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 1 , 2 , which are time consuming and costly , and have struggled to keep up with current industrial needs ., In 1991 , Jay Bailey proposed the term metabolic engineering to show how using recombinant DNA and other techniques could improve specific metabolic activity in cells by manipulating enzymes , transporters , and regulation to make cells meet human-specified goals 3 ., Rational strain design methods suggest particular genes or enzymes to alter in order to achieve desired strain characteristics for metabolic engineering 4 ., Systems 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 ) 5 and semi-automatic annotation techniques 6 have produced an increasing number of well annotated microbial genomes , enabling the collection of reasonably comprehensive information about which metabolic enzymes are encoded ., This information has greatly contributed to the reconstruction of the genome-scale metabolic models of various organisms 7 ., GEnome-scale metabolic Models ( GEMs ) are mathematical representations of the complete network of known biochemical reactions that can occur in a particular cell , assembled as a collection of metabolites , reaction stoichiometries , compartmentalizations , and gene-protein-reaction associations 8 , 9 ., One of the main analysis approaches of GEMs is the well-known Flux Balance Analysis ( FBA ) 10 , which can predict phenotypes for cells under different genetic and environmental conditions based on the stoichiometric matrix without requiring kinetic parameters 11 , 12 ., It has been demonstrated that computational simulation on GEMs can predict effective engineering strategies for strain design 13 , 14 ., Since the first strain design method OptKnock 15 was proposed in 2003 , several computational methods for efficient automated identification of genetic strain modifications have been developed , such as RobustKnock 16 , OptGene 17 , OptORF 18 , GDLS 19 , and FSEOF ( Flux Scanning based on Enforced Objective Flux ) 20 ., These algorithms have already yielded successful strain design applications ., In an early example , Fong et al . designed E . coli strains for lactate production with a maximum 73% increase by using OptKnock 21 ., Researchers from Tianjin University utilized a GEM of B . subtilis and elementary mode analysis to design an engineering strain for isobutanol production , and experimentally verified a 2 . 3-fold increase compared to wild type strain 22 ., Recently , Otero et al . designed a strain using OptGene to overproduce succinate in S . cerevisiae , and experimentally validated a 43-fold improvement in succinate yield on biomass after directed evolutions 23 ., However , a metabolic model alone has a significant limitation in revealing condition-specific metabolic activity 24 , 25 because gene regulation plays an important role in constraining the particular metabolism available under any given condition ., Also , the complex crosstalking mechanisms between gene regulation and metabolism are not captured by a metabolic model alone ., To overcome the limitation , methods that systematically integrate a transcriptional regulatory network and a metabolic network have been developed 26 , including regulatory Flux Balance Analysis ( rFBA ) 27 , steady-state rFBA ( SR-FBA ) 28 , Probabilistic Regulation of Metabolism ( PROM ) 29 , and Integrated Deduced REgulation And Metabolism ( IDREAM ) , developed by our group 30 ., Modification of gene regulatory circuits is an important strategy for transforming engineering strains 14 ., In fact , modifications of regulatory factors ( e . g . upregulation of biosynthetic pathway activators ) contribute to more than half of the genetic operations in E . coli and S . cerevisiae engineering strains , but most of these interventions are based on human intuition 31 ., Therefore , some strain design methods have utilized transcriptional regulation information to propose more effective metabolic engineering strategies ., OptORF 18 was the first approach using integrated regulatory-metabolic models , which followed the framework of two-layer optimization as did OptKnock ., In 2011 , the heuristic strain design method OptGene also updated a version which introduced integrated regulatory-metabolic models 32 ., In 2012 , a series of approaches based on minimal cut sets ( MCSs ) was further developed to include a new tool ( rcMCSs ) , that incorporates regulatory constraints 33 ., However , since the above algorithms used manually curated integrated regulatory-metabolic models , where the regulatory network is a Boolean network , there are some limitations to application ., Firstly , only some well-studied microorganisms may have existing integrated networks , such as E . coli 34 , M . tuberculosis35 , and yeast 36 ., Reconstructing such models requires extensive manual adjustment and additional information for generating Boolean logic rules in the regulatory network 37 , which hinders the ability of these algorithms to be broadly applicable across many organisms ., Secondly , these algorithms have to assume that the target gene is completely active or inactive , which ignores the range of possible regulatory intensities between regulatory factors and the target genes ., In addition , Boolean networks can only suggest the manipulation of transcription factor by knockouts ( ON to OFF ) and cannot provide guidance for more quantitative adjustment of transcriptional regulation ., Recently , a method named Beneficial Regulator Targeting ( BeReTa ) , used gene expression to infer the interaction between regulatory factors and target genes , combined with FSEOF 20 for identifying transcription regulators to enhance desired production ., According to the correlations between the different transcription factor expression levels and target reaction flux rates , beneficial scores are calculated to judge whether the transcription factor can enhance or inhibit the target reaction ., The algorithm was applied to E . coli , as well as S . coelicolor , which does not currently have an integrated metabolic-regulatory model ., BeReTa represents a significant advance , but it cannot predict an expected product rate or yield of the mutant , or make predictions about the combined manipulations of multiple sites ., Herein , we report a new strain design algorithm , named OptRAM ( Optimization of Regulatory And Metabolic Networks ) , which can identify combinatorial optimization strategies including overexpression , knockdown or knockout of both metabolic genes and transcription factors , based on our previous IDREAM integrated network 30 ., OptRAM also aims to achieve optimal coupling between biomass and target production , and can be used 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 achieved in experiments ., Saccharomyces cerevisiae S288c has been studied and simulated extensively through a series of models 38 reconstructed based on the genome sequence and literature annotations 39 ., We used the latest metabolic reconstruction , Yeast 7 . 6 , which includes 3493 metabolic reactions , 2220 metabolites and 909 metabolic genes ., Integration of a gene regulatory network with a metabolic network at the genome-scale poses significant challenges , in part because they are distinct network types requiring very different modeling frameworks ., PROM uses probabilities to represent gene states and TF–gene interactions from abundant gene expression data , and then uses these probabilities to constrain the fluxes through the reactions controlled by the target genes 29 ., A limitation of PROM is that a pre-built transcriptional regulatory network is required as an input ., In our previous work , we developed a framework called Integrated Deduced REgulation And Metabolism ( IDREAM ) , which uses bootstrapping-EGRIN-inferred 40 , 41 transcription factor ( TF ) regulation of enzyme-encoding genes , and then applies a PROM-like approach to apply regulatory constraints to the metabolic network ., In Yeast , we collected 2929 microarray datasets with 5939 yeast genes and evaluated 392 of those genes as possible regulators ., For each of the 5939 target genes , we constructed separate models from 200 randomly selected subsets of the 2929 experiments , as well as a 201st model constructed using the entire data set ., This resulted in 201 generated gene regulatory models for each of the 5939 yeast genes , for a total of 1 , 193 , 739 models ., For each gene , we estimated a false discovery rate ( FDR ) for each factor by tallying the fraction of models from random subsets that identified that factor as a regulator ., Thus , if factor X was predicted to regulate gene Y in 191 of 200 models , then X would have an FDR = 1–191/200 = 0 . 045 ., If X is predicted to activate Y with an FDR of 0 . 045 , only 4 . 5% of Y’s activity would be predicted to remain if X was deleted ., If X is predicted to deactivate Y , then we use the much larger 1-FDR ( e . g . , 95 . 5% of activity ) to represent that Y is somehow disturbed without a significant reduction in activity ., We included only those interactions that passed an FDR cutoff of 0 . 05 ., Then we predicted whether a factor was an activator or repressor by testing if its mRNA expression was correlated or anti-correlated with the expression of its target using the model from the entire expression dataset ., Finally , we retrieved an integrated regulatory-metabolic network including 2626 inferred influences consisting of 91 TFs transcriptionally regulating 803 genes encoding enzymes of the metabolic network ., It should be noted it is impossible to cleanly differentiate between the false discovery rate and the strength of the regulatory role for multi-cell microarrays or RNA-Seq because from bulk measurements we can’t differentiate a strong regulation occurring in a small portion of cells from a weak regulation happening in a large fraction of the cell population ., In other words , because we are using a ground up mixture of cells , then we might reasonably expect a few cells with high expression related to a rarely used promoter to appear similarly to a low level , frequently used promoter ., Perhaps single cell RNA-Seq will help disentangle this problem in the future ., OptRAM is a meta-heuristic strain optimization method based on an integrated model of an inferred regulatory network and a constraint-based metabolic network ., It aims to identify the modifications of TFs and metabolic genes , including overexpression , knockdown , and knockout , to achieve the maximal production of desired chemical ., OptRAM will simulate a series of mutations to get the optimized strategy for target overproduction by simulated annealing ., We adopt 11 kinds of mutations on gene expression of TFs or metabolic genes , represented as FC ( TF ) and FC ( G ) , with overexpression and knockdown fold change of 2 , 4 , 8 , 16 , 32 respectively , and 1 knockout , as shown in Table 1 . The expression level of these genes will be translated to corresponding metabolic reactions by the integrative network ., First , expression levels of metabolic genes are calculated according to expression of corresponding transcription factors ., In the EGRIN algorithm , a linear equation of the target gene and the TFs is generated:, target=coeff1TF1+coeff2TF2+⋯+coeffnTFn, ( 1 ), Where the variable target is the expression level of a target gene regulated by n TFs , TFi are the expression level of these TFs , and Coeffi are the corresponding coefficients of each TF ., In OptRAM , for a target gene regulated by one TF , tfExpr is the relative expression level of the mutated TF , then the relative expression level of the target gene is calculated as:, targExpr=2coeff×log2tfExpr, ( 2 ), When a target gene is affected by more than one TF , the expression level of the target gene is calculated as:, targExpr=2∑incoeffi×log2tfExpri, ( 3 ), Having the relative expression level of all metabolic genes , the change of relevant reactions , represented as FC ( R ) , is calculated according to the gene-reaction rules in the metabolic model ., For reactions with ‘AND’ rules of different genes , we selected the minimum value of relative gene expression level , since ‘AND’ indicates that the combination of multiple enzymes is required , so the enzyme with lower expression determines the upper bound of the reaction rate ., For reactions with ‘OR’ rules of different genes , the mean value of relative gene expression level is calculated , because ‘OR’ indicates that multiple enzymes have the same catalytic function and can be substituted for each other ., Therefore , the average expression level of enzymes in the set can better reflect upper bound of the reaction rate ., While the average was used herein , it would also be reasonable to use the max of the enzymes being expressed in this scenario ., In order to simulate the flux change of reactions induced by the gene expression mutation , we first need a reference flux value for each reaction , which is obtained by pFBA ( Parsimonious enzyme usage FBA ) method 42 ., pFBA is an algorithm based on FBA ., For a metabolic network with M metabolites and N reactions , the FBA formulation is shown below:, MaximizevobjectiveSubjectto∑j=1NSijvj=0 , i=1 , … , Mlbj≤vj≤ubj , j=1 , … , N, ( 4 ), Where Sij stands for the stoichiometric coefficient of metabolite i in reaction j , and vj stands for the flux of reaction j , lbj and ubj are the constraints for reaction j ., The most commonly used objective function ( vobjective ) is biomass synthesis 43 ., Here the simulation condition for yeast metabolic flux was set corresponding to the YPD medium , with glucose 20 g/L and blocking other carbon source ., The pFBA algorithm is divided into three steps ., First , the max biomass rate is obtained by FBA with the original model ., Secondly , the constraint of biomass is set equal to the max biomass value ., Finally , a new objective function is set as the minimization of total flux values carried by all reactions , to generate the flux distribution ., According to the reference flux values from pFBA and the level of expression change for mutated reactions , we set the new constraints of reactions as shown in Table 2 , where FC ( R ) is the change of reaction , v is the reference flux value , lb and ub are the original lower bound and upper bound for the reaction ., In the previous meta-heuristic strain optimization methods , such as OptGene , BPCY ( biomass-product coupled yield ) is used as the objective function 17:, BPCY=Product×GrowthSubstrate, ( 5 ), Where Product represents the flux of the reaction synthesizing the desired product , Growth represents the flux of biomass , and Substrate represents the uptake rate of the nutrient substrate ., The ultimate goal of the optimization algorithm is to identify the mutated solution with the largest BPCY value , which ensures a considerable growth when improving the target product ., A limitation of the simulation using pFBA is that this framework does not guarantee that the target reaction flux will be coupled to biomass ., That is , even if the BPCY score of a mutated solution is high , the flux value of the target reaction is unstable with the max biomass ., Because the flux variability of target reaction is a wide range and the minimum flux may even be zero , there is no guarantee that the target product can have a certain output under natural growth ., Moreover , since the objective function of pFBA is biomass , there is often no flux through the desired target reaction , although the flux range of that reaction may be 0 to a positive value ., In this situation , BPCY remains 0 and the algorithm reports no feasible solution ., Therefore , we defined a new objective function ( Eq 6 ) in OptRAM to couple maximizing biomass production and the target reaction of interest ., Where Target=Vmax+Vmin2 , Range=Vmax−Vmin2 , Vmax is the maximum flux value of target reaction and Vmin is the minimum flux value by FVA ( flux variability analysis ) 44 ., Target means the average flux value of target product ., Range is set to half of the interval between min and max target flux value ., When Vmin is 0 , RangeTarget=1 , the coefficient ( 1−logRangeTarget ) is 1 . And when Vmin is greater than 0 , RangeTarget>1 the coefficient will be greater than 1 , which is a reward coefficient for BPCY ., Compared to BPCY , this objective function will induce solutions to have a higher and narrower flux range of target product , which reduces the uncertainty caused by alternative solutions in constraint-based modeling ., Hence , by using the refined objective function , OptRAM can provide solutions with better biomass-product coupled ., Fig 1 illustrates the strategy of the OptRAM approach , and the detailed pipeline can be downloaded from supplemental files ( S1 Script and S1 Code ) ., OptRAM requires a transcriptional regulatory network ( or a gene expression data set ) and a genome-scale metabolic model as input ., Then IDREAM method will be run to get an integrated model ., For organisms with no existing TRN , users can input a set of expression data from which the IDREAM method will automatically infer the TRN and generate an integrated model ., Then the core strain design process within OptRAM will simulate a series of mutations to get the optimized strategy for target overproduction ., The output from OptRAM includes the maximized objective score , flux of the target reaction , and the corresponding mutated solution with suggested modification of TFs and/or metabolic genes ., We used simulated annealing for the core strain design part , which is able to accept a worse solution in the early stage ( avoiding getting stuck in local maximal ) , to pursue finding the global optimal screening for mutated models ., The simulated annealing algorithm is derived from the simulation of the solid annealing process , an idea first proposed by Metropolis in 1953 45 ., In 1983 , Kirkpatrick et al . introduced the idea of simulated annealing algorithm into the field of optimization problems , making the algorithm practical in engineering 46 ., The simulated annealing algorithm introduces Metropolis criterion , which help escaping local optimum , to accept new solutions , including not only better solutions but also worse ones , according to the probability ., By simulating the drop of temperature , the algorithm controls parameters during the process and gives an approximate optimal solution in polynomial time ., In our algorithm , we replace the internal energy of the annealing process with the refined objective function Obj ( Eq 6 ) , which is the prospective score for each mutated strain ., The following steps show the implementation of the simulated annealing algorithm in our specific optimization problem: 1 . Initialize the simulated annealing parameters , including the initial temperature T0 of the control parameter T , the attenuation factor ( α<1 ) , and the maximum number of iterations L at each temperature ., Then generate the initial solution , Ind0 ., 2 . When T = T ( k ) , search L times according to the following process: ( 1 ) For the current solution Indk , randomly mutate the expression of TFs and metabolic genes , translate to effects on reaction flux , and get a new Obj score and a new mutation solution Indk ., ( 2 ) Calculate ΔObj = Obj ( Indk ) —Obj ( Indk ) , where Obj ( Ind ) is the value of objective function for each solution ., ( 3 ) If ΔObj > 0 then Indk is received as the new solution , let Indk = Indk; otherwise generate a random number R on the even distribution in ( 0 , 1 ) , calculate the probability P according to Metropolis criterion:, P=eΔObjT ( k ), ( 7 ), If R<P , then accept the new solution , let Indk = Indk; otherwise keep the current solution; ( 4 ) If the number of iterations is less than L at this temperature , repeat step 2; otherwise , proceed to step 3 . 3 . If the convergence condition is satisfied , the algorithm ends and the current solution is the optimal solution , represented by a two-dimensional array of the mutated strain ., The first array contains all the mutated gene IDs ., The second array stores the expected fold change of expression level of the mutated gene over the wild type expression ., The convergence condition is that the value of objective function has not improved for a number of continuous temperatures ( here we set 100 temperatures ) ; otherwise , proceed to step 4 . 4 . Decrease the control parameter T , let T ( k + 1 ) = T ( k ) ×α , simultaneously reset the number of iterations and go to step 2 ., 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 ( Table 3 ) ., By using the IDREAM approach , we integrated the deduced regulatory network from 2929 microarray datasets with the Yeast7 . 6 metabolic network to generate an integrative gene regulatory-metabolic network ( Table 3 ) ., We performed 10 parallel simulated annealing runs , which set succinate as the target product , and obtained 10 solutions ( Supplemental S3 Table ) ., We first filtered the solutions with in 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 Table 4 ., The upper two rows show mutation sites with overexpression manipulation , while the rows below show mutation sites with knockdown manipulation ., Corresponding numbers are perturbation fold change of expression as defined in Table 1 ., PDR1 and PHO4 are TFs , and others are metabolic genes ., For this complicated combination of TFs and metabolic genes , we analyzed the critical sites corresponding to metabolic reactions ., A ‘critical reaction’ here means that the removal of the reaction can cause more than a 10% reduction in the objective function ( See Methods ) ., There are 5 critical reactions ( marked in Fig, 3 ) involved in this solution: up-regulation in cystathionine beta-synthase , down-regulation in ferrocytochrome-c: oxygen oxidoreductase , pyruvate decarboxylase , citrate transport , and oxoglutarate/malate exchange ., There are two main processes in the production of succinate from glucose ( Fig 3 ) ., First , glucose is used to make pyruvate in cytoplasm ., Next , pyruvate converts to oxaloacetate , then to malate , then to fumarate , and finally to succinate ., Comparison of the flux values in the two models revealed that the succinate exchange flux increases about 66-fold theoretically in the strain-optimized model ., All critical reactions we found are closer to the path from pyruvate to succinate ., Up-regulation in cystathionine beta-synthase will promote more L-cystathionine to pyruvate ., Decreasing the constraints of ferrocytochrome-c:oxygen oxidoreductase and pyruvate decarboxylase directly affects the pyruvate flux from lactate and acetaldehyde ., So pyruvate mostly comes from phosphoenolpyruvate in the mutated strain ., Meanwhile , citrate transport and oxoglutarate/malate exchange are limited to prevent malate in cytoplasm to transport to mitochondrion , which enable more malate flux to succinate synthesis ., We compared the best solution in Table 4 with the previous literature in which the succinate was successfully improved in yeast assisted by strain design method 23 ., This study used OptGene to get the optimized strategy including deletion of SDH3 , SER3 and SER33 ., SDH3 ( Succinate dehydrogenase ) 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 50 to find overexpression , knockdown , and knockout of metabolic genes ., For the mutant models generated from literature , OptFlux , and OptRAM , we used FVA to investigate the range of target reaction under constraints of being able to achieve 99% and 50% biomass respectively ., The 99% biomass constraint means that the lower bound of biomass reaction is set as the 99% of maximum biomass in each mutant model respectively , similarly for 50% biomass ., The 99% biomass is a relatively strict constraint on biomass and the 50% biomass is a loose constraint ., A strict constraint on biomass will make the flux range of target reaction much more narrow ., We compared the resulting flux range of these mutant models with the wild type model ( Table 5 ) ., 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 23 ., Although the max biomass flux is relatively low , it is still acceptable for strain growth ., When biomass is constrained to half of the theoretical max value , the model comparison suggests that our strategy makes succinate production strongly coupled with growth ., The performance of solution from OptFlux seems good , but the overall interval in OptRAM solution is much larger than other strategies ., We also compared all the mutation sites from 10 solutions with the experimental design in LASER database , as shown in S4 Table ., There are 9 genes presenting in more than 2 solutions matched with the experimental modifications in LASER , such as MDH2 , 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 51 and Otero et al 23 ., HAP2 is a global regulator responsible for respiration gene expression , one of its targets IDP1 has been validated to improve succinate 52 ., We performed 10 parallel simulated annealing runs with 2 , 3-butanediol as target product ( Supplemental S3 Table ) ., According to the filtering process similar with succinate case , we selected one optimized solution from the results of the ten runs ., Table 6 shows the mutation sites suggested by OptRAM , among which GLN3 and RTG3 are TFs ., There are 4 critical metabolic reactions ( marked in Fig, 4 ) involved in this mutated model , respectively catalyzed by mitochondrial alcohol dehydrogenase , ferrocytochrome-c:oxygen oxidoreductase , malate dehydrogenase , and cytoplasmic alcohol dehydrogenase ( acetaldehyde to ethanol ) , all of them are predicted to be knocked down ., There are two main processes in the production from glucose to 2 , 3-butanediol ( Fig 4 ) , just as for succinate ., Pyruvate comes from glucose and is converted to acetoin and then to 2 , 3-butanediol ., By comparing the flux values in the two models ( mutated and wild type ) , the flux of 2 , 3-butanediol exchange reaction increases about 61-fold theoretically in the mutated model ., Reduction of ferrocytochrome-c:oxygen oxidoreductase will affect pyruvate in cytoplasm coming from lactate ., Repressing alcohol dehydrogenase and mitochondrial alcohol dehydrogenase , both of which convert acetaldehyde to ethanol , will prevent acetaldehyde to ethanol ., Downregulation of malate dehydrogenase changes the direction of the reversible reaction from malate to more oxoglutarate , and promote the flux of pyruvate to final 2 , 3-butanediol ., We also compared the best solution in Table 6 with the previous literature in which Ng et al succeeded in improving 2 , 3-butanediol in yeast 53 ., They used OptKnock to explore optimization design including the deletion of ADH1 , ADH3 and ADH5 under an anaerobic condition ., We used OptRAM to identify knockdown of ADH3 , and the TF STE12 regulating ADH1 and ADH3 to improve 2 , 3-butanediol ., Also , we ran OptFlux to get strain design solution for comparison ., For the mutant models generated from literature , OptFlux , and OptRAM , we used FVA to obtain the range of 2 , 3-butanediol target under constraints of being able to achieve 99% and 50% biomass , respectively ., The comparison of mutant models with wild type model is shown in Table 7 ., Similarly , when biomass is constrained to 99% of the max theoretical value , even the minimal flux value of 2 , 3-butanediol exchange reaction by OptRAM is higher than other strategies ., While the minimum predicted target flux in OptFlux drops to 0 with the constraints of 99% max biomass , it means the good performance of OptFlux for succinate production coupled with biomass might be a case-specific outcome , whereas OptRAM can ensure the coupling for overproduction of different targets , because of the improved objective function ., We also compared all the mutation sites from 10 solutions with the experimental design in LASER database ( Supplemental S4 Table ) ., There are 6 genes presenting in more than 2 solutions matched with the experimental modifications in LASER , such as ADH3 , GPD2 ., Knockdown of ADH3 ( alcohol dehydrogenase ) 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 54 ., We also found several mutated TFs , such as 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 49 ., 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 performed 10 parallel simulated annealing runs with ethanol as target product ( Supplemental S3 Table ) ., According to the similar filtering process , we selected one optimized solution from the results of the ten runs ., Table 8 shows the mutation sites suggested by OptRAM ., There are 3 critical metabolic reactions ( marked in Fig, 5 ) involved in this mutated model , respectively catalyzed by ( R , R ) -butanediol dehydrogenase , ferrocytochrome-c:oxygen oxidoreductase and malate dehydrogenase in cytoplasm , all of them are predicted to be knocked down ., We experimentally implemented the OptRAM-based design by modifying yeast strains using CRISPR-Cas9 and measuring the effects on ethanol production by fermentation ., Three genes ( BDH1 , MDH2 and COX4 ) 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 compar
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 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 .
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 ( 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 .
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