Genome-scale modeling for metabolic engineering

Comparing genome-scale metabolic models of the non-resistant Enterococcus faecalis ATCC 19433 and the multi-resistant Enterococcus faecalis V583

Enterococcus faecalis is a versatile lactic acid bacterium with a large variety of implications for humans. While some strains of this species are pathobionts being resistant against most of the common antibiotics, other strains are regarded as biological protectants or even probiotics. Accordingly, E. faecalis strains largely differ in the size and content of their accessory genome. In this study, we describe the genome-scale metabolic network reconstruction of E. faecalis ATCC 19433, a non-resistant human-associated strain. A comparison of the genome-scale metabolic model (GSM) of E. faecalis ATCC 19433 with a previously published GSM of the multi-resistant pathobiontic E. faecalis V583 reveals high similarities in the central metabolic abilities of these two human associated strains. This is reflected, e.g., in the identical amino acid auxotrophies. The ATCC 19433 strain, however, has a 14.1 % smaller genome than V583 and lacks the multiple antibiotic resistance genes and genes involved in capsule formation. Based on the measured metabolic fluxes at different growth rates, the energy demand at zero growth was calculated to be about 40 % lower for the ATCC 19433 strain compared to V583. Furthermore, the ATCC 19433 strain seems less prone to the depletion of amino acids utilizable for energy metabolism. This might hint at a lower overall energy demand of the ATCC 19433 strain as compared to V583.

A long-term growth stable Halomonas sp. deleted with multiple transposases guided by its metabolic network model Halo-ecGEM

Microbial instability is a common problem during bio-production based on microbial hosts. Halomonas bluephagenesis has been developed as a chassis for next generation industrial biotechnology (NGIB) under open and unsterile conditions. However, the hidden genomic information and peculiar metabolism have significantly hampered its deep exploitation for cell-factory engineering. Based on the freshly completed genome sequence of H. bluephagenesis TD01, which reveals 1889 biological process-associated genes grouped into 84 GO-slim terms. An enzyme constrained genome-scale metabolic model Halo-ecGEM was constructed, which showed strong ability to simulate fed-batch fermentations. A visible salt-stress responsive landscape was achieved by combining GO-slim term enrichment and CVT-based omics profiling, demonstrating that cells deploy most of the protein resources by force to support the essential activity of translation and protein metabolism when exposed to salt stress. Under the guidance of Halo-ecGEM, eight transposases were deleted, leading to a significantly enhanced stability for its growth and bioproduction of various polyhydroxyalkanoates (PHA) including 3-hydroxybutyrate (3HB) homopolymer PHB, 3HB and 3-hydroxyvalerate (3HV) copolymer PHBV, as well as 3HB and 4-hydroxyvalerate (4HB) copolymer P34HB. This study sheds new light on the metabolic characteristics and stress-response landscape of H. bluephagenesis, achieving for the first time to construct a long-term growth stable chassis for industrial applications. For the first time, it was demonstrated that genome encoded transposons are the reason for microbial instability during growth in flasks and fermentors.

Balancing biomass reaction stoichiometry and measured fluxes in flux balance analysis

MOTIVATION

Flux balance analysis (FBA) is widely recognized as an important method for studying metabolic networks. When incorporating flux measurements of certain reactions into an FBA problem, it is possible that the underlying linear program may become infeasible, e.g. due to measurement or modeling inaccuracies. Furthermore, while the biomass reaction is of central importance in FBA models, its stoichiometry is often a rough estimate and a source of high uncertainty.

RESULTS

In this work, we present a method that allows modifications to the biomass reaction stoichiometry as a means to (i) render the FBA problem feasible and (ii) improve the accuracy of the model by corrections in the biomass composition. Optionally, the adjustment of the biomass composition can be used in conjunction with a previously introduced approach for balancing inconsistent fluxes to obtain a feasible FBA system. We demonstrate the value of our approach by analyzing realistic flux measurements of E.coli. In particular, we find that the growth-associated maintenance (GAM) demand of ATP, which is typically integrated with the biomass reaction, is likely overestimated in recent genome-scale models, at least for certain growth conditions. In light of these findings, we discuss issues related to the determination and inclusion of GAM values in constraint-based models. Overall, our method can uncover potential errors and suggest adjustments in the assumed biomass composition in FBA models based on inconsistencies between the model and measured fluxes.

AVAILABILITY AND IMPLEMENTATION

The developed method has been implemented in our software tool CNApy available from https://github.com/cnapy-org/CNApy.

Systematizing Microbial Bioplastic Production for Developing Sustainable Bioeconomy: Metabolic Nexus Modeling, Economic and Environmental Technologies Assessment

The excessive usage of non-renewable resources to produce plastic commodities has incongruously influenced the environment's health. Especially in the times of COVID-19, the need for plastic-based health products has increased predominantly. Given the rise in global warming and greenhouse gas emissions, the lifecycle of plastic has been established to contribute to it significantly. Bioplastics such as polyhydroxy alkanoates, polylactic acid, etc. derived from renewable energy origin have been a magnificent alternative to conventional plastics and reconnoitered exclusively for combating the environmental footprint of petrochemical plastic. However, the economically reasonable and environmentally friendly procedure of microbial bioplastic production has been a hard nut to crack due to less scouted and inefficient process optimization and downstream processing methodologies. Thereby, meticulous employment of computational tools such as genome-scale metabolic modeling and flux balance analysis has been practiced in recent times to understand the effect of genomic and environmental perturbations on the phenotype of the microorganism. In-silico results not only aid us in determining the biorefinery abilities of the model microorganism but also curb our reliance on equipment, raw materials, and capital investment for optimizing the best conditions. Additionally, to accomplish sustainable large-scale production of microbial bioplastic in a circular bioeconomy, extraction, and refinement of bioplastic needs to be investigated extensively by practicing techno-economic analysis and life cycle assessment. This review put forth state-of-the-art know-how on the proficiency of these computational techniques in laying the foundation of an efficient bioplastic manufacturing blueprint, chiefly focusing on microbial polyhydroxy alkanoates (PHA) production and its efficacy in outplacing fossil based plastic products.

Design and genome engineering of microbial cell factories for efficient conversion of lignocellulose to fuel

The gradually increasing need for fossil fuels demands renewable biofuel substitutes. This has fascinated an increasing investigation to design innovative energy fuels that have comparable Physico-chemical and combustion characteristics with fossil-derived fuels. The efficient microbes for bioenergy synthesis desire the proficiency to consume a large quantity of carbon substrate, transfer various carbohydrates through efficient metabolic pathways, capability to withstand inhibitory components and other degradation compounds, and improve metabolic fluxes to synthesize target compounds. Metabolically engineered microbes could be an efficient methodology for synthesizing biofuel from cellulosic biomass by cautiously manipulating enzymes and metabolic pathways. This review offers a comprehensive perspective on the trends and advances in metabolic and genetic engineering technologies for advanced biofuel synthesis by applying various heterologous hosts. Probable technologies include enzyme engineering, heterologous expression of multiple genes, CRISPR-Cas technologies for genome editing, and cell surface display.

Unlocking the magic in mycelium: Using synthetic biology to optimize filamentous fungi for biomanufacturing and sustainability

Filamentous fungi drive carbon and nutrient cycling across our global ecosystems, through its interactions with growing and decaying flora and their constituent microbiomes. The remarkable metabolic diversity, secretion ability, and fiber-like mycelial structure that have evolved in filamentous fungi have been increasingly exploited in commercial operations. The industrial potential of mycelial fermentation ranges from the discovery and bioproduction of enzymes and bioactive compounds, the decarbonization of food and material production, to environmental remediation and enhanced agricultural production. Despite its fundamental impact in ecology and biotechnology, molds and mushrooms have not, to-date, significantly intersected with synthetic biology in ways comparable to other industrial cell factories (e.g. Escherichia coli,Saccharomyces cerevisiae, and Komagataella phaffii). In this review, we summarize a suite of synthetic biology and computational tools for the mining, engineering and optimization of filamentous fungi as a bioproduction chassis. A combination of methods across genetic engineering, mutagenesis, experimental evolution, and computational modeling can be used to address strain development bottlenecks in established and emerging industries. These include slow mycelium growth rate, low production yields, non-optimal growth in alternative feedstocks, and difficulties in downstream purification. In the scope of biomanufacturing, we then detail previous efforts in improving key bottlenecks by targeting protein processing and secretion pathways, hyphae morphogenesis, and transcriptional control. Bringing synthetic biology practices into the hidden world of molds and mushrooms will serve to expand the limited panel of host organisms that allow for commercially-feasible and environmentally-sustainable bioproduction of enzymes, chemicals, therapeutics, foods, and materials of the future.

A novel SfaNI-like restriction-modification system in Caldicellulosiruptor extents the genetic engineering toolbox for this genus

Caldicellulosiruptor is a genus of thermophilic to hyper-thermophilic microorganisms that express and secrete an arsenal of enzymes degrading lignocellulosic biomasses into fermentable sugars. Because of this distinguished feature, strains of Caldicellulosiruptor have been considered as promising candidates for consolidated bioprocessing. Although a few Caldicellulosiruptor strains with industrially relevant characteristics have been isolated to date, it is apparent that further improvement of the strains is essential for industrial application. The earlier identification of the HaeIII-like restriction-modification system in C. bescii strain DSM 6725 has formed the basis for genetic methods with the aim to improve the strain's lignocellulolytic activity and ethanol production. In this study, a novel SfaNI-like restriction-modification system was identified in Caldicellulosiruptor sp. strain BluCon085, consisting of an endonuclease and two methyltransferases that recognize the reverse-complement sequences 5'-GATGC-3' and 5'-GCATC-3'. Methylation of the adenine in both sequences leads to an asymmetric methylation pattern in the genomic DNA of strain BluCon085. Proteins with high percentage of identity to the endonuclease and two methyltransferases were identified in the genomes of C. saccharolyticus strain DSM 8903, C. naganoensis strain DSM 8991, C. changbaiensis strain DSM 26941 and Caldicellulosiruptor sp. strain F32, suggesting that a similar restriction-modification system may be active also in these strains and respective species. We show that methylation of plasmid and linear DNA by the identified methyltransferases, obtained by heterologous expression in Escherichia coli, is sufficient for successful transformation of Caldicellulosiruptor sp. strain DIB 104C. The genetic engineering toolbox developed in this study forms the basis for rational strain improvement of strain BluCon085, a derivative from strain DIB 104C with exceptionally high L-lactic acid production. The toolbox may also work for other species of the genus Caldicellulosiruptor that have so far not been genetically tractable.

A genome-scale metabolic model of Cupriavidus necator H16 integrated with TraDIS and transcriptomic data reveals metabolic insights for biotechnological applications

Exploiting biological processes to recycle renewable carbon into high value platform chemicals provides a sustainable and greener alternative to current reliance on petrochemicals. In this regard Cupriavidus necator H16 represents a particularly promising microbial chassis due to its ability to grow on a wide range of low-cost feedstocks, including the waste gas carbon dioxide, whilst also naturally producing large quantities of polyhydroxybutyrate (PHB) during nutrient-limited conditions. Understanding the complex metabolic behaviour of this bacterium is a prerequisite for the design of successful engineering strategies for optimising product yields. We present a genome-scale metabolic model (GSM) of C. necator H16 (denoted iCN1361), which is directly constructed from the BioCyc database to improve the readability and reusability of the model. After the initial automated construction, we have performed extensive curation and both theoretical and experimental validation. By carrying out a genome-wide essentiality screening using a Transposon-directed Insertion site Sequencing (TraDIS) approach, we showed that the model could predict gene knockout phenotypes with a high level of accuracy. Importantly, we indicate how experimental and computational predictions can be used to improve model structure and, thus, model accuracy as well as to evaluate potential false positives identified in the experiments. Finally, by integrating transcriptomics data with iCN1361 we create a condition-specific model, which, importantly, better reflects PHB production in C. necator H16. Observed changes in the omics data and in-silico-estimated alterations in fluxes were then used to predict the regulatory control of key cellular processes. The results presented demonstrate that iCN1361 is a valuable tool for unravelling the system-level metabolic behaviour of C. necator H16 and can provide useful insights for designing metabolic engineering strategies.

Genome-scale metabolic models (GSMs) provide a tool for unravelling the complex metabolic behaviour of bacteria and how they adapt to changing environments and genetic perturbations, and thus offer invaluable insights for biotechnology applications. For a GSM to be used efficiently for strain development purposes, however, the model must be easily readable and reusable by other researchers, whilst being able to predict metabolic behaviour with a high level of accuracy. In this work, we developed a GSM for Cupriavidus necator H16 that is linked to the BioCyc database, which provides an efficient way of application, model update, integration of experimental data and network visualisation for other researchers. Using our model, we demonstrate how integrating experimental observations, including Transposon-directed Insertion site Sequencing (TraDIS) and omics data, can be used to compensate for the lack of regulatory, kinetic and thermodynamic information in GSMs, and thus improve model accuracy. Importantly, we found that TraDIS in vivo screening and GSM analysis are complementary approaches, which can be used in combination to provide reliable gene essentiality predictions. Overall, our results offer an informed strategy for the deliberate manipulation of C. necator H16 metabolic capabilities, towards its industrial application to convert greenhouse gases into biochemicals and biofuels.

Refactoring transcription factors for metabolic engineering

Due to the ability to regulate target metabolic pathways globally and dynamically, metabolic regulation systems composed of transcription factors have been widely used in metabolic engineering and synthetic biology. This review introduced the categories, action principles, prediction strategies, and related databases of transcription factors. Then, the application of global transcription machinery engineering technology and the transcription factor-based biosensors and quorum sensing systems are overviewed. In addition, strategies for optimizing the transcriptional regulatory tools' performance by refactoring transcription factors are summarized. Finally, the current limitations and prospects of constructing various regulatory tools based on transcription factors are discussed. This review will provide theoretical guidance for the rational design and construction of transcription factor-based metabolic regulation systems.

Enhancement of Ethanol Production Using a Hybrid of Firefly Algorithm and Dynamic Flux Balance Analysis

Many high-demand industrial products are generated by microorganisms, including fuels, food, vitamins, and other chemicals. Metabolic engineering is the method of circumventing cellular control to manufacture a desirable product or to create a new product that the host cells do not normally need to produce. One of the objectives of microorganism metabolic engineering is to maximise the production of a desired product. However, owing to the structure of the regulatory cellular and metabolic network, identifying specific genes to be knocked out is difficult. The development of optimization algorithms often confronts issues such as easily trapping in local maxima and handling multivariate and multimodal functions inefficiently. To predict the gene knockout list that can generate high yields of desired product, a hybrid of Firefly Algorithm and Dynamic Flux Balance Analysis (FADFBA) is proposed. This paper focuses on the ethanol production of Escherichia coli (E. coli). The findings of the experiments include gene lists, ethanol production, growth rate, and the performance of FADFBA.

Genome-Scale Reconstruction of Microbial Dynamic Phenotype: Successes and Challenges

This review is a part of the SI 'Genome-Scale Modeling of Microorganisms in the Real World'. The goal of GEM is the accurate prediction of the phenotype from its respective genotype under specified environmental conditions. This review focuses on the dynamic phenotype; prediction of the real-life behaviors of microorganisms, such as cell proliferation, dormancy, and mortality; balanced and unbalanced growth; steady-state and transient processes; primary and secondary metabolism; stress responses; etc. Constraint-based metabolic reconstructions were successfully started two decades ago as FBA, followed by more advanced models, but this review starts from the earlier nongenomic predecessors to show that some GEMs inherited the outdated biokinetic frameworks compromising their performances. The most essential deficiencies are: (i) an inadequate account of environmental conditions, such as various degrees of nutrients limitation and other factors shaping phenotypes; (ii) a failure to simulate the adaptive changes of MMCC (MacroMolecular Cell Composition) in response to the fluctuating environment; (iii) the misinterpretation of the SGR (Specific Growth Rate) as either a fixed constant parameter of the model or independent factor affecting the conditional expression of macromolecules; (iv) neglecting stress resistance as an important objective function; and (v) inefficient experimental verification of GEM against simple growth (constant MMCC and SGR) data. Finally, we propose several ways to improve GEMs, such as replacing the outdated Monod equation with the SCM (Synthetic Chemostat Model) that establishes the quantitative relationships between primary and secondary metabolism, growth rate and stress resistance, process kinetics, and cell composition.

Path to improving the life cycle and quality of genome-scale models of metabolism

Genome-scale models of metabolism (GEMs) are key computational tools for the systems-level study of metabolic networks. Here, we describe the "GEM life cycle," which we subdivide into four stages: inception, maturation, specialization, and amalgamation. We show how different types of GEM reconstruction workflows fit in each stage and proceed to highlight two fundamental bottlenecks for GEM quality improvement: GEM maturation and content removal. We identify common characteristics contributing to increasing quality of maturing GEMs drawing from past independent GEM maturation efforts. We then shed some much-needed light on the latent and unrecognized but pervasive issue of content removal, demonstrating the substantial effects of model pruning on its solution space. Finally, we propose a novel framework for content removal and associated confidence-level assignment which will help guide future GEM development efforts, reduce duplication of effort across groups, potentially aid automated reconstruction platforms, and boost the reproducibility of model development.

Knowledge extraction from literature and enzyme sequences complements FBA analysis in metabolic engineering

Flux balance analysis (FBA) using genome-scale metabolic model (GSM) is a useful method for improving the bio-production of useful compounds. However, FBA often does not impose important constraints such as nutrients uptakes, by-products excretions and gases (oxygen and carbon dioxide) transfers. Furthermore, important information on metabolic engineering such as enzyme amounts, activities, and characteristics caused by gene expression and enzyme sequences is basically not included in GSM. Therefore, simple FBA is often not sufficient to search for metabolic manipulation strategies that are useful for improving the production of target compounds. In this study, we proposed a method using literature and enzyme search to complement the FBA-based metabolic manipulation strategies. As a case study, this method was applied to shikimic acid production by Corynebacterium glutamicum to verify its usefulness. As unique strategies in literature-mining, overexpression of the transcriptional regulator SugR and gene disruption related to by-products productions were complemented. In the search for alternative enzyme sequences, it was suggested that those candidates are searched for from various species based on features captured by deep learning, which are not simply homologous to amino acid sequences of the base enzymes.

Enhancing in silico strain design predictions through next generation metabolic modeling approaches

The reconstruction and analysis of metabolic models has garnered increasing attention due to the multitude of applications in which these have proven to be practical. The growing number of generated metabolic models has been accompanied by an exponentially expanding arsenal of tools used to analyze them. In this work, we discussed the biological relevance of a number of promising modeling frameworks, focusing on the questions and hypotheses each method is equipped to address. To this end, we critically analyzed the steady-state modeling approaches focusing on resource allocation and incorporation of thermodynamic considerations which produce promising results and aid in the generation and experimental validation of numerous predictions. For smaller networks involving more complex regulation, we addressed kinetic modeling techniques which show encouraging results in addressing questions outside the scope of steady-state modeling. Finally, we discussed the potential application of the discussed frameworks within the field of strain design. Adoption of such methodologies is believed to significantly enhance the accuracy of in silico predictions and hence decrease the number of design-build-test cycles required.

Synthetic biology approaches to actinomycete strain improvement

Their biochemical versatility and biotechnological importance make actinomycete bacteria attractive targets for ambitious genetic engineering using the toolkit of synthetic biology. But their complex biology also poses unique challenges. This mini review discusses some of the recent advances in synthetic biology approaches from an actinomycete perspective and presents examples of their application to the rational improvement of industrially relevant strains.

Altered Metabolic Strategies: Elaborate Mechanisms Adopted by Oenococcus oeni in Response to Acid Stress

Oenococcus oeni plays a key role in inducing malolactic fermentation in wine. Acid stress is often encountered under wine conditions. However, the lack of systematic studies of acid resistance mechanisms limits the downstream fermentation applications. In this study, the acid responses of O. oeni were investigated by combining transcriptome, metabolome, and genome-scale metabolic modeling approaches. Metabolite profiling highlighted the decreased abundance of nucleotides under acid stress. The gene-metabolite bipartite network showed negative correlations between nucleotides and genes involved in ribosome assembly, translation, and post-translational processes, suggesting that stringent response could be activated under acid stress. Genome-scale metabolic modeling revealed marked flux rerouting, including reallocation of pyruvate, attenuation of glycolysis, utilization of carbon sources other than glucose, and enhancement of nucleotide salvage and the arginine deiminase pathway. This study provided novel insights into the acid responses of O. oeni, which will be useful for designing strategies to address acid stress in wine malolactic fermentation.

Metabolic Modeling to Interrogate Microbial Disease: A Tale for Experimentalists

The explosion of microbiome analyses has helped identify individual microorganisms and microbial communities driving human health and disease, but how these communities function is still an open question. For example, the role for the incredibly complex metabolic interactions among microbial species cannot easily be resolved by current experimental approaches such as 16S rRNA gene sequencing, metagenomics and/or metabolomics. Resolving such metabolic interactions is particularly challenging in the context of polymicrobial communities where metabolite exchange has been reported to impact key bacterial traits such as virulence and antibiotic treatment efficacy. As novel approaches are needed to pinpoint microbial determinants responsible for impacting community function in the context of human health and to facilitate the development of novel anti-infective and antimicrobial drugs, here we review, from the viewpoint of experimentalists, the latest advances in metabolic modeling, a computational method capable of predicting metabolic capabilities and interactions from individual microorganisms to complex ecological systems. We use selected examples from the literature to illustrate how metabolic modeling has been utilized, in combination with experiments, to better understand microbial community function. Finally, we propose how such combined, cross-disciplinary efforts can be utilized to drive laboratory work and drug discovery moving forward.

Experimentally Validated Reconstruction and Analysis of a Genome-Scale Metabolic Model of an Anaerobic Neocallimastigomycota Fungus

Anaerobic gut fungi in the phylum Neocallimastigomycota typically inhabit the digestive tracts of large mammalian herbivores, where they play an integral role in the decomposition of raw lignocellulose into its constitutive sugar monomers. However, quantitative tools to study their physiology are lacking, partially due to their complex and unresolved metabolism that includes the largely uncharacterized fungal hydrogenosome. Modern omics approaches combined with metabolic modeling can be used to establish an understanding of gut fungal metabolism and develop targeted engineering strategies to harness their degradation capabilities for lignocellulosic bioprocessing. Here, we introduce a high-quality genome of the anaerobic fungus Neocallimastix lanati from which we constructed the first genome-scale metabolic model of an anaerobic fungus. Relative to its size (200 Mbp, sequenced at 62× depth), it is the least fragmented publicly available gut fungal genome to date. Of the 1,788 lignocellulolytic enzymes annotated in the genome, 585 are associated with the fungal cellulosome, underscoring the powerful lignocellulolytic potential of N. lanati. The genome-scale metabolic model captures the primary metabolism of N. lanati and accurately predicts experimentally validated substrate utilization requirements. Additionally, metabolic flux predictions are verified by ¹³C metabolic flux analysis, demonstrating that the model faithfully describes the underlying fungal metabolism. Furthermore, the model clarifies key aspects of the hydrogenosomal metabolism and can be used as a platform to quantitatively study these biotechnologically important yet poorly understood early-branching fungi.

IMPORTANCE Recent genomic analyses have revealed that anaerobic gut fungi possess both the largest number and highest diversity of lignocellulolytic enzymes of all sequenced fungi, explaining their ability to decompose lignocellulosic substrates, e.g., agricultural waste, into fermentable sugars. Despite their potential, the development of engineering methods for these organisms has been slow due to their complex life cycle, understudied metabolism, and challenging anaerobic culture requirements. Currently, there is no framework that can be used to combine multi-omic data sets to understand their physiology. Here, we introduce a high-quality PacBio-sequenced genome of the anaerobic gut fungus Neocallimastix lanati. Beyond identifying a trove of lignocellulolytic enzymes, we use this genome to construct the first genome-scale metabolic model of an anaerobic gut fungus. The model is experimentally validated and sheds light on unresolved metabolic features common to gut fungi. Model-guided analysis will pave the way for deepening our understanding of anaerobic gut fungi and provides a systematic framework to guide strain engineering efforts of these organisms for biotechnological use.

Bioprospecting of microbial strains for biofuel production: metabolic engineering, applications, and challenges

The issues of global warming, coupled with fossil fuel depletion, have undoubtedly led to renewed interest in other sources of commercial fuels. The search for renewable fuels has motivated research into the biological degradation of lignocellulosic biomass feedstock to produce biofuels such as bioethanol, biodiesel, and biohydrogen. The model strain for biofuel production needs the capability to utilize a high amount of substrate, transportation of sugar through fast and deregulated pathways, ability to tolerate inhibitory compounds and end products, and increased metabolic fluxes to produce an improved fermentation product. Engineering microbes might be a great approach to produce biofuel from lignocellulosic biomass by exploiting metabolic pathways economically. Metabolic engineering is an advanced technology for the construction of highly effective microbial cell factories and a key component for the next-generation bioeconomy. It has been extensively used to redirect the biosynthetic pathway to produce desired products in several native or engineered hosts. A wide range of novel compounds has been manufactured through engineering metabolic pathways or endogenous metabolism optimizations by metabolic engineers. This review is focused on the potential utilization of engineered strains to produce biofuel and gives prospects for improvement in metabolic engineering for new strain development using advanced technologies.

A Genome-Scale Metabolic Model of 2,3-Butanediol Production by Thermophilic Bacteria Geobacillus icigianus

The thermophilic strain of the genus Geobacillus, Geobacillus icigianus is a promising bacterial chassis for a wide range of biotechnological applications. In this study, we explored the metabolic potential of Geobacillus icigianus for the production of 2,3-butanediol (2,3-BTD), one of the cost-effective commodity chemicals. Here we present a genome-scale metabolic model iMK1321 for Geobacillus icigianus constructed using an auto-generating pipeline with consequent thorough manual curation. The model contains 1321 genes and includes 1676 reactions and 1589 metabolites, representing the most-complete and publicly available model of the genus Geobacillus. The developed model provides new insights into thermophilic bacterial metabolism and highlights new strategies for biotechnological applications of the strain. Our analysis suggests that Geobacillus icigianus has a potential for 2,3-butanediol production from a variety of utilized carbon sources, including glycerine, a common byproduct of biofuel production. We identified a set of solutions for enhancing 2,3-BTD production, including cultivation under anaerobic or microaerophilic conditions and decreasing the TCA flux to succinate via reducing citrate synthase activity. Both in silico predicted metabolic alternatives have been previously experimentally verified for closely related strains including the genus Bacillus.

An extended and generalized framework for the calculation of metabolic intervention strategies based on minimal cut sets

The concept of minimal cut sets (MCS) provides a flexible framework for analyzing properties of metabolic networks and for computing metabolic intervention strategies. In particular, it has been used to support the targeted design of microbial strains for bio-based production processes. Herein we present a number of major extensions that generalize the existing MCS approach and broaden its scope for applications in metabolic engineering. We first introduce a modified approach to integrate gene-protein-reaction associations (GPR) in the metabolic network structure for the computation of gene-based intervention strategies. In particular, we present a set of novel compression rules for GPR associations, which effectively speedup the computation of gene-based MCS by a factor of up to one order of magnitude. These rules are not specific for MCS and as well applicable to other computational strain design methods. Second, we enhance the MCS framework by allowing the definition of multiple target (undesired) and multiple protected (desired) regions. This enables precise tailoring of the metabolic solution space of the designed strain with unlimited flexibility. Together with further generalizations such as individual cost factors for each intervention, direct combinations of reaction/gene deletions and additions as well as the possibility to search for substrate co-feeding strategies, the scope of the MCS framework could be broadly extended. We demonstrate the applicability and performance benefits of the described developments by computing (gene-based) Escherichia coli strain designs for the bio-based production of 2,3-butanediol, a chemical, that has recently received much attention in the field of metabolic engineering. With our extended framework, we could identify promising strain designs that were formerly unpredictable, including those based on substrate co-feeding.

Dynamic Flux Balance Analysis to Evaluate the Strain Production Performance on Shikimic Acid Production in Escherichia coli

Flux balance analysis (FBA) is used to improve the microbial production of useful compounds. However, a large gap often exists between the FBA solution and the experimental yield, because of growth and byproducts. FBA has been extended to dynamic FBA (dFBA), which is applicable to time-varying processes, such as batch or fed-batch cultures, and has significantly contributed to metabolic and cultural engineering applications. On the other hand, the performance of the experimental strains has not been fully evaluated. In this study, we applied dFBA to the production of shikimic acid from glucose in Escherichia coli, to evaluate the production performance of the strain as a case study. The experimental data of glucose consumption and cell growth were used as FBA constraints. Bi-level FBA optimization with maximized growth and shikimic acid production were the objective functions. Results suggest that the shikimic acid concentration in the high-shikimic-acid-producing strain constructed in the experiment reached up to 84% of the maximum value by simulation. Thus, this method can be used to evaluate the performance of strains and estimate the milestones of strain improvement.

Construction, comparison and evolution of networks in life sciences and other disciplines

Network approaches have become pervasive in many research fields. They allow for a more comprehensive understanding of complex relationships between entities as well as their group-level properties and dynamics. Many networks change over time, be it within seconds or millions of years, depending on the nature of the network. Our focus will be on comparative network analyses in life sciences, where deciphering temporal network changes is a core interest of molecular, ecological, neuropsychological and evolutionary biologists. Further, we will take a journey through different disciplines, such as social sciences, finance and computational gastronomy, to present commonalities and differences in how networks change and can be analysed. Finally, we envision how borrowing ideas from these disciplines could enrich the future of life science research.

Comparative Metabolic Network Flux Analysis to Identify Differences in Cellular Metabolism

Metabolic network flux analysis uses genome-scale metabolic reconstructions to integrate transcriptomics, proteomics, and/or metabolomics data to allow for comprehensive interpretation of genotype to metabolic phenotype relationships. The compilation of many Constraint-based model analysis methods into one MATLAB package, the COBRAtoolbox, has opened the possibility of using these methods to the many biologists with some knowledge of the commonly used statistical program, MATLAB. Here we outline the steps required to take a published genome-scale metabolic reconstruction and interrogate its consistency and biological feasibility. Subsequently, we demonstrate how mRNA expression data and metabolomics data, relating to one or more cell types or biological contexts, can be applied to constrain and generate metabolic models descriptive of metabolic flux phenotypes. Finally, we describe the comparison of the resulting models and model outputs with the aim of identifying metabolic biomarkers and changes in cellular metabolism.

Metabolic model guided strain design of cyanobacteria

Cyanobacteria are oxygenic photoautotrophs that serve as potential platforms for the production of biochemicals from cheap and renewable raw materials - sunlight, water, and carbon dioxide. Systems level analysis of the metabolic network of these organisms could enable the successful engineering of these organisms for the enhanced production of target chemicals. Metabolic modeling techniques including both stoichiometric and kinetic modeling with a genome-wide coverage enable a global assessment of metabolic capabilities. Recent studies guided by such modeling techniques have engineered strains for the enhanced production of valuable chemicals such as ethanol, n-butanol, 1,3-propanediol, glycerol, limonene, and isoprene from CO₂.

A Genome-Scale Metabolic Model of Soybean (Glycine max) Highlights Metabolic Fluxes in Seedlings

Until they become photoautotrophic juvenile plants, seedlings depend upon the reserves stored in seed tissues. These reserves must be mobilized and metabolized, and their breakdown products must be distributed to the different organs of the growing seedling. Here, we investigated the mobilization of soybean (Glycine max) seed reserves during seedling growth by initially constructing a genome-scale stoichiometric model for this important crop plant and then adapting the model to reflect metabolism in the cotyledons and hypocotyl/root axis (HRA). A detailed analysis of seedling growth and alterations in biomass composition was performed over 4 d of postgerminative growth and used to constrain the stoichiometric model. Flux balance analysis revealed marked differences in metabolism between the two organs, together with shifts in primary metabolism occurring during different periods postgermination. In particular, from 48 h onward, cotyledons were characterized by the oxidation of fatty acids to supply carbon for the tricarboxylic acid cycle as well as production of sucrose and glutamate for export to the HRA, while the HRA was characterized by the use of a range of imported amino acids in protein synthesis and catabolic processes. Overall, the use of flux balance modeling provided new insight into well-characterized metabolic processes in an important crop plant due to their analysis within the context of a metabolic network and reinforces the relevance of the application of this technique to the analysis of complex plant metabolic systems.

Condition-specific series of metabolic sub-networks and its application for gene set enrichment analysis

MOTIVATION

Genome-scale metabolic networks and transcriptomic data represent complementary sources of knowledge about an organism's metabolism, yet their integration to achieve biological insight remains challenging.

RESULTS

We investigate here condition-specific series of metabolic sub-networks constructed by successively removing genes from a comprehensive network. The optimal order of gene removal is deduced from transcriptomic data. The sub-networks are evaluated via a fitness function, which estimates their degree of alteration. We then consider how a gene set, i.e. a group of genes contributing to a common biological function, is depleted in different series of sub-networks to detect the difference between experimental conditions. The method, named metaboGSE, is validated on public data for Yarrowia lipolytica and mouse. It is shown to produce GO terms of higher specificity compared to popular gene set enrichment methods like GSEA or topGO.

AVAILABILITY AND IMPLEMENTATION

The metaboGSE R package is available at https://CRAN.R-project.org/package=metaboGSE.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

RAVEN 2.0: A versatile toolbox for metabolic network reconstruction and a case study on Streptomyces coelicolor

RAVEN is a commonly used MATLAB toolbox for genome-scale metabolic model (GEM) reconstruction, curation and constraint-based modelling and simulation. Here we present RAVEN Toolbox 2.0 with major enhancements, including: (i) de novo reconstruction of GEMs based on the MetaCyc pathway database; (ii) a redesigned KEGG-based reconstruction pipeline; (iii) convergence of reconstructions from various sources; (iv) improved performance, usability, and compatibility with the COBRA Toolbox. Capabilities of RAVEN 2.0 are here illustrated through de novo reconstruction of GEMs for the antibiotic-producing bacterium Streptomyces coelicolor. Comparison of the automated de novo reconstructions with the iMK1208 model, a previously published high-quality S. coelicolor GEM, exemplifies that RAVEN 2.0 can capture most of the manually curated model. The generated de novo reconstruction is subsequently used to curate iMK1208 resulting in Sco4, the most comprehensive GEM of S. coelicolor, with increased coverage of both primary and secondary metabolism. This increased coverage allows the use of Sco4 to predict novel genome editing targets for optimized secondary metabolites production. As such, we demonstrate that RAVEN 2.0 can be used not only for de novo GEM reconstruction, but also for curating existing models based on up-to-date databases. Both RAVEN 2.0 and Sco4 are distributed through GitHub to facilitate usage and further development by the community (https://github.com/SysBioChalmers/RAVEN and https://github.com/SysBioChalmers/Streptomyces_coelicolor-GEM).

Metabolic network reconstruction and phenome analysis of the industrial microbe, Escherichia coli BL21(DE3)

Escherichia coli BL21(DE3) is an industrial model microbe for the mass-production of bioproducts such as biofuels, biorefineries, and recombinant proteins. However, despite its important role in scientific research and biotechnological applications, a high-quality metabolic network model for metabolic engineering is yet to be developed. Here, we present the comprehensive metabolic network model of E. coli BL21(DE3), named iHK1487, based on the latest genome reannotation and phenome analysis. The metabolic model consists of 1,164 unique metabolites, 2,701 metabolic reactions, and 1,487 genes. The model was validated and improved by comparing the simulation results with phenome data from phenotype microarray tests. Previous transcriptome profile data was incorporated during model reconstruction, and flux prediction was simulated using the model. iHK1487 was simulated to explore the metabolic features of BL21(DE3) such as broad spectrum amino acid utilization and enhanced flux through the upper glycolytic pathway and TCA cycle. iHK1487 will contribute to systematic understanding of cellular physiology and metabolism of E. coli BL21(DE3) and highlight its biotechnological applications.

Metabolic engineering of Corynebacterium glutamicum for anthocyanin production

Background

Anthocyanins such as cyanidin 3-O-glucoside (C3G) have wide applications in industry as food colorants. Their current production heavily relies on extraction from plant tissues. Development of a sustainable method to produce anthocyanins is of considerable interest for industrial use. Previously, E. coli-based microbial production of anthocyanins has been investigated extensively. However, safety concerns on E. coli call for the adoption of a safe production host. In the present study, a GRAS bacterium, Corynebacterium glutamicum, was introduced as the host strain to synthesize C3G. We adopted stepwise metabolic engineering strategies to improve the production titer of C3G.

Results

Anthocyanidin synthase (ANS) from Petunia hybrida and 3-O-glucosyltransferase (3GT) from Arabidopsis thaliana were coexpressed in C. glutamicum ATCC 13032 to drive the conversion from catechin to C3G. Optimized expression of ANS and 3GT improved the C3G titer by 1- to 15-fold. Further process optimization and improvement of UDP-glucose availability led to ~ 40 mg/L C3G production, representing a > 100-fold titer increase compared to production in the un-engineered, un-optimized starting strain.

Conclusions

For the first time, we successfully achieved the production of the specialty anthocyanin C3G from the comparatively inexpensive flavonoid precursor catechin in C. glutamicum. This study opens up more possibility of C. glutamicum as a host microbe for the biosynthesis of useful and value-added natural compounds.

Electronic supplementary material

The online version of this article (10.1186/s12934-018-0990-z) contains supplementary material, which is available to authorized users.

Development and validation of an updated computational model of Streptomyces coelicolor primary and secondary metabolism

BACKGROUND

Streptomyces species produce a vast diversity of secondary metabolites of clinical and biotechnological importance, in particular antibiotics. Recent developments in metabolic engineering, synthetic and systems biology have opened new opportunities to exploit Streptomyces secondary metabolism, but achieving industry-level production without time-consuming optimization has remained challenging. Genome-scale metabolic modelling has been shown to be a powerful tool to guide metabolic engineering strategies for accelerated strain optimization, and several generations of models of Streptomyces metabolism have been developed for this purpose.

RESULTS

Here, we present the most recent update of a genome-scale stoichiometric constraint-based model of the metabolism of Streptomyces coelicolor, the major model organism for the production of antibiotics in the genus. We show that the updated model enables better metabolic flux and biomass predictions and facilitates the integrative analysis of multi-omics data such as transcriptomics, proteomics and metabolomics.

CONCLUSIONS

The updated model presented here provides an enhanced basis for the next generation of metabolic engineering attempts in Streptomyces.

Dynamic metabolic control: towards precision engineering of metabolism

Advances in metabolic engineering have led to the synthesis of a wide variety of valuable chemicals in microorganisms. The key to commercializing these processes is the improvement of titer, productivity, yield, and robustness. Traditional approaches to enhancing production use the “push–pull-block” strategy that modulates enzyme expression under static control. However, strains are often optimized for specific laboratory set-up and are sensitive to environmental fluctuations. Exposure to sub-optimal growth conditions during large-scale fermentation often reduces their production capacity. Moreover, static control of engineered pathways may imbalance cofactors or cause the accumulation of toxic intermediates, which imposes burden on the host and results in decreased production. To overcome these problems, the last decade has witnessed the emergence of a new technology that uses synthetic regulation to control heterologous pathways dynamically, in ways akin to regulatory networks found in nature. Here, we review natural metabolic control strategies and recent developments in how they inspire the engineering of dynamically regulated pathways. We further discuss the challenges of designing and engineering dynamic control and highlight how model-based design can provide a powerful formalism to engineer dynamic control circuits, which together with the tools of synthetic biology, can work to enhance microbial production.

Microbial Methylotrophic Metabolism: Recent Metabolic Modeling Efforts and Their Applications In Industrial Biotechnology

Developing methylotrophic bacteria into cell factories that meet the chemical demand of the future could be both economical and environmentally friendly. Methane is not only an abundant, low-cost resource but also a potent greenhouse gas, the capture of which could help to reduce greenhouse gas emissions. Rational strain design workflows rely on the availability of carefully combined knowledge often in the form of genome-scale metabolic models to construct high-producer organisms. In this review, the authors present the most recent genome-scale metabolic models in aerobic methylotrophy and their applications. Further, the authors present models for the study of anaerobic methanotrophy through reverse methanogenesis and suggest organisms that may be of interest for expanding one-carbon industrial biotechnology. Metabolic models of methylotrophs are scarce, yet they are important first steps toward rational strain-design in these organisms.

Genome-scale model-driven strain design for dicarboxylic acid production in Yarrowia lipolytica

Background

Recently, there have been several attempts to produce long-chain dicarboxylic acids (DCAs) in various microbial hosts. Of these, Yarrowia lipolytica has great potential due to its oleaginous characteristics and unique ability to utilize hydrophobic substrates. However, Y. lipolytica should be further engineered to make it more competitive: the current approaches are mostly intuitive and cumbersome, thus limiting its industrial application.

Results

In this study, we proposed model-guided metabolic engineering strategies for enhanced production of DCAs in Y. lipolytica. At the outset, we reconstructed genome-scale metabolic model (GSMM) of Y. lipolytica (iYLI647) by substantially expanding the previous models. Subsequently, the model was validated using three sets of published culture experiment data. It was finally exploited to identify genetic engineering targets for overexpression, knockout, and cofactor modification by applying several in silico strain design methods, which potentially give rise to high yield production of the industrially relevant long-chain DCAs, e.g., dodecanedioic acid (DDDA). The resultant targets include (1) malate dehydrogenase and malic enzyme genes and (2) glutamate dehydrogenase gene, in silico overexpression of which generated additional NADPH required for fatty acid synthesis, leading to the increased DDDA fluxes by 48% and 22% higher, respectively, compared to wild-type. We further investigated the effect of supplying branched-chain amino acids on the acetyl-CoA turn-over rate which is key metabolite for fatty acid synthesis, suggesting their significance for production of DDDA in Y. lipolytica.

Conclusion

In silico model-based strain design strategies allowed us to identify several metabolic engineering targets for overproducing DCAs in lipid accumulating yeast, Y. lipolytica. Thus, the current study can provide a methodological framework that is applicable to other oleaginous yeasts for value-added biochemical production.

Electronic supplementary material

The online version of this article (10.1186/s12918-018-0542-5) contains supplementary material, which is available to authorized users.

Genome-Scale, Constraint-Based Modeling of Nitrogen Oxide Fluxes during Coculture of Nitrosomonas europaea and Nitrobacter winogradskyi

Modern agriculture is sustained by application of inorganic nitrogen (N) fertilizer in the form of ammonium (NH₄⁺). Up to 60% of NH₄⁺-based fertilizer can be lost through leaching of nitrifier-derived nitrate (NO₃⁻), and through the emission of N oxide gases (i.e., nitric oxide [NO], N dioxide [NO₂], and nitrous oxide [N₂O] gases), the latter being a potent greenhouse gas. Our approach to modeling of nitrification suggests that both biotic and abiotic mechanisms function as important sources and sinks of N oxides during microaerobic conditions and that previous models might have underestimated gross NO production during nitrification.

Nitrification, the aerobic oxidation of ammonia to nitrate via nitrite, emits nitrogen (N) oxide gases (NO, NO₂, and N₂O), which are potentially hazardous compounds that contribute to global warming. To better understand the dynamics of nitrification-derived N oxide production, we conducted culturing experiments and used an integrative genome-scale, constraint-based approach to model N oxide gas sources and sinks during complete nitrification in an aerobic coculture of two model nitrifying bacteria, the ammonia-oxidizing bacterium Nitrosomonas europaea and the nitrite-oxidizing bacterium Nitrobacter winogradskyi. The model includes biotic genome-scale metabolic models (iFC578 and iFC579) for each nitrifier and abiotic N oxide reactions. Modeling suggested both biotic and abiotic reactions are important sources and sinks of N oxides, particularly under microaerobic conditions predicted to occur in coculture. In particular, integrative modeling suggested that previous models might have underestimated gross NO production during nitrification due to not taking into account its rapid oxidation in both aqueous and gas phases. The integrative model may be found at https://github.com/chaplenf/microBiome-v2.1.

IMPORTANCE Modern agriculture is sustained by application of inorganic nitrogen (N) fertilizer in the form of ammonium (NH₄⁺). Up to 60% of NH₄⁺-based fertilizer can be lost through leaching of nitrifier-derived nitrate (NO₃⁻), and through the emission of N oxide gases (i.e., nitric oxide [NO], N dioxide [NO₂], and nitrous oxide [N₂O] gases), the latter being a potent greenhouse gas. Our approach to modeling of nitrification suggests that both biotic and abiotic mechanisms function as important sources and sinks of N oxides during microaerobic conditions and that previous models might have underestimated gross NO production during nitrification.

Genome-scale biological models for industrial microbial systems

The primary aims and challenges associated with microbial fermentation include achieving faster cell growth, higher productivity, and more robust production processes. Genome-scale biological models, predicting the formation of an interaction among genetic materials, enzymes, and metabolites, constitute a systematic and comprehensive platform to analyze and optimize the microbial growth and production of biological products. Genome-scale biological models can help optimize microbial growth-associated traits by simulating biomass formation, predicting growth rates, and identifying the requirements for cell growth. With regard to microbial product biosynthesis, genome-scale biological models can be used to design product biosynthetic pathways, accelerate production efficiency, and reduce metabolic side effects, leading to improved production performance. The present review discusses the development of microbial genome-scale biological models since their emergence and emphasizes their pertinent application in improving industrial microbial fermentation of biological products.

Prediction of metabolic fluxes from gene expression data with Huber penalty convex optimization function

As one of the critical parameters of a metabolic pathway, the metabolic flux in a metabolic network serves as an essential role in physiology and pathology. Constraint-based metabolic models are the widely used frameworks for predicting metabolic fluxes in genome-scale metabolic networks. Integrating the transcriptomic data into the constraint-based metabolic models can effectively predict context-specific fluxes across different conditions. However, these methods always need user-defined thresholds to identify the expression levels of metabolic genes or restrain the rate of biomass production, and the predictive results are sensitive to the thresholds. In this work, we present the Huber penalty convex optimization function (HPCOF) combined with the flux minimization principle to predict metabolic fluxes. Our HPCOF method integrates gene expression profiles into the genome-scale metabolic models (GEMs) to reduce the sensitivity to outliers, and uses continuous expression data to avoid selection of arbitrary threshold parameters. In the case studies of Saccharomyces cerevisiae (S. cerevisiae) and Escherichia coli (E. coli) strains under different conditions, the results show that our HPCOF method has a better fit to the experimentally measured values, and has a higher Pearson correlation coefficient, a smaller P-value and a lower sum of squared error than other methods. The HPCOF code can be freely downloaded from for academic users.

Applications of a metabolic network model of mesenchymal stem cells for controlling cell proliferation and differentiation

Mesenchymal stem cells (MSCs) can be isolated from several tissues of adults. In addition, MSCs have the potential of differentiation into several cell types. Therefore, MSCs are very useful in stem cell therapy and regenerative medicine. MSCs have also been used as gene or protein carriers. As a result, maintaining MSCs in a desirable metabolic state has been the subject of several studies. Here, we used a genome scale metabolic network model of bone marrow derived MSCs for exploring the metabolism of these cells. We analyzed metabolic fluxes of the model in order to find ways of increasing stem cell proliferation and differentiation. Consequently, the experimental results were in consistency with computational results. Therefore, analyzing metabolic models was proven to be a promising field in biomedical researches of stem cells.

Synthetic Biology to Improve the Production of Lipases and Esterases (Review)

Synthetic biology is an emergent field of research whose aim is to make biology an engineering discipline, thus permitting to design, control, and standardize biological processes. Synthetic biology is therefore expected to boost the development of biotechnological processes such as protein production and enzyme engineering, which can be significantly relevant for lipases and esterases.

OptPipe - a pipeline for optimizing metabolic engineering targets

BACKGROUND

We propose OptPipe - a Pipeline for Optimizing Metabolic Engineering Targets, based on a consensus approach. The method generates consensus hypotheses for metabolic engineering applications by combining several optimization solutions obtained from distinct algorithms. The solutions are ranked according to several objectives, such as biomass and target production, by using the rank product tests corrected for multiple comparisons.

RESULTS

OptPipe was applied in a genome-scale model of Corynebacterium glutamicum for maximizing malonyl-CoA, which is a valuable precursor for many phenolic compounds. In vivo experimental validation confirmed increased malonyl-CoA level in case of ΔsdhCAB deletion, as predicted in silico.

CONCLUSIONS

A method was developed to combine the optimization solutions provided by common knockout prediction procedures and rank the suggested mutants according to the expected growth rate, production and a new adaptability measure. The implementation of the pipeline along with the complete documentation is freely available at https://github.com/AndrasHartmann/OptPipe .

Amino acid catabolism-directed biofuel production in Clostridium sticklandii: An insight into model-driven systems engineering

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Model-driven systems engineering has been more fascinating process for microbial biofuel production.

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Clostridium sticklandii is a potential strain for the solventogenesis and acidogenesis.

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The present review provides an insight for the protein catabolism-directed biofuel production.

Model-driven systems engineering has been more fascinating process for the microbial production of biofuel and bio-refineries in chemical and pharmaceutical industries. Genome-scale modeling and simulations have been guided for metabolic engineering of Clostridium species for the production of organic solvents and organic acids. Among them, Clostridium sticklandii is one of the potential organisms to be exploited as a microbial cell factory for biofuel production. It is a hyper-ammonia producing bacterium and is able to catabolize amino acids as important carbon and energy sources via Stickland reactions and the development of the specific pathways. Current genomic and metabolic aspects of this bacterium are comprehensively reviewed herein, which provided information for learning about protein catabolism-directed biofuel production. It has a metabolic potential to drive energy and direct solventogenesis as well as acidogenesis from protein catabolism. It produces by-products such as ethanol, acetate, n-butanol, n-butyrate and hydrogen from amino acid catabolism. Model-driven systems engineering of this organism would improve the performance of the industrial sectors and enhance the industrial economy by using protein-based waste in environment-friendly ways.

Inference and interrogation of a coregulatory network in the context of lipid accumulation in Yarrowia lipolytica

Complex phenotypes, such as lipid accumulation, result from cooperativity between regulators and the integration of multiscale information. However, the elucidation of such regulatory programs by experimental approaches may be challenging, particularly in context-specific conditions. In particular, we know very little about the regulators of lipid accumulation in the oleaginous yeast of industrial interest Yarrowia lipolytica. This lack of knowledge limits the development of this yeast as an industrial platform, due to the time-consuming and costly laboratory efforts required to design strains with the desired phenotypes. In this study, we aimed to identify context-specific regulators and mechanisms, to guide explorations of the regulation of lipid accumulation in Y. lipolytica. Using gene regulatory network inference, and considering the expression of 6539 genes over 26 time points from GSE35447 for biolipid production and a list of 151 transcription factors, we reconstructed a gene regulatory network comprising 111 transcription factors, 4451 target genes and 17048 regulatory interactions (YL-GRN-1) supported by evidence of protein-protein interactions. This study, based on network interrogation and wet laboratory validation (a) highlights the relevance of our proposed measure, the transcription factors influence, for identifying phases corresponding to changes in physiological state without prior knowledge (b) suggests new potential regulators and drivers of lipid accumulation and