Systems biology approaches integrated with artificial intelligence for optimized metabolic engineering

Artificial Intelligence in Metabolomics: A Current Review

Metabolomics and artificial intelligence (AI) form a synergistic partnership. Metabolomics generates large datasets comprising hundreds to thousands of metabolites with complex relationships. AI, aiming to mimic human intelligence through computational modeling, possesses extraordinary capabilities for big data analysis. In this review, we provide a recent overview of the methodologies and applications of AI in metabolomics studies in the context of systems biology and human health. We first introduce the AI concept, history, and key algorithms for machine learning and deep learning, summarizing their strengths and weaknesses. We then discuss studies that have successfully used AI across different aspects of metabolomic analysis, including analytical detection, data preprocessing, biomarker discovery, predictive modeling, and multi-omics data integration. Lastly, we discuss the existing challenges and future perspectives in this rapidly evolving field. Despite limitations and challenges, the combination of metabolomics and AI holds great promises for revolutionary advancements in enhancing human health.

Application of modern synthetic biology technology in aromatic amino acids and derived compounds biosynthesis

Aromatic amino acids (AAA) and derived compounds have enormous commercial value with extensive applications in the food, chemical and pharmaceutical fields. Microbial production of AAA and derived compounds is a promising prospect for its environmental friendliness and sustainability. However, low yield and production efficiency remain major challenges for realizing industrial production. With the advancement of synthetic biology, microbial production of AAA and derived compounds has been significantly facilitated. In this review, a comprehensive overview on the current progresses, challenges and corresponding solutions for AAA and derived compounds biosynthesis is provided. The most cutting-edge developments of synthetic biology technology in AAA and derived compounds biosynthesis, including CRISPR-based system, genetically encoded biosensors and synthetic genetic circuits, were highlighted. Finally, future prospects of modern strategies conducive to the biosynthesis of AAA and derived compounds are discussed. This review offers guidance on constructing microbial cell factory for aromatic compound using synthetic biology technology.

Learning metabolic dynamics from irregular observations by Bidirectional Time-Series State Transfer Network

Modeling microbial metabolic dynamics is important for the rational optimization of both biosynthetic systems and industrial processes to facilitate green and efficient biomanufacturing. Classical approaches utilize explicit equation systems to represent metabolic networks, enabling the quantification of pathway fluxes to identify metabolic bottlenecks. However, these white-box models, despite their diverse applications, have limitations in simulating metabolic dynamics and are intrinsically inaccurate for industrial strains that lack information on network structures and kinetic parameters. On the other hand, black-box models do not rely on prior mechanistic knowledge of strains but are built upon observed time-series trajectories of biosynthetic systems in action. In practice, these observations are typically irregular, with discontinuously observed time points across multiple independent batches, each time point potentially containing missing measurements. Learning from such irregular data remains challenging for existing approaches. To address this issue, we present the Bidirectional Time-Series State Transfer Network (BTSTN) for modeling metabolic dynamics directly from irregular observations. Using evaluation data sets derived from both ideal dynamic systems and a real-world fermentation process, we demonstrate that BTSTN accurately reconstructs dynamic behaviors and predicts future trajectories. This approach exhibits enhanced robustness against missing measurements and noise, as compared to the state-of-the-art methods.IMPORTANCEIndustrial biosynthetic systems often involve strains with unclear genetic backgrounds, posing challenges in modeling their distinct metabolic dynamics. In such scenarios, white-box models, which commonly rely on inferred networks, are thereby of limited applicability and accuracy. In contrast, black-box models, such as statistical models and neural networks, are directly fitted or learned from observed time-series trajectories of biosynthetic systems in action. These methods typically assume regular observations without missing time points or measurements. If the observations are irregular, a pre-processing step becomes necessary to obtain a fully filled data set for subsequent model training, which, at the same time, inevitably introduces errors into the resulting models. BTSTN is a novel approach that natively learns from irregular observations. This distinctive feature makes it a unique addition to the current arsenal of technologies modeling metabolic dynamics.

Can digital twin efforts shape microorganism-based alternative food?

With the continuous increment in global population growth, compounded by post-pandemic food security challenges due to labor shortages, effects of climate change, political conflicts, limited land for agriculture, and carbon emissions control, addressing food production in a sustainable manner for future generations is critical. Microorganisms are potential alternative food sources that can help close the gap in food production. For the development of more efficient and yield-enhancing products, it is necessary to have a better understanding on the underlying regulatory molecular pathways of microbial growth. Nevertheless, as microbes are regulated at multiomics scales, current research focusing on single omics (genomics, proteomics, or metabolomics) independently is inadequate for optimizing growth and product output. Here, we discuss digital twin (DT) approaches that integrate systems biology and artificial intelligence in analyzing multiomics datasets to yield a microbial replica model for in silico testing before production. DT models can thus provide a holistic understanding of microbial growth, metabolite biosynthesis mechanisms, as well as identifying crucial production bottlenecks. Our argument, therefore, is to support the development of novel DT models that can potentially revolutionize microorganism-based alternative food production efficiency.

Strategies, Achievements, and Potential Challenges of Plant and Microbial Chassis in the Biosynthesis of Plant Secondary Metabolites

Diverse secondary metabolites in plants, with their rich biological activities, have long been important sources for human medicine, food additives, pesticides, etc. However, the large-scale cultivation of host plants consumes land resources and is susceptible to pest and disease problems. Additionally, the multi-step and demanding nature of chemical synthesis adds to production costs, limiting their widespread application. In vitro cultivation and the metabolic engineering of plants have significantly enhanced the synthesis of secondary metabolites with successful industrial production cases. As synthetic biology advances, more research is focusing on heterologous synthesis using microorganisms. This review provides a comprehensive comparison between these two chassis, evaluating their performance in the synthesis of various types of secondary metabolites from the perspectives of yield and strategies. It also discusses the challenges they face and offers insights into future efforts and directions.

AI in cellular engineering and reprogramming

During the last decade, artificial intelligence (AI) has increasingly been applied in biophysics and related fields, including cellular engineering and reprogramming, offering novel approaches to understand, manipulate, and control cellular function. The potential of AI lies in its ability to analyze complex datasets and generate predictive models. AI algorithms can process large amounts of data from single-cell genomics and multiomic technologies, allowing researchers to gain mechanistic insights into the control of cell identity and function. By integrating and interpreting these complex datasets, AI can help identify key molecular events and regulatory pathways involved in cellular reprogramming. This knowledge can inform the design of precision engineering strategies, such as the development of new transcription factor and signaling molecule cocktails, to manipulate cell identity and drive authentic cell fate across lineage boundaries. Furthermore, when used in combination with computational methods, AI can accelerate and improve the analysis and understanding of the intricate relationships between genes, proteins, and cellular processes. In this review article, we explore the current state of AI applications in biophysics with a specific focus on cellular engineering and reprogramming. Then, we showcase a couple of recent applications where we combined machine learning with experimental and computational techniques. Finally, we briefly discuss the challenges and prospects of AI in cellular engineering and reprogramming, emphasizing the potential of these technologies to revolutionize our ability to engineer cells for a variety of applications, from disease modeling and drug discovery to regenerative medicine and biomanufacturing.

Towards multi-omics synthetic data integration

Across many scientific disciplines, the development of computational models and algorithms for generating artificial or synthetic data is gaining momentum. In biology, there is a great opportunity to explore this further as more and more big data at multi-omics level are generated recently. In this opinion, we discuss the latest trends in biological applications based on process-driven and data-driven aspects. Moving ahead, we believe these methodologies can help shape novel multi-omics-scale cellular inferences.

The triggering pathway, the metabolic amplifying pathway, and cellular transduction in regulation of glucose-dependent biphasic insulin secretion

Introduction: Insulin secretion is a highly regulated process critical for maintaining glucose homeostasis. This abstract explores the intricate interplay between three essential pathways: The Triggering Pathway, The Metabolic Amplifying Pathway, and Cellular Transduction, in orchestrating glucose-dependent biphasic insulin secretion.Mechanism: During the triggering pathway, glucose metabolism in pancreatic beta-cells leads to ATP production, closing ATP-sensitive potassium channels and initiating insulin exocytosis. The metabolic amplifying pathway enhances insulin secretion via key metabolites like NADH and glutamate, enhancing calcium influx and insulin granule exocytosis. Additionally, the cellular transduction pathway involves G-protein coupled receptors and cyclic AMP, modulating insulin secretion.Result and Conclusion: These interconnected pathways ensure a dynamic insulin response to fluctuating glucose levels, with the initial rapid phase and the subsequent sustained phase. Understanding these pathways' complexities provides crucial insights into insulin dysregulation in diabetes and highlights potential therapeutic targets to restore glucose-dependent insulin secretion.

Identifying Key In Silico Knockout for Enhancement of Limonene Yield Through Dynamic Metabolic Modelling

Living cells display dynamic and complex behaviors. To understand their response and to infer novel insights not possible with traditional reductionist approaches, over the last few decades various computational modelling methodologies have been developed. In this chapter, we focus on modelling the dynamic metabolic response, using linear and nonlinear ordinary differential equations, of an engineered Escherichia coli MG1655 strain with plasmid pJBEI-6409 that produces limonene. We show the systems biology steps involved from collecting time-series data of living cells, to dynamic model creation and fitting the model with experimental responses using COPASI software.

Recent advances in systems metabolic engineering

Systems metabolic engineering, which integrates metabolic engineering with systems biology, synthetic biology, and evolutionary engineering, has revolutionized the sustainable production of fuels and materials through the creation of efficient microbial cell factories. Recent advancements in systems metabolic engineering targeting different biological components of the host cell have enabled the creation of highly productive microbial cell factories. This article provides a review of the recent tools and strategies used for enzyme-, genetic module-, pathway-, flux-, genome-, and cell-level engineering, supported by illustrative examples. Furthermore, we highlight recent trends in systems metabolic engineering, which involve the application of multiple tools discussed in this review. Finally, the paper addresses the challenges and perspectives of transitioning academic-level metabolic engineering studies to commercial-scale production.

Perspectives for Using CO2 as a Feedstock for Biomanufacturing of Fuels and Chemicals

Microbial cell factories offer an eco-friendly alternative for transforming raw materials into commercially valuable products because of their reduced carbon impact compared to conventional industrial procedures. These systems often depend on lignocellulosic feedstocks, mainly pentose and hexose sugars. One major hurdle when utilizing these sugars, especially glucose, is balancing carbon allocation to satisfy energy, cofactor, and other essential component needs for cellular proliferation while maintaining a robust yield. Nearly half or more of this carbon is inevitably lost as CO2 during the biosynthesis of regular metabolic necessities. This loss lowers the production yield and compromises the benefit of reducing greenhouse gas emissions-a fundamental advantage of biomanufacturing. This review paper posits the perspectives of using CO2 from the atmosphere, industrial wastes, or the exhausted gases generated in microbial fermentation as a feedstock for biomanufacturing. Achieving the carbon-neutral or -negative goals is addressed under two main strategies. The one-step strategy uses novel metabolic pathway design and engineering approaches to directly fix the CO2 toward the synthesis of the desired products. Due to the limitation of the yield and efficiency in one-step fixation, the two-step strategy aims to integrate firstly the electrochemical conversion of the exhausted CO2 into C1/C2 products such as formate, methanol, acetate, and ethanol, and a second fermentation process to utilize the CO2-derived C1/C2 chemicals or co-utilize C5/C6 sugars and C1/C2 chemicals for product formation. The potential and challenges of using CO2 as a feedstock for future biomanufacturing of fuels and chemicals are also discussed.

The potentials and challenges of using fermentation to improve the sensory quality of plant-based meat analogs

Despite the advancements made in improving the quality of plant-based meat substitutes, more work needs to be done to match the texture, appearance, and flavor of real meat. This review aims to cover the sensory quality constraints of plant-based meat analogs and provides fermentation as a sustainable approach to push these boundaries. Plant-based meat analogs have been observed to have weak and soft textural quality, poor mouth feel, an unstable color, and unpleasant and beany flavors in some cases, necessitating the search for efficient novel technologies. A wide range of microorganisms, including bacteria such as Lactobacillus acidophilus and Lactiplantibacillus plantarum, as well as fungi like Fusarium venenatum and Neurospora intermedia, have improved the product texture to mimic fibrous meat structures. Additionally, the chewiness and hardness of the resulting meat analogs have been further improved through the use of Bacillus subtilis. However, excessive fermentation may result in a decrease in the final product's firmness and produce a slimy texture. Similarly, several microbial metabolites can mimic the color and flavor of meat, with some concerns. It appears that fermentation is a promising approach to modulating the sensory profiles of plant-derived meat ingredients without adverse consequences. In addition, the technology of starter cultures can be optimized and introduced as a new strategy to enhance the organoleptic properties of plant-based meat while still meeting the needs of an expanding and sustainable economy.

A critical review of machine-learning for "multi-omics" marine metabolite datasets

During the last decade, genomic, transcriptomic, proteomic, metabolomic, and other omics datasets have been generated for a wide range of marine organisms, and even more are still on the way. Marine organisms possess unique and diverse biosynthetic pathways contributing to the synthesis of novel secondary metabolites with significant bioactivities. As marine organisms have a greater tendency to adapt to stressed environmental conditions, the chance to identify novel bioactive metabolites with potential biotechnological application is very high. This review presents a comprehensive overview of the available "-omics" and "multi-omics" approaches employed for characterizing marine metabolites along with novel data integration tools. The need for the development of machine-learning algorithms for "multi-omics" approaches is briefly discussed. In addition, the challenges involved in the analysis of "multi-omics" data and recommendations for conducting "multi-omics" study were discussed.

Construction and application of high-quality genome-scale metabolic model of Zymomonas mobilis to guide rational design of microbial cell factories

High-quality genome-scale metabolic models (GEMs) could play critical roles on rational design of microbial cell factories in the classical Design-Build-Test-Learn cycle of synthetic biology studies. Despite of the constant establishment and update of GEMs for model microorganisms such as Escherichia coli and Saccharomyces cerevisiae, high-quality GEMs for non-model industrial microorganisms are still scarce. Zymomonas mobilis subsp. mobilis ZM4 is a non-model ethanologenic microorganism with many excellent industrial characteristics that has been developing as microbial cell factories for biochemical production. Although five GEMs of Z. mobilis have been constructed, these models are either generating ATP incorrectly, or lacking information of plasmid genes, or not providing standard format file. In this study, a high-quality GEM iZM516 of Z. mobilis ZM4 was constructed. The information from the improved genome annotation, literature, datasets of Biolog Phenotype Microarray studies, and recently updated Gene-Protein-Reaction information was combined for the curation of iZM516. Finally, 516 genes, 1389 reactions, 1437 metabolites, and 3 cell compartments are included in iZM516, which also had the highest MEMOTE score of 91% among all published GEMs of Z. mobilis. Cell growth was then predicted by iZM516, which had 79.4% agreement with the experimental results of the substrate utilization. In addition, the potential endogenous succinate synthesis pathway of Z. mobilis ZM4 was proposed through simulation and analysis using iZM516. Furthermore, metabolic engineering strategies to produce succinate and 1,4-butanediol (1,4-BDO) were designed and then simulated under anaerobic condition using iZM516. The results indicated that 1.68 mol/mol succinate and 1.07 mol/mol 1,4-BDO can be achieved through combinational metabolic engineering strategies, which was comparable to that of the model species E. coli. Our study thus not only established a high-quality GEM iZM516 to help understand and design microbial cell factories for economic biochemical production using Z. mobilis as the chassis, but also provided guidance on building accurate GEMs for other non-model industrial microorganisms.

Significance of Fermentation in Plant-Based Meat Analogs: A Critical Review of Nutrition, and Safety-Related Aspects

Plant-based meat analogs have been shown to cause less harm for both human health and the environment compared to real meat, especially processed meat. However, the intense pressure to enhance the sensory qualities of plant-based meat alternatives has caused their nutritional and safety aspects to be overlooked. This paper reviews our current understanding of the nutrition and safety behind plant-based meat alternatives, proposing fermentation as a potential way of overcoming limitations in these aspects. Plant protein blends, fortification, and preservatives have been the main methods for enhancing the nutritional content and stability of plant-based meat alternatives, but concerns that include safety, nutrient deficiencies, low digestibility, high allergenicity, and high costs have been raised in their use. Fermentation with microorganisms such as Bacillus subtilis, Lactiplantibacillus plantarum, Neurospora intermedia, and Rhizopus oryzae improves digestibility and reduces allergenicity and antinutritive factors more effectively. At the same time, microbial metabolites can boost the final product's safety, nutrition, and sensory quality, although some concerns regarding their toxicity remain. Designing a single starter culture or microbial consortium for plant-based meat alternatives can be a novel solution for advancing the health benefits of the final product while still fulfilling the demands of an expanding and sustainable economy.

Biosimilars for the next decade in Latin America: a window of opportunity

INTRODUCTION

Biosimilars are gaining popularity in Latin America (LA). The biosimilars market is expected to grow rapidly over the next decade as a cost-effective alternative to expensive patented biologics. The drivers for the growing demand include needs for affordable health care, the prevalence of chronic diseases, expiration of patents for numerous biologic medicines and the advent of artificial intelligence (AI). Countries such as Argentina, Brazil and Mexico have implemented regulatory frameworks for the approval of biosimilars as well as for investment in local manufacturing capacity, sale, and distribution. Some LA countries face challenges related to low quality institutional frameworks and deficient public policies for regulatory harmonization of these medicines.

AREAS COVERED

The aim of this article is to analyze the broad window of opportunity for biosimilars in LA (Brazil, Mexico and Argentina) in the next decade, considering their regulations and institutional quality, as well as an affordable cost for patients with chronic diseases and highlight the biosimilars approved in the three countries studied. Likewise, the future contribution of AI in the drug R&D process is considered.

EXPERT OPINION

Preparing the next decade of biosimilars in LA will involve improving international regulatory frameworks, institutional quality, investments and capacity in R&D (competencies, infrastructure, and AI).

Microsystem Advances through Integration with Artificial Intelligence

Microfluidics is a rapidly growing discipline that involves studying and manipulating fluids at reduced length scale and volume, typically on the scale of micro- or nanoliters. Under the reduced length scale and larger surface-to-volume ratio, advantages of low reagent consumption, faster reaction kinetics, and more compact systems are evident in microfluidics. However, miniaturization of microfluidic chips and systems introduces challenges of stricter tolerances in designing and controlling them for interdisciplinary applications. Recent advances in artificial intelligence (AI) have brought innovation to microfluidics from design, simulation, automation, and optimization to bioanalysis and data analytics. In microfluidics, the Navier-Stokes equations, which are partial differential equations describing viscous fluid motion that in complete form are known to not have a general analytical solution, can be simplified and have fair performance through numerical approximation due to low inertia and laminar flow. Approximation using neural networks trained by rules of physical knowledge introduces a new possibility to predict the physicochemical nature. The combination of microfluidics and automation can produce large amounts of data, where features and patterns that are difficult to discern by a human can be extracted by machine learning. Therefore, integration with AI introduces the potential to revolutionize the microfluidic workflow by enabling the precision control and automation of data analysis. Deployment of smart microfluidics may be tremendously beneficial in various applications in the future, including high-throughput drug discovery, rapid point-of-care-testing (POCT), and personalized medicine. In this review, we summarize key microfluidic advances integrated with AI and discuss the outlook and possibilities of combining AI and microfluidics.

Peptidomics

Peptides are biopolymers, typically consisting of 2-50 amino acids. They are biologically produced by the cellular ribosomal machinery or by non-ribosomal enzymes and, sometimes, other dedicated ligases. Peptides are arranged as linear chains or cycles, and include post-translational modifications, unusual amino acids and stabilizing motifs. Their structure and molecular size render them a unique chemical space, between small molecules and larger proteins. Peptides have important physiological functions as intrinsic signalling molecules, such as neuropeptides and peptide hormones, for cellular or interspecies communication, as toxins to catch prey or as defence molecules to fend off enemies and microorganisms. Clinically, they are gaining popularity as biomarkers or innovative therapeutics; to date there are more than 60 peptide drugs approved and more than 150 in clinical development. The emerging field of peptidomics comprises the comprehensive qualitative and quantitative analysis of the suite of peptides in a biological sample (endogenously produced, or exogenously administered as drugs). Peptidomics employs techniques of genomics, modern proteomics, state-of-the-art analytical chemistry and innovative computational biology, with a specialized set of tools. The complex biological matrices and often low abundance of analytes typically examined in peptidomics experiments require optimized sample preparation and isolation, including in silico analysis. This Primer covers the combination of techniques and workflows needed for peptide discovery and characterization and provides an overview of various biological and clinical applications of peptidomics.

Missing data in multi-omics integration: Recent advances through artificial intelligence

Biological systems function through complex interactions between various 'omics (biomolecules), and a more complete understanding of these systems is only possible through an integrated, multi-omic perspective. This has presented the need for the development of integration approaches that are able to capture the complex, often non-linear, interactions that define these biological systems and are adapted to the challenges of combining the heterogenous data across 'omic views. A principal challenge to multi-omic integration is missing data because all biomolecules are not measured in all samples. Due to either cost, instrument sensitivity, or other experimental factors, data for a biological sample may be missing for one or more 'omic techologies. Recent methodological developments in artificial intelligence and statistical learning have greatly facilitated the analyses of multi-omics data, however many of these techniques assume access to completely observed data. A subset of these methods incorporate mechanisms for handling partially observed samples, and these methods are the focus of this review. We describe recently developed approaches, noting their primary use cases and highlighting each method's approach to handling missing data. We additionally provide an overview of the more traditional missing data workflows and their limitations; and we discuss potential avenues for further developments as well as how the missing data issue and its current solutions may generalize beyond the multi-omics context.

A Review on Artificial Intelligence Enabled Design, Synthesis, and Process Optimization of Chemical Products for Industry 4.0

With the development of Industry 4.0, artificial intelligence (AI) is gaining increasing attention for its performance in solving particularly complex problems in industrial chemistry and chemical engineering. Therefore, this review provides an overview of the application of AI techniques, in particular machine learning, in chemical design, synthesis, and process optimization over the past years. In this review, the focus is on the application of AI for structure-function relationship analysis, synthetic route planning, and automated synthesis. Finally, we discuss the challenges and future of AI in making chemical products.

Application of GeneCloudOmics: Transcriptomic Data Analytics for Synthetic Biology

Research in synthetic biology and metabolic engineering require a deep understanding on the function and regulation of complex pathway genes. This can be achieved through gene expression profiling which quantifies the transcriptome-wide expression under any condition, such as a cell development stage, mutant, disease, or treatment with a drug. The expression profiling is usually done using high-throughput techniques such as RNA sequencing (RNA-Seq) or microarray. Although both methods are based on different technical approaches, they provide quantitative measures of the expression levels of thousands of genes. The expression levels of the genes are compared under different conditions to identify the differentially expressed genes (DEGs), the genes with different expression levels under different conditions. DEGs, usually involving thousands in number, are then investigated using bioinformatics and data analytic tools to infer and compare their functional roles between conditions. Dealing with such large datasets, therefore, requires intensive data processing and analyses to ensure its quality and produce results that are statistically sound. Thus, there is a need for deep statistical and bioinformatics knowledge to deal with high-throughput gene expression data. This represents a barrier for wet biologists with limited computational, programming, and data analytic skills that prevent them from getting the full potential of the data. In this chapter, we present a step-by-step protocol to perform transcriptome analysis using GeneCloudOmics, a cloud-based web server that provides an end-to-end platform for high-throughput gene expression analysis.

Perspective: Multiomics and Machine Learning Help Unleash the Alternative Food Potential of Microalgae

Food security has become a pressing issue in the modern world. The ever-increasing world population, ongoing COVID-19 pandemic, and political conflicts together with climate change issues make the problem very challenging. Therefore, fundamental changes to the current food system and new sources of alternative food are required. Recently, the exploration of alternative food sources has been supported by numerous governmental and research organizations, as well as by small and large commercial ventures. Microalgae are gaining momentum as an effective source of alternative laboratory-based nutritional proteins as they are easy to grow under variable environmental conditions, with the added advantage of absorbing carbon dioxide. Despite their attractiveness, the utilization of microalgae faces several practical limitations. Here, we discuss both the potential and challenges of microalgae in food sustainability and their possible long-term contribution to the circular economy of converting food waste into feed via modern methods. We also argue that systems biology and artificial intelligence can play a role in overcoming some of the challenges and limitations; through data-guided metabolic flux optimization, and by systematically increasing the growth of the microalgae strains without negative outcomes, such as toxicity. This requires microalgae databases rich in omics data and further developments on its mining and analytics methods.

Machine Learning and Hybrid Methods for Metabolic Pathway Modeling

Computational cell metabolism models seek to provide metabolic explanations of cell behavior under different conditions or following genetic alterations, help in the optimization of in vitro cell growth environments, or predict cellular behavior in vivo and in vitro. In the extremes, mechanistic models can include highly detailed descriptions of a small number of metabolic reactions or an approximate representation of an entire metabolic network. To date, all mechanistic models have required details of individual metabolic reactions, either kinetic parameters or metabolic flux, as well as information about extracellular and intracellular metabolite concentrations. Despite the extensive efforts and the increasing availability of high-quality data, required in vivo data are not available for the majority of known metabolic reactions; thus, mechanistic models are based primarily on ex vivo kinetic measurements and limited flux information. Machine learning approaches provide an alternative for derivation of functional dependencies from existing data. The increasing availability of metabolomic and lipidomic data, with growing feature coverage as well as sample set size, is expected to provide new data options needed for derivation of machine learning models of cell metabolic processes. Moreover, machine learning analysis of longitudinal data can lead to predictive models of cell behaviors over time. Conversely, machine learning models trained on steady-state data can provide descriptive models for the comparison of metabolic states in different environments or disease conditions. Additionally, inclusion of metabolic network knowledge in these analyses can further help in the development of models with limited data.This chapter will explore the application of machine learning to the modeling of cell metabolism. We first provide a theoretical explanation of several machine learning and hybrid mechanistic machine learning methods currently being explored to model metabolism. Next, we introduce several avenues for improving these models with machine learning. Finally, we provide protocols for specific examples of the utilization of machine learning in the development of predictive cell metabolism models using metabolomic data. We describe data preprocessing, approaches for training of machine learning models for both descriptive and predictive models, and the utilization of these models in synthetic and systems biology. Detailed protocols provide a list of software tools and libraries used for these applications, step-by-step modeling protocols, troubleshooting, as well as an overview of existing limitations to these approaches.

A Novel Method to Screen Strong Constitutive Promoters in Escherichia coli and Serratia marcescens for Industrial Applications

Promoters serve as the switch of gene transcription, playing an important role in regulating gene expression and metabolites production. However, the approach to screening strong constitutive promoters in microorganisms is still limited. In this study, a novel method was designed to identify strong constitutive promoters in E. coli and S. marcescens based on random genomic interruption and fluorescence-activated cell sorting (FACS) technology. First, genomes of E. coli, Bacillus subtilis, and Corynebacterium glutamicum were randomly interrupted and inserted into the upstream of reporter gene gfp to construct three promoter libraries, and a potential strong constitutive promoter (P_BS) suitable for E. coli was screened via FACS technology. Second, the core promoter sequence (P_BS76) of the screened promoter was identified by sequence truncation. Third, a promoter library of P_BS76 was constructed by installing degenerate bases via chemical synthesis for further improving its strength, and the intensity of the produced promoter P_BS76-100 was 59.56 times higher than that of the promoter P_{BBa_J23118}. Subsequently, promoters P_BBa__J23118, P_BS76, P_BS76-50, P_BS76-75, P_BS76-85, and P_BS76-100 with different strengths were applied to enhance the metabolic flux of L-valine synthesis, and the L-valine yield was significantly improved. Finally, a strong constitutive promoter suitable for S. marcescens was screened by a similar method and applied to enhance prodigiosin production by 34.81%. Taken together, the construction of a promoter library based on random genomic interruption was effective to screen the strong constitutive promoters for fine-tuning gene expression and reprogramming metabolic flux in various microorganisms.

Metabolomics and modelling approaches for systems metabolic engineering

Metabolic engineering involves the manipulation of microbes to produce desirable compounds through genetic engineering or synthetic biology approaches. Metabolomics involves the quantitation of intracellular and extracellular metabolites, where mass spectrometry and nuclear magnetic resonance based analytical instrumentation are often used. Here, the experimental designs, sample preparations, metabolite quenching and extraction are essential to the quantitative metabolomics workflow. The resultant metabolomics data can then be used with computational modelling approaches, such as kinetic and constraint-based modelling, to better understand underlying mechanisms and bottlenecks in the synthesis of desired compounds, thereby accelerating research through systems metabolic engineering. Constraint-based models, such as genome scale models, have been used successfully to enhance the yield of desired compounds from engineered microbes, however, unlike kinetic or dynamic models, constraint-based models do not incorporate regulatory effects. Nevertheless, the lack of time-series metabolomic data generation has hindered the usefulness of dynamic models till today. In this review, we show that improvements in automation, dynamic real-time analysis and high throughput workflows can drive the generation of more quality data for dynamic models through time-series metabolomics data generation. Spatial metabolomics also has the potential to be used as a complementary approach to conventional metabolomics, as it provides information on the localization of metabolites. However, more effort must be undertaken to identify metabolites from spatial metabolomics data derived through imaging mass spectrometry, where machine learning approaches could prove useful. On the other hand, single-cell metabolomics has also seen rapid growth, where understanding cell-cell heterogeneity can provide more insights into efficient metabolic engineering of microbes. Moving forward, with potential improvements in automation, dynamic real-time analysis, high throughput workflows, and spatial metabolomics, more data can be produced and studied using machine learning algorithms, in conjunction with dynamic models, to generate qualitative and quantitative predictions to advance metabolic engineering efforts.

Machine learning alternative to systems biology should not solely depend on data

In recent years, artificial intelligence (AI)/machine learning has emerged as a plausible alternative to systems biology for the elucidation of biological phenomena and in attaining specified design objective in synthetic biology. Although considered highly disruptive with numerous notable successes so far, we seek to bring attention to both the fundamental and practical pitfalls of their usage, especially in illuminating emergent behaviors from chaotic or stochastic systems in biology. Without deliberating on their suitability and the required data qualities and pre-processing approaches beforehand, the research and development community could experience similar 'AI winters' that had plagued other fields. Instead, we anticipate the integration or combination of the two approaches, where appropriate, moving forward.

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.

Non-target molecular network and putative genes of flavonoid biosynthesis in Erythrina velutina Willd., a Brazilian semiarid native woody plant

Erythrina velutina is a Brazilian native tree of the Caatinga (a unique semiarid biome). It is widely used in traditional medicine showing anti-inflammatory and central nervous system modulating activities. The species is a rich source of specialized metabolites, mostly alkaloids and flavonoids. To date, genomic information, biosynthesis, and regulation of flavonoids remain unknown in this woody plant. As part of a larger ongoing research goal to better understand specialized metabolism in plants inhabiting the harsh conditions of the Caatinga, the present study focused on this important class of bioactive phenolics. Leaves and seeds of plants growing in their natural habitat had their metabolic and proteomic profiles analyzed and integrated with transcriptome data. As a result, 96 metabolites (including 43 flavonoids) were annotated. Transcripts of the flavonoid pathway totaled 27, of which EvCHI, EvCHR, EvCHS, EvCYP75A and EvCYP75B1 were identified as putative main targets for modulating the accumulation of these metabolites. The highest correspondence of mRNA vs. protein was observed in the differentially expressed transcripts. In addition, 394 candidate transcripts encoding for transcription factors distributed among the bHLH, ERF, and MYB families were annotated. Based on interaction network analyses, several putative genes of the flavonoid pathway and transcription factors were related, particularly TFs of the MYB family. Expression patterns of transcripts involved in flavonoid biosynthesis and those involved in responses to biotic and abiotic stresses were discussed in detail. Overall, these findings provide a base for the understanding of molecular and metabolic responses in this medicinally important species. Moreover, the identification of key regulatory targets for future studies aiming at bioactive metabolite production will be facilitated.

A Feature Selection Approach Guided an Early Prediction of Anthocyanin Accumulation Using Massive Untargeted Metabolomics Data in Mulberry

Identifying the early predictive biomarkers or compounds represents a pivotal task for guiding a targeted agricultural practice. Despite the various available tools, it remains challenging to define the ideal compound combination and thereby elaborate an effective predictive model fitting that. Hence, we employed a stepwise feature selection approach followed by a maximum relevance and minimum redundancy (MRMR) on the untargeted metabolism in four mulberry genotypes at different fruit developmental stages (FDSs). Thus, we revealed that 7 out of 226 differentially abundant metabolites (DAMs) explained up to 80% variance of anthocyanin based on linear regression model and stepwise feature selection approach accompanied by an MRMR across the genotypes over the FDSs. Among them, the phosphoenolpyruvate, d-mannose and shikimate show the top 3 attribution indexes to the accumulation of anthocyanin in the fruits of these genotypes across the four FDSs. The obtained results were further validated by assessing the regulatory genes expression levels and the targeted metabolism approach. Taken together, our findings provide valuable evidences on the fact that the anthocyanin biosynthesis is somehow involved in the coordination between the carbon metabolism and secondary metabolic pathway. Our report highlights as well the importance of using the feature selection approach for the predictive biomarker identification issued from the untargeted metabolomics data.

Genome Editing Technology and Its Application to Metabolic Engineering in Rice

Genome editing technology can be used for gene engineering in many organisms. A target metabolite can be fortified by the knockout and modification of target genes encoding enzymes involved in catabolic and biosynthesis pathways, respectively, via genome editing technology. Genome editing is also applied to genes encoding proteins other than enzymes, such as chaperones and transporters. There are many reports of such metabolic engineering using genome editing technology in rice. Genome editing is used not only for site-directed mutagenesis such as the substitution of a single base in a target gene but also for random mutagenesis at a targeted region. The latter enables the creation of novel genetic alleles in a target gene. Recently, genome editing technology has been applied to random mutagenesis in a targeted gene and its promoter region in rice, enabling the screening of plants with a desirable trait from these mutants. Moreover, the expression level of a target gene can be artificially regulated by a combination of genome editing tools such as catalytically inactivated Cas protein with transcription activator or repressor. This approach could be useful for metabolic engineering, although expression cassettes for inactivated Cas fused to a transcriptional activator or repressor should be stably transformed into the rice genome. Thus, the rapid development of genome editing technology has been expanding the scope of molecular breeding including metabolic engineering. In this paper, we review the current status of genome editing technology and its application to metabolic engineering in rice.

Toward improved terpenoids biosynthesis: strategies to enhance the capabilities of cell factories

Terpenoids form the most diversified class of natural products, which have gained application in the pharmaceutical, food, transportation, and fine and bulk chemical industries. Extraction from naturally occurring sources does not meet industrial demands, whereas chemical synthesis is often associated with poor enantio-selectivity, harsh working conditions, and environmental pollutions. Microbial cell factories come as a suitable replacement. However, designing efficient microbial platforms for isoprenoid synthesis is often a challenging task. This has to do with the cytotoxic effects of pathway intermediates and some end products, instability of expressed pathways, as well as high enzyme promiscuity. Also, the low enzymatic activity of some terpene synthases and prenyltransferases, and the lack of an efficient throughput system to screen improved high-performing strains are bottlenecks in strain development. Metabolic engineering and synthetic biology seek to overcome these issues through the provision of effective synthetic tools. This review sought to provide an in-depth description of novel strategies for improving cell factory performance. We focused on improving transcriptional and translational efficiencies through static and dynamic regulatory elements, enzyme engineering and high-throughput screening strategies, cellular function enhancement through chromosomal integration, metabolite tolerance, and modularization of pathways.

Saccharomyces cerevisiae as a Heterologous Host for Natural Products

Cell factories can provide a sustainable supply of natural products with applications as pharmaceuticals, food-additives or biofuels. Besides being an important model organism for eukaryotic systems, Saccharomyces cerevisiae is used as a chassis for the heterologous production of natural products. Its success as a cell factory can be attributed to the vast knowledge accumulated over decades of research, its overall ease of engineering and its robustness. Many methods and toolkits have been developed by the yeast metabolic engineering community with the aim of simplifying and accelerating the engineering process.In this chapter, a range of methodologies are highlighted, which can be used to develop novel natural product cell factories or to improve titer, rate and yields of an existing cell factory with the goal of developing an industrially relevant strain. The addressed topics are applicable for different stages of a cell factory engineering project and include the choice of a natural product platform strain, expression cassette design for heterologous or native genes, basic and advanced genetic engineering strategies, and library screening methods using biosensors. The many engineering methods available and the examples of yeast cell factories underline the importance and future potential of this host for industrial production of natural products.

Systems Biology to Understand and Regulate Human Retroviral Proinflammatory Response

The majority of human genome are non-coding genes. Recent research have revealed that about half of these genome sequences make up of transposable elements (TEs). A branch of these belong to the endogenous retroviruses (ERVs), which are germline viral infection that occurred over millions of years ago. They are generally harmless as evolutionary mutations have made them unable to produce viral agents and are mostly epigenetically silenced. Nevertheless, ERVs are able to express by still unknown mechanisms and recent evidences have shown links between ERVs and major proinflammatory diseases and cancers. The major challenge is to elucidate a detailed mechanistic understanding between them, so that novel therapeutic approaches can be explored. Here, we provide a brief overview of TEs, human ERVs and their links to microbiome, innate immune response, proinflammatory diseases and cancer. Finally, we recommend the employment of systems biology approaches for future HERV research.

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.

Revolutionizing enzyme engineering through artificial intelligence and machine learning

The combinatorial space of an enzyme sequence has astronomical possibilities and exploring it with contemporary experimental techniques is arduous and often ineffective. Multi-target objectives such as concomitantly achieving improved selectivity, solubility and activity of an enzyme have narrow plausibility under approaches of restricted mutagenesis and combinatorial search. Traditional enzyme engineering approaches have a limited scope for complex optimization due to the requirement of a priori knowledge or experimental burden of screening huge protein libraries. The recent surge in high-throughput experimental methods including Next Generation Sequencing and automated screening has flooded the field of molecular biology with big-data, which requires us to re-think our concurrent approaches towards enzyme engineering. Artificial Intelligence (AI) and Machine Learning (ML) have great potential to revolutionize smart enzyme engineering without the explicit need for a complete understanding of the underlying molecular system. Here, we portray the role and position of AI techniques in the field of enzyme engineering along with their scope and limitations. In addition, we explain how the traditional approaches of directed evolution and rational design can be extended through AI tools. Recent successful examples of AI-assisted enzyme engineering projects and their deviation from traditional approaches are highlighted. A comprehensive picture of current challenges and future avenues for AI in enzyme engineering are also discussed.

A Retrospective Literature Evaluation of the Integration of Stress Physiology Indices, Animal Welfare and Climate Change Assessment of Livestock

Simple Summary

Rapidly expanding global human population has led to increased supply chain demands on animal-based farming systems and the desire for environmentally friendly products. This has also resulted because of socio-political pressure and increased public concerns over the impacts of conventional agriculture on the environment. In order to be sustainable, animal production systems must also advance animal welfare, avoiding physically and psychologically stressful situations for the animals and apply innovative methods of reducing contribution of farming practices to global climate change while also functioning at optimum productivity. Consequently, to achieve a practical and effective improvement towards environmental sustainability, animal-based agriculture should consider animal welfare assessment, objective measures of physiological stress, climate change evaluation and animal productivity in a multi-dimensional and holistic approach.

Abstract

In this retrospective study, we conducted a desktop-based analysis of published literature using the ScienceDirect™ search engine to determine the proportion of livestock research within the last 7 years (2015–2021) that have applied animal welfare assessment combining objective measures of physiological stress and evaluation of climate change factors in order to provide an account of livestock productivity. From the search results, 563 published articles were reviewed. We found that the majority of the literature had discussed animal production outcomes (n = 491) and animal welfare (n = 453) either individually or in conjunction with another topic. The most popular occurrence was the combination of animal welfare assessment, objective measures of stress physiology and production outcomes discussed collectively (n = 218). We found that only 125 articles had discussed the impact of climate change (22.20%) on livestock production and/or vice versa. Furthermore, only 9.4% (n = 53) of articles had discussed all four factors and published research was skewed towards the dairy sector. Overall, this retrospective paper highlights that although research into animal welfare assessment, objective measures of stress and climate change has been applied across livestock production systems (monogastrics and ruminants), there remains a shortfall of investigation on how these key factors interact to influence livestock production. Furthermore, emerging technologies that can boost the quantitative evaluation of animal welfare are needed for both intensive and extensive production systems.

Internet of Nonthermal Food Processing Technologies (IoNTP): Food Industry 4.0 and Sustainability

With the introduction of Industry 4.0, and smart factories accordingly, there are new opportunities to implement elements of industry 4.0 in nonthermal processing. Moreover, with application of Internet of things (IoT), smart control of the process, big data optimization, as well as sustainable production and monitoring, there is a new era of Internet of nonthermal food processing technologies (IoNTP). Nonthermal technologies include high power ultrasound, pulsed electric fields, high voltage electrical discharge, high pressure processing, UV-LED, pulsed light, e-beam, and advanced thermal food processing techniques include microwave processing, ohmic heating and high-pressure homogenization. The aim of this review was to bring in front necessity to evaluate possibilities of implementing smart sensors, artificial intelligence (AI), big data, additive technologies with nonthermal technologies, with the possibility to create smart factories together with strong emphasis on sustainability. This paper brings an overview on digitalization, IoT, additive technologies (3D printing), cloud data storage and smart sensors including two SWOT analysis associated with IoNTPs and sustainability. It is of high importance to perform life cycle assessment (LCA), to quantify (En)—environmental dimension; (So)—social dimension and (Ec)—economic dimension. SWOT analysis showed: potential for energy saving during food processing; optimized overall environmental performance; lower manufacturing cost; development of eco-friendly products; higher level of health and safety during food processing and better work condition for workers. Nonthermal and advanced thermal technologies can be applied also as sustainable techniques working in line with the sustainable development goals (SDGs) and Agenda 2030 issued by United Nations (UN).