We identified the thermodynamically feasible co-utilization ratio ranges of CH4, CO, and CO2 toward production of the target metabolite(s) as a function of ferric uptake. A revised version of the iMAC868 genome-scale metabolic model of Methanosarcina acetivorans was chosen to assess co-utilization of CH4, CO, and CO2 and their conversion into selected target products using the optStoic pathway design tool. This revised version contains the latest information on electron flow mechanisms by the methanogen while supplied with methane as the sole carbon source. The interplay between different gas co-utilization ratios and the energetics of reverse methanogenesis were also analyzed using the same metabolic model.The abundance of methane in shale gas and of other gases such as carbon monoxide, hydrogen, and carbon dioxide as chemical process byproducts has motivated the use of gas fermentation for bioproduction. Recent advances in metabolic engineering and synthetic biology allow for engineering of microbes metabolizing a variety of chemicals including gaseous feeds into a number of biorenewables and transportation liquid fuels. New computational tools enable the systematic exploration of all feasible conversion alternatives. Here we computationally assessed all thermodynamically feasible ways of co-utilizing CH The global increase in oil production, fossil fuel combustion, biomass burning, and hydraulic fracturing of shale gas and climate change concerns has motivated the reduction of emissions from anthropogenic sources. Mitigation of gaseous emissions  from the environment and their microbial conversion into useful products provides a sustainable and transformative solution that avoids the “food vs. fuel” dilemma. Methane, the major constituent of natural gas, has the highest oxidation potential amongst carbon dioxide, carbon monoxide, and glucose to be converted into a wide range of products including liquid fuels such as ethanol and butanol. Carbon monoxide, often as synthesis gas with varying levels of carbon dioxide and hydrogen  technologies  require high operating temperatures and pressures, involve high CapEx costs, yield generally low carbon conversion efficiency, and cannot directly convert methane into the desired bioproducts Dry, . The bio4, CO, and CO2. Note that there exist many other computational tools for pathway design  into target products, the optStoic procedure  solver was used to solve the optimization problems written in Python 2.7.In the postulated overall stoichiometry sM. acetivorans growing on methane  between ferredoxin  and heterodisulfide   solver in Matlab using COBRA toolbox  Figure . Therefo4, CO, and CO2 for the production of target chemicals listed in Table 4-CO-CO2 triplet lead to thermodynamically feasible production of the target molecules  at varying levels of ferric, which is further explained in the following sections.The minimum and maximum moles of ferric required to maintain any thermodynamically feasible gas co-utilization are given in Table 4, CO, or CO2, depends on the target molecule C/O ratio and reduction level. Figure The maximum amount of carbon that can be incorporated to target products from CH2 (since it has the lowest Gibbs free energy of formation among CO and CH4) leading to a decline in maximum co-utilization ratios of CO2-to-CH4. Such information could be useful in designing and/or modifying bioconversions based on varying compositions of industrial gas waste streams , glyceraldehyde 3-phosphate (GAP), and acetyl-CoA (ACA)  based on their distinct differences as shown in Figures 4, CO, and CO2 at an arbitrary ferric level of 4 moles at which co-utilization of the three gases for the production of OXA, ACA, and GAP was predicted by optStoic to be thermodynamically feasible , respectively, which is only 11.5, and 11% less than the optimal overall stoichiometries obtained by optStoic. This difference is due to the inclusion of many more cofactors and intermediate metabolites in the metabolic network compared to the consideration of one simple overall stoichiometry as that shown in Equation 1. Further analysis of the flux through the formation of biomass, as another product of the metabolic network, reveals a maximum biomass yield of 0.217 at a ferric level of 4.2 (mol per 10 C-mol of gases) when using the gas ratios optimized for ACA production predicted by optStoic , F420-dependent methylene-H4MPT reductase (Mer), F420-dependent methylene-H4MPT dehydrogenase (Mtd), or F420-dependent NADP reductase enzyme complexes according to the metabolic model predictions.It has been postulated that M. acetivorans, we decided to analyze the theoretical limits of ethanol and butanol co-production during CH4 and CO co-utilization by the iMAC868 model. The biological co-production of alcohols has been reported in the literature where acetone/butanol/ethanol (ABE) fermentation process by clostridial strains has been studied the most and implemented industrially  results in production of one mole butanol while maximizing the production of ethanol and several other alcohol molecules as co-products  ending up in the production of precursors used for making high-value biorenewables. We targeted ten key branch point metabolites that have been used extensively as building blocks for the production of many commodity and specialty chemicals such as acetate, terpenoids, and synthetic sugars among others. We showed that the proper choice of an electron acceptor  could bypass the thermodynamic barriers for electron flow in the gas-to-chemicals conversions. Furthermore, we showed that there exist well defined gas co-utilization ranges, which are feasible at varying levels of ferric, dependent on the choice of target product. Maximum ferric usage as well as maximum carbon contribution from each of the CH4, CO2, and CO was analyzed that could lead to new or improved gas co-utilization designs. Using optStoic designs as a guide, metabolic capacities of M. acetivorans as the model host was examined owing to its diverse substrate utilization abilities and the progress in its genetic engineering tools. Equipped with latest electron flow mechanisms during growth with methane, the iMAC868 metabolic model of M. acetivorans provided information on the partitioning of electrons within the methanogenesis reversal pathway as well as on distribution of carbons coming from co-utilization of mixtures of gases toward selected products. The combined use of optStoic and metabolic modeling presented in this work puts forth an efficient platform for quickly exploring in silico the feasibility and limits of various gaseous substrate utilization options.In this work, we have demonstrated the utility of deploying computational tools such as optStoic along with “genome-scale” metabolic modeling to inform optimal metabolic engineering designs and strategies satisfying an overall desired bioconversion. The optStoic formulation allowed for the exploration of all overall conversions rooting from the co-utilization of low-value CHN-B wrote computer scripts, performed the simulations and analyses, designed and generated the figures and tables, and wrote the manuscript. CM supervised and contributed to the design of the study, wrote the manuscript, and critically revised the manuscript. Both authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Kluyveromyces marxianus and Ogataea polymorpha have been described to grow at reasonable rates above 40 °C. However, the complex mechanisms of thermotolerance in yeast impede its full comprehension and the rare physiological data at elevated temperatures has so far not been matched with corresponding metabolic analyses.Thermotolerance is a highly desirable trait of microbial cell factories and has been the focus of extensive research. Yeast usually tolerate only a narrow temperature range and just two species, Kluyveromyces and Ogataea strains were generated and used for 13C-metabolic flux analyses. While the maximum growth temperature was very similar in all investigated strains, the metabolic network response to elevated temperatures was not conserved among the different species. In fact, metabolic flux distributions were remarkably irresponsive to increasing temperatures in O. polymorpha, while the K. marxianus strains exhibited extensive flux rerouting at elevated temperatures.To elaborate on the metabolic network response to increased fermentation temperatures of up to 49 °C, comprehensive physiological datasets of several While a clear mechanism of thermotolerance is not deducible from the fluxome level alone, the generated data can be valued as a knowledge repository for using temperature to modulate the metabolic activity towards engineering goals.The online version of this article (10.1186/s12866-019-1453-3) contains supplementary material, which is available to authorized users. For centuries, yeasts have been essential to many applications for the betterment of human life. With the rise of metabolic engineering and due to manifold beneficial traits, ranging from ethanol tolerance to protein glycosylation, these organisms have also found various applications outside the baking and beverage industries , 2. WhilKluyveromyces marxianus (synonym: Candida macedoniensis) and Ogataea polymorpha  are the two most prominent examples of thermotolerant yeasts and both have beneficial traits besides their thermotolerance. K. marxianus can grow at staggering growth rates and produces ethanol if cultivated at temperatures above 40 °C [O. polymorpha is methylotrophic and exhibits desirably low levels of hyperglycosylation [Thermotolerance is a rare trait among yeasts, but has been described for few species and was improved for example in baker’s yeast , 9. Therve 40 °C . O. polysylation , 14. Consylation .The relationships of these taxa are shown in Fig. Both species seem to have surpassed their non-conventional status and have been successfully targeted with novel metabolic engineering tools –24. HoweK. marxianus and O. polymorpha perform well at high temperatures [Available literature on thermotolerant yeasts clearly suggests that eratures , 25. ThiOgataea strains showed a phenotype that was highly stable over the investigated range of temperatures, especially up to 40 °C, with growth rates narrowing around 0.4 1/h, glucose uptake rates of 4 mmol/gCDW/h, and oxygen uptake rates of around 8 mmol/gCDW/h. No glycerol production was observed, and significant amounts of acetate were produced only by O. polymorpha KCTC 17233 at 37 °C and 40 °C. O. polymorpha KCTC 17233 was furthermore unique in having a maximum growth rate at 47 °C, while all other strains showed a maximum growth rate at 40 °C. Although O. polymorpha KCTC 17233 was still able to grow at 49 °C, this dataset is excluded here because growth was not exponential. The data of O. polymorpha CLIB 421 at 40 °C has been used in a previous study to validate a 13C-based metabolic flux analysis model [O. polymorpha NCYC 495 but did not produce any byproducts at the tested temperatures. As an example of the common Ogataea phenotype, a comprehensive flux map of O. polymorpha KCTC 17233 at 47 °C is given in Fig. The is model  and is iOgataea strains, the Kluyveromyces strains were highly adaptive to temperature changes exhibiting a variety of phenotypes with large differences also between the individual strains. Two outstanding phenotypes, one defined by a very high growth rate and a second, defined by a high ethanol yield, have already been subject of extensive research [K. marxianus strains at 40 °C and 47 °C, respectively. Two strains, specifically K. marxianus CBS 712 and K. marxianus ATCC 748, showed growth rates above 0.9 1/h at 40 °C, which is approximately double the maximal growth rate reported for S. cerevisiae [CDW/h) and oxygen uptake rates (40 mmol/gCDW/h), and TCA cycle rates reaching 9.5 mmol/gCDW/h  for all strains and for all temperatures. Both genera showed distinctive flux distributions characterized by a very high pentose phosphate pathway (PPP) flux in the Ogataea strains and comparably higher glycolytic and TCA cycle fluxes for Kluyveromyces. Compared with the Crabtree-positive yeast S. cerevisiae at 30 °C under otherwise identical culture conditions, these oxidative metabolic schemes sustained much higher rates of redox cofactor reduction in all experiments  [Kluyveromyces strains  , 29. Howins Fig. , while sAn excerpt of the flux distribution data of all investigated strains is presented in Fig. Ogataea and Kluyveromyces strains to increased cultivation temperature was the remarkable metabolic variability of Kluyveromyces strains, which is absent in Ogataea. Special focus of the discussion around the metabolic basis of thermotolerance lies on the activity of the PPP, which was increased in all investigated strains compared to S. cerevisiae [S. cerevisiae [Ogataea and no correlation with temperature was found for Kluyveromyces. Secretion of pyruvate, as described by Fonseca et al. [K. marxianus strain and during growth on higher sugar concentrations, was not observed in the present study. This indicates that the response to increased temperature is not conserved among thermotolerant yeast species, much less among yeasts in general.The most notable difference in the metabolic response of revisiae , but it revisiae , such a revisiae , none ofa et al.  for a diKluyveromyces strains, the temperature increase resulted in a stark increase of metabolic activities, thereby being in accordance with the temperature dependence of reaction rates described by Arrhenius. The optimal temperature of all three Kluyveromyces strains was around 40 °C at which they showed approximately doubled glucose uptake and growth rates compared to growth at 30 °C. Apparently, at mesophilic temperatures, the strain is not exploiting its maximal metabolic capacity. Additionally, we observed an up to 2-fold increase of the TCA cycle activity indicating that the elevated temperatures cause a typical metabolic stress response. Specifically, the cells experience an increased mitochondrial membrane permeability, concomitant proton influx, protein damage, and lipid peroxidation, which needs to be counteracted by ATPase activity and cell repair mechanisms [Kluyveromyces . The flasks were filled with 50 mL of Verduyn medium [4)2SO4, 3 g KH2PO4, 0.5 g MgSO4∙7H2O, 4.5 mg ZnSO4∙7H2O, 0.3 mg CoCl2∙6H2O, 1.0 mg MnCl2∙4H2O, 0.3 mg CuSO4∙5H2O, 4.5 mg CaCl2∙2H2O, 3.0 mg FeSO4∙7H2O, 0.4 mg NaMoO4∙2H2O, 1.0 mg H3BO3, 0.1 g KI, 15 mg EDTA, 0.05 mg biotin, 1.0 mg calcium pantothenate, 1.0 mg nicotinic acid, 25 mg inositol, 1.0 mg pyridoxine, 0.2 mg p-aminobenzoic acid, 1.0 mg thiamine. The medium was supplemented with 5 g/L glucose consisting of 80% (n/n) 1-13C-glucose and 20% (n/n) U-13C-glucose , because it allows the determination of intracellular fluxes with good resolution at reasonable costs per experiment [600) of 0.05. Biomass samples for determination of the 13C-labeling of proteinogenic amino acids by GC-MS analysis were taken during the exponential growth phase no earlier than five generations after inoculation.The strains used in this study were n medium , and shan medium . The clo600 was measured immediately after sampling. The samples were centrifuged at 13,000 × g for 5 min in a Thermo Scientific Heraeus Pico 17 tabletop centrifuge. The biomass pellet and the supernatant were stored separately at − 20 °C until further analysis. The pellet was used for GC-MS analysis of proteinogenic amino acids (see below). The concentrations of glucose and the excreted metabolites ethanol, glycerol, and acetate were determined by HPLC analysis of the supernatant. A constant flow of 0.8 mL/min of 5 mM H2SO4 was used to separate the metabolites on an Aminex HPX-87H column  at 60 °C. Glucose, ethanol, and glycerol were quantified with a Shodex RI-101 detector, and a variable wavelength detector of an UltiMate 3000 HPLC system  was used at 210 nm to quantify acetate. The cell dry weight (CDW) of samples was calculated from the measured OD600 and calibration curves, which were specific for the experimental conditions. The off-gas analysis system provided by BlueSens gas sensor GmbH  and included BCP-O2 and BCP-CO2 sensors connected to a BACCom12 communication box to obtain signlas in units Vol-% using the FermVis software. These signlas were transformed into molar concentrations as described by J Heyland, J Fu and LM Blank [2 production rate was corrected for the insensitivity of the sensors towards labeled CO2, as described by M Lehnen, BE Ebert and LM Blank [During the exponential growth phase, samples were taken via the sample port to not disturb the gas composition in the head space as described by Lehnen et al. . The OD6LM Blank . ExponenLM Blank .13C-labeling patterns of proteinogenic amino acids were determined from biomass samples taken in the mid-exponential growth phase. Approximately 0.3 mg of biomass was hydrolyzed at 105 °C for 6 h after resuspension of the pellet in 6 M HCl and then dried overnight at 80 °C. Derivatization of the dried hydrolysate was done in a mix of 30 μL acetonitrile and 30 μL N-methyl-N-tert-butyldimethylsilyl-trifluoroacetamide  for one our at 85 °C for 1 h to allow derivatization. The derivatized samples were analyzed on a gas chromatography-mass spectrometry (GC-MS) triple quadrupole system. The GC-MS system consisted of a TRACE™ GC Ultra and a TSQ triple quadrupole MS with electron impact ionization  equipped with a TraceGOLD TG-5SilMS fused silica column  and was operated as described by Schmitz et al. [z et al. . Helium z et al.  was usedO. polymorpha NCYC 495, available from the homepage of the Joint Genome Institute (JGI)  and the genome sequence of K. marxianus KCTC 17555, which is deposited at DDBJ/EMBL/GenBank under the accession number AKFM00000000 [Ogataea and Kluyveromyces datasets, respectively, is given in the supplement.To compute the intracellular fluxes, OpenFLUX was used , 57, bas00000000 , pathway00000000  and WoLF00000000  for all 00000000 . DetaileAdditional file 1:Figure S1. Supplementary physiological data of K. marxianus strains and O. polymorpha strains from batch experiments on glucose at different temperatures. Figures S2-S25. Flux maps of all strains for all tested temperatures. (DOCX 6050 kb)Additional file 2:Table S1. Physiological rates of K. marxianus strains from batch experiments on glucose at different temperatures. Depending on the strain, the cultivation temperature was varied between 30 °C and 49 °C in different experiments. Table S2. Physiological rates of O. polymorpha strains from batch experiments on glucose at different temperatures. Depending on the strain, the cultivation temperature was varied between 30 °C and 49 °C in different experiments. (DOCX 26 kb)Additional file 3:Optimal flux solutions for all simulated data sets, including the corresponding smallest sum of residual errors  and the constraints for uptake and production rates of metabolites and biomass constituents, which were used in the simulations. (XLSX 516 kb)


Saccharomyces cerevisiae––an important model organism and widely used cell-factory. Yeast8 tracks community development with version control, setting a standard for how GEMs can be continuously updated in a simple and reproducible way. We use Yeast8 to develop the derived models panYeast8 and coreYeast8, which in turn enable the reconstruction of GEMs for 1,011 different yeast strains. Through integration with enzyme constraints (ecYeast8) and protein 3D structures (proYeast8DB), Yeast8 further facilitates the exploration of yeast metabolism at a multi-scale level, enabling prediction of how single nucleotide variations translate to phenotypic traits.Genome-scale metabolic models (GEMs) represent extensive knowledgebases that provide a platform for model simulations and integrative analysis of omics data. This study introduces Yeast8 and an associated ecosystem of models that represent a comprehensive computational resource for performing simulations of the metabolism of  Saccharomyces cerevisiae.Genome-scale metabolic models provide a platform to study metabolism through simulations and analysis of omics data. Here the authors introduce Yeast8 with its model ecosystem, a comprehensive computational resource for simulating the metabolism of  As a bottom-up systems biology tool, genome scale metabolic models (GEMs) connect genes, proteins and reactions, enabling metabolic and phenotypic predictions based on specified constraints5. The quality and scope of GEMs have improved as demonstrated by the models for human6, yeast7, and E. coli8. With their thousands of reactions, genes and proteins, GEMs also represent valuable organism-specific databases. Therefore, it is important to keep track of changes as new knowledge is added to GEMs, in order to make model development recorded, repeatable, free and open to the community. Approaches to record updates of a GEM exist for this purpose10, albeit with some limitations on their simplicity and flexibility.In the era of big data, computational models are instrumental for turning different sources of data into valuable knowledge for e.g. biomedicalS. cerevisiae is a widely used cell factory12 and is extensively used as a model organism in basic biological and medical research14. Recently, the emergence of technologies, such as CRISPR15 and single cell omics data generation16, have accelerated the developments in systems biology. Consistent with strong research interests in yeast, the relevant GEMs have also undergone numerous rounds of curation since the first published version in 200317. These GEMs have contributed significantly to systems biology studies of yeast including their use as platforms for multi-omics integration19, and use for in silico strain design21. However, the hitherto latest version, Yeast722, with only 909 genes, falling behind the latest genome annotation, presents a bottleneck for the use of yeast GEMs as a scaffold for integrating omics datasets.24. Traditional GEMs only consist of reactions and their related gene and protein identifiers, and therefore cannot accurately predict cellular phenotypes under varied environmental conditions other than nutritional conditions, e.g. simulating the impact of temperature on growth rates25. Recently, enzyme constraints26 and protein 3D structures27 have been integrated into GEMs, thereby expanding their scope of application and laying the foundation for whole cell modelling. GEMs constrained with kcat values and enzyme abundances have been able to directly integrate proteomics data and correctly predict cellular phenotypes under conditions of stress26. GEMs with protein 3D structures connect the structure-related parameters and genetic variation27 with cellular metabolism6, thus enlarging the prediction scope of GEMs. However, it remains challenging to directly predict cellular metabolism based on changes in protein sequences with current GEMs. Advanced functional mutation cluster analysis constrained with protein 3D structures is therefore needed to integrate knowledge on protein structures into GEMs.Metabolism is complex and regulated at several different levelsS. cerevisiae30. We also introduce a model ecosystem around this GEM, including ecYeast8, a model incorporating enzyme constraints; panYeast8 and coreYeast8, representing the pan and core metabolic networks of 1011 S. cerevisiae strains; and proYeast8DB, a database containing 3D structures of metabolic proteins. This model ecosystem has the ability to meet wide application demands from the large scientific yeast community in systems and synthetic biology of yeast.This study presents Yeast8, the latest release of the consensus GEM of DB, multiple parameters related to protein kinetics and 3D structures could be integrated based on gene–protein-reaction relations. Furthermore, with panYeast8 and coreYeast8, 1011 strain-specific GEMs were reconstructed and compared. Thus, with Yeast8 and its model ecosystem, we demonstrate that the metabolism of yeast can be characterised and explored in a systematic way.Yeast8 is a consensus GEM maintained in an open and version-controlled way. Through ecYeast8 and proYeast8https://git-scm.com/), a version control system, and GitHub (https://github.com/), a hosting service for Git repositories  to ensure that fluxes are kept at physiologically possible levels. Compared with Yeast8, ecYeast8 has a large reduction in flux variability for most of the metabolic reactions in both glucose-limited and glucose-excess conditions  on 322 different combinations of carbon and nitrogen sources using microtiter plates Fig. . Here, tS. cerevisiae strains from a recent genome-sequencing project and designed a pipeline to reconstruct GEMs for each strain  could be generated automatically  based on subsystems defined in the KEGG database and found that the accessory genes were primarily engaged with alternative sugar metabolism and other secondary metabolism pathways  as well as the yield of 26 metabolites and biomass for growth on minimal media Fig. . From th40 revealed that variations in the simulated maximum yields of amino acids are primarily due to differences in the energy pathways used for ATP regeneration and the absence of some essential genes in amino acid synthesis  and glycolysis , indicating that these pathways are conserved in the evolution of S. cerevisiae. By comparison, genes with the most nsSNPs were enriched in organic acid biosynthetic process , carboxylic acid biosynthetic process , etc.To get further insight into which parts of metabolism may be sensitive or prone to mutations we chose the top 30 genes with the smallest and largest number of relative nsSNPs and conducted a GO-term analysis using DAVID 6.7DB using CLUMPS method45, which could find proteins with significant SNP clusters within the protein structures. As an example, using the above nsSNP data as input, we ran the CLUMPS pipeline for three different groups of S. cerevisiae strains: Wild, Wine, and Bioethanol strains  and ‘glutathione metabolic process’  suggested that mutation at site of 422 in YML100W occurred in a conserved zone  belonging to the GO term ‘cellular response to heat’  from 50 strains with highest relative growth rate, among which YJL052W, one of the isozymes of glyceraldehyde-3-phosphate dehydrogenase (GAPDH), is located in the key pathway related to glycerol utilisation Fig. . When weWe queried whether there were correlations between mutations occurring in hotspot zones and the growth phenotype. From the remaining 746 strains which were not used for the above hotspot analysis, we found eight strains that exhibit more than one mutation in the hotspot zone and another 15 strains having two mutations in YJL052W, but in the non-hotspot zone. Comparing the relative growth rate of these two groups of strains with the remaining, we saw that the former 8 strains grows faster on complex medium with glycerol as the carbon source than both the latter 15 strains, as well as all other strains not containing the mutations in hotspot zone Fig. , indicat50. Integrating consistent model evaluation with community model development will be instrumental to accelerate high quality development of GEMs.As part of this study, we have developed Yeast8 aided by version control and open collaboration, which has provided a platform for a continued community-driven expansion of the model. This platform can greatly accelerate iterative updates of the model, and we believe that this approach should become the future standard for developing GEMs for other organisms. Yeast8 is the currently most comprehensive reconstruction of yeast metabolism, but it also represents a model that can be used for simulations. The platform provided through the GitHub repository enables addition of new knowledge when it is acquired as well as using this for further improving the model for simulations. Yeast8 is in line with the latest trend of performing model quality-control analysis in a standardised manner with memoteS. cerevisiae. As more evidences from experiments and bioinformatics analyses are revealed and utilised to update GEMs, these models will move closer to the in vivo network. Based on Yeast8, we developed strain specific GEMs, enzyme-constrained GEMs (ecYeast8), etc., which together form a model ecosystem around the yeast GEM and improve cellular phenotype predictions. As an example, ecYeast8 verifies that the yeast phenotypes are to a large extent determined by protein resources allocation, which is consistent with recent research39. We expect predictions of ecYeast8 to further improve as more organism-specific kinetic data becomes available, hopefully generated in a high-throughput and systematic way51. By comparing panYeast8, coreYeast8 and 1,011 strain specific models, it can be concluded that metabolic capabilities are largely conserved for all S. cerevisiae strains, which is consistent with a recent study52. However, through strain specific GEM simulations, we have found subtle metabolic differences among the strains in the utilisation of substrates and the maximum yield of 26 chemicals. Exploring these differences constrained with more physiological data can guide future metabolic engineering and help to evaluate the potential of any given strain for any desired product, as well as provide clues about the mechanisms of evolutionary adaption. Currently, only in silico simulations were conducted using 1011 yeast ssGEMs; therefore, future experimental evidence for other non-reference strains will be important to evaluate the reliability of our model predictions.Through developing Yeast8, we have significantly improved the metabolic scope of the consensus GEM of DB by collecting and evaluating the yeast metabolic protein 3D structures from public databases54. This enabled identification of mutational hotspots associated with specific phenotypes. Furthermore, through combining the predicted targets from ecYeast8 and proYeast8DB, we demonstrated how to identify mutations in enzymes with high flux control over a given pathway, which may also be associated with desirable phenotypes. It should be noted that although proYeast8DB is useful for connecting GEMs to protein structure information like PDB identifiers and protein parameters, it is still challenging to directly predict phenotypes using the model with protein structure variations as input. High quality 3D protein structures at genome scale are still scarce53, thus the breakthrough in 3D proteins structure simulations55 and the related residues functional predictions are strongly expected. Nevertheless, like Recon3D6 and the E. coli GEM-PRO27, the proYeast8DB holds value as a means to explore the relation between the cell genotype and phenotype with clear evidence.With the increasing use of adaptive laboratory evolution in metabolic engineering it is important to have methods for rapid assessment of mutations in strains with improved phenotypes. Our multi-scale model analysis will be easily applicable in this area. As a further extension of Yeast8, we developed proYeast8Yeast8 will continue to be developed together with its ecosystem of models. As a whole, they are expected be a solid basis for developing a whole cell model of an eukaryal cell, which may serve as a stepping stone to a wider use of model simulations in life sciences, resulting in reducing the costs of developing biotechnology processes and drug discovery.Git and GitHub were used to develop yeast-GEM in a traceable way. Git is used to track any changes of yeast-GEM, which are stored online in a GitHub repository  ComplementaryData, which contains the related database annotation and physiological data used for yeast-GEM updates. This data is generally stored as tab-separated value (.tsv) format for easier tracking of changes; (2) ComplementaryScripts, which contains all the scripts used to update yeast-GEM; (3) ModelFiles, which contains different formats of yeast-GEM for various applications. The.txt and.yml (YAML) formats make it convenient to visualise any changes in GitHub or Git local clients. The.xml (SBML) format makes it easy to import the model across different toolboxes and programming languages.As a standard step, a commit is needed when updating yeast-GEM. To make commits easy to understand, semantic commit messages are used  and the latest metabolomics research  G/G: in vivo growth/in silico growth (true positive); (2) NG/NG: in vivo no growth/in silico no growth (true negative); (3) G/NG: in vivo growth/in silico no growth ; (4) NG/G: in vivo no growth/in silico growth ;61 (Eq. 2). Accuracy ranges from 0 (worst accuracy) to 1 (best accuracy). MCC ranges from −1  to + 1 (perfect prediction).The model quality is then evaluated based on accuracy (Eq. 1) and the Matthews’ Correlation Coefficient (MCC)http://www-sequence.stanford.edu/group/yeast_deletion_project/downloads.html, which was generated from experiments using a complete medium. Accuracy and MCC were computed as described above.To conduct gene essentiality analysis, we used the essential gene list from the Yeast Deletion Project, available at 62. The following procedure was employed to simulate chemostat growth in glucose-limited conditions. Firstly set the lower bound of glucose and O2 uptake reactions using experimental values. Glucose and oxygen uptake fluxes are negative and therefore the lower bounds are fixed to represent the maximum uptake rates. Secondly maximise the growth rate.The simulated aerobic and anaerobic growth under glucose-limited and nitrogen-limited conditions were compared with reference data63, then set the lower bound as measured for NH3 and O2 uptake reactions using experimental values and finally maximise the growth rate.As for nitrogen-limited conditions, since protein content in biomass drops dramatically under nitrogen-limited conditions, the biomass composition was rescaled according to reference conditions38  are defined as a ratio between a relative change in the flux of interest and a relative change in the correspondent 40. In Peter’s study, 1011 yeast strains genomes had been sequenced and analysed. A pan-genome was obtained from all these strains, made up of by 6081 non-redundant ORFs from S. cerevisiae S288C reference genome, and 1715 non-reference ORFs (nrORFs) from the other strains. For the 7796 ORFs, a panID was given for each of them. By comparison, 4940 ORFs are conserved in all these strains while 2846 ORFs are variables across all these strains. The annotation of non-redundant 6081 ORFs can be taken directly from the latest S. cerevisiae S288C genome annotation, while related gene–protein-reactions (GPR) can be obtained from Yeast8 directly.To construct the pan model of yeast (panYeast8), the latest genomics research by Peter et al has consultedS. cerevisiae S288C genome40. The blast analysis, along with the gene annotation of KEGG web service35, and EggNOG web service65, were employed to check and improve the original ortholog relation. To evaluate the ortholog gene relations qualitatively, the bi-directional blast hit (BBH) analysis was further conducted using Diamond66. Here the best hit in BBH analysis with pidentity larger than 80% were finally chosen and prepared for a panYeast8 formulation.As mentioned in Peter’s article there are 774 nrORFs with the ortholog genes from https://www.genome.jp/tools/kaas/) and EggNOG (http://eggnogdb.embl.de/#/app/emapper). For the KEGG annotation, a BBH  assignment method with the default parameters was used. For the EggNOG annotation, the HMMER with the default parameters was used. In the EggNOG annotation, each protein will be mapped onto KO ID and BiGG reaction ID while for the KEGG annotation, each protein will be given a unique KO ID. So if the KO ID for a protein is different between KEGG and EggNOG, then the KO ID given by KEGG will be preferred in the further analysis. If the KO ID was given for one protein by EggNOG, but not in KEGG, then this annotation will be also used for the pan-genome annotation. When the KO ids are obtained, the lists of KOs from nrORFs are compared with the reference ORFs. New KO ids for the nrORFs were subsequently extracted. Following this, the rxnID was obtained based on KO-rxnID mapping from KEGG database.To further search reliable new reactions connected with nrORFs, the annotation results from KEGG and the EggNOG web service were used. According to the format request for the two web services, the protein fasta files of pan-genome were uploaded onto KEGG (9 were used to represent the gene name.For ortholog genes (e.g. gene C) obtained from pan-genome annotation, they can be merged based on the reference gene (e.g. gene A) function in the original model according to the following rules: (1) if A or B catalyze the same isoenzyme, the GPR rule could be changed to ‘A or B or C’ in panYeast8; (2) if A and B belong to a complex, the GPR rule should be updated from ‘A and B’ into ‘(A and B) or (C and B)’. Secondly, 51 new reactions with 13 new genes were merged into panYeast8. As for the genes identity in the model, in order to reduce chaos, the original gene IDs and gene names from original Yeast8 were kept, while for newly added genes, the panIDs defined in Peter’s workCollapsed genes in pan-genome but could be found in yeast GEM, and will be replaced with the corresponding ortholog genes defined in pan-genome. ssGEMs for 1011 strains were reconstructed based on panYeast8 along with the related strains specific genes list . For the PCA analysis of strains based gene (or reaction) existence in ssGEMs, R function-prcomp has been used in this article. The decision tree classification of strains according to the maximum growth rate on different carbon sources was carried out using the R package––rpart (https://cran.r-project.org/web/packages/rpart/). For the hyperparameters tuning, two R packages—ParamHelpers (https://CRAN.R-project.org/package = ParamHelpers) and mlr (https://CRAN.R-project.org/package=mlr) were further used.The hierarchical cluster analysis based on the reaction existence in ssGEMs for yeast strains is based on R package––dendextend (S. cerevisiae S288C from the SWISS-MODEL database67 (https://Swissmodel.expasy.org) on 20 July 2018 were downloaded. The total number is about 20332 PDB files including the 8109 modelling homology PDB files (PDB_homo) and 12223 experimental PDB files (PDB_ex). Meanwhile all the PDB_ex of S. cerevisiae S288C stored in RCSB PDB54 database were further downloaded. The protein sequences contained in each PDB_ex were also downloaded. The above two sources of PDB files were merged to obtain the comprehensive PDB files database for S. cerevisiae S288C. With the metabolic gene list of S. cerevisiae S288C to query PDB files database, most genes, with the exception of roughly 217 proteins (in Yeast8.3) could be found in the related PDB files. To fill this gap, the SWISS-MODEL web service was further used to build the PDB_homo for 217 proteins. As a result, each of metabolic protein could have at least one PDB file. All the original proteins annotation, like the residues sequence and protein length, were downloaded from the SGD database.To establish the protein 3D structure models for all genes from yeast GEM (and a few metabolic genes not included in current Yeast8), all the protein structures of Once the PDB files were collected, the parameters of PDBs were extracted and calculated for quality analysis. As for the PDB_homo, the default parameters from the ftp of the SWISS-MODEL database were obtained, and included the protein UniProt ID, the protein length, the related PDB ID (connected with chainID), the structure sources, the coordinates of proteins residues covered with PDB structures, the coverage, the resolution, and QMEAN. As for PDB_homo, besides the above default parameters from the SWISS-MODEL database, a greater number of parameters were obtained by parsing the PDB_homo atom files provided by the SWISS-MODEL with an in-house python script, which included the methods used to obtain the PDB files, the model template, the protein oliga state, the GMQE, QMN4, sequence identity (SID), and sequence similarity (SIM). In summary, each PDB_homo contains 18 parameters for further PDB quality analysis.https://github.com/williamgilpin/pypdb). The chainID for each PDB_ex was downloaded from the SIFTS database68.Some of PDB_ex parameters, like coverage and template ID can also be found from the SWISS-MODEL database. The other important parameters like resolution, ligands, and oliga state were obtained by parsing PDB_ex files from RCSB PDB database using , sequence similarity (SS), resolution, and QMEAN, were used to classify the PDB_homo. By using a simple normal distribution to describe all these parameters of PDB_homo, a Z score test can be done to calculate the threshold value for 69 was used to obtain residue sequences for each chain of one PDB file. Next, all residue sequences were blasted with original protein sequences for S. cerevisiae S288C from SGD with the aid of Diamond66 in order to check whether there existed gaps (mismatches or mutations) in the residue sequences from PDB_ex when compared with the original residue sequences. The PDB_ex has been chosen with the thresholds: pidentity = 100 and resolution ≤ 3.4 Å; otherwise a PDB_homo from SWISS-MODEL database will be used.In order to check whether there exists a gap in the PDB_ex files, all residue sequences from PDB databases for each chain of one PDB file were downloaded. At some points, however, residue sequences provided by PDB databases were not consistent with residue sequences contained in the structure. To solve this issue, a Biopython package70 (https://pfam.xfam.org/) was mainly used to annotate the domain information of proteins from S. cerevisiae S288C. If a structure covered all residues of any given domain, it was assigned to that very domain. For each domain, the coordinates of start and end, the name, the domain function description, the domain type, e_value, the related PDB ID, and protein ID, were all summarised. According to the GPRs of Yeast8, the relation between gene ID and reaction ID could be obtained. Following this, the domain information could be connected with each pair of gene and reaction based on the ID mapping.In this work, the Pfam32.0 database40 the homozygous SNP from the massive data file  from NCBI. If the SNP was not located on CDS zone of gene, it was classified as a type of ‘INTEGENIC’. If not this classification, it was otherwise given a gene systematic name, consistent with the gene name format in Yeast8. Based on the above SNP annotation information only those belonging to the metabolic genes (gene list in Yeast8 and some other metabolic genes not contained in Yeast8 until now) were chosen. According to the SNP annotation information and the protein sequences of the related genes, the SNPs are classified as the sSNP (synonymous single nucleotide polymorphism) and nsSNP (nonsynonymous single nucleotide polymorphism). The relative numbers of sSNPs and nsSNPs for each gene were calculated, which is equal to the total sSNPs or nsSNPs divided by the related protein length.After filtration, the reliable SNP can be obtained for each strain. The data furthermore contains each SNP’s strain name, chromosome, coordinates, ref, and alt nucleotide base. In the annotation phase, the SNP type and related gene names were further annotated based on the coordinates and the annotation information of S. cerevisiae Before mapping, the coordinates of mutated residues from each nsSNP need to be calculated. Firstly, the relative coordinates of mutated residues on the original protein sequence can be obtained based on the coordinates of nsSNP on the chromosome. Following this, according to the coordinates mapping between the original protein sequences and the relative residues coordinates in the proteins structure, the relative coordinates of the mutated residues in the protein structures can be estimated and used in the following calculation.45, a WAP score to calculate the pairwise distances between mutated residues for a protein 3D structure.dq,r in this article is defined as the Euclidean distance (in Å) between α carbons of any two mutated residues. t is defined as a ‘soft’ distance threshold, which equals to 6 Å. nq and nr are the normalised numbers of samples contains the mutations using the followed sigmoidal Hill function:Nq is the number of samples with a missense mutation impacting residue q of the protein and θ = 2 and m = 3 are parameters of the Hill function controlling the critical point (centre) and steepness of the sigmoid function, respectively. Formula (2) was used to normalise the sample number contained in residue mutations q and r, both of which can avoid the impact of higher frequent mutated residues in the samples. A detailed description of each formula can be found in Kamburov’s article45.Referring to Kamburov’s methodP value in the null distribution for the given mutated protein structures based on the original WAP score and all the sampled WAP scores. The right tailed P value is defined as the number of samples with WAP scores larger than the original WAP scored, divided by the total number of samples.The CLUMPS method can be divided into four steps. Firstly, prepare the needed SNP information and structure information of one protein. Secondly, with the normalised mutation number occurring in specific positions, calculate the WAP scores of the samples. Next, assuming that the uniform distribution of mutations across the protein residues covers the given structure, calculate each WAP score in 10 randomisations to obtain the null distribution. During the sampling process, the mutation number of residues occurring in random locations was kept the same as the original values. Lastly, calculate the right tailed P value smaller than 0.05 from strains group of “Bioethonal” and “Wine”, GO-enrichment analysis using DAVID6.7 on-line web service72 was carried out.For proteins with 49. All the SNP and structure information  were prepared for a group of strains with specific phenotypes. Before carrying out the cluster analysis, the mutated paired residues of significance were filtered according to reference49. These important paired residues should meet the followed three criteria: the distance between two residues should be smaller than 10 Å for all the intramolecular clusters analysis; the two residues should be separated by at least 20 residues in the original protein sequence; and a permutation method ought to be used to calculate the P value for each paired residues . For each cluster, its closeness can be calculated using the function of ‘closeness.residual’ from the R package entiserve73. The detailed principle could be also find in the original research49. As the last step, when a cluster was estimated, the P value was calculated based on the CLUMPS analysis pipeline in this work.Once the paired residues of significance have been obtained, the clusters made up of paired residues were obtained based on the undirected graph theory, which was realised using the function ‘decompose.graph’ from the R package igraph (48 (http://mutfunc.com/) and SIFT (http://snpeff.sourceforge.net/SnpSift.html) were employed to predict the potential effects of nsSNP on protein function.To look into the effect of the mutations, mutfunc74. A total of 190 carbon sources, 95 nitrogen sources, 95 phosphorus, and sulphur sources were tested. The PM procedures for S. cerevisiae S288C were based on the protocol of Yeast version of the PM system.The Phenotype MicroArray (PM) system was used to test growth on every carbon, nitrogen, phosphorus and sulphur sources−1 glucose) and ammonium sulphate (7.5 g L−1 (NH4)2SO4), respectively. For all other substrate sources, the same minimal medium was used 75. Strains were cultivated in 96-well plates, and growth performance was determined with Growth Profiler 960 . The maximum specific growth rate (μmax) was calculated with the R package—growthrates (https://github.com/tpetzoldt/growthrates).A total of 14 carbon sources and 23 nitrogen sources were combined by orthogonal experiments. Every carbon source and nitrogen source used in the medium were the same C-mole and N-mole as glucose (20 g LFor two group comparison in this work, a two tailed Wilcoxon rank sum test was used.Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Reporting Summary


Such data can be integrated using 13C Metabolic Flux Analysis (13C MFA) to provide quantitative metabolic maps of flux distributions. However, 13C MFA might be unable to reduce the solution space towards a unique solution either in large metabolic networks or when small sets of measurements are integrated. Here we present parsimonious 13C MFA (p13CMFA), an approach that runs a secondary optimization in the 13C MFA solution space to identify the solution that minimizes the total reaction flux. Furthermore, flux minimization can be weighted by gene expression measurements allowing seamless integration of gene expression data with 13C data. As proof of concept, we demonstrate how p13CMFA can be used to estimate intracellular flux distributions from 13C measurements and transcriptomics data. We have implemented p13CMFA in Iso2Flux, our in-house developed isotopic steady-state 13C MFA software. The source code is freely available on GitHub (https://github.com/cfoguet/iso2flux/releases/tag/0.7.2).Deciphering the mechanisms of regulation of metabolic networks subjected to perturbations, including disease states and drug-induced stress, relies on tracing metabolic fluxes. One of the most informative data to predict metabolic fluxes are  13C Metabolic Flux Analysis (13C MFA) is a well-established technique that has proven to be a valuable tool in quantifying the metabolic flux profile of central carbon metabolism. When a biological system is incubated with a 13C-labeled substrate, 13C propagates to metabolites throughout the metabolic network in a flux and pathway-dependent manner. 13C MFA integrates measurements of 13C enrichment in metabolites to identify the flux distributions consistent with the measured 13C propagation. However, there is often a range of flux values that can lead to the observed 13C distribution. Indeed, either when the metabolic network is large or a small set of measurements are integrated, the range of valid solutions can be too wide to accurately estimate part of the underlying flux distribution. Here we propose to use flux minimization to select the best flux solution in the13C MFA solution space. Furthermore, this approach can integrate gene expression data to give greater weight to the minimization of fluxes through enzymes with low gene expression evidence in order to ensure that the selected solution is biologically relevant. The concept of using flux minimization to select the best solution is widely used in flux balance analysis, but it had never been applied in the framework of 13C MFA. We have termed this new approach parsimonious 13C MFA (p13CMFA). Fluxomics is the omics field that analyses metabolic fluxes  which are a close reflection of the metabolic phenotype. As such, quantitative tracking of metabolic fluxes is vital for deciphering the regulation mechanisms of metabolic networks subjected to perturbations, including disease states and drug-induced stress. However, unlike other omics data that can be quantified directly, the fluxome can only be estimated through an indirect interpretation of experimental data–3.13C Metabolic Flux Analysis (13C MFA). Both methods use stoichiometric, thermodynamic and experimental constraints to find the range of feasible fluxes across a metabolic network and then find the flux distributions within that space that optimize a given objective function. However, both techniques differ in the type of objective function optimized.There are two main model-based approaches to quantifying metabolic fluxes, Flux Balance Analysis (FBA) and In FBA, the objective function is a set of fluxes to be minimized or maximized. These fluxes must represent a biological objective deemed desirable in the conditions of study . A signi13C MFA, the objective function is to minimize the difference between simulated and measured 13C enrichment in metabolites -glul dataset . However, unlike pathway activity analysis, p13CMFA is able to integrate all quantified isotopologue fractions and flux measurements (e.g. rates of metabolite uptake and secretion) to generate network-wide flux maps consistent with such data. Furthermore, p13CMFA is highly flexible; for instance, here we show that it can be used to seamlessly integrate gene expression data by giving higher weight to the minimization of the fluxes through lowly expressed enzymes.Here we describe p13CMFA can be used to estimate flux distributions integrating only limited sets of 13C measurements in a test case where traditional 13C MFA was unable to provide a narrow solution space. Furthermore, we demonstrated that, when a limited set of measurements are integrated, p13CMFA can yield more accurate flux predictions than both 13C MFA and GIMME.As a proof of concept, we exemplified how p13C MFA does not aim to be a replacement of 13C MFA; instead, it seeks to supplement it by identifying the more straightforward solution in parts of the network that cannot be uniquely determined. In that regard, it can be used to quantitatively study flux distributions in instances where not enough information can be obtained with conventional 13C MFA. Nor does it aim to replace the direct interpretation of 13C data. The latter is still a suitable technique when the goal of the analysis is to compare the relative activity of well-established pathways across conditions or quantify substrate contributions rather than to generate complete flux maps.p13C data has been widely used to assist in drug discovery. In this regard, tracer analysis coupled with regression and correlation analyses is frequently used to characterize drug response -glucose (Sigma-Aldrich) and cells were incubated for 40h in a humidified atmosphere with 5% CO2 and 1% O2 at 37°C. Both at the beginning (t = 0h) and the end (t = 40h) of incubation, media and pellets were collected. On the one hand, media and cell pellets were used for analyzing isotopologue abundances for glucose, lactate, glutamate, RNA ribose and glycogen. Raw data are publicly available in the MetaboLights repository at http://www.ebi.ac.uk/metabolights [13C-D7]-glucose as recovery standard[-1·million-cells-1)–were 0.463, 0.099, 0.050 and 1.169 for glucose uptake, glutamine uptake, glutamate secretion, and lactate secretion, respectively, and a net transformation of glycogen of 0.000175.Human Umbilical Vein Endothelial Cells  were maintained on 1% gelatin-coated flasks at 37°C in a humidified atmosphere of 5% CObolights . Also, t standard. All bioS1 Figa and Cb) are condensed into a bi-carbon metabolite (Ca-Cb) through a reaction with a flux v1. Metabolite Ca-Cb is removed from the system at a rate of v2. For each metabolite, isotopologue fractions (Mx) are defined as the relative abundance of the metabolite with x number of 13C substitutions. Isotopologue balances for metabolite Ca-Cb are indicated. Under the assumption of isotopic steady state  and given v1 and v2, and a set of isotopologue fractions for Ca and Cb (assumed a constant input), the system can be solved to identify the steady-state isotopologue fractions for metabolite Ca-Cb.In this toy metabolic network, two mono-carbon metabolites (C(TIF)Click here for additional data file.S1 Table-1·million-cells-1.Fluxes are expressed in μmol·h(XLSX)Click here for additional data file.S2 Table-1·million-cells-1.Fluxes are indicated in μmol·h(XLSX)Click here for additional data file.S3 Table-1·million-cells-1.Fluxes are indicated in μmol·h(XLSX)Click here for additional data file.S1 ZIP13CMFA on Iso2Flux.The files are inputs for running p(ZIP)Click here for additional data file.S2 ZIP13CMFA on Iso2Flux.The files are inputs for running p(ZIP)Click here for additional data file.


In order to improve the accuracy of constraint-based metabolic models, several approaches have been developed which intend to integrate additional biological information. Two of these methods, MOMENT and GECKO, incorporate enzymatic (kcat) parameters and enzyme mass constraints to further constrain the space of feasible metabolic flux distributions. While both methods have been proven to deliver useful extensions of metabolic models, they may considerably increase size and complexity of the models and there is currently no tool available to fully automate generation and calibration of such enzyme-constrained models from given stoichiometric models.E. coli genome-scale model iJO1366 and analyze its key properties and differences with the standard model. In particular, we show that the enzyme constraints improve flux predictions  and demonstrate, for the first time, that these constraints can markedly change the spectrum of metabolic engineering strategies for different target products.In this work we present three major developments. We first conceived short MOMENT (sMOMENT), a simplified version of the MOMENT approach, which yields the same predictions as MOMENT but requires significantly fewer variables and enables direct inclusion of the relevant enzyme constraints in the standard representation of a constraint-based model. When measurements of enzyme concentrations are available, these can be included as well leading in the extreme case, where all enzyme concentrations are known, to a model representation that is analogous to the GECKO approach. Second, we developed the AutoPACMEN toolbox which allows an almost fully automated creation of sMOMENT-enhanced stoichiometric metabolic models. In particular, this includes the automatic read-out and processing of relevant enzymatic data from different databases and the reconfiguration of the stoichiometric model with embedded enzymatic constraints. Additionally, tools have been developed to adjust (kcat and enzyme pool) parameters of sMOMENT models based on given flux data. We finally applied the new sMOMENT approach and the AutoPACMEN toolbox to generate an enzyme-constrained version of the The methodological and tool developments presented herein pave the way for a simplified and routine construction and analysis of enzyme-constrained metabolic models. This information is readily available for many (organism-specific) enzymes in databases such as SABIO-RK  of an enzyme Ek (mmol/gDW) and that this enzyme is the only catalyst in the reaction(s) it catalyzes, this immediately sets an upper bound for the sum of all reaction fluxes catalyzed by enzyme Ek:R(Ek) denotes the set of reactions catalyzed by enzyme Ek. Similar as we did for the overall protein pool ))Ek] of aEk is not the only catalyzing enzyme in a reaction i it catalyzes, we split this reaction in two reactions with the same stoichiometry, one reaction is now (exclusively) catalyzed by enzyme Ek while the other reaction is catalyzed by all other enzymes of the former reaction i \Ek). Thereby, the rule (11) has to be applied again for both of the new reactions and the respective (possibly adapted) enzyme cost values have to be used in eq. (i had a limited flux bound (vi < ∞), additional constraints must be introduced (e.g. “arm” reactions as used in the GECKO approach) to ensure that this constraint is met by the sum of all the reactions obtained by splitting reaction i.For the case that d in eq.  and in tThe procedure outlined above has to be repeated for all enzymes with measured concentrations. With a growing set of concentration measurements, this will add several new columns and reactions in the stoichiometric matrix. However, concentration measurements are often available only for a small fraction of all enzymes. In these cases, the size of the augmented sMOMENT model as described above will still be significantly smaller than a fully expanded GECKO model. If concentrations are specified for all enzymes then the resulting model will, in fact, be an analogon to a GECKO model with the same number of reactions and metabolites. In principle, when using the AutoPACMEN toolbox (see below), very high (non-limiting) concentrations can be defined during model generation to enforce explicit inclusion of some or of all enzymes ; these concentration values can later be adapted for a given set of measurements.The AutoPACMEN (Automatic integration of Protein Allocation Constraints in Metabolic Networks) toolbox implements the sMOMENT method. It consists of two parts Fig. : (1) thecat values are central for the enzyme constraints, AutoPACMEN includes a specifically engineered automatic kcat retrieval method. AutoPACMEN can access the publicly available enzymatic databases SABIO-RK [cat database(s). Using the collected kcat data from all these sources, AutoPACMEN chooses the kcat values according to the number of entries for a reaction’s EC (Enzyme Commission) number as well as according to the substrates and the organism in which the kcat values were measured. The substrate-depending kcat search is supported using the BIGG database metabolite identifiers [cat search uses NCBI TAXONOMY [cat values to enzymes and reactions is described in the Additional file cat selection algorithm works as follows: For each EC number of a reaction, kcat values are collected from SABIO-RK and BRENDA. Then, for every reaction, its substrates and EC numbers are read out and standardized using BIGG identifiers. For every reaction’s EC number, the collected kcat values are determined. Additionally, for every enzyme catalyzing the reaction, the optional custom kcat values are retrieved, if available. Generally, from all these kcat values, the ones measured with the reaction’s substrate and with enzymes from the metabolic model’s organism are preferred. If no value could be found for the given substrate and organism, then the values from the taxonomically nearest species are preferred. The constraints for the taxonomic distance are also relaxed if there are less than a minimum of 10 kcat values for the given reaction. Finally, the mean value of all collected kcat values is chosen. For all reactions for which no kcat value could be found, a default kcat representing the median of all found kcat values is set.The AutoPACMEN model generator needs as main input the metabolic model as SBML file . This SBSABIO-RK  and BRENSABIO-RK . Optionantifiers , while tTAXONOMY . A full Furthermore, if enzyme concentration measurements are given by the user, then AutoPACMEN includes explicit enzyme (concentration) variables in the model as explained in the Methods section.The described AutoPACMEN model generator is written in Python 3 and requires a Python version > = 3.7. It can be used as console program or as Python module. Aside of Python’s standard library, the model generator also uses the modules biopython , cobrapyP as well as the kcat values both used in eq. ..P as weli in scenario j, wij a weighting coefficient to optionally set preferences for certain measurements. This objective function ensures that the relative error of predicted vs. measured fluxes is minimized. The model calibrator makes use of MATLAB’s fmincon function, requires MATLAB version 2017a or higher and depends on the MATLAB metabolic modeling package CellNetAnalyzer [fmincon. A separate Python script, which has the same dependencies as the AutoPACMEN model generator, is used for making a preselection of (sensitive) kcat parameters for fitting : https://github.com/ARB-Lab/autopacmenAutoPACMEN is free and open source under the Apache License. A GitHub repository has been created for AutoPACMEN  to the model’s stoichiometric matrix Fig. . Thus, sIn order to facilitate the construction of sMOMENT models, we developed AutoPACMEN (Automatic integration of Protein Allocation Constraints in Metabolic Networks). It consists of (1) the AutoPACMEN model generator for automatic generation of an sMOMENT-enhanced version of a stoichiometric metabolic model, and 2) the model calibrator which helps adjusting parameters of the included enzyme constraints based on measured data  was available in [cat values obtained from SABIO-RK and BRENDA resources. Note that kapp values reflect the actual (effective) turnover numbers as calculated from flux and proteomics data and may thus help to reduce overestimations from maximal turnover numbers (kcat).An exemplary run of AutoPACMEN was performed with the genome-scale  iJO1366 . The SBMlable in  these daiJO1366 can be found in Additional file iJO1366 delivered by AutoPACMEN is denoted by iJO1366*. Compared to the parent model iJO1366  reactions representing the same metabolic capability. The true change in the behavior of the model stems from the integration of the protein pool pseudo-metabolite and of the pseudo reaction for synthesis of this metabolite with an upper limit determined by the maximum protein pool .A detailed step-by-step description and explanation of the AutoPACMEN run with  to adjust the original kcat values to some extent to improve the agreement of model predictions with experimental data. As input for the parameter fitting of iJO1366* we used two sources, namely flux data  for aerobic and anaerobic growth of E. coli K-12 MG1655 on glucose given in [E. coli exhibited on 23 additional substrates [Generally, enzyme-constrained models need model validation, i.e. some fitting to experimental data to (a) determine an appropriate upper limit for the protein pool given in  as well P  turnover number kapp,i is therefore typically lower and can be written as a saturation-corrected value of kcat,i: kapp,i = σi ∙ kcat,i with 0 ≤ σi ≤ 1. Equation  by fittl in eq. . With thG1655 in . It is iEquation  then reaσi are not known (and not fitted as independent variables), fitting the protein pool P in eq. . With more biological data  this manual adjustment could be replaced with the automated workflow described in the following paragraph.The (effective) protein pool variable was fixed to the determined value of 0.095 g/gDW in all subsequent analyses. Next, in order to obtain realistic model behavior also for anaerobic growth on glucose, we manually identified four kcat values to improve the predictions with respect to the growth rates for 24 different substrates . As a preliminary step, the model calibrator identifies reactions whose kcat value can be optimized for a growth rate prediction of one substrate without changing the results for other substrates. As a result of this selection process, only 96 out of the 1155 reactions with kcat values were eventually selected for calibration. The adjusted kcat values can be interpreted either as correction of the original kcat values or as an adaptation of the kcat values to the apparent turnover numbers (kapp) under saturation levels of the respective growth conditions. The resulting model iJO1366* with the adapted kcat values is provided in SBML format in Additional file In a subsequent step we finally further optimized the kirs from ). Here wiJO1366* for 25 growth scenarios for which measurements were available  until a critical glucose uptake rate is reached beyond which acetate excretion takes place. Thus, unlike iJO1366 and without adding further (e.g. oxygen uptake) constraints, iJO1366* can explain this well-known overflow metabolism of E. coli [Going beyond maximal growth rate predictions shown in the previous section, we next intended to compare predicted vs. measured exchange fluxes (for substrate and major fermentation products) for aerobic and anaerobic growth on glucose Fig. . Here, w E. coli  solely b E. coli  at a gluiJO1366* predicts a dominant excretion of ethanol, formate and acetate as fermentation products for a wide range of substrate uptake rates. The combined operation of these pathways gives the maximum (anaerobic) yield of 2.5 ATP per molecule glucose. For a substrate uptake rate of 16.69 mmol/(gDW*h) the predicted exchange fluxes agree again very well with measurements from [iJO1366* predicts increasing lactate production rates  for very high glucose uptake rates, however, the net gain in growth rate for this shift is only marginal and thus probably not relevant in vivo. However, in [Afterwards, we performed the same simulations for anaerobic growth with different glucose uptake rates. Consistent with biological knowledge, nts from . Interesever, in  it was siJO1366 and the sMOMENT-enhanced iJO1366* model, we performed flux variability analysis in both models for aerobic growth on glucose with a maximal glucose uptake rate of 9.53 mmol/(gDW*h)  only 3 of those fluxes exist in iJO1366*. Moreover, every reaction in iJO1366* has either a reduced or identical flux range compared to iJO1366. These results highlight that the introduced enzyme constraints, consisting just of a single additional reaction and metabolite, significantly narrow down the flux space.As further step to compare the solution spaces of the original value in ). In botE. coli genome-scale model with and without enzyme constraints. As application example we calculated MCSs with up to 6 reaction knockouts for the growth-coupled production of the commodity chemicals ethanol and succinate as well as for the amino acids leucine and valine in both iJO1366  and iJO1366*. The MCS were calculated for anaerobic conditions with the following constraints: the maximal glucose uptake rate in iJO1366 was set to 15 mmol/(gDW*h), while this rate was not explicitly bounded in iJO1366*. In both models, as in the FVA study, the exchange reactions for all carbon metabolites were disabled except for the standard fermentation products  and the respective target product. For each target product, a minimal growth rate of 0.1 h− 1 and a specific minimal product yield  was demanded, irrespective of the growth rate  approach , 21, 34 coupling ). The MAAnalyzer , 36 can iJO1366* cause a significantly higher number of metabolic engineering strategies (58% more MCS in iJO1366* compared to iJO1366). A closer look at the interrelationships of the MCSs reveals that approximately a quarter of the 7168 MCS in iJO1366* are shared with iJO1366 while the largest fraction (~ 60%) represents MCS with knockout strategies that do not exist  in iJO1366. Especially interesting is the fact that there are 231 MCS in iJO1366* that are (proper) subsets of (1516) MCS in iJO1366. The reduced number of required interventions in these MCS indicate that ethanol secretion is already enforced to a certain extent by the enzyme constraints. On the other hand, a few of such cases also exist in the other direction where (11) MCS of iJO1366 are subsets of (101) MCS in iJO1366*. Similar results are obtained for succinate as target product, although the fraction of identical MCS in both models is larger.The complete results of the MCS computations can be found in Additional file iJO1366* while at least 196 could be computed for iJO1366. Here it is to be expected that pathways for leucine synthesis enforced by MCS in iJO1366 are not valid in iJO1366* due to some limitation by the enzyme costs. Using FBA we found that it is generally possible to reach the given leucine yield in the iJO1366* under the given minimal growth rate, however, coupling cannot be enforced, at least not with up to 6 knockouts. In the case of valine, the number of MCS (3712) in iJO1366* is relatively high but markedly reduced compared to iJO1366 (29290). Importantly, while 3664 MCS are identical in both models, not a single MCS that exists only in either model is a reduced version (subset) of the other indicating that also rather different strategies arise in both models. The results of the MCS study thus demonstrate that the application of sMOMENT may lead to new biotechnological metabolic engineering strategies which would not have been found without enzyme allocation constraints.A different picture is seen for the amino acids leucine and valine. First, not a single MCS is found for leucine in iJO1366* of the genome-scale E. coli model iJO1366 and compared these two models demonstrating how the added enzyme allocation constraints affect major model properties and predictions.In this work we presented three major developments. First, we introduced the sMOMENT method for simplified inclusion of (enzymatic) protein allocation constraints in metabolic models. We then developed the AutoPACMEN toolbox allowing automatic construction and calibration of sMOMENT models. Finally, we applied AutoPACMEN to construct the enzyme-constrained version MOMENT , a furthiJO1366 concentration measurements) contains 7728 reactions and 4166 metabolites, which is an enormous increase compared to 3178 reactions and 1806 metabolites in the sMOMENT model iJO1366*  with the respective GECKO version of the iJO1366 model  and did not find any differences if no protein measurements are provided. Furthermore, although not used herein, we described in the Methods section how given enzyme concentration measurements can be properly included during the automated construction of an sMOMENT model while still keeping the model as small as possible. As mentioned above and described in the Methods section, a fully expanded GECKO model can also be generated with AutoPACMEN if needed.A related method to MOMENT and sMOMENT is GECKO  where thAs for MOMENT and GECKO, sMOMENT models focus on protein mass constraints and are therefore simpler than the more advanced resource balance analysis  and Mecat data, but only from the BRENDA database and it does not include the capability to automatically calibrate kcat values. Furthermore, the Python scripts seem not be compatible with current versions of Python 3.The AutoPACMEN toolbox is, to our knowledge, the first program suite providing a virtually fully automated workflow for the integration and calibration of enzyme constraints in a given stoichiometric metabolic model. No such comprehensive toolbox was available for MOMENT whereas a set of manually editable and partly automated scripts were provided for generating GECKO models . This GEapp values) from external databases is not possible.Another related toolbox was recently published for (semi-)automated construction of RBA models (RBApy ). As expiJO1366* model, however, AutoPACMEN is ready to be used with any other constraint-based metabolic model (with standardized name space and gene-enzyme-reaction associations), regardless of the species they represent.Since the model generator of AutoPACMEN can be used either as console program or as Python modules, it can be easily integrated with other metabolic modeling programs. As the program suite depends on cobrapy , it can E. coli, iJO1366*, constructed herein with AutoPACMEN, is provided in SBML format in Additional file iJO1366* is, to the best of our knowledge, the E. coli genome-scale model based on (simple) enzyme constraints with the widest coverage of kcat values. If enzyme concentration measurements are available, AutoPACMEN can be used to integrate them, with minimal model extensions, in iJO1366*. Furthermore, by relaxing the protein pool variable P to a very high value, iJO1366* behaves as the original model iJO1366 thus allowing simultaneous simulation of E. coli’s metabolism with and without enzyme constraints.The calibrated enzyme-constrained genome-scale model for iJO1366* already revealed interesting properties and several key differences to the original model iJO1366. The explanation and predictions of phenomena such as overflow metabolism with enzyme constraints is not new [iJO1366* under the given conditions. Moreover, the phenomenon of increased lactate synthesis under anaerobic conditions with high substrate uptake rates could be predicted. Furthermore, the conducted analysis of intervention strategies for different target products is the most comprehensive done so far for enzyme-constraint models and revealed important insights. In particular, while some strategies might be valid in both models, a significantly altered spectrum of minimal cut sets may result when enzyme constraints are included and enforcement of growth-coupled product synthesis may become easier (less interventions required) or harder (more interventions required). It thus seems worth to rigorously include enzyme constraints for computational strain design in metabolic engineering.The basic analyses conducted herein with  not new , 11, 38,P, kcat values and the molecular weight of the enzymes). While the molecular weights can often be determined accurately, the kcat values retrieved from the databases usually have a much higher uncertainty. They are difficult to measure (often only in vitro and not in vivo) and reported measurements sometimes differ by orders of magnitudes. Moreover, specific kcat values are often not available for the organism under study and must then be taken from related species. Calibration of the original kcat values and estimating the protein pool P from available flux measurements is thus essential to obtain meaningful predictions of enzyme-constrained models and is supported by AutoPACMEN. Moreover, AutoPACMEN also provides options to use different modes of kcat value assignment  which can then be used to test the effect of different kcat distributions on the model predictions.While enzyme-constrained models may exhibit a higher predictive and explanatory power than classical constraint-based models, they require as additional input three different types of enzyme parameters  in SBML format.


Optimization of Regulatory And Metabolic Networks), which can identify combinatorial optimization strategies including overexpression, knockdown or knockout of both metabolic genes and transcription factors. OptRAM is based on our previous IDREAM integrated network framework, which makes it able to deduce a regulatory network from data. OptRAM uses simulated annealing with a novel objective function, which can ensure a favorable coupling between desired chemical and cell growth. The other advance we propose is a systematic evaluation metric of multiple solutions, by considering the essential genes, flux variation, and engineering manipulation cost. We applied OptRAM to generate strain designs for succinate, 2,3-butanediol, and ethanol overproduction in yeast, which predicted high minimum predicted target production rate compared with other methods and previous literature values. Moreover, most of the genes and TFs proposed to be altered by OptRAM in these scenarios have been validated by modification of the exact genes or the target genes regulated by the TFs, for overproduction of these desired compounds by in vivo experiments cataloged in the LASER database. Particularly, we successfully validated the predicted strain optimization strategy for ethanol production by fermentation experiment. In conclusion, OptRAM can provide a useful approach that leverages an integrated transcriptional regulatory network and metabolic network to guide metabolic engineering applications.The ultimate goal of metabolic engineering is to produce desired compounds on an industrial scale in a cost effective manner. To address challenges in metabolic engineering, computational strain optimization algorithms based on genome-scale metabolic models have increasingly been used to aid in overproducing products of interest. However, most of these strain optimization algorithms utilize a metabolic network alone, with few approaches providing strategies that also include transcriptional regulation. Moreover previous integrated approaches generally require a pre-existing regulatory network. In this study, we developed a novel strain design algorithm, named OptRAM ( Optimization of Regulatory And Metabolic Network), which can identify combinatorial optimization strategies including overexpression, knockdown or knockout of both transcription factors and metabolic genes, based on our previous IDREAM integrated network framework. OptRAM uses simulated annealing with a novel objective function, which can ensure a favorable coupling between the production of a desired chemical and cell growth. This strategy can be deployed for strain design of bacteria, archaea or eukaryotes. The other advantage of OptRAM compared with previous heuristic approaches is that we systematically evaluated the implementation cost of different solutions and selected strain designs which are more likely to be achievable in experiments. Through the in-silico strain design case studies for producing succinate, 2,3-butanediol, and ethanol in yeast, we demonstrated that OptRAM can identify strategies that increase production beyond what is seen currently, or found as potential designs using alternative methods. We also validated the modified genes chosen by OptRAM in example cases against previous in vivo experiments in the LASER database. Additionally, we experimentally validated the ethanol strain design by evaluating its performance in fermentation. OptRAM provides a robust approach to strain design across gene regulatory network modification and metabolic engineering.Computational strain design algorithms based on genome-scale metabolic models have increasingly been used to guide rational strain design for metabolic engineering. However, most strain optimization algorithms only utilize a metabolic network alone and cannot provide strategies that also involve transcriptional regulation. In this paper, we developed a novel strain design algorithm, named OptRAM ( Microbial-based cell factories can be used to advance environmentally friendly and economically viable industrial bioprocesses. Various strategies have been suggested to modify industrial strains to improve desired product yields. Traditional methods of strain screening mainly rely on mating, hybridization and mutagenesis techniques ,2, whichSystems biology is a powerful approach to uncover genotype-phenotype relationships, which can guide rational design-build-test iterations on strains to improve phenotypic properties in metabolic engineering. Next-Generation Sequencing (NGS)  and semiE. coli strains for lactate production with a maximum 73% increase by using OptKnock }). A smaller score means a higher concentrated level of the engineered branches, which corresponds to a lower cost implementation.Where the numerator is the average distance between all junctions of branch paths and the main path  in shake flasks and inoculated into the fermenter with 1 L working volume. A 10% inoculum was provided, and culturing was performed at 30°C, pH 4.5, 150 r/min, without aeration. The E. coli DH5α was used for construction of gRNA expression vector, and was routinely grown on LB medium at 37°C. YPD medium (g/L): 20 glucose, 10 yeast extract, 20 peptone; YPD plates: YPD medium and agar 15 g/L. LB medium (g/L): 5 yeast extract, 10 tryptone, 10 NaCl; LB plates: LB medium and agar 15 g/L. Yeast fermentation medium (g/L): 90 glucose, 10 yeast extract, and 20 peptone. Selection plates containing corresponding antibiotics were used for selecting transformants. All experiments were triplicates.The 2SO4 served as mobile phase at the flow rate of 0.6 mL/min. The cell growth was measured using the optical density at 600 nm.A 2-mL of the sample was collected from the fermentation broth at 24 h. The cells were removed by centrifugation (10000 × g for 5 min), and the supernatant was subjected to glucose, ethanol quantification using HPLC (Model: Waters 1525) with an RI detector (Model: Waters 2414) at 50 ºC. An ion-exclusion column  was employed to separate metabolites. While 10 mmol/L HBDH1 knock-out strain was constructed through homologous recombination of a resistance gene Kan MX4, which was amplified from HO plasmid using primer 1 and 2 containing homologous sequence of BDH1. The PCR products were purified and transformed into S288c. S288cΔBDH1 were selected by YPD with G418.MDH2 and COX4 knock-out strain were constructed by CRISPR/Cas9 as Zhang et. al described [MDH2 or COX4 gRNA DNA fragments into the BsaⅠ digested pRS42H-gRNA. Second, double-stranded 90 bp repair DNA for MDH2 or COX4 disruption was amplified by introducing stop codon TAA into the PAM sequence, respectively. Third, Cas9-NAT was transformed into cells to generate Cas9 protein. Lastly, pRS42H-gRNA-MDH2 or pRS42H-gRNA-COX4 were transformed together with the 90 bp donor DNA (~2 μg) into Cas9-expressing strain and selected by nourseothricin and hygromycin, respectively.escribed . First, We used flux variability analysis on the Yeast7.6 model to analyze the range of possible flux values for all reactions and excluded the reactions that could not have a non-zero value since these would not have any effect on the strain optimization calculations. The processed metabolic model has fewer reactions, metabolites and genes compared to the original Yeast7.6 model . By usinin silico knockdown or knockout of known essential genes. Then we selected solutions with the maximal fluxes of the target reaction. For solutions with the same predicted yield in succinate, we chose the one with a smaller implementation cost and a larger Cosine function representing smaller variation of genetic manipulation. Finally, we selected one optimized strain design from ten runs, with the mutation sites and expression modifications shown in We performed 10 parallel simulated annealing runs, which set succinate as the target product, and obtained 10 solutions  catalyzes the reaction from succinate to fumaric acid, its deletion will obviously promote flux to succinate. Deletion of SER3 and SER33 can facilitate TCA Cycle, and improve succinate. We also identified SER1 as engineering site by OptRAM. To compare with other strain design methods, we chose the widely used OptFlux [There are two main processes in the production of succinate from glucose . First,  OptFlux  to find When biomass is constrained to 99% of the maximal theoretical value, our strategy makes an obvious improvement for predicted succinate production, even the minimal succinate exchange flux is higher than the max value in wild type and the strategy in literature . AlthougMDH2, SDH3, and several mutated TFs, including CAT8, HAP2, have targets modification improving succinate validated in LASER. Overexpression of CAT8 can promote succinate production by increasing flux of glycolysis and TCA Cycle, DIC1 and ICL1 are regulated by CAT8, which have significant effects on succinate reported by Agren et al [et al [HAP2 is a global regulator responsible for respiration gene expression, one of its targets IDP1 has been validated to improve succinate [We also compared all the mutation sites from 10 solutions with the experimental design in LASER database, as shown in en et al  and Oterl [et al . HAP2 isuccinate .GLN3 and RTG3 are TFs. There are 4 critical metabolic reactions  can keep more flux to acetaldehyde and impress the flux to ethanol. Deletion of GPD1/2(glycerol-3-phosphate dehydrogenase) can improving ethanol by effectively decreasing flux to glycerol [STE12 and OAF1, have targets modification improving 2,3-butanediol validated in LASER. STE12 is an important global regulator for yeast growth, whose target genes including ADH1/2, ALD6, BDH1, GPD1/2, have been reported as effective modification for improving 2,3-butanediol [OAF1 also regulates ADH1/2. It demonstrated that by our strain design method based on integrated model, the global TF could be identified for modification to accomplish the roles of several metabolic genes.We also compared all the mutation sites from 10 solutions with the experimental design in LASER database  in totally different pathways were firstly considered simultaneously to enhance the ethanol pathway, which were deleted separately as well as in combination. The growth of the modified strains was slightly reduced compared to wildtype with all of the strains showing an increase in ethanol production  is the top recurrent mutation found by all 10 solutions, whose deletion represses the TCA Cycle, increases flux of anaerobic respiration, and improves ethanol production [BDH2 is another recurrent mutation found by 8 solutions, whose knockdown can increase flux from acetaldehyde to ethanol. There are also some TFs having targets whose modification improves ethanol validated in LASER. HCM1 is found as a TF to modulate using OptRAM and it regulates ACC1, GLN1, and TKL1, all of which have been reported to improve ethanol production as reported in LASER [HCM1 can repress ACC1 (acetyl-CoA carboxylase), and make more flux from pyruvate to ethanol.In addition, we compared all the mutation sites from 10 OptRAM solutions with the experimental design in the LASER database  with GUROBI version of 7.5, the average time of one SA process is 3.7 hours. Overall, the computation time is comparable to the time running optimization algorithm once on the OptFlux platform. The processor of PC is i7-6700 CPU with 3.40GHz frequency, and the RAM is 16.0GB.E.coli model iMC1010 by OptORF to simulate the modification strategy for ethanol production with OptRAM. We found the best solution of OptRAM includes knockdown of Pta, TpiA, AldB, ZntA, YbiV, Fre, OmpL, AceA, and GpmB, as well as overexpression of Acs. The ethanol production improved 1.8 fold with 7% reduction in biomass compared to wild-type. In comparison, OptORF suggested deletion of ArcA, Pta, TpiA, EutD, and PtsH, and overexpressing gene Edd, which was predicted to improve ethanol production by 2.2 fold with 54% reduction in biomass. However, ArcA has positive regulation on AckA, which is one of the essential genes for E.coli (from DEG). Hence, this solution from OptORF may cause death of E.coli. While there are benefits to both approaches, in this case it may be that OptRAM identified a more biologically feasible modification strategy with similar improvement on target production compared to OptORF.The performance of OptRAM has been compared with OptFlux by three strain design cases in yeast, including production of succinate, 2,3-butanediol, and ethanol. To make a more suitable comparison with a previous strain optimization method using an integrated regulatory-metabolic model, we compared OptRAM with OptORF, the first strain design method utilizing regulatory information, for ethanol overproduction. We used the integrated in silico strain design case studies for producing succinate, 2,3-butanediol, and ethanol in yeast, we demonstrated that OptRAM can identify solutions containing both TF and metabolic gene manipulations that are predicted to increase production beyond what is seen currently, or found as potential designs using alternative methods.With the development of industrial biotechnology, there is an increasing need to design high producing strains in an economic and efficient manner. Computational strain optimization algorithms have been developed for this purpose as an important application of metabolic network reconstructions and constraint-based modeling. These methods can automatically search for sites of genetic modification for increasing any desired product. However, most strain optimization algorithms can only utilize a metabolic network alone and cannot provide strategies also involving transcriptional regulation. Although some methods can utilize gene regulatory information now, they have some limitations since they are based on integrating a boolean regulatory network, which is not suitable for TF overexpression (e.g. OptORF). Reconstructing such models requires extensive manual adjustment and additional information for generating boolean logic rules in the regulatory network , which hMDH2, BDH1, and COX4 has significant improvement on ethanol production. Therefore, OptRAM provides in silico predictions of improved strains over other methods tested.OptRAM used simulated annealing with a novel objective function, which can ensure a favorable coupling between desired chemical production and cell growth. We applied OptRAM in succinate, 2,3-butanediol, and ethanol overproduction in yeast. By setting a loose constraint at least 50%) and a strict one 99%) to biomass respectively, we compared the flux ranges of target reaction in different mutant models by different methods. In both cases, strategies from OptRAM led to higher minimum fluxes for target production under the strict constraints  for modeling the contribution of allosteric regulation for flux control in the central carbon metabolism of E. coli has been reported [Despite the above highlights of our new algorithm, OptRAM can be further strengthened in various ways. First, the performance of integrative regulatory-metabolic modeling for phenotype simulation can be improved by introducing more information such as kinetic parameters to set more precise ranges for critical reactions in a kinetic model. Also, by introducing more potential sites, the solution space for exploration increases sharply. It is also favorable to seek new ideas for optimization framework other than focusing on the branches from previously proposed computational strain design methods . On the reported . Most imS1 Table(XLSX)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 TableThere are 10 solutions for each case.(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S1 Script(DOCX)Click here for additional data file.S1 Code(RAR)Click here for additional data file.