This systems view of biochemical evolution reveals strong epistatic interactions between metabolic genes that shape evolutionary trajectories and influence the magnitude of evolved kcats. Diminishing returns epistasis prevents enzymes from developing higher kcats in all reactions and keeps the organism far from the potential fitness optimum. Multifunctional enzymes cause synergistic epistasis that slows down adaptation. The resulting fitness landscape allows kcat evolution to be convergent. Predicted kcat parameters show a significant correlation with experimental data, validating our modeling approach. Our analysis reveals how evolutionary forces shape modern kcats and the whole of metabolism.Systems biology describes cellular phenotypes as properties that emerge from the complex interactions of individual system components. Little is known about how these interactions have affected the evolution of metabolic enzymes. Here, we combine genome-scale metabolic modeling with population genetics models to simulate the evolution of enzyme turnover numbers ( The catalytic efficiency of many enzymes is lower than the theoretical maximum. Here, the authors combine genome-scale metabolic modeling with population genetics models to simulate enzyme evolution, and find that strong epistasis limits turnover numbers due to diminishing returns of fitness gains. The topography of the fitness landscape is determined by epistasis13, i.e., the extent to which the fitness effect of a mutation depends on the genetic background. Understanding epistasis is thus crucial for understanding evolutionary dynamics and constraints, and systems models can serve as a key tool to understand these interactions15.The biological systems we observe today are the results of evolutionary trajectories that were shaped by their underlying genotype-to-fitness map, termed the fitness landscape. The components of the system constantly change to increase fitness in the current environment. It is thus tempting to assume that, given the right environment, biological systems can be described as the state that results in the highest fitness possible under all biophysical constraints. Whereas such optimality assumptions were successfully applied to understand a variety of systems properties like bacterial growth rateskcats) of metabolic enzymes constitute an example of a system state that is distant from a potential optimum, as the efficiency of most enzymes remains far from its theoretical maximum17. Enzyme turnover numbers span over six orders of magnitude and are essential for understanding biological processes on a quantitative level, as they quantitatively describe the proteomic demands of reaction flux, growth, and thus fitness23. In contrast to this high variability and functional importance, experimental data on kcat is scarce (data in the enzyme kinetics database BRENDA24 accounts for about 10% of the reactions in the E. coli model iJO136625) and exhibits high noise16. An improved understanding of the evolutionary and biophysical forces that shape the distribution of kinetic parameters on a systems scale would thus constitute an important step towards quantitative understanding of cellular metabolism. A meta-analysis of databases of kcats showed two major patterns16. On the one hand, kcats in primary metabolism are consistently higher than those in pathways of secondary metabolism, a finding that can be interpreted as the result of differential selection pressure on the respective genes. On the other hand, the underlying biochemical mechanism has a measurable effect on kcat, suggesting that an interplay between biophysical and evolutionary constraints determines metabolic kcats. How these factors have acted mechanistically to result in the diverse kinetic turnover numbers we observe today is unknown.It was suggested that the catalytic turnover numbers , and kcat evolution is simulated with a Markov Chain Monte Carlo approach  number to decide on a candidate set of 569 biophysically unconstrained reactions (see Methods). The remaining 1087 enzymes were considered biophysically constrained and were fixed to the median of in vitro kcat measurements (13.7 s−1). In the context of evolutionary predictions, the number of enzymes in the constrained and unconstrained set are more meaningfully compared in terms of reactions that are contributing to growth. Flux variability analysis38 for aerobic growth on glucose reveals that 278 growth-relevant reactions (see Methods) are unconstrained, while 183 carry a biophysical constraint; the majority of in silico growth-relevant reactions is thus evolving without upper limit.As biological catalysts are limited to natural amino acids to stabilize transition states, it is expected that many reactions will have a distinct biophysical upper limit to the turnover rate that is lower than the theoretical limit resulting from diffusion rate of collisions. As certain reaction mechanisms were shown to consistently exhibit high kcat evolution with the MCMC algorithm, we can trace the dynamics of adaptation through evolutionary trajectories of growth rates  without achieving a growth rate >0.5 h−1 , showing that they are indeed responsible for the irregular adaptation dynamics. Investigation of metabolic network model and gene-protein-reaction context of these genes revealed that all of them are multifunctional enzymes that catalyze multiple reactions in the same linear pathways. These genes are involved in histidine biosynthesis (histb), purine biosynthesis (purH), cell wall biosynthesis (glmU), and fatty acid biosynthesis (fabG). The irregular behavior in adaptive trajectories thus has a mechanistic reason that lies in the structure of the underlying network: protein cost of the linear pathway cannot be mitigated by increasing an individual kcat of a single active site, resulting in a fitness landscape that shows synergistic epistasis  and compared prediction performance of this diverse environment simulation with the simulations under constant aerobic glucose conditions.How well do our simulated end points of kcat were previously mined from the BRENDA database and filtered for natural substrates16. We compared the simulated end points for both constant and diverse environments to this dataset while focusing on reactions without data-driven biophysical constraints to avoid circular conclusions. We found that the predictions agree in magnitude  with the in vitro data  became recently available based on the combination of proteomics data and flux predictions across multiple conditions39. The predicted kcats from both diverse and constant evolutionary environments agree with this in vivo data in magnitude . Like in the case of in vitro measurements, a model of diverse environments explains in vivo data better than constant environments.In vitro measurements of ude Fig.  A and shata Fig. . Simulatude Fig.  and showkcat until system-wide DRE prevents further adaptation? We find that the kcats in the end points of evolution in diverse environments are correlated with enzyme molecular weight  and with the mean of fluxes of parsimonious FBA40 across diverse growth environments , indicating that these two factors are the major determinants of selection pressure on a given reaction. This finding explains why the enzymes that catalyze high flux reactions in central metabolism are associated with high in vitro kcats16. When we repeat our evolutionary simulations in models with random perturbations of reaction stoichiometries and biomass components, agreement with experimental data are abolished .Finally, the convergent behavior we found for evolution in a static environment  decreases, it approaches the neutral boundary that lies around 1/Ne41, and mutations that yield large improvements in kcat are rendered effectively neutral. Metabolic control theory42 has been used in the past to postulate the occurrence of diminishing fitness returns when the activity of a single enzyme changes, e.g., explaining the genetic dominance of metabolic genes43 and the frequency of neutral mutations41. In our framework, that situation is comparable to assigning a single reaction to the unconstrained set.It has been suggested that the suboptimal turnover number of many enzymes is the result of an increasing difficulty to achieve 13, and our results on kcat evolution give a mechanistic example of how diminishing returns can arise, even when the population is still distant from a global optimum. In terms of experimental data, intergenic diminishing returns epistasis has been found to play a crucial row in a long-term evolutionary experiment44 and in adaptation to heterologous pathway optimization45. In the latter example, the expression cost of a heterologous pathway was reduced by reducing over-expression, a process conceptually similar to the reduction of protein costs through the increase in kinetic parameters. Whereas the adjustment of expression levels is a mechanism commonly found in experimental evolution, kinetic parameter evolution is a smaller mutational target and thus more difficult to study in such a framework.Diminishing returns are often implicitly assumed in quantitative models of adaptation, e.g. in the form of Gaussian fitness landscapes46. Our model shows that kinetic parameter evolution is likely to similarly exhibit convergent behavior. The evolutionary end points show a high correlation of kcats across replicates—even though some reactions diverge— and find a median Pearson correlation of 0.7 between flux distributions on log scale, indicating that this similarity in flux underlies the observed high level of convergence.Structural genomics studies have found convergent evolution of function to be a common pattern in enzyme evolution47, intragenic sign epistasis—where the sign of a mutation’s effect depends on the genetic background—could cause a more rugged landscape.Even though diminishing returns epistasis arises for the growth rate effect of mutations, epistatic effects of mutations in the same gene are not modeled explicitly. Thus, even though structural models argue against thiskcat improvements can yield fitness gains  will affect enzyme saturation where our model assumes full saturation. Undersaturation is thus expected to influence kcat evolution by increasing the selection pressure on kcat. A similar effect is expected for the backward flux in thermodynamically unfavorable reactions; e.g., the simulations predict a kcat for the thermodynamically unfavorable malate dehydrogenase reaction of 805 s−1 that underestimates in vitro data (931 s−153), whereas in vivo data suggest a much lower effective turnover rate of 7 s−139, probably caused by substantial backward flux39. Whereas computational feasibility will be a challenge, modeling the interaction between kcat, Km, metabolite concentrations, and allosteric regulation is a promising topic for future studies that could also shed light on the co-evolution of isozymes that often vary in Km54. As gene duplication is frequently observed in short-term adaptation55, we assume that most kcats evolved before isozymes emerged and model kcat mutation at the reaction level. Furthermore, our model has to make an assumption about the identity of biophysically constrained reactions. Whereas EC numbers serve as a first approximation for estimating this set, there is still a high level of uncertainty in its true identity. It is in fact possible that a growth-limiting process outside of metabolism causes diminishing returns epistasis, e.g., the expression machinery of the cell. Encouragingly, sensitivity analyses indicate that the qualitative adaptation dynamics and agreement of simulated kcats with experimental data are robust against the identity of the constrained set  we use to model adaptation dynamics does not account for the effects of clonal interference, like a decreased rate of adaptation and higher fitness gains of fixed mutations16 for discussion) we found a significant agreement with in vitro data and in vivo estimates, where the model explained about 45% of the observed variance in in vivo kcats. In vitro kcats were shown to correlate with enzyme molecular weight and reaction flux . Similarly, predicted kcats in our model for diverse environments are correlated with enzyme molecular weight  and with the mean of fluxes of parsimonious FBA40 across diverse growth environments . This result indicates that enzyme usage and size determine the selection pressure on individual reactions and thus the magnitude of final kcats, a hypothesis that we confirmed by sensitivity analysis: randomly perturbing network stoichiometry, biomass components, and enzyme molecular weights abolishes the correlation with experimental data  instead of model-derived flux, a high correlation with model-derived kapp,max was found (R2 = 0.85)39. Sensitivity analyses , in that it maximizes the growth rate µ by maximizing flux into a biomass reaction (vz) given a set of constraints (vmin and vmax):In the simulation of kinetic parameter evolution, the growth rate that results from a given vector of catalytic turnover rates S represents the stoichiometric matrix and v the vector of fluxes. MOMENT extends FBA by introducing enzyme concentrations as model variables  and recursively parsing gene-protein-reaction (GPR) rules to obtain upper limit constraints on metabolic fluxes:Gi represents the set of genes involved in catalyzing reaction i. The respective GPR is parsed by using the maximum of enzyme concentrations to represent AND relations and the sum to model OR relations. Finally, the total weight of the metabolic proteome  and the respective enzyme molecular weights (MW) are used to constrain enzyme concentrations:Here, iJO1366, a genome-scale model of E. coli K-12 MG1655 metabolism25. Enzyme molecular weights were calculated based on the E. coli K12 MG1655 protein sequences (NCBI Reference Sequence NC_000913.3), and C was set to 0.32 gproteingDW−1 in accordance with the E. coli metabolic protein fraction across diverse growth conditions60. Linear programming problems were constructed using the R61 packages sybil62 and sybilccFBA and solved using IBM CPLEX version 12.7. The growth rate μ . A single iteration of the Markov Chain Monte Carlo (MCMC) algorithm starts as follows: A mutation affecting the kcat of a single randomly chosen reaction i is simulated as multiplying an original kcat (= κi) by a factor α that is drawn from a lognormal distribution with mean and standard deviation in log scale log(3/2) and 0.3, respectively. This distribution determines the jump size in the space of kcats, but not the ratio between deleterious to advantageous mutations (see below).We assume a genetically homogenous population of cells with a population size equal to the effective population size estimated for 63, kcats of forward and backward directions and respective Kms cannot change independently from each other. To account for the Haldane relationship, we implement mutations that affect the forward and backward kcat of reversible reactions equally. The growth rate of the original strain (µ) and the strain carrying the mutation affecting κi (µmut) is then calculated by solving the MOMENT problem detailed above  number to set the reactions belonging to the three (out of six) top level codes with the highest median in vitro kcat—namely oxidoreductases, hydrolases, and isomerases—as biophysically unconstrained. In order to allow an unbiased comparison to experimental data, all reactions for which data was available were also set as unconstrained. The remaining reactions were considered biophysically constrained and were fixed to the median of in vitro kcat measurements (13.7 s−1). The kcats of unconstrained reactions were initialized to 10−3 s−1. See Supplementary Figures The high population size allowed us to optimize simulation performance by heuristically setting the ratio of deleterious to advantageous mutations: the growth rate for a deleterious mutation was simulated once, but their fixation was sampled multiple times to arrive at a 100:1 ratio between deleterious and advantageous mutations (see Supplementary Table ments 13. s−1. TheIn order to simulate diverse environments, we applied random sampling of a new environment every 1000 iterations. Here, oxygen uptake was allowed with probability 1/2, and the environment always contained at least one randomly chosen source of each carbon, nitrogen, sulfur, and phosphate. A number of additional sources were drawn from a binomial of size 2 with success probability 1/2. This process was repeated until a growth sustaining environment was found and the following 1000 mutations were simulated in this novel environment.R was used to test for significant correlation with a two-sided t-test as implemented in the cor.test function of the R environment61.Pearson’s R code for the simulations presented in this study is available from the authors upon request.Supplementary InformationPeer Review FileReporting Summary


Living organisms need to allocate their limited resources in a manner that optimizes their overall fitness by simultaneously achieving several different biological objectives. Examination of these biological trade-offs can provide invaluable information regarding the biophysical and biochemical bases behind observed cellular phenotypes. A quantitative knowledge of a cell system’s critical objectives is also needed for engineering of cellular metabolism, where there is interest in mitigating the fitness costs that may result from human manipulation.Rhodopseudomonas palustris, a metabolically versatile gram-negative purple non-sulfur bacterium capable of growing phototrophically on various carbon sources, including inorganic carbon and aromatic compounds. To quantitatively assess trade-offs among a set of important biological objectives during different metabolic growth modes, we used our new model to conduct an 8-dimensional multi-objective flux analysis of metabolism in R. palustris. Our results revealed that phototrophic metabolism in R. palustris is light-limited under anaerobic conditions, regardless of the available carbon source. Under photoheterotrophic conditions, R. palustris prioritizes the optimization of carbon efficiency, followed by ATP production and biomass production rate, in a Pareto-optimal manner. To achieve maximum carbon fixation, cells appear to divert limited energy resources away from growth and toward CO2 fixation, even in the presence of excess reduced carbon. We also found that to achieve the theoretical maximum rate of biomass production, anaerobic metabolism requires import of additional compounds (such as protons) to serve as electron acceptors. Finally, we found that production of hydrogen gas, of potential interest as a candidate biofuel, lowers the cellular growth rates under all circumstances.To study metabolism in photoheterotrophs, we developed and validated a genome-scale model of metabolism in R. palustris is primarily regulated by the amount of light it can absorb and not the availability of carbon. However, despite carbon’s secondary role as a regulating factor, R. palustris’ metabolism strives for maximum carbon efficiency, even when this increased efficiency leads to slightly lower growth rates.Photoheterotrophic metabolism of The online version of this article (10.1186/s12859-019-2844-z) contains supplementary material, which is available to authorized users. The high-throughput “omics” revolution has resulted in a deluge of system-level information about the components of living organisms. Optimally, integration and interpretation of these data can provide mechanistic insights about cellular behaviors and function . This nei.e, those modes of metabolism that use renewable resources and release part of the generated energy in useful forms. Metabolism in phototrophic organisms, as well as those that can catabolize aromatic compounds (a major component of plant biomass), are such modes of metabolism.These positive attributes have resulted in significant interest in conducting system-level analyses of those modes of metabolism that can sustainably result in generation of biofuels, To manipulate cellular metabolism to achieve a desired biological task (or objective), while mitigating the fitness costs that may result from our tampering, it is necessary to have a quantitative knowledge of a system’s critical objectives. This is because it is important to ensure our goals do not significantly alter the natural balance of objectives in a system for a given environment. Life is based on a series of trade-offs that represent the price living organisms pay in term of fitness when improvement in one of their traits results in detrimental change in another; a system state called ‘Pareto efficient’. Evolution ensures that all living organisms are Pareto efficient; otherwise, on an evolutionary timescale the non-efficient would be outcompeted and outlasted by organisms with better performance in all or some biological tasks.Escherichia coli [Rhodobacter sphaeroides [Synechococcus sp. PCC 7002 [Knowing the nature and magnitude of biological trade-offs can provide invaluable information regarding the biophysical and biochemical bases behind observed cellular phenotypes. This type of knowledge can be gained through the use of genome-scale models (GSM) and system-level multi-objective analyses of cellular processes. Genome-scale mathematical modeling of metabolic networks is a key tool of systems biology that has been used to examine the biochemical underpinnings of cellular phenotypes in microbes ranging from model organisms like hia coli , 3 and bhia coli , to thoshia coli –8, and ehia coli , 10. A naeroides  and cyanPCC 7002 . When usPCC 7002 , 14, thePCC 7002 ..Despite its many uses , 16, staTo date, there have been a number of important MO analyses of biological processes using constraint-based models –24. Phenn-dimensional (n = number of objectives) solution space of the model is discretized, and a Pareto optimal solution is calculated for each section of the space. The combined set of these Pareto optimal solutions forms the n-dimensional Pareto front for the MO analysis. A variety of different methods are used to identify the examination loci and hence constrain the values for n-1 objectives. Subsequently, like FBA, the value of the one remaining objective is optimized.All these MO analyses (including MOFA) are based on a reconstruction of the system’s metabolic network. Like FBA, the networks are constrained based on fundamental physico-chemical laws and experimental observations and measurements. For all MO analyses, the Rhodopseudomonas palustris (RP), is a purple non-sulfur (PNS) proteobacterium from the Rhodospirillaceae family. RP’s metabolism is extremely versatile and serves as a model for several important biological phenomena, including biodegradation of industrial waste [2) [2) and nitrogen gas [In this study, we report the first results of a MOFA examination of metabolic trade-offs in a phototrophic organism. The modeled organism, al waste , 34, eleal waste , and proaste [2) –38. RP haste [2)  compoundogen gas , 42.Finally, RP can metabolize aromatic compounds as a carbon source in a light-dependent fashion under anaerobic conditions (LN). New insights gained through our MO system-level analyses of this type of RP metabolism are important for industrial and environmental reasons, since microbial production of biofuels as well as bioremediation of aromatic pollutants usually occur in low oxygen environments.. Although previously a model of central carbon metabolism in RP had been developed [To determine carbon and energy fluxes among RP’s different metabolic modes, we developed a GSM of metabolism in RPeveloped , that moeveloped , 45), poAlthough system-level MOFA analyses of metabolism have expanded analyses of biosystems beyond FBA’s canonical single objective optimization, to date, the maximum number of objective trade-offs that have been simultaneously examined has not exceeded eight . In one Given RP’s versatile metabolism, our system-level MOFA analysis of its metabolism meant we needed to simultaneously examine trade-offs among more than five interdependent biological objectives and environmental constraints. To this end, we developed a computational algorithm for MOFA that (like another published MO systems biology study ) uses th2.In addition to our MOFA analyses of metabolic objective trade-offs, we used the RP GSM to: a) investigate RP’s metabolism of different carbon sources, b) examine the role of proton availability in affecting mode of metabolism, and c) study RP’s capacity to produce H2 export flux measurements to constrain our model based on an experimentally observed metabolic phenotype. This allowed us to assess the metabolic limitations of the system. Using this new-found insight and MOFA we then examined the relative importance of different biological objectives during different forms of mixotrophic metabolism. The results of these analyses are detailed below. We also used FBA to examine the robustness of RP’s metabolism to genetic perturbations. The results of these analyses are included in the Additional files First, we used available RP flux measurements  for phot2 gas). We examined the RP’s metabolism on 4 different carbon sources  and the role of proton economy in each case. We also used GX-FBA [Recently Shuetz et al.  showed td GX-FBA , an in s2 export, 3) ATP production, 4) nutrient allocation , 5) H2 export, 6) pyruvate export, 7) succinate export, and 8) α-ketoglutarate export. The latter three objectives were included to examine the role of carbon fixation as a sink for excess electrons. For the nutrient allocation objective, we minimized the sum of the absolute value of the transport rates. In a complex medium, the optimum value of this objective can be degenerate, i.e., different combinations of metabolite imports/exports could result in the same optimum value. However, for our minimal medium, each optimum value results in a singular transport profile.One of the most detailed studies of metabolism in RP is McKinlay et al.’s examination of photoheterotrophic growth on acetate . The flu2+, Cl−, and all the metal cations  that are necessary for cellular biomass production.The growth medium for the MOFA study included acetate as the sole carbon source. It also included ammonium, sulfate, and phosphate as the sole nitrogen, sulfur, and phosphorus sources. Finally, the medium included unlimited photons, as well as CaThe majority of the examined objectives have a negative trade-off with respect to growth, i.e., if their values increase then the growth rate reduces.The negative effects of these objectives are additive and hence small improvements to the value of all of them will drastically reduce the growth rate of RP.Some of imported carbon is exported as reduced organic byproducts, which removes energy needed for biomass formation.Figure Given the fact the measured growth phenotype is very close to the theoretical growth archetype (a phenotype that optimizes a single task) , it can By examining the predicted Pareto optimal phenotypes, we can gain a better understanding of the quantitative trade-offs among the different objectives we examined. Figure −1.h−1), the cell did not produce any small carbon byproducts  process fixed the majority of the produced CO2 (> 70%). Conversion to bicarbonate by carbonate anhydrase (E.C. 4.2.1.1), and pyruvate via pyruvate synthase (E.C. 1.2.7.1) fixed the remaining produced CO2. However, the predicted growth rate for the carbon-limited growth archetype was higher than experimentally measured values . This i ± 0.6 h  for thei2 export was 21% of the rate of uptake of acetate. This value matches the total measured amount of CO2 produced . Elimination of CBB increased the predicted doubling time to 7.2 h  Fig. b, Row C.ate flux ).2, we set the rate of CO2 export equal to the measured value (0.23 mmol.gDW− 1.h− 1). With unlimited light, the predicted doubling time (6.8 h) was still smaller than the measured value . As we When succinate export is blocked, we found a 1% reduction in maximum growth rate, and pyruvate is exported instead  higher growth rate. This result suggest that the measured rate of acetate import is higher than the amount needed for achieving the experimentally measured growth rate.2 export is removed, CO2 export increases, growth rate increases by ~ 3%, and the rate of small organic acid export drops to ~ 13% of imported carbon  and allowed for export of small organic compounds. In contrast, Hadicke et al. [2 and did not fix the rates of light absorption or CO2 export. Thus, they were able to predict photoheterotrophic growth with only CO2 and biomass as byproducts (similar to our outcome for condition 1), whereas our model with fixed experimental measurements and light absorption, required export of excess imported carbon via means other than CO2.This result slightly differs from that found in another in silico analysis of redox balancing and biohydrogen production in PNS . Hadicke biomass . The dife et al.  blocked Our 8-dimensional MOFA analysis of LN growth of RP on acetate revealed a pattern consistent with Schuetz et al. , i.e., mOptimization of this fourth objective diminishes the ability of the cell to achieve the growth archetype for the amount of resources that are imported. Model predictions suggest that the cell by design diverts some of the absorbed light energy toward the production of reduced organic compounds. We can better understand this process by examining the degree of reduction of various carbon-based compounds imported and exported by the system. The degree of reduction for a compound per carbon atom (κ) can be quantified using the concept developed by Roels .2 (κ = 0). One possible explanation for this behavior could be that the cell stores some of the available energy as easily metabolized compounds for consumption during dark periods, a behavior that has been observed in cyanobacteria [We have noted that our predictions indicate that the cell imports extra carbon and electrons in the form of acetate (κ = 4), uses some of this resource for biomass (κ = 4.19) production and then uses light energy to export the remaining electrons as organic compounds like succinate (κ = 3.5) that although more oxidized than acetate are significantly more reduced than CObacteria .2 is the sole byproduct [Another point to keep in mind is that the amount of carbon predicted by the model to be used for the production of reduced byproducts is small and well within the range of uncertainties of experimental measurements. Sensitive experimental analyses to identity and quantity the metabolic byproducts of LN metabolism could either validate our MOFA prediction of organic carbon export or support the previous assumption that COyproduct .We also conducted a MOFA analysis to examine the metabolic trade-offs of different biological objectives while consuming 3 different aromatic compounds Table . The gro2 and CO2 diminish growth. The former siphons electrons and protons needed for growth; while the latter removes carbon that can be fixed back into biomass.Exports of H2 (a byproduct of chemotrophic metabolism) would lead to export of reduced organic byproducts, which removes energy needed for biomass formation.Excess import of carbon in the absence of absorbing more light or increased export of COFigure The results of our MOFA analyses also indicate that, as with metabolism of acetate, efficient carbon utilization is a primary objective of RP metabolizing aromatic carbon sources. Below we describe the results of our analysis in more detail.2 while the majority of the produced CO2 (83%) was incorporated into biomass. Unexpectedly, at the growth archetype  and 2-oxoglutarate synthase (E.C. 1.2.7.3). These enzymes are associated with the reductive citric acid cycle (rTCA) which is a pathway for carbon fixation in some photoautotrophic organisms such as the green-sulfur bacterium Chlorobium limicola [Under light-limited anaerobic conditions with 4-coumarate as the sole carbon source, a small fraction (4.6%) of the imported carbon was exported as COlimicola  and chemlimicola .Rhodobacter sphaeroides. One advantage of using rTCA for carbon fixation is that it requires less energy than CBB [Presence of rTCA in RP is intriguing since it is not present in another well-studied, closely related species, than CBB . Thus, g2). The switch to CBB also resulted in a 5% improvement in carbon utilization efficiency. Gene expression analyses have verified extensive use of CBB during LN metabolism of 4-coumarate [However, the rTCA-based optimum theoretical growth rate was 4% higher than the measured value Table . At the oumarate . Thus, o2 while approximately 90% of the produced CO2 was fixed into biomass. However, as with 4-coumarate, due to energy considerations, 67% of CO2 was fixed by rTCA. MOFA results at the measured growth rate . This mode of metabolism is about 2% more carbon efficient than the metabolism at the optimum theoretical growth rate.MOFA analyses of growth archetype with benzoate as the sole carbon source under light-limited anaerobic conditions Fig. b, Row A 2. Only about half of the CO2 that was produced was fixed through the activity of rTCA while around 20% is fixed through the formation of carbonic acid. The results at the measured growth rate . This mode of metabolism is 8% more carbon efficient than the one used to achieve theoretical optimum growth rate.Using the model to examine flux patterns in the growth archetype when RP consumes 4-hydroxybenzoate (4HBZ) under light-limited anaerobic conditions Fig. b, Row B,− 1.h− 1) photoheterotrophic growth of RP on aromatic compounds, with exception of metabolism on 4HBZ, the model predicts growth rates that are reasonably close to measured values. This can be viewed as further proof of light-limited nature of RP’s phototrophic metabolism.Overall, when simulating light-limited ) contain more reduced carbon than α-ketoglutarate. The model’s prediction that RP imports protons during LN acetate metabolism should lead to a pH increase in the growth media, consistent with our experimental observation. Phototrophic growth of RP on acetate in poorly buffered minimal media leads to a medium pH increase (from 6.7 to 7.2) , due to excess oxygen being exported as α-ketoglutarate, wasting carbon and electrons that could otherwise be used for growth. α-ketoglutarate is exported because it has a low degree of reduction (κ = 3.2). If we eliminate export of α-ketoglutarate, this further reduces the growth rate (10.5 h doubling time) because alternative oxygen carriers  results in production of protons that are exported. This is due to the low (compared to biomass) hydrogen and oxygen content of these aromatic compounds. The reactions for metabolism of 4-coumarate and benzoate are:Examining the proton economy of LN metabolism of aromatic compounds1.5O) and thus water needs to be used to make up for this shortcoming. Our experiments verified that LN metabolism of benzoate indeed reduces the pH of the growth medium   Fig. b, Row G.gas Fig. b, Row G.2 production in PNS [2 production. Our simulations concur with these findings. Simulating nitrogen starvation while growing on acetate, our model predicts that RP will produce the subunits (C4H6O2) of PHB, if this compound is allowed to be exported as a metabolic byproduct. When we optimized PHB production while minimizing the total exchange of nutrients and byproducts (at fixed rates of carbon and photon import), RP produced water, PHB (1 PHB/6 acetate), and succinate.It is known that production of highly reduced energy and carbon storage compounds such a polyhydroxybutyrate (PHB) can negatively affect Hn in PNS . PHB pro2 gas along with PHB and α-ketoglutarate and CO2 as metabolic byproducts [Previous studies have also shown that nitrogen starved, non-growing RP cells produce Hproducts . To test2 production during LN metabolism of three aromatic compounds indicate that regardless of carbon source, at the theoretical growth archetypes, RP would not produce H2 gas   w2 production as long as the redox state of the system provides it with reducing agents [It appears that expression of nitrogenase automatically results in Hg agents , 65. To g agents .2 [2. However, these resources are essential for a variety of other important functions. This could explain why layers of transcriptional control  and posOur MOFA analyses described herein examined the trade-offs between 8 biological objectives for RP’s photoheterotrophic metabolism of acetate (matching the maximum number of objective trade-offs that have been simultaneously examined to date ) and 7 oOur examination of differences between RP’s metabolism of aromatic and aliphatic carbon sources using GX-FBA  is based on the annotated genome of Rhodopseudomonas palustris CGA009 [The metabolic network reconstruction for s CGA009 . Of the E. coli.The initial foundation of the model is a draft model obtained from Model SEED . So, allThe same curation scheme was used to ensure that multiple genes associated with a reaction are correctly labeled as either isozymes or components of an enzyme complex. In cases where multiple reactions were combined into one reaction , the reaction in the GPR was treated as if it was catalyzed by a multi active-site enzyme complex.In some cases, we used KEGG to improve/update the originally published annotations. For example, byproducts of some genes in the original annotation have been assigned a clearly annotated function  but have not been assigned an EC number. In these cases, we used KEGG to assign the correct EC number for the enzyme and the associated reactions were added to the metabolic network reconstruction. In some other cases, the EC numbers assigned in the original annotation have become obsolete. For example, the original annotation assigns the EC number 1.3.99.1 to the byproduct of gene RPA0216. That EC number is no longer in use and has been changed to 1.3.5.1. For such cases we used KEGG to identify and correct these dated assignments and added the correct reactions to our model.Finally, in some cases KEGG had assigned a function to a protein that did not have one in the original annotation. We did not blindly accept KEGG’s assignment, and instead examined the initial experimental basis for the KEGG ortholog (KO) assignment and verified the KEGG association using BLAST., badR (RPA0655) mutants grow slowly on benzoate [In cases where the roles of essential regulatory genes were known, such as the need for hbaR (RPA0673) and aadR (RPA4234) for growth with 4-hydroxybenzoate as the carbon source , 75, thebenzoate ), then tThe biomass equation for RP was developed using a variety of data sources. The overall breakdown of cellular components is from McKinlay and Harwood . The amiIt is known that oxygen is not required for the oxidative reactions that are involved in biosynthesis of carotenoids, different forms of quinones, nicotinates, and nicotinamides –86. HoweOne significant challenge encountered during the course of our RP GSM development was the unique structure of RP’s Lipid A. The lipid A base of lipopolysaccharides (LPS) in RP has been shown to be composed of 2,3-diamino-2,3-dideoxyglucose ; however1.93O0.54N0.22. This formula is somewhat different from that measured for the elemental composition of RP strain 42OL (CH1.8O0.38N0.18) [1.8O0.5N0.2 [Our model’s biomass has an elemental composition of CH38N0.18) . HoweverO0.5N0.2  (κ = 4.2Escherichia coli (7.6 mmol.gDW− 1.h− 1) [We set the value for non-growth associated maintenance ATP usage to that previously used for  1.h− 1) . VariatiWe curated the model extensively to ensure absolute mass balance, including proper proton balance under physiological pH values. We also imposed the loop law on the model and eliminated all thermodynamically infeasible type III extreme pathways .An SBML file of the model is included in the Additional file Comparing the predicted metabolic phenotypes with experimental observations validated the model. We examined the model’s ability to consume a variety of different carbon sources as reported in the literature , 96. It S (m × n), where m is the number of metabolites and n the number of different reactions. Applying the assumptions of mass balance and metabolic steady-state, the following set of linear equations govern the system’s behavior:Xi is the concentration of metabolite i. For FBA, other limitations are imposed on the system based on experimental studies, including a limit on the amount of flux that courses through a reaction, as well as constraints on the amount of nutrients imported, and the waste products secreted from the system. These constraints are formulated as:bi and νi are the export/import flux of metabolite species i, and the flux through internal reaction i respectively, and α, β, χ, and φ are the lower and upper limits for these fluxes. Finally, FBA utilizes linear programming to determine a feasible steady-state flux vector that optimizes an objective function, most commonly chosen to be the production of biomass, i.e. cellular growth. FBA was used with our RP GSM to analyze single gene knockout phenotypes for all the genes in the model. Several reviews [.The FBA modeling approach uses a genome-scale metabolic reconstruction as its basis. The reconstruction is developed using elementary functional information derived from annotated genomes and available knowledge of enzymology. The reconstruction of an organism’s metabolic reactions is represented as a stoichiometric matrix,  reviews , 113 proIn order to assess differences in RP metabolism when growing on aliphatic and aromatic carbon sources, we used the GX-FBA modeling methodology  with avaGenerate the flux distribution i ), flux variability analysis (FVA)  with minT for which an mRNA expression value can be associated. For protein complexes and reactions catalyzed by isozymes, the maximal up- or down-regulation value is used unless the mRNA expression values are inconsistent (mixture of up- and down-regulation). In the latter case, the reaction is excluded from T.Identify the set of reactions Construct a new objective function:A brief description of GX-FBA steps is:i is zero, If the flux value for condition s (2012) .The catalytic site identification (CATSID) web server , 90 rapin-dimensional Pareto front of the competing metabolic objectives. NNC generates an even distribution of Pareto solutions on convex or non-convex Pareto frontiers for problems of n-objectives. Additionally, NNC is usable for an arbitrary number of objectives and its results are entirely independent of the scales of the examined objectives scales.As with the effort by Nagrath et al. , our MOFThe mathematical representation of a generic multi-objective optimization problem is:x denotes the design variables (fluxes) and zn denotes the nth objective function. Equations Vector n objectives defines an n-dimensional volume in which all Pareto solutions to the problem are found. Next, n anchor points are identified. Anchor Points are feasible solutions, in the objective space, that correspond to the best possible values for respective individual objectives. The values of the anchor points are normalized to eliminate deficiencies associated with scales of individual objectives. The solution space volume is then reduced through the use of an n-dimensional “Utopia” hyperplane. The Utopia plane is defined such that it contains all n anchor points. Finally, a set of evenly distributed points on the Utopia hyperplane serve to constrain the all but one of the objectives under consideration. Solving for the optimal value of the lone objective at each one of these points will result in calculation of a Pareto solution. For a full mathematical description of NNC see the manuscripts by Dr. Achille Messac and coworkers [NNC can be briefly described as a method where an investigator’s choice of a set of oworkers , 115.It is interesting to note that Shoval et al.  recentlyn objectives we were examining; the algorithm would determine the appropriate n anchor points to define the Utopia plane for the problem. At each point on the hyperplane, the value of n-1 objectives would be constrained, and then (like FBA) linear programming will be used to solve for the optimum value of the remaining objective.For our MOFA analyses, we used the RP GSM and changed the constraints based on the growth medium we were examining. Based on the choice of the Rhodopseudomonas palustris CGA009 was grown in modified photosynthetic medium [c medium  with lowAdditional file 1:System-level Analyses of robustness of metabolism to environmental and genetic perturbations. (DOCX 3179 kb)Additional file 2:RP model and gene knockout results. (XLSX 133 kb)Additional file 3:SBML format of the RP model. (XML 1614 kb)


Escherichia coli strain lacking the major glucose transport system, grown on complex media.Classic metabolic engineering strategies often induce significant flux imbalances to microbial metabolism, causing undesirable outcomes such as suboptimal conversion of substrates to products. Several mathematical frameworks have been developed to understand the physiological and metabolic state of production strains and to identify genetic modification targets for improved bioproduct formation. In this work, a modeling approach was applied to describe the physiological behavior and the metabolic fluxes of a shikimic acid overproducing The obtained flux distributions indicate the presence of high fluxes through the pentose phosphate and Entner-Doudoroff pathways, which could limit the availability of erythrose-4-phosphate for shikimic acid production even with high flux redirection through the pentose phosphate pathway. In addition, highly active glyoxylate shunt fluxes and a pyruvate/acetate cycle are indicators of overflow glycolytic metabolism in the tested conditions. The analysis of the combined physiological and flux response surfaces, enabled zone allocation for different physiological outputs within variant substrate conditions. This information was then used for an improved fed-batch process designed to preserve the metabolic conditions that were found to enhance shikimic acid productivity. This resulted in a 40% increase in the shikimic acid titer (60 g/L) and 70% increase in volumetric productivity (2.45 gSA/L*h), while preserving yields, compared to the batch process.Escherichia coli strain.The combination of dynamic metabolic modeling and experimental parameter response surfaces was a successful approach to understand and predict the behavior of a shikimic acid producing strain under variable substrate concentrations. Response surfaces were useful for allocating different physiological behavior zones with different preferential product outcomes. Both model sets provided information that could be applied to enhance shikimic acid production on an engineered shikimic acid overproducing The online version of this article (10.1186/s12918-018-0632-4) contains supplementary material, which is available to authorized users. Illicium verum, employing classic extraction processes with yields of only 30 mg/Kg approximately ) and the exponential consumption rate Figure mentclass2pt{minimmentclass2pt{minimXmax) response surface , surface morphology is similar to the consumed GLC surface, but exhibits a maximum critical point at 110:40 g/L GLC:YE initial concentrations  displays a minimum critical point on 105:21 g/L GLC:YE initial concentrations  values lower than 1% and p-values below 0.05. These statistical values and the depicted models from Fig. 0 presented the highest error, probably because in experiments with high initial [GLC] and [YE] no AC was produced on stationary phase and surfaces constructed with the polynomial equation cannot properly render these behavior values. Yp/s also had relatively high errors above 10%. This may be related to the contribution of YE since it does not only contains the aromatic amino acids needed for growth, but also other amino acids and some carbohydrates that could contribute to some of the previously discussed effects. However, the two-tailed t-student test for the comparison of experimental and modeled parameters validated all parameters as similar, with p-values over 0.05. This means that the predicted values can be used within reason to compare and study the behavior of AR36 under the limits of the experimental design and that the constructed surfaces can be used to obtain further insights on cell behavior. Parameter data and statistical values for all experiments can be found on Additional file The descriptive viability of the constructed response surfaces was validated by performing fermentations using three conditions not included in the experimental design . Figure R2 from Pearson linear regressions were found to be above 0.9 and their significance p-values were all found to be below 0.05. Regarding SPD, the highest values were found for GLC and AC profiles. Specifically, a 21% deviation was found for GLC in the 100:15 condition, where the model presents higher GLC consumption at the last part of the fermentation compared to the experimental values. As it can be seen on Fig. To get further insight into these different output zoned behaviors, dynamic flux models were constructed. It is evident that AR36 strain regulation showed no linear borders and contributions between predominant outcomes. Since data on internal fluxes, constraints on regulation or other kinetic data were not available, a cybernetic modeling approach was used See “” sectionCalculated fluxes were normalized against GLC consumption derived fluxes for their analysis and surface construction on three different fermentation stages: initial exponential (IEx), mid exponential (MEx) and mid stationary (MST). IEx and MEx presented highly similar behaviors, so their description is similar and only IEx surfaces were addressed. However all surfaces and contour plots for all reactions and time sets can be found in Additional file zwf and the 6-phosphogluconolactonase (Pgl). G6Pdh presents relative flux values from ≈75 to 95%  flux surfaces show the same morphology as G6Pdh, a marked tendency of maximization towards higher initial [YE] Fig. 6666. An inte666ΔpoxB strain was constructed and cultured under high [GLC] and high [YE], conditions that maximize AC production according to the response surfaces. Interestingly, the initial AC concentration peak observed in all previous experiments was not detected in this case with the mutant strain  and lower exponential GLC consumption rates (0.61 g/gh) , accounting for ≈28–38% from the NADPH dependent enzyme (MaeB) Fig. l and ≈0-6777− strain , 36–38. glta coded citrate synthase (Csyn) reaction presents relative flux values from ≈56 to 75%  reaction is non-existent, suggesting no gluconeogenic flux from PYR towards PEP is obtained in any condition. To the same extent, PckA reaction catalyzing carbon flux from oxaloacetate (OA) towards PEP is quite low with relative fluxes between ≈0–5% and only being present under high [YE] initial conditions Fig. k. On the88999Regarding TCA and GSP, their surfaces exhibit ringed type maximization or minimization morphologies towards the upper left corner of the experimental design. Specifically, Csyn and Acn reactions present ≈54 to 70% relative fluxes with the minimization morphology behavior towards low [GLC] and high [YE] initial experimental conditions corner Fig. f. The GS9In contrast to the growth phase, IcdA, KGdh and SucCD reactions present flux directions towards GLC oxidation on all the experimental area, with relative fluxes between ≈10 to 55% Fig. g, i, j, 9The dynamic cybernetic model on this phase showed two predominant production zones. The observations made by the dynamic flux models allocate a SA production zone above an imaginary diagonal line, cutting the experimental design area from low to high initial substrate concentration, and a predominantly AC production zone was found below the same imaginary diagonal, in accordance to the physiological models surface allocations.To assess the utility of the previously described models and considerations towards SA production enhancement, a fed-batch fermentation process was performed with initial conditions of 80 g/L and 40 g/L initial [GLC] and [YE]. Surfaces revealed higher growth rates have been found under high [YE] and low [GLC] conditions Fig. k. Under 2228891010610As mentioned, fed-batch operation was designed to favor the biomass preferential production during growth phase and then use the SA production preferential zone during no-growth conditions. The hypothesis was that this would help to stabilize the flux distributions described on the modeled surfaces and therefore maintain yields with higher process productivity as more cells would be present. Upon ending the feed, a stationary or non-growth phase would in theory be expected to show similar physiological and flux distribution behavior as described by the stationary modeled response surfaces and in this way enhance SA production with controlling AC production at high yields and process substrate conversion. This was achieved by designing a pseudo-exponential feeding profile with concentrated solutions of GLC and YE that considered substrate addition from the beginning of the fermentation to maintain the initial concentration conditions as long as possible. It is important to notice that all models consider YE as a unique metabolite so balancing flux for this complex substrate was just an approximation.−1, which is in range of the ones predicted by models (0.8–0.85 h −1), while the second growth rate is only about 0.18 h −1, suggesting limitation by an unknown substrate. Regarding the SA production rate, it also responded to this 8–16 h stall. Despite this, high GLC consumption rates on stationary phase were maintained and GLC was completely exhausted after only 24 h. A total of 180.5 g of GLC were consumed and 59.1 g/L of SA were produced. Although this titer is the highest obtained with this strain, it is 30% below the maximum titer reported on E. coli by Chandran et al.  where only at high [YE], high [GLC] initial concentrations can be exhausted.Regarding specific rates a diminution of the GLC consumption rate under increasing [YE] was also found. This could be related to YE components competing for transport energy or amino acid allocation for their transporters. This may be possible since as mentioned, AR36 does not present catabolic repression  cannot be mechanistically described but some systematical characteristics can be approximately modeled , 48, 49.zwf gene overexpressed by a strong promoter on a high copy number plasmid  and between 35–40 g/L of initial [YE] have been found. It is interesting to notice that 9 EMs were enough to describe all the patterns conformed by the surfaces by only modifying the parameters which alter their combination across time. These changes, although cannot be used to describe regulatory mechanisms, unveil relevant systems characteristics and interestingly also suggest the existence of metabolic state multiplicity derived from changing extracellular conditions .− laboratory evolved E. coli engineered strain for SA overproduction  wem (PTS) , 40. AR3 wem  and an aminex HPX-87H column , using 5 mMH2SO4 as mobile phase at 50 ∘C; either UV or refractive index detectors were used for qualitative and quantitative determination. All measured parameters were volumetrically corrected for the acid or base added by pH control pumps.Physiological behavior characterization was performed with a central composite design experimental matrix with 3 levels for each substrate source. Experimental condition levels were: 75, 100 and 125 μmax and maximum biomass Xmax were obtained by adjusting a logistic growth model to experimental data. Since the fermentation processes use complex media, calculation of yields and production/consumption rates by classical calculations were difficult to address. Therefore, to provide a more accurate parametrization, GLC consumption and SA production integrated models were constructed. The integrated model equations used were: For fermentation data parametrization and analysis, a set of modeling approaches was constructed. The maximum growth rate S refers to substrate, in this case GLC, and P refers to product, SA on this experimental design. Xt) and the sum of the square of the experimental points (SSEP). Model approximation was then mathematically described by the linear regression between experimental points and model points. A percentile deviation from the expected slope  descriptive indicator constructed from regression (SDP) as well as Pearson regression coefficient (describing dispersion) and the regression significance proved by AC presented a dynamic behavior (simultaneous production and consumption) in all fermentations that could not be described by any of the previously described equations. Nevertheless, initial exponential where yield was calculated by linear regression for AC vs biomass and volumetric rate was calculated by linear regression for AC vs time on the first experimental data points for each phase.p-values and square sums of error and percentile error. Surfaces were validated by prediction of parameters for three fermentations not contained on the set of the experimental design . Surface calculated parameters were introduced to the logistic biomass, consumption and production models and compared to the experimental data sets. Surface calculated parameters were also compared to the ones calculated directly from experimental data by error percentage, estimated by the ratio between standard deviation between each calculation and experimental parameter. A two-tailed t-student test using the experimental design standard deviation, calculated from central point, was used to determine if the experimental parameters and surface predicted parameters were significantly different.Parameters describing the physiological behavior were then used to construct individual three-dimensional surfaces. A second-order bivariate polynomial equation was used for surface construction and its approximation was addressed and qualified by regression coefficients, u≡ and v≡ referring to them as cybernetic variables, associated with fractional allocations of resources for enzyme synthesis and activity, respectively t). This inter-crossed set passed onto the next generation where another round of individual parameter perturbations was made. The algorithm was cycled until SSE was found constant . Finally, these parameters were subjected to a final SSE nonlinear numeric minimization to model the flux rates of each EM and the final metabolic dynamic flux model for each fermentation.where indexes a et al. , 58, 60.entclass1pt{minimat=0 and MEx time, and MSt as the middle point between the end of the fermentation and the initial time of stationary phase. Constructed surfaces were statistically qualified and used to analyze and study the behavior of strain metabolism and SA production.Flux distributions were used to construct three-dimensional behavioral surfaces with the second order two variable polynomial equation at three fermentation times: initial exponential (IEx), mid exponential (MEx) and mid stationary (MSt). Calculation of MEx time was made by obtaining the maximum point of the second derivative vs. time for each biomass model, IEx was set as the mid time between To assess the utility of the metabolic models developed, a fermentation process was designed with the information acquired by physiological models and flux distribution surfaces to optimize SA productivity. Process was designed to maintain constant the initial GLC:YE conditions. This would in theory, maintain constant the cybernetic variables (which are a representation of the regulation parameters) and therefore the internal flux distributions accordingly. Initial conditions around 80:40 g/L GLC:YE were used. A pseudo-exponential flux was operated and regulated by a peristaltic pump manually set every 15 min to calculated exponential feeding needs, with measured GLC and calculated growth and consumption rates. Feed consisted on two simultaneously added solutions, one containing mineral media with GLC 400 g/L and the second containing phosphate buffer solution with YE at 400 g/L. After 12 h, the feed was stopped and the fermentation was allowed to enter stationary phase to consume the remaining GLC. Oxygen was added when needed to maintain dissolved oxygen tension (DOT) over 20% along with an agitation cascade. Other media compounds, salts, buffer and antibiotics were used as described by Rodriguez et. al. [Additional file 1Model and validation data, parameters and statistical values. Description: Parameters and statistical value tables for physiological models. Response surface parameters and statistical values for the polynomial approximation. Response surface prediction validation data. Response surface critical points calculations. Dynamic Flux models parameters and statistics. (PDF 186 kb)Additional file 2Metabolic network definition for dynamic model. Description: Definition of reactions, internal and external metabolites comprehending the metabolic network. Matlab program section for the genetic algorithm used for parameter approximation. (PDF 173 kb)Additional file 3Response surfaces and contour plots for all fluxes. Description: Response surfaces and contour plots for all reactions detailed on the metabolic network for initial exponential, mid exponential and mid stationary fermentation stages. Additional file 4poxB− figures. Description: Table for all plasmids and oligonucleotides used. AR36 ΔpoxB strain construction. Fermentation profiles for AR36 ΔpoxB and AR36 on highly concentrated substrate media. (PDF 215 kb)Strains, plasmids and oligonucleotides used on this work, AR36


Cellular metabolism is tightly regulated by hard-wired multiple layers of biological processes to achieve robust and homeostatic states given the limited resources. As a result, even the most intuitive enzyme-centric metabolic engineering endeavours through the up-/down-regulation of multiple genes in biochemical pathways often deliver insignificant improvements in the product yield. In this regard, targeted engineering of transcriptional regulators (TRs) that control several metabolic functions in modular patterns is an interesting strategy. However, only a handful of in silico model-added techniques are available for identifying the TR manipulation candidates, thus limiting its strain design application.Escherichia coli and Corynebacterium glutamicum. h-BeReTa suggested several promising TR targets, many of which have been validated through literature evidences. h-BeReTa considers the hierarchy of TRs in the TRN and also accounts for alternative metabolic pathways which may divert flux away from the product while identifying suitable metabolic fluxes, thereby performing superior in terms of global TR target identification.We developed hierarchical-Beneficial Regulatory Targeting (h-BeReTa) which employs a genome-scale metabolic model and transcriptional regulatory network (TRN) to identify the relevant TR targets suitable for strain improvement. We then applied this method to industrially relevant metabolites and cell factory hosts, In silico model-guided strain design framework, h-BeReTa, was presented for identifying transcriptional regulator targets. Its efficacy and applicability to microbial cell factories were successfully demonstrated via case studies involving two cell factory hosts, as such suggesting several intuitive targets for overproducing various value-added compounds.The online version of this article (10.1186/s12934-018-1015-7) contains supplementary material, which is available to authorized users. FadR, the TR regulating a number of genes in fatty acid biosynthesis including fabA, fabB and iclR, in Escherichia coli, fatty acid titres could be enhanced up to 73% of the theoretical yield which was not achieved by overexpressing any of the metabolic gene combinations [cra, was targeted to channel more carbon flux via phosphoenolpyruvate carboxylation and the glyoxylate pathway in E. coli, thereby improving succinate yields [galP and glk, along with a TR, TyrR, which represses the expression of multiple l-phenylalanine pathway genes in E. coli, enhanced the yield of this amino acid significantly [Currently, a variety of value-added products can be newly synthesized and overproduced in microbial expression hosts at near-commercial levels through various pathway modifications such as gene up-/down-regulation and deletion in a serial and/or iterative manner , 2. Howeinations . Similare yields . Anotherficantly . HoweverE. coli for overproducing ethanol, isobutanol and 2-phenylethanol. Later, Vilaça et al. [Constraint-based metabolic modeling (CBM) is a simple and widely used approach that requires only metabolic network stoichiometry and environmental constraints to describe the cellular phenotype from genotype, and thus can be readily exploited to characterize and predict cellular behaviours under perturbed conditions , 8. In ta et al.  used evoa et al. . A systeWhile the unintegrated approach presented in BeReTa could effectively identify several relevant TR candidates for up-/down-regulation compared to OptORF, it still suffers from certain limitations. Firstly, BeReTa does not consider the inherent hierarchical structure of TRN; unlike metabolic genes, TRs are known to operate in a regulatory cascade when certain global TRs regulate multiple downstream TRs, all of which in turn can modulate the expression of target genes , 21. HerE. coli and C. glutamicum. Finally, we compare the resulting targets obtained from h-BeReTa to its preceding methods and discuss their performance.In this work, we propose “hierarchical-Beneficial Regulatory Targeting” (h-BeReTa), which extends the BeReTa by addressing the abovementioned shortcomings for identifying efficient TR targets. Specifically, h-BeReTa utilizes a TRN with hierarchies of TR clearly defined and a metabolic model to identify target candidates. Moreover, it also account for the negatively correlated reactions with the product flux, in addition to the positively correlated reactions because the flux through these reactions need to be minimized to improve product synthesis. Here, we first describe the methodology of h-BeReTa, and then demonstrate its applicability by identifying promising TR manipulation targets for overproducing various compounds in h-BeReTa aims to identify the relevant TR targets for up-/down-regulation to overproduce the desired product using an unintegrated approach which was previously proposed by BeReTa . InitialnRAP represents the extent of control a particular reaction flux could have on the product flux. Calculation of nRAP involves a constraint-based flux analysis formulation as shown below:vtarget represents the product forming flux, vbiomass is flux through biomass forming reaction and vmin and vmax are minimum and maximum feasible flux values of reaction ‘r’ (vr), whose effect on vtarget is to be determined. vmin and vmax are determined using flux variability analysis [r’, the normalized Reaction Activity on Product flux (nRAPr) are calculated as slopes of linear plots for vtarget versus k as shown previously [k’ is chosen instead of the absolute change in the reaction flux in order to avoid unrealistically large nRAP values for reactions carrying small fluxes. nRAP can take any real value depending upon the bounds used for model simulation; reactions potentially favouring product formation take a positive value and those that have potential negative impact on product flux take a negative value. Reactions which does not influence the product flux take a value, ‘0’. Here, it should be highlighted that the solutions obtained by solving optimization problem P1 will identify only the reactions which are present in the shortest path to the product will be ranked with positive or negative nRAP values. However, there could be reactions that are part of alternative  flux modes which could also be positively or negatively correlated to product formation  can be computed for each gene ‘j’ that are associated with reaction ‘r’ using gene-protein-reaction (GPR) association information available in the metabolic model using below equation:GPRfj is GPR factor of gene ‘j’, which distributes the weightage of each gene ‘j’ associated with the reaction ‘r’. For example, if a reaction has GPR ((A and (B or (C and D)) or E), then, GPRf of E is ‘1’ as it can form a fully functional enzyme. On the other hand, GPRf of A and B is ‘2’ as each can constitute half of the multi-subunit enzyme complex; and GPRf of C and D is ‘4’, as they constitute quarter each.Once the i’ regulating the metabolic gene ‘j’, the normalized regulatory strength (nRSij) can be calculated as below:TRi,prod and TRi,n-prod are the expression intensities of the TR ‘i’ at producer and non-producer product conditions, respectively. Genei,prod and Genei,n-prod are the expression intensities of the gene ‘j’ regulated by TR ‘i’ at producing and non-producing conditions. It should be noted that the “producer” and “non-producer” phenotypes can even be replaced by “slightly-better producer” and “producer” phenotypes, respectively. The absolute changes of expression intensities seldom determine the extent of the ‘overall effect’ of the change in gene expression. Hence, a normalization factor nRSij. Here, the normalization is important because the TRs with low expression values are bound to receive relatively higher regulatory strengths compared to those with high expression levels, although the actual extent of regulation of their respective genes could be similar. Furthermore, the activator/repressor information obtained from RegulonDB is used to eliminate the TRs with nRS values having sign opposite to its known functionality.The regulatory strength (RS) represents the effect of TR expression on the expression of the downstream regulated gene. For a TR, ‘nRSij) and its downstream metabolic gene activities on product flux (nGAP):nRSij·nGAPjr represents the effect of the TR ‘i’ on the product flux via the gene-associated reaction ‘r’.The effect of each TR on product flux is calculated by combining the effect of normalized regulatory strength  of nRAP values that are randomly generated within the observed ranges. TRE scores for each TR were then obtained for the 1000 randomly generated nGAP sets. Subsequently, the probability of the randomly generated TRE to fall in ± 10% range of the actual TRE scores of the corresponding TR is calculated. TRs with this probability less than 0.05 (5%) are considered true positives and therefore carry forwarded to Step 5.In order to ensure that the gTRE) would be the sum of its own effect on product flux (TRE) and gTREs of different TRs in the immediate downstream level of the TR-hierarchy that it regulates. Hence, as depicted in Fig. gTREs should be approached from the bottom-most level which only includes TRs that regulate “non-TR” genes to the top-most level which includes global TRs along with those TRs, which are not under the control of any known TRs. This effect can be calculated as follows:p’ can take any value from 1 to ‘h’ and is the level immediately upstream to ‘p + 1’ in the TR-hierarchy  is the normalized regulatory strength of TR at level ‘p’ on TR at level ‘p + 1’. The summation term in the above expression exists only for those TRs that regulate other TRs and does not exist for TRs at level ‘h’, i.e. the last downstream level in the hierarchy. The gTREs of all TRs in the TRN, thus calculated, can be used to rank them as either overexpression or downregulation target depending on whether they receive high positive or high negative values, respectively.Transcriptional regulation of metabolism includes not only the TR-metabolic gene interactions but also TR–TR interactions. Such interactions are effectively represented by TRN, which also provide information about the hierarchies of TR–TR interactions. In such hierarchies, the effect of a certain global TR on product flux , TRN along with the reconstructed TR-hierarchies and gene expression datasets of two reference strains.iJO1366 [iAF1260 [nGAP values for the E. coli case studies, and the iCW773 [C. glutamicum case studies. All constraint-based simulations were performed using COBRA toolbox [http://www.mathworks.com) with Gurobi5 (http://www.gurobi.com) as the optimization solver. Note that FVA was performed by employing the FastLooplessFVA function, implemented in COBRA toolbox [The iJO1366  and iAF1[iAF1260  GEMs were iCW773  GEM was  toolbox .E. coli was downloaded from RegulonDB version 9.0 [C. glutamicum was obtained from the Abasy Atlas database [The TRN information of sion 9.0  includindatabase  which acApart from a metabolic model and TRN, h-BeReTa requires two specific gene-expression datasets relevant to the desired phenotype, i.e. “producer vs. non-producer”, for the identification of promising TR-manipulation targets. Such datasets can be obtained from the two different phases of a cell culture, e.g. growth vs. stationary phase, which shows differential transcriptional regulation. Alternatively, gene expression datasets obtained while comparing a wild-type to that of a transcriptional regulator engineered mutant can also be used for this purpose. Note that the gene expression datasets used are product-specific unlike BeReTa, which uses a general gene expression compendium for all products. The expression datasets for the case studies involving the production of tyrosine, acetate and fatty acids were downloaded using the GEO accessions provided in references cited for the respective case studies see “”.Escherichia coli is one of the well-studied microbes, and a commonly used cell factory for producing various value-added compounds due to the ease of gene manipulations with abundantly available genetic engineering tools. Hence, to take advantage of such valuable resources, we used E. coli to demonstrate the applicability of h-BeReTa (The Matlab code for h-BeReTa is provided in https://github.com/lokanandk/h-BeReTa). We used the RegulonDB information to manually retrieve several hierarchies from the E. coli TRN. A total of 13 levels of TR hierarchy were obtained as compared to the previously described five levels  constrained to zero, in order to mimic the metabolic state of the engineered parental strain. Interestingly, many of the TR targets identified correspond to those regulating the pool of phosphoenolpyruvate (PEP), an early precursor for tyrosine biosynthesis  and chorismate mutase/prephenate dehydrogenase (tyrAfbr) [rpoA14, rpoA27, and rpoD3) [Tyrosine has been used for a wide range of industrial and pharmaceutical applications as dietary supplements and precursors for the synthesis of benzylisoquinoline alkaloids and polyketides. Several metabolic engineering strategies have been carried out to increase the production of tyrosine in l strain . The contyrAfbr) , it stild rpoD3) , we furtE. coli. The fatty acid metabolism in E. coli is extensively regulated at transcriptional level, and hence their overproduction would require significant interventions in the associated TRs [iJO1366 GEM to perform constraint-based simulations. h-BeReTa identified relevant TR overexpression and downregulation targets for fatty acid overproduction where at least three out of the four TR targets for fatty acid overproduction that were validated using experimental evidences either activate or repress fatty acid degradation  involved in the flux limiting branch point of lycopene (isoprenoid) biosynthesis [Recently, it was reported that overexpression of the housekeeping sigma factor, lycopene . Hence, um Table . Here, iynthesis , 51.E. coli case studies. Initial comparison of the individual targets for various products from h-BeReTa and BeReTa showed a significant overlap among the resulting TRs due to the similarity in implementation , false positives (FP) and false negatives (FN) . While iJO1366 yielded unrealistic nRAP values for h-BeReTa possibly due to the dubious feasible flux ranges obtained using FVA, the BeReTa results using iJO1366 were contradictory to those of iAF1260, cautioning the user to ensure the reliability of the nRAP values or flux slopes derived from the model by evaluating them on a case-by-case basis. Moreover, h-BeReTa predictions are largely affected by the completeness of the gene expression data. When random partial gene expression datasets were used in the acetate case study, h-BeReTa predicts inferior TR targets compared to those obtained from the original datasets , ihfA (positive) and fur (negative), which occupy the upper levels of the TR hierarchy. It is furthermore clear from Table fnr as an experimentally validated downregulation target for ethanol production has been consistently translated to ihfB and fur as downregulation and overexpression targets, respectively. Another important difference between the two approaches is that h-BeReTa uses a different constraint-based flux analysis formulation in which it also takes into account reactions with negative nRAP scores, i.e., those reactions whose fluxes compete with product formation. The inclusion of reactions with negative nRAP scores is important because high-value products are often secondary metabolites which the cells does not produce naturally and experiences direct competition from a large part of the fluxes in the metabolic network which are associated with biomass precursor biosynthesis. Furthermore, the accounting of negatively correlated fluxes in h-BeReTa allows it to rank the global TRs accordingly, considering that it could regulate multiple genes in other parts of the metabolic network in addition to the product flux. In contrast, since BeReTa does not consider the negatively correlated reaction fluxes there could be a bias for TRs to be identified just by considering the positive beneficial scores calculated.As mentioned earlier, h-BeReTa utilizes an unintegrated approach which treats the cellular metabolism and regulation as two modules in the framework and then combines them systematically as previously proposed in BeReTa algorithm. However, h-BeReTa still encompasses several differences at various levels of the formulation, thereby resulting in improved performance. First, the TR hierarchy information is newly incorporated into the framework, thus identifying TR targets with higher regulatory impact on the product formation. The importance of such considerations can be perceived from h-BeReTa results which exclusively include global TRs such as re Table . In addiTRE scores was arbitrary and can be subjected to scrutiny. However, with this cut-off range we observed a minimal rejection of true positive  TR targets. Additionally, using more than one set of transcriptomic data representing the desired phenotype, i.e. producer and non-producer, to calculate the regulatory strength (nRS) values may increase the accuracy of TR candidate predictions. Alternatively, if no relevant datasets could be found for the desired phenotype, a general gene expression compendium can be used as it is in BeReTa.Although h-BeReTa is able to identify efficient TR targets consistently, one major limitation is the inability to predict the extent of changes to product yields as a function of TR manipulation which is mainly due to the unintegrated nature of the methodology. However, the actual increase in product yield might mainly depend on several contributing factors, including the degree of correlation between the mRNA and protein levels of the TR, nature of interaction, saturation kinetics between the TR and its regulatory targets, and the intracellular metabolite concentrations, which are generally ignored in CBM approaches. Therefore, further improvements in h-BeReTa predictions could be made possible by incorporating concepts such as the metabolite dilution  or molecnRAP scores. We believe that the less-stringent resource requirements and the computationally less-intensive methodology make h-BeReTa to be more readily employed in comparison to the existing methods for identifying non-intuitive TR targets, thereby advancing metabolic engineering applications.Despite its limitations and scope for further improvements, the agreement of h-BeReTa predictions with experimental evidences from literature was substantial. Although the gene expression datasets used in this study for various case studies correspond to exponentially growing cells cultures, the method can also be readily extended to those of stationary phase cultures, provided an appropriate objective function is employed during the computation of Additional file 1. Table S1. False-negative TRs for the E. coli case studies from literature evidences. Figure S1. Simplified toy network showing alternate routes positively correlated to product formation. Table S2. Randomly selected Partial  gene expression datasets for acetate case study. Table S3. TR-hierarchy of C. glutamicum.


Producing ‘designer cells’ with specific functions is potentially feasible in the near future. Recent developments, including whole-cell models, genome design algorithms and gene editing tools, have advanced the possibility of combining biological research and mathematical modelling to further understand and better design cellular processes. In this review, we will explore computational and experimental approaches used for metabolic and genome design. We will highlight the relevance of modelling in this process, and challenges associated with the generation of quantitative predictions about cell behaviour as a whole: although many cellular processes are well understood at the subsystem level, it has proved a hugely complex task to integrate separate components together to model and study an entire cell. We explore these developments, highlighting where computational design algorithms compensate for missing cellular information and underlining where computational models can complement and reduce lab experimentation. We will examine issues and illuminate the next steps for genome engineering. Synthetic biology is the rational design and engineering of cells and cellular systems using genetic manipulations ,2. It isBox 1Genome engineering: Extensive and intentional genetic modification of a replicating system for a specific purpose [ purpose .Minimal genomes: Reduced genomes containing only the genetic material essential for survival, with an appropriately rich medium and no external stresses. No single gene can be removed without loss of viability [iability .Recoded genomes: Genomes with codon/s that have been freed, substituting codons for synonymous codons that encode the same amino acid, so that they can be assigned to new functions [unctions ,22.Platform cell/cell factory/chassis (interchangeable): A bacterial species that can efficiently convert raw materials into a product of interest, through genome engineering or hosting genetic components [mponents ,23–26.Multiplex gene editing: Simultaneous introduction of multiple distinct modifications to a genome [a genome .Algorithm: Series of steps or rules to attempt to solve a problem, often implemented in a computer.Model: Mathematical description of a system.Metabolic flux: Metabolic reaction rate (i.e. turnover of molecules through a metabolic reaction).Flux vector: A vector where each element corresponds to the metabolic flux of a reaction in the model.Genome-scale biological models: Category of models containing: metabolic models, transcription regulatory networks, protein–protein interaction networks, integrated cellular models, and whole-cell models [l models .Genome-scale metabolic models: Models representing all active reactions in a cell/organism as a matrix of stoichiometric coefficients of each reaction, and linking reactions with gene products that catalyse them. Abbreviated to GSMMs or GEMs [ or GEMs ,29.Whole-cell models: Describe the life cycle of a single cell, modelling individual molecules and interactions, and includes the function of every known gene product [ product .Genome-driven cell engineering encompasses both metabolic engineering (control of cellular production processes) and genome engineering . It encompasses diverse types and scales of genetic modifications and underscores the genome as the major driver of cellular events .Metabolic engineering attempts to improve titre, accumulation rate, and yield of a specific metabolite, often from microorganisms in an industrial setting . Genome in silico design and simulation  [in vivo construction methodologies  [Here, we review metabolic engineering and genome engineering from both biological and computational perspectives. Metabolic engineering, with established orithms) ,11 and ispecies) , could ispecies) , genome species) ,15, and species) . FinallyMetabolic engineering enhances the production of native or introduced metabolites, often in a microbial strain . GeneticEscherichia coli (E. coli), Bacillus subtilis (B. subtilis), Streptomyces sp., Pseudomonas putida, Corynebacterium glutamicum [Saccharomyces cerevisiae and Aspergillus niger [Vibrio natriegens , and Roseobacter and Halomonas  [E. coli [B. subtilis [Only a small number of microorganisms are ‘industry ready’, such as: utamicum , Saccharus niger . Requireus niger . Also reus niger ,32, a nulerance) . Metabol[E. coli  and B. ssubtilis .The metabolic production pathway is constructed, reconstructed, or tweaked in the strain, and can then be iterated upon to produce improvements in titre, rate, and yield. There are six strategies  for imprThe development of an ‘industry ready’ strain takes several years and is costly. Strains for Artemisinin and 1,3-propanediol production took 10 years and $50 million, and 15 years and $130 million, to develop respectively , though in silico design and in vivo construction. [Recently, the availability of accurate, genome scale metabolic models, refined with data captured using omics technologies, has begun to overcome these limitations and support rounds of ruction. ,34.in silico models which can predict cellular phenotypes. Metabolic models are based on biological knowledge and experimental data; metabolic kinetic models describe how metabolites vary in time using differential equations, while metabolic constraint-based models formalise behaviour at steady-state  [Recent advances have allowed the reconstruction of genome-scale metabolic networks, and subsequently te loss) . There aGenome-scale metabolic models (GSMMs/GEMs) aim to form a solution space of flux values for each reaction in a metabolic model  can be found, based on the stoichiometric matrix—these are the set of non-decomposable reactions that trace input metabolites to output metabolites which can be used to break a metabolic network into its component pathways . In the To perform FBA, first an objective function is defined that can maximise or minimise the flux through any reaction in the system. When simulating a wild-type unicellular organism in the exponential growth phase, the rate of biomass production is maximised as a proxy for cellular fitness , and useFBA optimisation generates flux vectors that are used to optimise a defined objective function. The flux vectors give insight into the dynamics of the system when equilibrium is reached, indicate which pathways the metabolites are involved in, and can also predict the behaviour of the simulated cell when grown in different culture conditions.FBA is available in the COBRA  toolbox for Python or MATLAB numerical computing environments. For an overview of the COBRA ecosystem see Lewis et al. .in vivo knockouts, which can be different from cell behaviour over a longer time scale [GEMs can be used for metabolite optimisation by analysing the effects of adding or removing genes. FBA can be used to directly calculate fluxes in cells with gene knockouts, or FBA wild-type fluxes can be used as input for other methods to calculate fluxes after gene knockouts: MOMA (minimisation of metabolic adjustment ) and ROOme scale .E. coli strains such as MG1655, where gene deletions simulated in silico correctly predicted the essentiality of 86% of single gene deletions [Helicobacter pylori [Saccharomyces cerevisiae [B. subtilis [C. glutamicum [Metabolic models can be used also to predict gene essentiality—gene knockouts can be simulated and then cell survival assessed based on the end production of biomass . This has successfully been shown for eletions . Similarr pylori , Saccharrevisiae , B. subtsubtilis , and C. utamicum , showingE. coli model (iJL1678b-ME) that is more efficient than the first E. coli ME model , contaiThe main limitation to this approach is the lack of well-curated databases: while genome and gene product information can be retrieved  can be found. Competing reaction pathways can then be found and removed, thereby producing a streamlined strain with minimal functionality. Although this process involves significant computational power, especially for genome-scale models, it has been shown to have success in improving lysine production in utamicum .Whereas FBA, MOMA and ROOM take gene modifications as their input and output a corresponding flux distribution, other algorithms designed specifically for metabolic engineering take a metabolite (other than biomass) as their input, and output a set of gene modifications that optimise its production.in silico using OptKnock were consistent with laboratory results [For example, OptKnock maximises the production of a specified metabolite and biomass by deleting genes to re-route metabolites through certain reaction pathways . Genome  results .Several algorithms for metabolite optimisation use linear programming and couple cell growth and biochemical production using bilevel mixed-integer linear program (MILP), a nested framework where an outer optimisation problem (e.g. maximise metabolite) is constrained by another inner optimisation problem (e.g. maximise biomass), as shown in Alternatively, ReacKnock  uses LagEMILiO  also useOptReg  and OptFE. coli strain GEMs contain over 2000 reactions, but the possible combinations for only five gene knockouts is 1015, making the solution space huge. However, metaheuristic algorithms do not guarantee a globally optimal solution and they require significant computational power.Other approaches are based on metaheuristic algorithms  Table 2Table 2, Additional approaches for algorithmic metabolite optimisation include OptStrain , which sGenetic Design through Local Search (GDLS)  iterativChoosing an algorithm to use  has to tGenome engineering is the production of modified genomes using either a prescriptive genome design or a clear laboratory-based algorithm to design gene edits, and accurate genetic tools that can be used repeatedly.Mycoplasma strains were found to share only 196 genes [Genome engineering builds on historical gene essentiality research see . The seq96 genes . This is96 genes ,97. This96 genes ); nevertSingle gene knockout studies  are stiConsequently, non-essential and essential classifications have been expanded to no essentiality, low essentiality, high essentiality, and complete essentiality , with otMycoplasma genitalium only synthesises DNA, RNA, and proteins from imported precursors, in order to replicate itself [Research for understanding  and production (chassis development) see  both inve itself , which ie itself ; useful JCVI-Syn3.0 [B. subtilis PG10 and PS38 [E. coli Δ 33a [E. coli DGF-298 [Of the largest scale reductions to date see : JCVI-SyI-Syn3.0 , and B. and PS38  were proli Δ 33a  and E. c DGF-298 ,115  substitutes synonymous codons (encoding the same amino acid) across an entire genome resulting in: virus resistance , prevenGenome recoding is possible due to the development of MAGE (multiplex automated genome engineering)  and CAGECombining genome engineering research together can give insights into what an ‘optimal’ cellular chassis could look like see  and suggin silico cellular research, as the first integration of mathematical models to simulate an entire cell’s components. The recency is due to the immense complexity of individual cells. There are many well-characterised cellular subsystem models, such as ordinary differential equation (ODE) or network models for protein interactions [The first whole-cell model  has beenractions , but comMycoplasma genitalium whole-cell model [The ll model  consistsM. genitalium whole-cell model is in silico genome reduction [in vivo experiments. Although modelling is never 100% accurate, it can help to shed light on unexplained phenomena and guide the design of lab experiments, producing research more efficiently [M. genitalium (525 genes), the number of possible gene knockout combinations at genome-scale is of the order of ten [A recent application of the eduction . This isiciently . Howeverr of ten , making M. genitalium genome while still producing a dividing in silico cell. Minesweeper approximates a divide-and-conquer algorithm by knocking out gene sets of varying sizes, then combining sets that produced a dividing cell, generating knockout sets of greater size . GAMA begins similarly, knocking out gene sets of varying sizes, followed by a genetic algorithm to combine these sets iteratively over multiple generations, to make the genome smaller.Algorithms to reduce the solution space can be used. Minesweeper and GAMA (Guess Add Mate Algorithm)  identifiThese approaches are similar to the metaheuristic algorithms used for metabolic engineering, they are purely input/output dependent. These algorithms could potentially be applied to any model, regardless of its formulation, so theoretically scalable from the GEM level to the whole-cell model level. OptGene and GAMA both use a genetic algorithm to achieve their results, except with a different objective function. It is plausible that GAMA could be modified to maximise a metabolite at the whole-cell level, and equally possible that FOCuS and GACOFBA could be applied to whole-cell models for similar purposes or for genome minimisation.Whole-cell models are a vital new approach for genome design. Combined with flexible algorithms  have genes with unknown functions and essentiality; bacterial genomes have on an average 33% genes of unknown function [There is a clear need for greater species-specific understanding of the metabolism and the genome. Even well-studied organisms  along tM. genitalium in the lab [E. coli whole-cell model, as well as other whole-cell models [in silico and in vivo genome engineering for the first time.Currently, genome engineering has not combined computational and biological research, due to how recently the required tools were developed  and the  the lab . This isl models . In combl models  this migin silico and in vivo research will soon be possible in genome engineering. With the release of increasingly refined whole-cell models, genome engineers will have an appropriate model, computational design algorithms, and scalable genetic editing technologies. Following the path of metabolic engineering, better design, and construction processes in silico and in vivo could be applied to a larger scale problem, replacing large quantities of lab work.Combining in silico designs in vivo [in vivo genome reductions for species that will soon have whole-cell models.The next steps for genome engineering are: (i) the production and publication of new whole-cell models ; (ii) th in vivo ; (iv) anin silico design and in vivo construction.Metabolic engineering has an established process of In silico design informing in vivo construction is the future of genome engineering.Whole-cell models and algorithms for genome design will widen the field of genome engineering.in silico predictions in vivo, and uniting in silico and in vivo research in E. coli, are the next steps in genome engineering.Testing current The present study did not generate any new data.