismanagement and lowering carbon footprint of plastics. However, the current catalytic route for the synthesis of PEF is impeded with technical challenges including high cost of pretreatment and catalyst refurbishment. On the other hand, the semi-biosynthetic route of PEF plastic production is of increased biotechnological interest. In particular, the PEF monomers (Furan dicarboxylic acid and ethylene glycol) can be synthesized via microbial-based biorefinery and purified for subsequent catalyst-mediated polycondensation into PEF. Several bioengineering and bioprocessing issues such as efficient substrate utilization and pathway optimization need to be addressed prior to establishing industrial-scale production of the monomers. This review highlights current advances in semi-biosynthetic production of PEF monomers using consolidated waste biorefinery strategies, with an emphasis on the employment of omics-driven systems biology approaches in enzyme discovery and pathway construction. The roles of microbial protein transporters will be discussed, especially in terms of improving substrate uptake and utilization from lignocellulosic biomass, as well as from depolymerized plastic waste as potential bio-feedstock. The employment of artificial bioengineered microbial consortia will also be highlighted to provide streamlined systems and synthetic biology strategies for bio-based PEF monomer production using both plant biomass and plastic-derived substrates, which are important for circular and new plastics economy advances.

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.

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.

Integration of graph neural networks and genome-scale metabolic models for predicting gene essentiality

Genome-scale metabolic models are powerful tools for understanding cellular physiology. Flux balance analysis (FBA), in particular, is an optimization-based approach widely employed for predicting metabolic phenotypes. In model microbes such as Escherichia coli, FBA has been successful at predicting essential genes, i.e. those genes that impair survival when deleted. A central assumption in this approach is that both wild type and deletion strains optimize the same fitness objective. Although the optimality assumption may hold for the wild type metabolic network, deletion strains are not subject to the same evolutionary pressures and knock-out mutants may steer their metabolism to meet other objectives for survival. Here, we present FlowGAT, a hybrid FBA-machine learning strategy for predicting essentiality directly from wild type metabolic phenotypes. The approach is based on graph-structured representation of metabolic fluxes predicted by FBA, where nodes correspond to enzymatic reactions and edges quantify the propagation of metabolite mass flow between a reaction and its neighbours. We integrate this information into a graph neural network that can be trained on knock-out fitness assay data. Comparisons across different model architectures reveal that FlowGAT predictions for E. coli are close to those of FBA for several growth conditions. This suggests that essentiality of enzymatic genes can be predicted by exploiting the inherent network structure of metabolism. Our approach demonstrates the benefits of combining the mechanistic insights afforded by genome-scale models with the ability of deep learning to infer patterns from complex datasets.

Machine learning identifies key metabolic reactions in bacterial growth on different carbon sources

Carbon source-dependent control of bacterial growth is fundamental to bacterial physiology and survival. However, pinpointing the metabolic steps important for cell growth is challenging due to the complexity of cellular networks. Here, the elastic net model and multilayer perception model that integrated genome-wide gene-deletion data and simulated flux distributions were constructed to identify metabolic reactions beneficial or detrimental to Escherichia coli grown on 30 different carbon sources. Both models outperformed traditional in silico methods by identifying not just essential reactions but also nonessential ones that promote growth. They successfully predicted metabolic reactions beneficial to cell growth, with high convergence between the models. The models revealed that biosynthetic pathways generally promote growth across various carbon sources, whereas the impact of energy-generating pathways varies with the carbon source. Intriguing predictions were experimentally validated for findings beyond experimental training data and the impact of various carbon sources on the glyoxylate shunt, pyruvate dehydrogenase reaction, and redundant purine biosynthesis reactions. These highlight the practical significance and predictive power of the models for understanding and engineering microbial metabolism.

Genome-Scale Model of Rhizopus microsporus: Metabolic integration of a fungal holobiont with its bacterial and viral endosymbionts

Rhizopus microsporus often lives in association with bacterial and viral symbionts that alter its biology. This fungal model represents an example of the complex interactions established among diverse organisms in functional holobionts. We constructed a Genome-Scale Model (GSM) of the fungal-bacterial-viral holobiont (iHol). We employed a constraint-based method to calculate the metabolic fluxes to decipher the metabolic interactions of the symbionts with their host. Our computational analyses of iHol simulate the holobiont's growth and the production of the toxin rhizoxin. Analyses of the calculated fluxes between R. microsporus in symbiotic (iHol) versus asymbiotic conditions suggest that changes in the lipid and nucleotide metabolism of the host are necessary for the functionality of the holobiont. Glycerol plays a pivotal role in the fungal-bacterial metabolic interaction, as its production does not compromise fungal growth, and Mycetohabitans bacteria can efficiently consume it. Narnavirus RmNV-20S and RmNV-23S affected the nucleotide metabolism without impacting the fungal-bacterial symbiosis. Our analyses highlighted the metabolic stability of Mycetohabitans throughout its co-evolution with the fungal host. We also predicted changes in reactions of the bacterial metabolism required for the active production of rhizoxin. This iHol is the first GSM of a fungal holobiont.

Metabolomic-Based Approaches for Endometrial Cancer Diagnosis and Prognosis: A Review

Endometrial cancer, the most prevalent gynecological malignancy in developed countries, is experiencing a sustained rise in both its incidence and mortality rates, primarily attributed to extended life expectancy and lifestyle factors. Currently, the absence of precise diagnostic tools hampers the effective management of the expanding population of women at risk of developing this disease. Furthermore, patients diagnosed with endometrial cancer require precise risk stratification to align with optimal treatment planning. Metabolomics technology offers a unique insight into the molecular landscape of endometrial cancer, providing a promising approach to address these unmet needs. This comprehensive literature review initiates with an overview of metabolomic technologies and their intrinsic workflow components, aiming to establish a fundamental understanding for the readers. Subsequently, a detailed exploration of the existing body of research is undertaken with the objective of identifying metabolite biomarkers capable of enhancing current strategies for endometrial cancer diagnosis, prognosis, and recurrence monitoring. Metabolomics holds vast potential to revolutionize the management of endometrial cancer by providing accuracy and valuable insights into crucial aspects.

From observational to actionable: rethinking omics in biologics production

As the era of omics continues to expand with increasing ubiquity and success in both academia and industry, omics-based experiments are becoming commonplace in industrial biotechnology, including efforts to develop novel solutions in bioprocess optimization and cell line development. Omic technologies provide particularly valuable 'observational' insights for discovery science, especially in academic research and industrial R&D; however, biomanufacturing requires a different paradigm to unlock 'actionable' insights from omics. Here, we argue the value of omic experiments in biotechnology can be maximized with deliberate selection of omic approaches and forethought about analysis techniques. We describe important considerations when designing and implementing omic-based experiments and discuss how systems biology analysis strategies can enhance efforts to obtain actionable insights in mammalian-based biologics production.

Recent advances in data- and knowledge-driven approaches to explore primary microbial metabolism

With the rapid progress in metabolomics and sequencing technologies, more data on the metabolome of single microbes and their communities become available, revealing the potential of microorganisms to metabolize a broad range of chemical compounds. The analysis of microbial metabolomics datasets remains challenging since it inherits the technical challenges of metabolomics analysis, such as compound identification and annotation, while harboring challenges in data interpretation, such as distinguishing metabolite sources in mixed samples. This review outlines the recent advances in computational methods to analyze primary microbial metabolism: knowledge-based approaches that take advantage of metabolic and molecular networks and data-driven approaches that employ machine/deep learning algorithms in combination with large-scale datasets. These methods aim at improving metabolite identification and disentangling reciprocal interactions between microbes and metabolites. We also discuss the perspective of combining these approaches and further developments required to advance the investigation of primary metabolism in mixed microbial samples.

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.

From Spatial-Temporal Multiscale Modeling to Application: Bridging the Valley of Death in Industrial Biotechnology

The Valley of Death confronts industrial biotechnology with a significant challenge to the commercialization of products. Fortunately, with the integration of computation, automation and artificial intelligence (AI) technology, the industrial biotechnology accelerates to cross the Valley of Death. The Fourth Industrial Revolution (Industry 4.0) has spurred advanced development of intelligent biomanufacturing, which has evolved the industrial structures in line with the worldwide trend. To achieve this, intelligent biomanufacturing can be structured into three main parts that comprise digitalization, modeling and intellectualization, with modeling forming a crucial link between the other two components. This paper provides an overview of mechanistic models, data-driven models and their applications in bioprocess development. We provide a detailed elaboration of the hybrid model and its applications in bioprocess engineering, including strain design, process control and optimization, as well as bioreactor scale-up. Finally, the challenges and opportunities of biomanufacturing towards Industry 4.0 are also discussed.

Machine learning in bioprocess development: from promise to practice

Fostered by novel analytical techniques, digitalization, and automation, modern bioprocess development provides large amounts of heterogeneous experimental data, containing valuable process information. In this context, data-driven methods like machine learning (ML) approaches have great potential to rationally explore large design spaces while exploiting experimental facilities most efficiently. Herein we demonstrate how ML methods have been applied so far in bioprocess development, especially in strain engineering and selection, bioprocess optimization, scale-up, monitoring, and control of bioprocesses. For each topic, we will highlight successful application cases, current challenges, and point out domains that can potentially benefit from technology transfer and further progress in the field of ML.

Trimming the genomic fat: minimising and re-functionalising genomes using synthetic biology

Naturally evolved organisms typically have large genomes that enable their survival and growth under various conditions. However, the complexity of genomes often precludes our complete understanding of them, and limits the success of biotechnological designs. In contrast, minimal genomes have reduced complexity and therefore improved engineerability, increased biosynthetic capacity through the removal of unnecessary genetic elements, and less recalcitrance to complete characterisation. Here, we review the past and current genome minimisation and re-functionalisation efforts, with an emphasis on the latest advances facilitated by synthetic genomics, and provide a critical appraisal of their potential for industrial applications.

A General Hybrid Modeling Framework for Systems Biology Applications: Combining Mechanistic Knowledge with Deep Neural Networks under the SBML Standard

In this paper, a computational framework is proposed that merges mechanistic modeling with deep neural networks obeying the Systems Biology Markup Language (SBML) standard. Over the last 20 years, the systems biology community has developed a large number of mechanistic models that are currently stored in public databases in SBML. With the proposed framework, existing SBML models may be redesigned into hybrid systems through the incorporation of deep neural networks into the model core, using a freely available python tool. The so-formed hybrid mechanistic/neural network models are trained with a deep learning algorithm based on the adaptive moment estimation method (ADAM), stochastic regularization and semidirect sensitivity equations. The trained hybrid models are encoded in SBML and uploaded in model databases, where they may be further analyzed as regular SBML models. This approach is illustrated with three well-known case studies: the Escherichia coli threonine synthesis model, the P58IPK signal transduction model, and the Yeast glycolytic oscillations model. The proposed framework is expected to greatly facilitate the widespread use of hybrid modeling techniques for systems biology applications.

High-throughput genetic engineering tools for regulating gene expression in a microbial cell factory

With the rapid advances in biotechnological tools and strategies, microbial cell factory-constructing strategies have been established for the production of value-added compounds. However, optimizing the tradeoff between the biomass, yield, and titer remains a challenge in microbial production. Gene regulation is necessary to optimize and control metabolic fluxes in microorganisms for high-production performance. Various high-throughput genetic engineering tools have been developed for achieving rational gene regulation and genetic perturbation, diversifying the cellular phenotype and enhancing bioproduction performance. In this paper, we review the current high-throughput genetic engineering tools for gene regulation. In particular, technological approaches used in a diverse range of genetic tools for constructing microbial cell factories are introduced, and representative applications of these tools are presented. Finally, the prospects for high-throughput genetic engineering tools for gene regulation are discussed.

SBML2HYB: a Python interface for SBML compatible hybrid modeling

SUMMARY

Here, we present sbml2hyb, an easy-to-use standalone Python tool that facilitates the conversion of existing mechanistic models of biological systems in Systems Biology Markup Language (SBML) into hybrid semiparametric models that combine mechanistic functions with machine learning (ML). The so-formed hybrid models can be trained and stored back in databases in SBML format. The tool supports a user-friendly export interface with an internal format validator. Two case studies illustrate the use of the sbml2hyb tool. Additionally, we describe HMOD, a new model format designed to support and facilitate hybrid models building. It aggregates the mechanistic model information with the ML information and follows as close as possible the SBML rules. We expect the sbml2hyb tool and HMOD to greatly facilitate the widespread usage of hybrid modeling techniques for biological systems analysis.

AVAILABILITY AND IMPLEMENTATION

The Python interface, source code and the example models used for the case studies are accessible at: https://github.com/r-costa/sbml2hyb.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Genome-scale models as a vehicle for knowledge transfer from microbial to mammalian cell systems

With the plethora of omics data becoming available for mammalian cell and, increasingly, human cell systems, Genome-scale metabolic models (GEMs) have emerged as a useful tool for their organisation and analysis. The systems biology community has developed an array of tools for the solution, interrogation and customisation of GEMs as well as algorithms that enable the design of cells with desired phenotypes based on the multi-omics information contained in these models. However, these tools have largely found application in microbial cells systems, which benefit from smaller model size and ease of experimentation. Herein, we discuss the major outstanding challenges in the use of GEMs as a vehicle for accurately analysing data for mammalian cell systems and transferring methodologies that would enable their use to design strains and processes. We provide insights on the opportunities and limitations of applying GEMs to human cell systems for advancing our understanding of health and disease. We further propose their integration with data-driven tools and their enrichment with cellular functions beyond metabolism, which would, in theory, more accurately describe how resources are allocated intracellularly.

Metabolic Engineering: Methodologies and Applications

Metabolic engineering aims to improve the production of economically valuable molecules through the genetic manipulation of microbial metabolism. While the discipline is a little over 30 years old, advancements in metabolic engineering have given way to industrial-level molecule production benefitting multiple industries such as chemical, agriculture, food, pharmaceutical, and energy industries. This review describes the design, build, test, and learn steps necessary for leading a successful metabolic engineering campaign. Moreover, we highlight major applications of metabolic engineering, including synthesizing chemicals and fuels, broadening substrate utilization, and improving host robustness with a focus on specific case studies. Finally, we conclude with a discussion on perspectives and future challenges related to metabolic engineering.

Progress toward rapid, at-line N-glycosylation detection and control for recombinant protein expression

Proteins continue to represent a large fraction of the therapeutics market, reaching over a hundred billion dollars in market size globally. One key feature of protein modification that can affect both structure and function is the addition of glycosylation following protein folding, leading to regulatory requirements for the accurate assessment of protein attributes, including glycan structures. The non-template-driven, innately heterogeneous N-glycosylation process thus requires accurate detection to robustly generate protein therapies. A challenge exists in the timely detection of protein glycosylation without labor-intensive manipulation. In this article, we discuss progress toward N-glycoprotein control, focusing on novel control strategies and the advancement of rapid, high-throughput analysis methods.

Applications of machine learning in metabolomics: Disease modeling and classification

Metabolomics research has recently gained popularity because it enables the study of biological traits at the biochemical level and, as a result, can directly reveal what occurs in a cell or a tissue based on health or disease status, complementing other omics such as genomics and transcriptomics. Like other high-throughput biological experiments, metabolomics produces vast volumes of complex data. The application of machine learning (ML) to analyze data, recognize patterns, and build models is expanding across multiple fields. In the same way, ML methods are utilized for the classification, regression, or clustering of highly complex metabolomic data. This review discusses how disease modeling and diagnosis can be enhanced via deep and comprehensive metabolomic profiling using ML. We discuss the general layout of a metabolic workflow and the fundamental ML techniques used to analyze metabolomic data, including support vector machines (SVM), decision trees, random forests (RF), neural networks (NN), and deep learning (DL). Finally, we present the advantages and disadvantages of various ML methods and provide suggestions for different metabolic data analysis scenarios.

Genome-scale modeling of Chinese hamster ovary cells by hybrid semi-parametric flux balance analysis

Flux balance analysis (FBA) is currently the standard method to compute metabolic fluxes in genome-scale networks. Several FBA extensions employing diverse objective functions and/or constraints have been published. Here we propose a hybrid semi-parametric FBA extension that combines mechanistic-level constraints (parametric) with empirical constraints (non-parametric) in the same linear program. A CHO dataset with 27 measured exchange fluxes obtained from 21 reactor experiments served to evaluate the method. The mechanistic constraints were deduced from a reduced CHO-K1 genome-scale network with 686 metabolites, 788 reactions and 210 degrees of freedom. The non-parametric constraints were obtained by principal component analysis of the flux dataset. The two types of constraints were integrated in the same linear program showing comparable computational cost to standard FBA. The hybrid FBA is shown to significantly improve the specific growth rate prediction under different constraints scenarios. A metabolically efficient cell growth feed targeting minimal byproducts accumulation was designed by hybrid FBA. It is concluded that integrating parametric and nonparametric constraints in the same linear program may be an efficient approach to reduce the solution space and to improve the predictive power of FBA methods when critical mechanistic information is missing.

Machine learning, artificial intelligence, and chemistry: how smart algorithms are reshaping simulation and the laboratory

Machine learning and artificial intelligence are increasingly gaining in prominence through image analysis, language processing, and automation, to name a few applications. Machine learning is also making profound changes in chemistry. From revisiting decades-old analytical techniques for the purpose of creating better calibration curves, to assisting and accelerating traditional in silico simulations, to automating entire scientific workflows, to being used as an approach to deduce underlying physics of unexplained chemical phenomena, machine learning and artificial intelligence are reshaping chemistry, accelerating scientific discovery, and yielding new insights. This review provides an overview of machine learning and artificial intelligence from a chemist’s perspective and focuses on a number of examples of the use of these approaches in computational chemistry and in the laboratory.

Optimization and Scale-Up of Fermentation Processes Driven by Models

In the era of sustainable development, the use of cell factories to produce various compounds by fermentation has attracted extensive attention; however, industrial fermentation requires not only efficient production strains, but also suitable extracellular conditions and medium components, as well as scaling-up. In this regard, the use of biological models has received much attention, and this review will provide guidance for the rapid selection of biological models. This paper first introduces two mechanistic modeling methods, kinetic modeling and constraint-based modeling (CBM), and generalizes their applications in practice. Next, we review data-driven modeling based on machine learning (ML), and highlight the application scope of different learning algorithms. The combined use of ML and CBM for constructing hybrid models is further discussed. At the end, we also discuss the recent strategies for predicting bioreactor scale-up and culture behavior through a combination of biological models and computational fluid dynamics (CFD) models.

Data-Driven Synthetic Cell Factories Development for Industrial Biomanufacturing

Revolutionary breakthroughs in artificial intelligence (AI) and machine learning (ML) have had a profound impact on a wide range of scientific disciplines, including the development of artificial cell factories for biomanufacturing. In this paper, we review the latest studies on the application of data-driven methods for the design of new proteins, pathways, and strains. We first briefly introduce the various types of data and databases relevant to industrial biomanufacturing, which are the basis for data-driven research. Different types of algorithms, including traditional ML and more recent deep learning methods, are also presented. We then demonstrate how these data-based approaches can be applied to address various issues in cell factory development using examples from recent studies, including the prediction of protein function, improvement of metabolic models, and estimation of missing kinetic parameters, design of non-natural biosynthesis pathways, and pathway optimization. In the last section, we discuss the current limitations of these data-driven approaches and propose that data-driven methods should be integrated with mechanistic models to complement each other and facilitate the development of synthetic strains for industrial biomanufacturing.

Genome-Scale Metabolic Modeling Enables In-Depth Understanding of Big Data

Genome-scale metabolic models (GEMs) enable the mathematical simulation of the metabolism of archaea, bacteria, and eukaryotic organisms. GEMs quantitatively define a relationship between genotype and phenotype by contextualizing different types of Big Data (e.g., genomics, metabolomics, and transcriptomics). In this review, we analyze the available Big Data useful for metabolic modeling and compile the available GEM reconstruction tools that integrate Big Data. We also discuss recent applications in industry and research that include predicting phenotypes, elucidating metabolic pathways, producing industry-relevant chemicals, identifying drug targets, and generating knowledge to better understand host-associated diseases. In addition to the up-to-date review of GEMs currently available, we assessed a plethora of tools for developing new GEMs that include macromolecular expression and dynamic resolution. Finally, we provide a perspective in emerging areas, such as annotation, data managing, and machine learning, in which GEMs will play a key role in the further utilization of Big Data.

Yeast synthetic biology advances biofuel production

Increasing concerns of environmental impacts and global warming calls for urgent need to switch from use of fossil fuels to renewable technologies. Biofuels represent attractive alternatives of fossil fuels and have gained continuous attentions. Through the use of synthetic biology it has become possible to engineer microbial cell factories for efficient biofuel production in a more precise and efficient manner. Here, we review advances on yeast-based biofuel production. Following an overview of synthetic biology impacts on biofuel production, we review recent advancements on the design, build, test, learn steps of yeast-based biofuel production, and end with discussion of challenges associated with use of synthetic biology for developing novel processes for biofuel production.

Improving the performance of machine learning models for biotechnology: The quest for deus ex machina

Machine learning is becoming an integral part of the Design-Build-Test-Learn cycle in biotechnology. Machine learning models learn from collected datasets such as omics data and predict a defined outcome, which has led to both production improvements and predictive tools in the field. Robust prediction of the behavior of microbial cell factories and production processes not only greatly increases our understanding of the function of such systems, but also provides significant savings of development time. However, many pitfalls when modeling biological data - bad fit, noisy data, model instability, low data quantity and imbalances in the data - cause models to suffer in their performance. Here we provide an accessible, in-depth analysis on the problems created by these pitfalls, as well as means of their detection and mediation, with a focus on supervised learning. Assessing the state of the art, we show that, currently, in-depth analyses of model performance are often absent and must be improved. This review provides a toolbox for the analysis of model robustness and performance, and simultaneously proposes a standard for the community to facilitate future work. It is further accompanied by an interactive online tutorial on the discussed issues.

AI applications in functional genomics

We review the current applications of artificial intelligence (AI) in functional genomics. The recent explosion of AI follows the remarkable achievements made possible by "deep learning", along with a burst of "big data" that can meet its hunger. Biology is about to overthrow astronomy as the paradigmatic representative of big data producer. This has been made possible by huge advancements in the field of high throughput technologies, applied to determine how the individual components of a biological system work together to accomplish different processes. The disciplines contributing to this bulk of data are collectively known as functional genomics. They consist in studies of: i) the information contained in the DNA (genomics); ii) the modifications that DNA can reversibly undergo (epigenomics); iii) the RNA transcripts originated by a genome (transcriptomics); iv) the ensemble of chemical modifications decorating different types of RNA transcripts (epitranscriptomics); v) the products of protein-coding transcripts (proteomics); and vi) the small molecules produced from cell metabolism (metabolomics) present in an organism or system at a given time, in physiological or pathological conditions. After reviewing main applications of AI in functional genomics, we discuss important accompanying issues, including ethical, legal and economic issues and the importance of explainability.

Advances in Genome-Scale Metabolic Modeling toward Microbial Community Analysis of the Human Microbiome

A genome-scale metabolic model (GEM) represents metabolic pathways of an organism in a mathematical form and can be built using biochemistry and genome annotation data. GEMs are invaluable for understanding organisms since they analyze the metabolic capabilities and behaviors quantitatively and can predict phenotypes. The development of high-throughput data collection techniques led to an immense increase in omics data such as metagenomics, which expand our knowledge on the human microbiome, but this also created a need for systematic analysis of these data. In recent years, GEMs have also been reconstructed for microbial species, including human gut microbiota, and methods for the analysis of microbial communities have been developed to examine the interaction between the organisms or the host. The purpose of this review is to provide a comprehensive guide for the applications of GEMs in microbial community analysis. Starting with GEM repositories, automatic GEM reconstruction tools, and quality control of models, this review will give insights into microbe-microbe and microbe-host interaction predictions and optimization of microbial community models. Recent studies that utilize microbial GEMs and personalized models to infer the influence of microbiota on human diseases such as inflammatory bowel diseases (IBD) or Parkinson's disease are exemplified. Being powerful system biology tools for both species-level and community-level analysis of microbes, GEMs integrated with omics data and machine learning techniques will be indispensable for studying the microbiome and their effects on human physiology as well as for deciphering the mechanisms behind human diseases.

Exploiting unconventional prokaryotic hosts for industrial biotechnology

Developing cost-efficient biotechnological processes is a major challenge in replacing fossil-based industrial production processes. The remarkable progress in genetic engineering ensures efficient and fast tailoring of microbial metabolism for a wide range of bioconversions. However, improving intrinsic properties such as tolerance, handling, growth, and substrate consumption rates is still challenging. At the same time, synthetic biology tools are becoming easier applicable and transferable to nonmodel organisms. These trends have resulted in the exploitation of new and unconventional microbial systems with sophisticated properties, which render them promising hosts for the bio-based industry. Here, we highlight the metabolic and cellular capabilities of representative prokaryotic newcomers and discuss the potential and drawbacks of these hosts for industrial application.

Synergisms of machine learning and constraint-based modeling of metabolism for analysis and optimization of fermentation parameters

Recent noteworthy advances in the development of high-performing microbial and mammalian strains have enabled the sustainable production of bio-economically valuable substances such as bio-compounds, biofuels, and biopharmaceuticals. However, to obtain an industrially viable mass-production scheme, much time and effort are required. The robust and rational design of fermentation processes requires analysis and optimization of different extracellular conditions and medium components, which have a massive effect on growth and productivity. In this regard, knowledge- and data-driven modeling methods have received much attention. Constraint-based modeling (CBM) is a knowledge-driven mathematical approach that has been widely used in fermentation analysis and optimization due to its capabilities of predicting the cellular phenotype from genotype through high-throughput means. On the other hand, machine learning (ML) is a data-driven statistical method that identifies the data patterns within sophisticated biological systems and processes, where there is inadequate knowledge to represent underlying mechanisms. Furthermore, ML models are becoming a viable complement to constraint-based models in a reciprocal manner when one is used as a pre-step of another. As a result, more predictable model is produced. This review highlights the applications of CBM and ML independently and the combination of these two approaches for analyzing and optimizing fermentation parameters. This article is protected by copyright. All rights reserved.

From omics to Cellular mechanisms in mammalian cell factory development

Mammalian cells have been used widely as biopharmaceutical cell factories due to their ability to make complex biotherapeutic proteins with human-compatible modifications. However, their application for some products has been hampered by low protein yields. Numerous studies have aimed to characterize cellular bottlenecks in the hope of boosting protein productivity, but the complexity of the underlying pathways and the diversity of the modifications have complicated cell engineering when relying solely on traditional methodologies. Incorporating omics-based and systems approaches into cell engineering can provide valuable insights into desirable phenotypes of cell factories. Here, we discuss cell engineering strategies for enhancing protein productivity in mammalian cell factories, particularly CHO and HEK293, and the opportunities and limitations of the genome-wide screening and multi-omics approaches for guiding cell engineering. Systems biology strategies will also be discussed to show how they refine our understanding of the cellular mechanisms which will aid in effective engineering strategies.

Advanced strategies and tools to facilitate and streamline microbial adaptive laboratory evolution

Adaptive laboratory evolution (ALE) has served as a historic microbial engineering method that mimics natural selection to obtain desired microbes. The past decade has witnessed improvements in all aspects of ALE workflow, in terms of growth coupling, genotypic diversification, phenotypic selection, and genotype-phenotype mapping. The developing growth-coupling strategies facilitate ALE to a wider range of appealing traits. In vivo mutagenesis methods and multiplexed automated culture platforms open new gates to streamline its execution. Meanwhile, the application of multi-omics analyses and multiplexed genetic engineering promote efficient knowledge mining. This article provides a comprehensive and updated review of these advances, highlights newest significant applications, and discusses future improvements, aiming to provide a practical guide for implementation of novel, effective, and efficient ALE experiments.

Virulence factor-related gut microbiota genes and immunoglobulin A levels as novel markers for machine learning-based classification of autism spectrum disorder

Autism spectrum disorder (ASD) is a neurodevelopmental condition for which early identification and intervention is crucial for optimum prognosis. Our previous work showed gut Immunoglobulin A (IgA) to be significantly elevated in the gut lumen of children with ASD compared to typically developing (TD) children. Gut microbiota variations have been reported in ASD, yet not much is known about virulence factor-related gut microbiota (VFGM) genes. Upon determining the VFGM genes distinguishing ASD from TD, this study is the first to utilize VFGM genes and IgA levels for a machine learning-based classification of ASD. Sequence comparisons were performed of metagenome datasets from children with ASD (n = 43) and TD children (n = 31) against genes in the virulence factor database. VFGM gene composition was associated with ASD phenotype. VFGM gene diversity was higher in children with ASD and positively correlated with IgA content. As Group B streptococcus (GBS) genes account for the highest proportion of 24 different VFGMs between ASD and TD and positively correlate with gut IgA, GBS genes were used in combination with IgA and VFGMs diversity to distinguish ASD from TD. Given that VFGM diversity, increases in IgA, and ASD-enriched VFGM genes were independent of sex and gastrointestinal symptoms, a classification method utilizing them will not pertain only to a specific subgroup of ASD. By introducing the classification value of VFGM genes and considering that VFs can be isolated in pregnant women and newborns, these findings provide a novel machine learning-based early risk identification method for ASD.