In silico cell factory design driven by comprehensive genome‑scale metabolic models:development and challenges

Genome-scale metabolic models(GEMs)have been widely used to design cell factories in silico.However,initial flux balance analysis only considers stoichiometry and reaction direction constraints,so it cannot accurately describe the distribution of metabolic flux under the control of various regulatory mechanisms.In the recent years,by introducing enzymology,thermodynamics,and other multiomics-based constraints into GEMs,the metabolic state of cells under different conditions was more accurately simulated and a series of algorithms have been presented for microbial phenotypic analysis.Herein,the development of multiconstrained GEMs was reviewed by taking the constraints of enzyme kinetics,thermodynamics,and transcriptional regulatory mechanisms as examples.This review focused on introducing and summarizing GEMs application tools and cases in cell factory design.The challenges and prospects of GEMs development were also discussed.

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

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

Advances and applications of machine learning and intelligent optimization algorithms in genome‑scale metabolic network models

Due to the increasing demand for microbially manufactured products in various industries,it has become important to find optimal designs for microbial cell factories by changing the direction of metabolic flow and its flux size by means of metabolic engineering such as knocking out competing pathways and introducing exogenous pathways to increase the yield of desired products.Recently,with the gradual cross-fertilization between computer science and bioinformatics fields,machine learning and intelligent optimization-based approaches have received much attention in Genome-scale metabolic network models(GSMMs)based on constrained optimization methods,and many high-quality related works have been published.Therefore,this paper focuses on the advances and applications of machine learning and intelligent optimization algorithms in metabolic engineering,with special emphasis on GSMMs.Specifically,the development history of GSMMs is first reviewed.Then,the analysis methods of GSMMs based on constraint optimization are presented.Next,this paper mainly reviews the development and application of machine learning and intelligent optimization algorithms in genome-scale metabolic models.In addition,the research gaps and future research potential in machine learning and intelligent optimization methods applied in GSMMs are discussed.

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

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

Flux Balance Analysis Reveals Potential Anti-HIV-1 Metabolic Targets

Background:Human immunodeficiency virus type 1(HIV-1)remains a persistent global health challenge.Therefore,a continuous exploration of novel therapeutic strategies is essential.A comprehensive understanding of how HIV-1 utilizes the cellular metabolism machinery for replication can provide insights into new therapeutic approaches.Methods:In this study,we performed a flux balance analysis using a genome-scale metabolic model(GEM)integrated with an HIV-1 viral biomass objective function to identify potential targets for anti–HIV-1 interventions.We generated a GEM by integrating an HIV-1 production reaction into CD4+T cells and optimized for both host and virus optimal states as objective functions to depict metabolic profiles of cells in the status for optimal host biomass maintenance or for optimal HIV-1 virion production.Differential analysis was used to predict biochemical reactions altered optimal for HIV-1 production.In addition,we conducted in silico simulations involving gene and reaction knock-outs to identify potential anti–HIV-1 targets,which were subsequently validated by human phytohemagglutinin(PHA)blasts infected with HIV-1.Results:Differential analysis identified several altered biochemical reactions,including increased lysine uptake and oxidative phosphorylation(OXPHOS)activities in the virus optima compared with the host optima.In silico gene and reaction knock-out simulations revealed de novo pyrimidine synthesis,and OXPHOS could serve as potential anti–HIV-1 metabolic targets.In vitro assay confirmed that targeting OXPHOS using metformin could suppress the replication of HIV-1 by 56.6%(385.4±67.5 pg/mL in the metformintreated group vs.888.4±32.3 pg/mL in the control group,P<0.001).Conclusion:Our integrated host-virus genome-scale metabolic study provides insights on potential targets(OXPHOS)for anti-HIV therapies.

Systems-based approaches to study immunometabolism

Technical advances at the interface of biology and computation,such as single-cell RNA-sequencing(scRNA-seq),reveal new layers of complexity in cellular systems.An emerging area of investigation using the systems biology approach is the study of the metabolism of immune cells.The diverse spectra of immune cell phenotypes,sparsity of immune cell numbers in vivo,limitations in the number of metabolites identified,dynamic nature of cellular metabolism and metabolic fluxes,tissue specificity,and high dependence on the local milieu make investigations in immunometabolism challenging,especially at the single-cell level.In this review,we define the systemic nature of immunometabolism,summarize cell-and system-based approaches,and introduce mathematical modeling approaches for systems interrogation of metabolic changes in immune cells.We close the review by discussing the applications and shortcomings of metabolic modeling techniques.With systems-oriented studies of metabolism expected to become a mainstay of immunological research,an understanding of current approaches toward systems immunometabolism will help investigators make the best use of current resources and push the boundaries of the discipline.

Development of fungal cell factories for the production of secondary metabolites:Linking genomics and metabolism

The genomic era has revolutionized research on secondary metabolites and bioinformatics methods have in recent years revived the antibiotic discovery process after decades with only few new active molecules being identified.New computational tools are driven by genomics and metabolomics analysis,and enables rapid identification of novel secondary metabolites.To translate this increased discovery rate into industrial exploitation,it is necessary to integrate secondary metabolite pathways in the metabolic engineering process.In this review,we will describe the novel advances in discovery of secondary metabolites produced by filamentous fungi,highlight the utilization of genome-scale metabolic models(GEMs)in the design of fungal cell factories for the production of secondary metabolites and review strategies for optimizing secondary metabolite production through the construction of high yielding platform cell factories.

Towards applications of genome-scale metabolic model-based approaches in designing synthetic microbial communities

Background:Synthetic microbial communities,with different strains brought together by balancing their nutrition and promoting their interactions,demonstrate great advantages for exploring complex performance of communities and for further biotechnology applications.The potential of such microbial communities has not been explored,due to our limited knowledge of the extremely complex microbial interactions that are involved in designing and controlling effective and stable communities.Results:Genome-scale metabolic models(GEM)have been demonstrated as an effective tool for predicting and guiding the investigation and design of microbial communities,since they can explicitly and efficiently predict the phenotype of organisms from their genotypic data and can be used to explore the molecular mechanisms of microbehabitats and microbe-microbe interactions.In this work,we reviewed two main categories of GEM-based approaches and three uses related to design of synthetic microbial communities:predicting multi-species interactions,exploring environmental impacts on microbial phenotypes,and optimizing community-level performance.Conclusions:Although at the infancy stage,GEM-based approaches exhibit an increasing scope of applications in designing synthetic microbial communities.Compared to other methods,especially the use of laboratory cultures,GEM-based approaches can greatly decrease the trial-and-error cost of various procedures for designing synthetic communities and improving their functionality,such as identifying community members,determining media composition,evaluating microbial interaction potential or selecting the best community configuration.Future efforts should be made to overcome the limitations of the approaches,ranging from quality control of GEM reconstructions to community-level modeling algorithms,so that more applications of GEMs in studying phenotypes of microbial communities can be expected.

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.

Multi-scale modeling of Arabidopsis thaliana response to different CO2 conditions： From gene expression to metabolic flux

Multi-scale investigation from gene transcript level to metabolic activity is important to uncover plant response to environment perturbation. Here we integrated a genome-scale constraint-based metabolic model with transcriptome data to explore Arabidopsis thaliana response to both elevated and low CO2 conditions. The four condition-specific models from low to high CO2 concentrations show differences in active reaction sets, enriched pathways for increased/decreased fluxes, and putative post-transcriptional regulation, which indicates that condition-specific models are necessary to reflect physiological metabolic states. The simulated CO2 fixation flux at different CO2 concentrations is consistent with the measured Assim- ilation-CO2intercellular curve. Interestingly, we found that reac- tions in primary metabolism are affected most significantly by CO2 perturbation, whereas secondary metabolic reactions are not influenced a lot. The changes predicted in key pathways are consistent with existing knowledge. Another interesting point is that Arabidopsis is required to make stronger adjustment on metabolism to adapt to the more severe low CO2 stress than elevated CO2. The challenges of identifying post-transcriptional regulation could also be addressed by the integrative model. In conclusion, this innovative application of multi-scale modeling in plants demonstrates potential to uncover the mechanisms of metabolic response to different conditions.

Kinetic analysis of anaerobic phosphorus release during biological phosphorus removal process

Enhanced biological phosphorus removal(EBPR)is a commonly used and sustainable method for phosphorus removal from wastewater.Poly-β-hydroxybutyrate(PHB),polyphosphate,and glycogen are three kinds of intracellular storage polymers in phosphorus accumulation organisms.The variation of these polymers under different conditions has an apparent influence on anaerobic phosphorus release,which is very important for controlling the performance of EBPR.To obtain the mechanism and kinetic character of anaerobic phosphorus release,a series of batch experiments were performed using the excessively aerated sludge from the aerobic unit of the biological phosphorus removal system in this study.The results showed that the volatile suspended solid(VSS)had an increasing trend,while the mixed liquid suspended sludge(MLSS)and ashes were reduced during the anaerobic phosphorus release process.The interruption of anaerobic HAc-uptake and phosphorus-release occurs when the glycogen in the phosphorus-accumulating-organisms is exhausted.Under the condition of lower initial HAc-COD,HAc became the limiting factor after some time for anaerobic HAc uptake.Under the condition of higher initial HAc-COD,HAc uptake was stopped because of the depletion of glyco-gen in the microorganisms.The mean ratio ofΔ_(ρP)/Δ_(ρPHB),Δ_(ρ)GLY/ΔρPHB,Δ_(ρP)/ΔCOD,andΔ_(ρPHB)/ΔCOD was 0.48,0.50,0.44,and 0.92,respectively,which was nearly the same as the theoretical value.The calibrated kinetic parameters of the HAc-uptake and phosphorus-release model were evaluated as follows:QHAc,max was 164 mg/(g·h),QP,max was 69.9 mg/(g·h),Kgly was 0.005,and KCOD was 3 mg/L.An apparently linear correlation was observed between the ratio ofΔ_(ρP)/ΔCOD and pH of the solution,and the equation between them was obtained in this study.Enhanced biological phosphorus removal(EBPR)is a commonly used and sustainable method for phosphorus removal from wastewater.Poly-β-hydroxybutyrate(PHB),polyphosphate,and glycogen are three kinds of intracellular storage polymers in phosphorus accumulation organisms.The variation of these polymers under different conditions has an apparent influence on anaerobic phosphorus release,which is very important for controlling the performance of EBPR.To obtain the mechanism and kinetic character of anaerobic phosphorus release,a series of batch experiments were performed using the excessively aerated sludge from the aerobic unit of the biological phosphorus removal system in this study.

An analysis of the mechanism underlying photocatalytic disinfection based on integrated metabolic networks and transcriptional data

Photocatalytic disinfection has long been used to combat pathogenic bacteria.However,the specific mechanism underlying photocatalytic disinfection and its corresponding targets remain unclear.In this study,an analysis of the potential mechanism underlying photocatalytic disinfection was performed based on integrated metabolic networks and transcriptional data.Two sets of RNA-seq data(wild type and a photocatalysis-resistant mutant mediated by titanium dioxide(TiO2))were processed to constrain the genome scale metabolic models(GSMM)of E.coli.By analyzing the metabolic network,the differential metabolic flux of every reaction was computed in constrained GSMM,and several significantly differential metabolic fluxes in reactions were extracted and analyzed.Most of these reactions were involved in the transmembrane transport of substances and occurred on the inner membrane or were an important component of the cell membrane.These results,which are consistent with the reported information,validated our analysis process.In addition,our work also identified other new and valuable metabolic pathways,such as the reaction ALCD2x,which has a great effect on the energy production process under bacterial anaerobic conditions.The DHAK reaction is also related to the metabolic process of ATP.These reactions with large differential metabolic fluxes merit further research.Additionally,to provide a strategy to address photocatalysis-resistant mutant bacteria,a metabolic compensation analysis was also performed.The metabolic compensation analysis results provided suggestions for a combined method that can effectively combat resistant bacteria.This method could also be used to explore the mechanisms of drug resistance in other microorganisms.

The future of genome-scale modeling of yeast through integration of a transcriptional regulatory network

Metabolism is regulated at multiple levels in response to the changes of internal or external conditions. Transcriptional regulation plays an important role in regulating many metabolic reactions by altering the concentrations of metabolic enzymes. Thus, integration of the transcriptional regulatory information is necessary to improve the accuracy and predictive ability of metabolic models. Here we review the strategies for the reconstruction of a transcriptional regulatory network （TRN） for yeast and the integration of such a reconstruction into a flux balance analysis-based metabolic model. While many large-scale TRN reconstructions have been reported for yeast, these reconstructions still need to be improved regarding the functionality and dynamic property of the regulatory interactions. In addition, mathematical modeling approaches need to be further developed to efficiently integrate transcriptional regulatory interactions to genome-scale metabolic models in a quantitative manner.

Genome-scale metabolic modeling in antimicrobial pharmacology

The increasing antimicrobial resistance has seriously threatened human health worldwide over the last three decades.This severe medical crisis and the dwindling antibiotic discovery pipeline require the development of novel antimicrobial treatments to combat life-threatening infections caused by multidrug-resistant micro-bial pathogens.However,the detailed mechanisms of action,resistance,and toxicity of many antimicrobials remain uncertain,significantly hampering the development of novel antimicrobials.Genome-scale metabolic model(GSMM)has been increasingly employed to investigate microbial metabolism.In this review,we discuss the latest progress of GSMM in antimicrobial pharmacology,particularly in elucidating the complex interplays of multiple metabolic pathways involved in antimicrobial activity,resistance,and toxicity.We also highlight the emerging areas of GSMM applications in modeling non-metabolic cellular activities(e.g.,gene expression),identi-fication of potential drug targets,and integration with machine learning and pharmacokinetic/pharmacodynamic modeling.Overall,GSMM has significant potential in elucidating the critical role of metabolic changes in antimi-crobial pharmacology,providing mechanistic insights that will guide the optimization of dosing regimens for the treatment of antimicrobial-resistant infections.