One of the hallmarks of sporadic Parkinson's disease is degeneration of dopaminergic neurons in the pars compacta of the substantia nigra. The aetiopathogenesis of this degeneration is still not fully understood, with dysfunction of many biochemical pathways in different subsystems suggested to be involved. Recent advances in constraint-based modelling approaches hold great potential to systematically examine the relative contribution of dysfunction in disparate pathways to dopaminergic neuronal degeneration, but few studies have employed these methods in Parkinson's disease research. Therefore, this review outlines a framework for future constraint-based modelling of dopaminergic neuronal metabolism to decipher the multi-factorial mechanisms underlying the neuronal pathology of Parkinson's disease. In turn, oxidation of reduced cofactors is kinetically coupled to generation of energy currency metabolites . Energy currency metabolites are used to drive otherwise thermodynamically unfavourable reactions that are required for maintenance of normal cellular functions, such as scavenging of reactive oxidative species (ROS), or in the case of DNs the synthesis, release and reuptake of dopamine Substantia nigra DNs consume a large amount of energy to maintain a tonic electrophysiological activity in their axonal terminals within the striatum, making these cells especially vulnerable to any impairment of energy metabolism +-dependent enzymes is currently being explored to treat neurological illnesses, e.g., the key NAD+-dependent enzymes SIRT1 and SIRT2, which have been associated with the α-synuclein aggregation process in PD l-DOPA  biosynthesis and alleviation of the disease motor symptoms Modulation of NADMoreover, degenerating DNs are accompanied by an increased iron accumulation 3Elucidation of the molecular aetiopathogenesis of PD requires an interdisciplinary systems approach An ultimate aim of the application of a systems approach to PD would be to be able to make non-trivial predictions of DN reactions, where quantitative modulation would either significantly imbalance normal DN energy supply, or re-balance DN energy supply in DNs generated from induced pluripotent cells derived from PD patients. At the core of this approach is an effort to reconstruct a state-of-the-art metabolic network of substantia nigra DNs, and then to apply computational modelling for generating experimentally testable hypotheses as to the aetiopathogenic nature of PD.Network reconstruction is a prerequisite for computational modelling. A high-quality reconstruction is built from a variety of biological knowledge sources such as genome annotations, metabolic databases  and biochemical literature manually curated in a quality-controlled manner Constraint-based modelling is a scalable computational modelling approach widely used for prediction of physicochemically and biochemically feasible steady-state metabolic fluxes (reaction rates) in living cells Software platforms have been developed to automate certain steps of the reconstruction process 4Relatively small scale computational models were developed and applied to investigate particular aspects of DN metabolic reaction kinetics, e.g., predicting dysregulation of dopamine homeostasis Research efforts have recently been devoted to reconstruct neuronal cell metabolism Comprehensive and anatomically resolved experimental gene expression datasets have recently become available, e.g., Allen brain atlas A typical set of core reactions is usually not flux consistent, i.e., not every reaction has a non-zero flux in at least one steady state flux vector. A number of methods have been developed to reconstruct context-specific models from existing genome-scale models by integrating various types of high-throughput data , in which intracellular fluxes are quantitatively determined by culturing cells in the presence of a 13C isotopically labelled carbon source and tracing the transition path of the labelled atoms between metabolites in the biochemical network using mathematical modelling Metabolic fluxes can be estimated using 4.2.2Extracellular metabolite abundances (the exometabolome) can be quantified using in vitro cultures of DNs derived from human neural stem cells. By analysing the exometabolomic data, the metabolomic profile of energy metabolism can be elucidated at different metabolic states of the cell, using previously developed protocols 4.2.3Extensive manual curation of the experimental literature provides valuable data for a cell-type specific dopaminergic neuron reconstruction, especially central metabolic pathways, including glycolysis, the TCA cycle and oxidative phosphorylation. Cell-type specific gene expression data can be manually retrieved from the Allen brain atlas,4.2.4It is important to develop and apply software to quantify spatiotemporally resolved morphological features from fluorescent images of dopaminergic neurons in cell culture. By quantification of the abundance and morphology of dopaminergic neurons in vitro, it is possible to compare metabolomic and mass isotopomer abundance data from different cultures, by normalising to the dopaminergic neuronal cell volume in each culture. Automated incorporation of the large-scale auxiliary information is important to the PD research community to adapt or calibrate the DN model to their own case-specific simulations in the future.Furthermore, it is necessary to qualitatively test the accuracy of a dopaminergic neuronal metabolic model, refine the reconstruction and corresponding constraint-based computational model, by comparing the pathways predicted to be active in the model with pathways established experimentally and in the literature. Many of the relevant in silico tests can be performed using existing software.4.3The degeneration of a single DN in PD may be the result of perturbations to energy metabolism. All else being equal, this could either be a decrease in the capacity to produce energy or an increase in demand for energy. Therefore, PD could either result from impaired function of metabolic reactions that generate energy or decreased energy efficiency in non-metabolic processes consuming energy, e.g., protein degradation. The molecular causes and metabolic pathways can be identified by understanding the relationship between metabolic reactions already hypothesised to be important in the pathogenesis of PD, e.g., mitochondrial oxidative phosphorylation, with other reactions included in the reconstruction. This can be done by calculating how a change in the rate of a given reaction would influence the rate of energy supply toward electrophysiological activity. This may uncover cascading effects of redox perturbation and over-expression of inflammation-associated enzymes such as cyclooxygenase-2 (COX-2).The metabolic mechanisms involved in neurophysiological activities of DN can be identified by a number of methods based on sensitivity analysis Specifically, robustness analysis, as exemplified by previous studies The steady states can be further constrained by metabolic uptake rates and the net direction of each metabolic reaction. Further thermodynamic constraints can be added such that steady states are also thermodynamically feasible. Energy balance analysis There is no human biomass equation available in the literature and a neuron does not proliferate like a cancer cell or a microorganism. The objective of the neural cell is thought to achieve different metabolic functions, such as secretion of a range of metabolites into surrounding environments. Different objectives have been used in studies applying constraint-based modelling to calculate an optimal flux distribution. These objectives include maximisation of fluxes through apoptosis, protein degradation, ATP, O2, α-synuclein and tyrosine production pathways In contrast to single-objective based studies, it is preferable to employ Pareto optimality analysis Then, a fractional benefit analysis, as described previously Furthermore, a number of PD-associated genes have already been reported, such as those encoding α-synuclein, parkin, PINK1, DJ-1, LRRK2, and ATP13A 5PD has a multi-factorial aetiology, involving interaction between multiple perturbed biochemical reactions. The latest human generic metabolic reconstruction There is a shortage of research activities in the DN reconstruction and modelling effort. The reconstruction requires not only the manual reconstruction from the classical neurobiochemical literature, but also a parallel and complementary effort to apply and adapt existing tools for automated reconstruction of metabolic networks in order to accelerate the comprehensiveness of the existing DN reconstruction and modelling effort. This permits the incorporation of high-throughput experimental data, which can be used as an independent means to establish the activity of individual reactions within DN susceptible to degeneration in PD. Once the parallel manual and automated reconstruction effort has resulted in an integrated computational model, it is possible to combine biochemistry and computational modelling to determine the relative importance of various factors implicated in neurodegeneration in PD.There are several potential challenges in the DN reconstruction. First, the pipeline methods for network reconstruction are well described by previous studies A range of age-related processes  might be linked to the pathophysiological mechanisms of PD, via natural and chronic alterations in whole-body metabolism It may be worth noting that the prevalence of PD is 1.5–2 times higher in men than women The authors declare that they have no competing interests.


The modeling of the chemical reactions involved in metabolism is a daunting task. Ideally, the modeling of metabolism would use kinetic simulations, but these simulations require knowledge of the thousands of rate constants involved in the reactions. The measurement of rate constants is very labor intensive, and hence rate constants for most enzymatic reactions are not available. Consequently, constraint-based flux modeling has been the method of choice because it does not require the use of the rate constants of the law of mass action. However, this convenience also limits the predictive power of constraint-based approaches in that the law of mass action is used only as a constraint, making it difficult to predict metabolite levels or energy requirements of pathways. An alternative to both of these approaches is to model metabolism using simulations of states rather than simulations of reactions, in which the state is defined as the set of all metabolite counts or concentrations. While kinetic simulations model reactions based on the likelihood of the reaction derived from the law of mass action, states are modeled based on likelihood ratios of mass action. Both approaches provide information on the energy requirements of metabolic reactions and pathways. However, modeling states rather than reactions has the advantage that the parameters needed to model states  are much easier to determine than the parameters needed to model reactions (rate constants). Herein, we discuss recent results, assumptions, and issues in using simulations of state to model metabolism. What is Life?, and used the concept of entropy to describe how order, in the form of high energy compounds in the environment, drives organization within organisms. Organisms dissipate that energy into lower forms. The concept of life as a non-equilibrium process has resonated with others as well, including Prigogine who described living organisms as dissipative structures that self-organize in response to large non-equilibrium driving forces  as A. The value A in Eq. jn. The relationship between A and the probability density of that microscopic state is,q = − ⟨E⟩B/BTk, we have the relationshipFor brevity, we will write J) is shorthand for Pr(n1 = n1(J), …, mn(J)|Ntotal, θ1, …, θm) and TN. Because the Gibbs entropy is an average over microstates, it is the entropy related to macroscopic observations , or if ensemble averages are used, the time-dependent ΔGS(t).For a set of coupled reactions such as,d ratio,−ΔSg,JK=lalently,PrJPrK=e−alently,PrJPrK=e−librium,PrJPrK=e−alently,PrJPrK=e−J to the new state K with some transition probability. We know that the system will eventually return to the steady state J, we just do not know specifically how. For the most part, a fluctuation away from the steady state will be along the direction of the non-equilibrium driving force. When the system returns to the steady state, an amount of energy will have been dissipated from the system. Note that if the system were to return to the steady state along the same path, no energy would have been dissipated; that is, the average likelihood of returning along the same path is not 1 as in the case for equilibrium , which is a measure of how far the system is away from detailed balance,For example, at a non-equilibrium steady state the average fluctuations of a system can still be characterized at times without knowing the actual probabilities of each state. Consider the fluctuation away from a steady state rium Eq. . Thus, fDq represents the dissipated energy due to the lack of detailed balance, then the odds of regaining that energy through a reversal of the trajectory are exponentially small. One could even think of the RHS of Eq. If i in the +direction as,Ji,netΔSg,i, where Ji,net = i+J − iJ−. Taking the ratio of the entropy production due to the forward and the reverse reaction, the odds of entropy being produced at reaction i are,When the time-dependent flux of material through reactions can be determined, the entropy production rate can be defined in several related ways . This relationship was originally proposed in discussions of reversible systems and discussed in the context of deterministic kinetics  are concordant. The relationship can be used to gain insight if used carefully. For instance, Noor et al. SgΓZ reactions, the state entropy increases as the system stabilizes, and reaches a maximum at equilibrium since equilibrium requires that each respective reaction is equally likely. In a non-equilibrium system, the neighborhood Γ is a reaction path and Eq. gS[Γ(𝕊)] over many trajectories such that GS[Γ(𝕊)] = ⟨gS[Γ(𝕊)]⟩. If the entropy change from equilibrium is In a system moving toward equilibrium through a trajectory of While its likely that no individual organism is at the apex of thermodynamic optimality, it is also likely, as discussed in the section “Beyond atomistic simulations, the application of statistical thermodynamics and fluctuation theory to biological systems is truly a frontier. To date, applications are mostly in the physics literature and include (but are not limited to) the study of molecular motors, mostly ATP synthase  cycles of bacteria. These cycles are central to the metabolism of most organisms and may be as close to a universal pathway as there is , the free energy changes are highly unfavorable. The TCA cycle of Chlorobium and other green sulfur bacteria, in fact, runs in the opposite direction , and these organisms use the cycle to fix CO2 and produce acetyl-CoA. Not only does a thermodynamic model allow us to understand each organism in its environment, but clearly designing an optimal pathway for metabolic engineering using statistical thermodynamics would be very useful.As an example of the potential use of statistical thermodynamics for both engineering and understand organisms in the context of their natural habitats, we compared three different versions of the TCA cycle used in three very different ecological niches: a typical heterotrophic TCA cycle from E. coli in Figures E. coli  must be paid for by the non-equilibrium driving forces.Enzymes catalyzing reactions far from equilibrium will need to increase the least since material flow is unidirectional. This is clearly the case for the enzyme-catalyzed reactions for transformation of oxaloacetate to citrate and 2-oxoglutarate to succinate: as the total metabolite pool increases, the concentrations of the reactants oxaloacetate and 2-oxoglutarate do not change markedly.If enzymes near equilibrium are expressed at a level just sufficient to catalyze its current load, then increasing the total pool of metabolites may require increased expression of these enzymes. However, these reactions are not likely to remain at equilibrium. This is apparent in Figure KM), then enzyme levels need to increase in order to maintain a steady state. This is the situation described by Flamholz et al.  on metabolomics measurements and carry out large-scale simulations of metabolism that would be identical to kinetic simulations based on rate constants. Experimentally measuring metabolite concentrations is an emerging area of great interest. Key to making the measurements useful for interpretation and modeling is reducing the uncertainty that the measured values reflect An alternative statistical thermodynamic approach is to model the process as thermodynamically optimal in which the rates are proportional to the thermodynamic driving force. In a thermodynamically optimal process, the maximum amount of energy is extracted from the environment with a minimal amount of dissipation of heat (Sivak and Crooks, en masse (Jankowski et al., As mentioned above, a challenge to using simulations based on statistical thermodynamics is determining accurate standard free energies of reaction or formation of each metabolite. Standard free energies based on group contribution methods are available The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


When applied to the genome-scale metabolic model of Mycoplasma genitalium, GlobalFit decreases unexplained gene knockout phenotypes by 79%, increasing accuracy from 87.3% (according to the current state-of-the-art) to 97.3%. While currently available computers do not allow a global optimization of the much larger metabolic network of E. coli, the main strengths of GlobalFit are already played out when considering only one growth and one non-growth case simultaneously. Application of a corresponding strategy halves the number of unexplained cases for the already highly curated E. coli model, increasing accuracy from 90.8% to 95.4%.Constraint-based metabolic modeling methods such as Flux Balance Analysis (FBA) are routinely used to predict the effects of genetic changes and to design strains with desired metabolic properties. The major bottleneck in modeling genome-scale metabolic systems is the establishment and manual curation of reliable stoichiometric models. Initial reconstructions are typically refined through comparisons to experimental growth data from gene knockouts or nutrient environments. Existing methods iteratively correct one erroneous model prediction at a time, resulting in accumulating network changes that are often not globally optimal. We present G lobalFit, a novel method that can consider all experiments and all possible changes simultaneously to identify model modifications that are globally optimal . This becomes computationally very hard when considering large metabolic models; however, a reduced application of GlobalFit that only looks at small subsets of experiments simultaneously works very well in practice. Allowing only changes that are conservative , GlobalFit halves the number of wrong growth/non-growth predictions for the state-of-the-art metabolic models of E. coli and Mycoplasma genitalium, increasing prediction accuracy to 95.4% and 93.0%, respectively. By additionally allowing less conservative changes, we are able to improve accuracy further to 97.3% for the M. genitalium model.Mathematical models that aim to describe the complete metabolism of a cell help us understand cellular metabolic capabilities and evolution, and aid the biotechnological design of microbial strains with desired properties. Draft models are frequently improved through adjustments that increase the agreement of growth/non-growth predictions with observations from gene knockout experiments. Automated methods for this task typically correct one erroneous prediction after the other. We present G Metabolism is the best understood large cellular system. Genome-scale metabolic models that largely rely on constraints for mass balance  are routinely applied to predict a wide range of metabolic phenomena . The mosin vivo, while the model is unable to produce biomass in silico in the same metabolic environment . Gap filling methods have been introduced to resolve individual FNp through a minimal number of network changes, making irreversible reactions reversible or adding reactions from a database [Network models are reconstructed by supplementing genomic annotation with information from biochemical characterizations and the organism-specific literature . The resdatabase –11.A second type of inconsistencies is the erroneous prediction of growth where the experiment shows no growth . Such cases can be rectified by deleting reactions, making reversible reactions unidirectional, or adding metabolites to the biomass . GrowMatch , the curMycoplasma genitalium below). Furthermore, experimental errors that happen to be consistent with the initial model can severely bias the results. Moreover, previous methods only alter the biomass equation independently of other network modifications [All currently available methods for network refinement based on growth data are greedy algorithms, solving one inconsistency between model and experiment at a time –15. Whilications , 16 and lobalFit, a novel bi-level optimization method capable of comparing flux-balance analysis (FBA) [lobalFit does not change gene-protein-reaction associations (GPRs), and thus isoenzymes should be identified and included in the model as a preprocessing step.We present Gis (FBA)  model prin silico growth for conditions with experimentally demonstrated growth, the biomass production for these conditions must be greater than a predefined threshold. For non-growth phenotypes, the inner optimization problem maximizes the biomass production to check whether it stays below a non-growth threshold. The outer optimization problem jointly minimizes the number of model changes and the number of experiments that are incorrectly predicted by the final model.The algorithm is first formulated as a bi-level linear problem, where each condition is represented by separate metabolites and fluxes .The penalties for individual network changes can be set independently. This allows, for example, to prefer reversibility changes over reaction additions, to preferentially remove reactions not associated with a gene, or to preferentially include additional reactions from metabolic network reconstructions of close relatives . FNp may first be solved alone. However, if a suggested solution for a FNp or a FPp converts other previously correct predictions to false predictions (TPp to FNp or TNp to FPp), the originally considered case should be solved again, this time contrasting it with the complete set of these conflicting cases. This last step must be repeated until no more additional false predictions occur (or until no solution is found).However, when searching for model changes that rectify a FPp, trivial but unhelpful solutions such as the deletion of essential reactions are already avoided by simultaneously requiring growth in one or more specified true positive cases. When searching for model changes that rectify a FNp, overly generous changes  are avoided by simultaneously requiring non-growth in one or more specified true negative cases. Thus, while a globally optimal solution is only guaranteed when simultaneously considering all experimental growth data, a good approximation may be found by solving subsets of inconsistencies. We explore this “subset strategy” below in our application to the n possible model changes for a single growth/non-growth case solves a problem with n binary variables. In comparison, the number of binary variables in a GlobalFit run that considers n possible model changes and contrasts m growth and non-growth cases is n+m. The number of binary variables can be further reduced by a set of preprocessing steps . Thus, a MILP strategy that considers ng steps .lobalFit can also provide a specified number of alternative optimal or sub-optimal solutions (using the integer cut method). Thus, users can choose the solution(s) that best agree with available evidence, or design additional experiments that distinguish between competing network modifications. In cases where all suggested alternatives appear excessive or unrealistic, users may also consider modifying individual GPR rules. The runtime for n alternative solutions is approximately n times the runtime for a single optimum. In the test cases reported below, we only examined a small range of alternative solutions and did not consider manual modifications.When reconciling a metabolic network with experimental data, the most parsimonious network modifications are not always those that best describe the true metabolic system. GlobalFit to the genome-scale metabolic network of Mycoplasma genitalium [lobalFit, we thus allowed the uptake of all nutrients for which transport reactions are included in the model. All other FBA parameters were set to the values used in [ZN2t4,INSK,LYSt3) to reversible reactions. With these modifications, the original model [in silico where a lethal phenotype was observed in vivo ; (ii) conversion of irreversible to reversible reactions for reactions that are at least classified as reversible with uncertainty in the E. coli model [In our first analysis, we used a very restrictive, conservative set of potential network changes: (i) addition of reactions from other network reconstructions that are catalyzed by enzymes with significant sequence similarity to the li model ; (iii) rli model , 20, 24.14 out of 24 FPp could be transformed to true negatives  that is reliably associated with a gene.Four of the remaining six reactions indicated for removal  were not associated with a gene; i.e., they had an empty gene-protein-reaction association (GPR). A fifth reaction, G3PD4, is associated with the gene MG260; however, this association is likely erroneous. G3PD4 is catalyzed by a glycerol-3-phosphate dehydrogenase (1.1.5.3), whereas MG260 is a lipoprotein without significant sequence similarity to any proteins with known catalytic functions. Thus, GlobalFit finds no network modification that predicts the lethality of MG124 knockouts. The gene MG124 encodes a thioreductase (THDPO) that is presumably used by Mycoplasma to protect itself from the consequences of self-generated oxidative challenges [Gallenges . Its metM. tuberculosis model [M. genitalium genome. Notably, PGAMT is an essential reaction in the original network reconstruction [The remaining three solved FPp cases were corrected by simultaneously adding one reaction (ACGAMPM) and removing another (PGAMT). Without PGAMT, ACGAMPM is the only reaction producing N-Acetyl-D-glucosamine 1-phosphate, a precursor of the biomass metabolites teichuronic acid and minor teichoic acid . ACGAMPMis model , one of truction , and woutruction . An altelobalFit to the M. genitalium model followed [in silico, they should be confirmed through further experiments. Previous studies [Our second application of Gfollowed  by allow studies , 12, 16  studies .The two remaining unexplained FPp correspond to knocked-out genes associated with the same reaction as another gene whose knockout was a true positive prediction; thus, these predictions cannot be rectified without changing the gene-reaction associations.lobalFit calculations for simultaneously solving all 11 feasible FPp cases  to the glycerol kinase of Trypanosoma brucei, which is known to indeed catalyze the reverse reaction [For FNp, the results of the conservative and non-conservative application of GNp cases , which t was set to 3.15 mmol gDW-1h-1. Gene essentiality was then calculated by FBA, considering any flux larger than 5% of the optimal biomass core reaction as growth. For the published iJO1366 model, we obtained the same accuracies as reported originally [To test the applicability of G iJO1366 . Again,  iJO1366 , 31 as u iJO1366 . The maxiginally : a combilobalFit to the iJO1366 model, we only allowed conservative network modifications . However, as the growth medium used in the E. coli experiments was chemically defined, we did not allow the removal of exchange reactions. We constructed a database of potential new reactions as for M. genitalium . Such highly conserved homologs are likely to be functionally very similar to the knocked-out gene [One simple explanation for FNp is the existence of un-annotated isoenzymes. To detect such cases, we identified all FNp where the knocked-out gene has a significant bi-directional blast hit with another gene in the genome  of a metabolite become unessential. Gg 19 FNp . For exalobalFit further indicated the removal of calcium and copper from the biomass, which was also suggested by the BioMog algorithm based on E. coli growth data on different media [E. coli chemotaxis [E. coli in its natural habitat, but to remove calcium from the biomass reaction when modeling E. coli in cell culture.Gnt media . Calciumemotaxis . HoweverFive FNp could be resolved by reversing existing reactions in the metabolic network . InteresE. coli metabolic network iJO1366 involved extensive literature and database searches to ensure a maximal inclusion of metabolic reactions. Second, we used the BiGG database as the source for potential additional reactions. Many networks in this database are based on the E. coli network reconstruction; this makes it unlikely that they provide new features relevant for E. coli. Third, the cut-off value for the similarity of enzymes to the E. coli genome used in the construction of the additional reaction database might have been too strict (10−13).GlobalFit could not improve the accuracy of knockout predictions by adding new reactions to the metabolic network. This may have several reasons. First, the reconstruction of the E. coli model [lobalFit through biomass additions catalyzed blocked reactions. When allowing the addition of biomass components not included in other BiGG biomass reactions or suggested by BioMog, GlobalFit was able to resolve 4 additional FPp ; however, as these suggested modifications did not meet our strict criteria, we did not consider them further.22 FPp could be resolved by adding metabolites as substrate or product to the biomass reaction . 17 of tli model . GrowMatli model . However25 FPp could be resolved by removing a total of 18 reactions from the metabolic network . At the E. coli was grown on glycerol, six of these genes were essential, while on glucose only three genes were found to be essential. Thus, overall accuracy is optimized if ATPS4rpp is essential for growth on glycerol, but non-essential for growth on glucose. We used GlobalFit to simultaneously solve a non-growth case of the ATPS4rpp knockout on glycerol, a wild-type growth case on glycerol, and a growth case of the ATPS4rpp knockout on glucose. GlobalFit found two alternative solutions that make the Phosphoglycerate kinase reaction irreversible (removing the backward direction of PGK) and also make the Fructose 6-phosphate aldolase reaction (F6PAback) or the Glucose 6-phosphate dehydrogenase (G6PDH2rfor) irreversible. By applying either of these two modifications, the two TPp of ATP synthase subunits for glycerol were converted to FNp.One example is the ATP synthase reaction ATPS4rpp, which is catalyzed by an enzyme complex encoded by eight genes. When For two of the 25 solved FPp (b0242 and b2913), alternative solutions are provided by adding metabolites to the biomass reaction . For exalobalFit does not allow changes to GPRs or inclusion of regulatory rules, and, consequently, could not find any solution for these genes.On glucose, 19 of the remaining 45 FPp corresponded to isoenzymes; on glycerol, 21 of the 33 remaining FPp corresponded to isoenzymes. FBA models do not account for gene regulation, and thus the corresponding reactions are assumed to remain active even when knocking out one of the isoenzymes. Thus, these FPp are due either to erroneous GPRs or to the isoenzymes not being expressed. GE. coli metabolism is provided as The resulting modified model of lobalFit does not reconcile the network model with inconsistent experiments iteratively, but finds a globally minimal set of network changes that resolves all inconsistencies simultaneously . To make GlobalFit applicable to large metabolic network reconstructions, we also explored a subset strategy, where individual false predictions are solved simultaneously with small subsets of growth/non-growth cases.In this work, we describe and implement a novel algorithm to automatically modify metabolic network models based on growth/non-growth data. The algorithm can utilize data from different growth environments and/or different gene knockouts. In contrast to previous approaches, the “global” mode of GM. genitalium [E. coli [lobalFit improved the accuracy of growth/non-growth predictions for M. genitalium from 87.3% to 93.6% (MCC from 0.56 to 0.68) and for E. coli from 90.8% to 95.4% (MCC from 0.67 to 0.84). If we allow a much wider range of possible network modifications—which is routinely done in alternative approaches [lobalFit can enumerate alternative optimal or sub-optimal solutions, such that expert knowledge or additional experiments can help select the biologically most realistic modifications.We demonstrate the utility of these approaches through applications to the previously published network models of nitalium   and E. [E. coli  . Alproaches , 21–evenE. coli would resolve three FPp on glycerol, while converting four TPp to FNp on glucose. Thus, the accuracy achievable for one growth medium could be further improved by sacrificing the accuracy for the other medium, albeit at a likely loss of biological correctness. This observation emphasizes the utility of combining gene knockout data across different nutritional environments to avoid problems of overfitting.For some inconsistencies, we found solutions that improved accuracy on one medium while decreasing accuracy on the other. For example, adding selenium to the biomass reaction of lobalFit may suggest a solution in this case, but this will simultaneously distort one or more true predictions. For example, the FPp for the E. coli gene b3560 (the α-subunit of glycine tRNA synthetase) could be resolved by adding the charged and uncharged glycine tRNA to the biomass reaction as substrate and product, respectively. This modification would at the same time transform the TPp of b3559 (the β-subunit) to a FNp, and would thus not improve accuracy.In other cases, several genes whose products act together in a protein complex had contradictory experimental results: in the same medium, some were found to be essential, while the rest was declared non-essential. Such contradictions may be caused either by experimental errors, by erroneous assignment of genes to reactions (incorrect GPRs), or by a residual function of the enzyme complex even with some of its components missing. GlobalFit, we adopted the in silico growth cutoffs used in the original model publications, i.e., one third of the mean growth rate for M. genitalium [E. coli [in silico growth from non-growth as an additional variable in the optimizations. For example, the knockout of E. coli ATPS4rpp reduced the biomass yield in glycerol below 10% of the wild-type yield. Such a substantial reduction in growth rate may explain why 6 out of 8 knockouts for the genes involved in the corresponding enzyme complex were labeled as essential in the experiment; however, following [a priori which in silico cutoff corresponds best to a given set of experimental data. Thus identifying the cutoff value that minimizes the necessary model changes seems most appropriate.In the applications of Gnitalium  and 5% o[E. coli . A more ollowing  in consilobalFit to the improvement of existing metabolic network reconstructions and showed that it can substantially reduce the number of false growth predictions even when restricted to conservative network changes. It is conceivable that GlobalFit can also be employed for other tasks related to metabolic model refinement. One possible such application is the initial reconstruction of a metabolic network model starting from a computer-generated template that is based on genome annotation . Globalnockouts  with genlobalFit compares flux-balance analysis (FBA) [Gis (FBA)  model prsubject to:with:subject to: Eqs and to the definitions following below.We thus solve the following bi-level problem:g. The sets used in this system of equations are listed in i.e., no concentration changes to internal metabolites) in all conditions g ∈ Gh ∈ NWhat is the purpose of each of the lines in the above system of equations? The network must be in a steady state , then the lower limit for reaction y in all conditions g :The penalties for each type of network change, and even for each individual change, can be set independently. This allows, for example, to prefer reversibility changes over reaction additions, or to preferentially include new reactions with stronger genomic evidence, or reactions from metabolic network reconstructions of close relatives. Users should choose appropriate penalties based on the details of the network reconstruction and the proposed changes. As a starting point, we include a list of suggested penalty values in M. genitalium network, we penalize these condition exclusions with very high values To guarantee a feasible solution, even if inconsistent growth cases are used, we implemented additional binary variables that allow the exclusion of individual growth δgG and non-lobalFit can put out not just one best solution, but, e.g., the five best solutions that can then be reviewed to identify the changes most compatible with existing knowledge. To speed up the calculations, network changes can also be limited to a maximal number.Metabolic network reconciliation with large-scale experimental data usually incorporates a manual curation stage, where experts for the physiology and biochemistry of the organism under study review network changes suggested by automated methods. To support this process, Gh, the inner problem is simply a linear program; thus, the assumptions in [No efficient software tools for general bi-level optimization problems are available. Solving the inner problem for each possible combination of network changes would be computationally too slow. We adapt the “Reduction Ansatz” of Section 4.3.4 in  to elimitions in  are trivlobalFit also allows using the GUROBI solver. Academic users can obtain both CPLEX and GUROBI free of charge.Because of the use of binary variables, algorithms to solve this type of optimization problem are termed mixed integer linear programming (MILP). MILP is NP hard ; while ni.e., a maximal set of conditions that can all be correctly predicted with the same modified metabolic network (regardless of the type and number of modifications). To identify such feasible condition sets, GlobalFit provides a simple mode, which only minimizes the number of erroneous predictions of growth regardless of the number of network changes. To speed up the calculation of a feasible condition set, it is possible to first solve individual wrong predictions against a “control” condition, thereby identifying conditions that cannot be reconciled with the network with the allowed modifications. We applied this strategy for the pre-processing of the M. genitalium data  set, data see .Furthermore, the number of binary variables can be reduced by a set of additional preprocessing steps. First, binary variables for changes to the network not allowed (such as reversibility changes to reactions strictly considered irreversible) should be constrained to zero. Second, we can consider a “supermodel” that encompasses the input model with all allowed reactions converted to reversible reactions and all reactions from the database of potential additional reactions. We can then reduce the number of binary variables further by (i) excluding all reactions that are blocked in this supermodel, (ii) constraining to zero the binary variables for the removal of reactions that are essential in this supermodel.lobalFit can optionally calculate a user-defined number n of alternative optimal or suboptimal solutions. The search for alternative solutions is executed using the integer cuts method. Thus, the complexity for each additional alternative solution is only increased through a single linear constraint. Consequently, the runtime for n alternative optimal or suboptimal solutions is approximately n times the runtime for a single optimum.GlobalFit, integrated with the sybil toolbox for constraint-based analyses [lobalFit/http://cran.r-project.org/web/packages/G). The optimized models for E. coli and M. genitalium are provided as SBML files that can be read, e.g., by sybil [We provide an implementation of Ganalyses , which ranalyses . The souby sybil  and the by sybil .S1 TableAs a starting point, this table list some suggested penalty values.(PDF)Click here for additional data file.S1 DatabaseM. genitalium genome . We removed globally blocked reactions, i.e., those reactions of the database that were not able to carry any flux in a supernetwork containing all reactions. Reversible reactions were represented as two independent irreversible reactions, corresponding to forward and backward directions. The database is provided as a tab-delimited text file with three columns: reaction ID; stoichiometric equation; gene-protein-reaction association (GPR).We then restricted this dataset to reactions that are catalyzed by enzymes with significant sequence similarity to the (TSV)Click here for additional data file.S2 DatabaseE. coli genome . We removed globally blocked reactions, i.e., those reactions of the database that were not able to carry any flux in a supernetwork containing all reactions. Reversible reactions were represented as two independent irreversible reactions, corresponding to forward and backward directions. The database is provided as a tab-delimited text file with three columns: reaction ID; stoichiometric equation; gene-protein-reaction association (GPR).We then restricted this dataset to reactions that are catalyzed by enzymes with significant sequence similarity to the (TSV)Click here for additional data file.S1 ModelThe two models differ only by their biomass reactions: “Biomass” for the non-conservative model; “Biomass_conservative” for the conservative model.(XML)Click here for additional data file.S2 Model(XML)Click here for additional data file.


The key role of sulfur metabolites in response to biotic and abiotic stress in plants, as well as their importance in diet and health has led to a significant interest and effort in trying to understand and manipulate the production of relevant compounds. Metabolic engineering utilizes a set of theoretical tools to help rationally design modifications that enhance the production of a desired metabolite. Such approaches have proven their value in bacterial systems, however, the paucity of success stories to date in plants, suggests that challenges remain. Here, we review the most commonly used methods for understanding metabolic flux, focusing on the sulfur assimilatory pathway. We highlight known issues with both experimental and theoretical approaches, as well as presenting recent methods for integrating different modeling strategies, and progress toward an understanding of flux at the whole plant level. Sulfur is an essential nutrient; available in the soil as sulfate, plants are able to reduce inorganic sulfur, for use in a large number of primary and secondary metabolites.Brassicaceae especially have large sulfur requirements, and the quality and yield of oilseed rape is known to be affected by low sulfur availability , as well as recent approaches to integrate different modeling strategies, and progress toward an understanding of flux at the whole plant level.O-acetylserine (OAS) to form cysteine, the precursor of all organic compounds containing reduced sulfur. Cysteine in the plastid may be converted into glutathione (GSH) via γ-glutamyl-cysteine, or reacts with phosphohomoserine, to form cystathionine, which can then be converted to methionine via homocysteine. Excess sulfite can be oxidized in the peroxisome back to sulfate by sulfite oxidase .The sulfur assimilatory pathway has been recently reviewed . In summ35S, from 35SO4, into various metabolite pools . These coefficients can be interpreted as a measure of the ‘rate limitingness’ of the enzyme to flux though the pathway, and potentially used to identify targets for overexpression to increase flux to metabolites of interest.Metabolic control analysis defines flux control coefficients (FCCs) as the sensitivity of flux through the pathway to an infinitesimal change in a given enzyme activity from the reference state . Ideally several magnitudes of perturbation would be made, and used to estimate the control coefficient in unperturbed conditions, however, this has not been done to date within the sulfur community , and experimental measurements . Aside fA number of approaches have been developed based on sensitivity of model predictions to parameter values to analyze parameter identifiability, and calculate confidence intervals for parameters and predictions . HoweverGiven the problem of estimating a large number of unknown parameters with limited data, most models tend to use lumped, empirical rate laws, which aim to capture the salient kinetic features, whilst minimizing the number of parameters required, rather than complicated, mechanistic laws . HoweverA minimal model of a subsystem should include everything that affects the internal variables of the model , howeverThe extent to which models can be simplified and still remain useful is unclear, as an overly reduced model system can result in inaccurate predictions. For example ignoring phosphohomoserine production meant that It is possible that a much larger metabolic network has to be considered when modeling sulfur assimilation than just the pathway itself, for example, GSH production at night is limited by availability of glycine, as its major source is photorespiration . IntegraUnfortunately, as model size increases, the problems of unknown parameters, and rate laws become extremely difficult to overcome. To generate a large kinetic model, simplifying assumptions about parameter values , and ratOther kinetic modeling frameworks acknowledge the inherently greater unknowns of a large system, and use the available data to define a cadre of related models, or sample feasible parameter space, reflecting either structural, or parameter and rate law uncertainty in their predictions . Some ofIn contrast, constraint-based modeling provides a number of hugely scalable, largely parameter free methods for understanding flux through large metabolic networks . Here weFlux balance analysis  is a powFlux balance analysis can be directly used in a number of areas, including understanding metabolic efficiency , interprIn spite of a profligacy of analytical methods and well-documented metabolic engineering case studies in microbes, application of FBA based methods to plants has been limited to date. This is likely due to challenges in genome scale model construction, and the assumptions of the FBA method, as will be discussed below.Despite a large number of available tools , construAlthough primary metabolism is well understood, the generally poorer understanding of the huge plant secondary metabolism , is reflAll published genome scale models of plant metabolism include compartmentalization to some extent, but the problem of biological unknowns again raises concern over the quality of some of the assignations. Wide variation between models in which compartments reactions occur  suggestsAdditionally, transport between compartments is often poorly understood; even in well-studied parts of metabolism, it is not always clear which metabolites can move between compartments, and the energetic costs of transport reactions are rarely known. This is shown in the sulfur assimilatory pathway by the only recent identification of PAPS transport between the plastid and cytosol .Objective functions. In addition to defining network structure, some biological knowledge of the system is required to choose an appropriate objective function. One commonly used objective is maximization of biomass production  approach makes a pseudo steady state assumption, that intracellular metabolism equilibrates several orders of magnitude faster than extracellular changes. Although it is likely that the pseudo steady state assumption might not be justified in all aspects of plant metabolism, a recent extension to the method potentially relaxes this requirement . GrafahrFlux is perhaps the most important metric to determine for a practical, applied understanding of plant biology. Through the study of sulfur metabolism we have seen that to understand flux requires a fusion of experimental and modeling approaches, but that to date, no integration of the two satisfactorily solves the problem to provide an accurate predictive framework. Each approach considered continues to advance independently both theoretically and experimentally, but currently perhaps most promising is the joining of kinetic and constraint-based approaches, which although an immature field, has the potential to finally deliver a useful facet of the famous ‘virtual plant.’The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The XTMS approach consists on determining the set of biochemical transformations that can potentially be processed in vivo as modeled by molecular signatures, a specific coding system for derivation of reaction rules for metabolic reactions and enumeration of all the corresponding substrates and products. Most promising routes are described in terms of metabolite exchange, maximum allowable pathway yield, toxicity and enzyme efficiency. By answering such critical design points, XTMS not only paves the road toward the rationalization of metabolic engineering, but also opens new processing possibilities for non-natural metabolites and novel enzymatic transformations.As metabolic engineering and synthetic biology progress toward reaching the goal of a more sustainable use of biological resources, the need of increasing the number of value-added chemicals that can be produced in industrial organisms becomes more imperative. Exploring, however, the vast possibility of pathways amenable to engineering through heterologous genes expression in a chassis organism is complex and unattainable manually. Here, we present XTMS, a web-based pathway analysis platform available at  Escherichia coli and Saccharomyces cerevisiae, through heterologous pathways requires of a careful selection and optimization of each step of the biosynthesis pathway (d) can be tuned to control the combinatorial explosion of possible compounds.Synthesis of value-added molecules in chassis organisms, such as  pathway . Success pathway , taxadie pathway , farneso pathway  or narin pathway . To rati pathway . Retrosy pathway  by the a pathway . The advThe degree of plasticity in metabolic networks that is uncovered by the signatures representation reveals an intrinsic feature of organisms linked to their adaptability, i.e. enzyme promiscuity . PromiscE. coli based on an Extended Metabolic Space, i.e. on the set of potential in vivo biochemical transformations as modeled by molecular signatures  Reactions and metabolites: MetaCyc version 16.0; (ii) Metabolism of chassis organism: EcoCyc version 16.1; (iii) Gibbs energies: MetaCyc version 17.5; (iv) 23609334  from Bio23609334 ; (v) Enzd. Reaction signatures  are computed as the net difference between the signatures of the products and of the substrates. The advantage of the signatures method is that the reaction rules describe the changes in the environments of the atoms belonging to the catalytic center of the reactions, whereas any group of atoms that remains essentially unchanged between substrates and products will not appear in the signatures. As the diameter d increases, the signature coding system becomes more specific and the size of the environment can be tuned to avoid a combinatorial explosion of possible compounds. The process of computing the Extended Metabolic Space for a given diameter d is computationally expensive and consists on the application of the reaction rules to all combinations of known metabolites so that the original set of metabolic reactions is extended with those additional putative promiscuous reactions that verify the rules at the given diameter d. The XTMS server implements the Extended Metabolic Spaces for diameters d = 14, 12 and 10, providing in that way a good trade-off solution between the information already available in metabolic databases and the ability of generalizing such information by including predicted promiscuous reactions that can be expected to be processed by those enzymes present in the network. The resulting databases are downloadable from the server.The basic steps that allow determining and ranking in a given metabolic space those heterologous enzymatic steps producing a target compound from a chassis organisms have been previously defined by the authors . These bE. coli. To that end, XTMS accepts as an input query either a molecular structure or a compound name. In the case of molecular structures, they can be both entered in MDL Molfile format or directly drawn through a chemical draw interface . Once the query is submitted, the user is presented with a list of compounds that corresponds to the matches in decreasing order of the query compound with chemicals that can be connected to the chassis in the Extended Metabolic Space. Matches for molecular structures are based on the Tanimoto similarity (E. coli). The former are naturally produced by E. coli, while for the latter, the number of available pathways at diameters d = 14, 12 and 10 is given and the user can select to retrieve the list of pathways at the desired diameter.The workflow of the XTMS server is shown in Figure milarity  search imilarity  between d = 14, 12 and 10. The workflow of RetroPath that finds and enumerates the pathways consists of (i) Computing the scope: the algorithm performs a two-step search, first a forward search to determine all compounds that are reachable, and then a backward step in order to add to the scope starting from the target compound any reaction that can be involved in a target-producing pathway imported into the chassis; (ii) Construction of the stoichiometry matrix corresponding to the target scope starting from precursors that are endogenous to the chassis; (iii) Apply elementary flux modes . Weights are chosen in order to normalize the final score; (ii) Gene score: The goal of this score is to provide an estimate of the performance of the pathway based on the combination of individual scores for the enzymes that putatively can catalyze each of the reaction steps r. The predictions are based on enzyme promiscuity through the tensor product technique  Number of putative steps: Number of enzymatic steps in the pathway that correspond to putative reactions predicted in the Extended Metabolic Space; (v) Estimated toxicity: Average toxicity log(IC50) for intermediate metabolites in the pathway, evaluated by using the EcoliTox server  Estimated free Gibbs energy: Standard ΔG Gibbs-free energy for reactions in kcal/mol were taken from MetaCyc database , according to their estimated Gibbs-free energy.Once a target compound and a diameter have been selected, XTMS will rank the enumerated pathways based on several criteria  and willechnique , 31. Thex server ; (vi) Madatabase , where vdatabase ; (viii) For the selected pathway, XTMS provides an interactive graphical depiction of the enzymatic steps, where the user can access reaction and compound information. In addition, four tables are given that correspond to (i) Reactions: list of reactions with associated Gibbs-free energy and EC (Enzyme Commission) number; (ii) Metabolite exchange: list of metabolites involved in the pathway with their net stoichiometric balance and predicted toxicity; (iii) Pathway yield: maximum allowable yield from precursors and main bottlenecks, i.e. precursors of the pathway that are also consumed for biomass production; (iv) Top constructs: list of 10 top constructs that can be obtained by combinations of predicted genes, based on the list of genes and individual scores for each pathway step. Each construct is downloadable as a file in SBML format  containiFor each individual reaction in the Extended Metabolic Space, XTMS provides information about products and substrates and top ranked genes that are predicted to correspond to enzymes catalyzing the reaction, with associated scores.The server runs under apache on a Linux machine running Ubuntu. The server was written in python by using the Django framework in combination with SQLite 3. It uses MetaCyc version 16.0  and enzyd = 12 was generated starting from a set of reaction rules and known metabolites from the MetaCyc database that comprised 6093 metabolites connected through 6078 reactions. After running the algorithm that performs the metabolic extension for 50 iterations, it converged to a total of 27 743 reactions, i.e. almost five times larger than the size of the initial network.A better understanding of the metabolic space and particularly the scope of metabolites that can be synthesized and transformed by enzymes is critical for future development in metabolic engineering and synthetic biology. To that end, the XTMS web server explores the regions in the extended metabolic space that can be reached through metabolic engineering. The computational procedure available at the web server provides a good balance between combinatorial complexity and predictive power from promiscuous reactions. For instance, the extended metabolic space that XTMS provides for diameter E. coli chassis. Concerning pathway enumeration, the number of pathways can substantially change from one diameter of the Extended Metabolic Space to another one. As the diameter d decreases, more pathways can be considered through new reactions. A case that illustrates this situation is the production of glycocholate (glycocholic acid). At diameter d = 14, only one pathway was found, while at decreasing diameters to d = 12 and d = 10, one additional pathway was found in each case. Therefore, the user should decide the degree of specificity in the reactions contained in the pathways, especially when dealing with cases where a large number of pathways can be formed . Such large numbers require of appropriate techniques to rank pathways, so that optimal solutions can be selected for implementation. To that end, XTMS implements a pathway objective function as described in Materials and Methods. The selection of optimal production circuits is facilitated in that way.A valuable result from XTMS is the determination of the extent of the chemical space that can be reached through biochemical transformations from the chassis organism. Based on the application of the retrosynthesis principles for scope determination  and pathd = 14. Raspberry ketone, which is found in some fruits, vegetables, berries and tree barks, is one of the most expensive flavor components used in the food industry (up to $20 000/kg may be paid) (Rubus idaeus) by condensation of coumaroyl-CoA with malonyl-CoA (BAS enzyme) to form p-hydroxybenzalacetone, which is further reduced into raspberry ketone (BAR enzyme). For this example, the XTMS identified the two-step natural route of raspberry ketone production that starts from coumaroyl-coA, a compound that is found in many plants tissues as intermediate of the lignin biosynthetic pathway. Coumaroyl-coA, however, is not readily available in E. coli, and thus XTMS proposed the insertion of 4-coumarate ligase (4CL enzyme) to produce it from 4-coumarate. In order to obtain this metabolite, three alternative heterologous pathways are proposed: (i) cinnamate 4-hydroxylase (EC 1.14.13.11); (ii) tyrosine ammonia lyase (EC 4.3.1.5); (iii) the two-step conversion hydroxyphenylpyruvate reductase (EC 1.1.1.237) followed by 3-(4-hydroxyphenyl) lactate hydrolase  XTMS was able to identify routes going from endogenous metabolites in the chassis to the target compounds, showing a better coverage of reactions databases and a good flexibility since the specific precursors do not need to be specified in advance; (ii) XTMS provided more alternative routes than FMM that can produce the target compound; (iii) XTMS ranks pathways in order to make easier the choice of the best pathways to implement; (iv) XTMS suggests enzyme sequence candidates for all the reactions in the pathway.As an example, Figure be paid) . RaspberThe XTMS web server provides a convenient and powerful way of dealing with the complexity of pathway design for metabolic engineering. Its ability to formalize the design of metabolic circuits could be applied as a future area of development into other applications of metabolic design, such as sensing and regulation. The main feature, thus, of the XTMS server as a metabolic engineering tool is its ability of predicting novel pathways producing a desired compound and the scoring of the pathway viability, including its unique ability to score gene constructs in order to select the most appropriate gene sequence combination. As such, this work provides for the first time an open web server to access the Extended Metabolic Space that introduces a proposed pathway ranking that expands our previous retrosynthesis-based strategy.Genopole [ATIGE]; Pôle de Recherche et d’Enseignement Supérieur (PRES) UniverSud Paris [PROMISENG]; abSYNTH; Agence Nationale de la Recherche (ANR) [Chair of Excellence]. Source of open access funding: ANR.Conflict of interest statement. None declared.


Yarrowia lipolytica is an emerging scope that has not been fully scrutinized, especially for what concerns cultivation conditions of newly generated engineered strains. In this study, by combining flux balance analysis (FBA) and Plackett-Burman design, we screened chemicals for lycopene production from a metabolically engineered strain of Y. lipolytica. Lycopene concentrations of 126 and 242 mg/L were achieved correspondingly from the FBA-independent and the FBA-assisted designed media in fed-batch cultivation mode. Transcriptional studies revealed upregulations of heterologous genes in media designed according to FBA, thus implying the efficiency of model predictions. Our study will potentially support upgraded lycopene and other terpenoids production from existing or prospect bioengineered strains of Y. lipolytica and/or closely related yeast species.Genome-scale metabolic models embody a significant advantage of systems biology since their applications as metabolic flux simulation models enable predictions for the production of industrially-interesting metabolites. The biotechnological production of lycopene from  Yarrowia lipolytica, categorized as GRAS , has been established as a promising fermentation platform. Recently, a complete procedure for engineering Y. lipolytica for lycopene production was developed. Nonetheless, apart from the culture medium and genetic manipulation procedure provided [Y. lipolytica under lipid accumulation conditions [Y. lipolytica. One of the most efficient tools for yielding high titers of carotenoids in engineered strains is based on knock-out or overexpression of specific genes coupled with computational approaches [Carotenoids represent a subcategory of tetraterpenoid pigmented lipid compounds made by plants and various fungi and bacteria . Majorlyprovided , no deepprovided . Furthernditions . Therefoproaches ,16,17. Mproaches ,19.In silico models are valuable for comprehending the biochemical aptitudes of an organism and to foresee the influence of genetic and/or medium perturbations on growth and metabolic fluxes [in silico channel for scrutinizing biological networks in a quantitative approach. Contrary to mechanistic simulations that rely on precise kinetic information, FBA is based on the stoichiometric matrix and a biologically important objective function for identifying optimal reaction flux distributions which are subsequently used to unravel the metabolic capabilities of the studied system [c fluxes ,21. Fluxd system .Y. lipolytica for lipid production and predicted a minimal culture medium for lipids biosynthesis. Afterward, we have updated this model for generating model information for terpenoids production. The updated model is apt to provide reliable information when farnesyl diphosphate (FPP), one of the key precursors of terpenoids backbone in Y. lipolytica, is resolved as the objective metabolite. We hypothesized that such a metabolic network model can support the prediction of culture media for ameliorating the yield of lycopene in biotechnological applications.The present-day FBA is in a wide way to guide designs of experiments. In a previous study, we constructed a genome scale metabolic model of in silico data rarely match with the in vivo metabolic behavior. However, by combining GSMN (FBA) results and statistical methods such as Plackett-Burman design, it is possible to develop an efficient culture medium for the production of valuable objective products.Statistical design methods, especially the Plackett-Burman design, are commonly employed for optimization of fermentation media. The Plackett-Burman designs are usually resolution III, highly efficient two level fractional factorial screening designs for studying N-1 variables using N runs, where N is a multiple of 4. They use the minimum number of runs to quickly identify the factors with a significant effect on the response. However, the choice of the initial set of chemical compounds is laborious since these compounds are generally randomly selected. In addition, though GSMN can predict the nutritional requirements for a given biological system, the accuracy of the amount of a single predicted compound to be added is difficult to determine because Y. lipolytica harboring only crtE, crtB, and crtI genes. As model predictions do not correctly reflect the in vivo situation, we combined modelling data with Plackett-Burman factorial design of experiments for optimizing cultivation media. Furthermore, we compared lycopene production in modeling suggested media with other media designed based on compounds usually reported in the literature through fermentation scale up experiments.Therefore, in this paper, we run FBA by means of COBRA Toolbox 2.0  to foresY. lipolytica has been realized. In order to establish a reference medium for evaluation of the FBA efficiency, we first selected a set of 23 factors in the light of preliminary experiments and literature review for the designing of FBA-independent cultivation medium. Bioprocess improvement experiments were carried out using the engineered strain Po1f-1312E-1269IB. In a first step, we screened a set of chemicals without considering the flux balance analysis (FBA). The matrix of the Plackett-Burman design used in regard of this achievement is reported in 10 or the root square (Sqrt) for data transformation. Subsequently, the Pareto chart was used for analyzing the effects of different components and selecting those to be included in the model. There are two different t limits plotted on the graph based on the Bonferroni or family-wise corrected t and a standard t for individual effects tested. Effects above the Bonferroni Limit were added to the model as certainly significant factors. Effects above the t-value Limit were possibly significant and added if they make sense. Effects below the t-value limit were likely insignificant. Pareto charts of all experimental data are reported in Up to date, no deep investigation for development of fermentation media for lycopene production from 3, MgSO4·7H2O, FeCl3·6H2O, FeSO4·7H2O, yeast extract, fructose, BaCl2·2H2O, NiCl2·6H2O, KH2PO4 and vitamin B1 in this decreasing order. The effect of MnCl2·4H2O, Na2MoO4·2H2O and KI were also positively significant  while the positive effect of K2HPO4 was not significant. The set of factors including CuCl2·2H2O, NiSO4·6H2O, CoCl2·6H2O, H3BO3, ZnCl2, MnSO4·7H20, NaCl, Glucose, (NH4)2SO4 were found as inhibitors of biomass production. The highest biomass concentration obtained was 16.14 g/L with max/min value of 25.22. The final equation in term of coded factors was: Sqrt (Biomass) = 2.24 − 0.38 × A − 0.18 × B − 0.043 × C + 0.29 × D + 0.15 × E − 0.13 × G − 0.095 × H + 0.072 × J + 0.089 × K + 0.28 × L + 0.082 × M + 0.29 × N + 0.37 × O − 0.095 × Q +0.22 × R + 0.25 × S − 0.20 × T + 0.14 × U + 0.020 × V + 0.12 × W + 0.19 × X.The results showed that biomass production was significantly (effects above the Bonferroni Limit) improved by the effects of KNO4·7H2O, FeCl3·6H2O, fructose, KNO3, NiCl2·6H2O, vitamin B1 and KH2PO4 had significant positive effects. The maximum lycopene concentration obtained in flasks was 9.18 mg/L. The final equation in terms of coded factors was: Sqrt (Lycopene)= 0.75 − 0.52 × A − 0.12 × B − 0.2 × C + 0.32 × D + 0.18 × E + 0.059 × G + 0.074 × H + 0.081 × J + 0.052 × K + 0.049 × L + 0.049 × M + 0.3 × N + 0.26 × O − 0.064 × P − 0.071 × Q + 0.28 × R − 0.15 × T + 0.11 × U − 0.17 × V + 0.14 × W + 0.072 × X.For lycopene production, MgSO4·7H2O, NiCl2·6H2O, ZnCl2, MnSO4·7H20, KI, MnCl2·4H2O, FeCl3·6H2O, KNO3, fructose, KH2PO4, vitamin B1, BaCl2·2H2O. Other components had negative effects.The maximal lycopene content obtained from the 24 runs was 1.11 mg/g dry cell weight (DCW). Lycopene content was positively promoted by the effects of MgSO4·7H2O (14.74), NiCl2·6H2O (0.097), H3BO3 (0.099), ZnCl2 (0.15), MnSO4·7H20 (0.4), KI (0.012), MnCl2·4H2O (0.2), Na2MoO4·2H2O (0.1), FeCl3·6H2O (2.87), KNO3 (0.05), Glucose (19.9), fructose (20), yeast extract (0.96), (NH4)2SO4 (0.51), KH2PO4 (10) and vitamin B1 (1.00E-02).The post-optimization experiments conducted to the optimized medium (PM medium) with the following composition (g/L): MgSOY. lipolytica (yli v1.7) that was updated from model iYL619_PCP by correcting topological network and parameters of model (model yli v1.7 is available at http://www.echaoceshi.com/ECLOSB/CN/Default.aspx). In model yli v1.7, R0763 is the only reaction, in which farnesyl diphosphate (FPP) is produced; R2000 is the reaction of growth (biomass formation reaction). Besides, the maximum flux of R2000 (specific growth rate) is 0.0350 h−1 in the glucose minimal media. The model yli v1.7 and iYL619_PCP predicted the equal glucose minimal media. The composition of minimal media in the experiment is as follows: glucose (20 g/L), NH4SO4 (3 g/L), KH2PO4 (2 g/L), in which the maximum specific growth rate is 0.0352 h−1. This indicates that model yli v1.7 predicted the maximum specific growth rate more accurately  was implemented to determine metabolic flux distribution with the genome-scale metabolic network model of curately .l-isoleucine, l-leucine, l-valine, l-asparagine, l-histidine, l-methionine, l-tryptophan, guanine, and l-lysine. The additional exchange reactions were those of O2, thiamine diphosphate, succinate, phosphate, ammonia, 4-aminobutanoate, ergosterol, ethanolamine, d-fructose, urea, d-glucose, hypoxanthine, and (R)-pantothenate. The number of negative exchange reactions and fluxes varied widely with the nature of the carbon source opted for. Putting aside the fact that FBA only gives one possible random solution instead of looking at all the possibilities , it seems that the only “non-zero” values in this table are those corresponding to O2 exchange, d-fructose exchange and d-glucose exchange. All other values from −8.05e-35 to −1.46e-13 are most probably rounding errors of the LP solver, and so all those values could be considered as zeros. However, in order to predict the potential benefit of supplementing the minimal media with of above amino acids or several cofactors in terms of FPP production based on FBA metabolic flux distributions, we relaxed the exchange reactions of model yli v1.7. We first tested effects of 18 amino acids (only 18 amino acid can be utilized in model yli v1.7) on FPP production in a definite specific growth rate (90% of the maximum specific growth rate), and found that all the 18 amino acids facilitated FPP production in different degrees  were found as amino acids that could be supplemented to the minimal medium owing to the guarantee of C/N ratio more than 10. Additionally, we also tested effects of 10 factors on FPP production in a definite specific growth rate  with average biomass production of 14.23 and 16.53 g/L leading to lycopene content of 2.90 and 2.45 mg/g. Approximately equal quantities of lipids (1.55 and 1.53 g/L) were yielded on both media and corresponded to lipid contents of 10.90% and 9.23%.Taking into account the results of the above simulations, we applied the Plackett-Burman design for experimental validation of FBA-predicted factors. The design matrix and responses obtained for biomass, lycopene and lipid production in Plackett-Burman design screening of FBA-predicted factors are correspondingly reported in 2PO4, ethanolamine, guanine, l-methionine, l-tryptophan, l-valine, l-asparagine, d-glucose, and d-fructose had positive effects on biomass production. Fructose was the most significant terms (effect above the Bonferroni Limit) followed by KH2PO4, l-valine, ethanolamine, nicotinate, l-asparagine, guanine, l-tryptophan, and glucose. The list of compounds composed of pyridoxine hydrochloride, succinate, 4-aminobutyric acid, Na2SO3, hypoxanthine, l-histidine, l-isoleucine, and l-leucine had negative effects on biomass production. The negative effects of 4-aminobutyric acid, l-histidine, and l-leucine were not significant. The effects of thiamine pyrophosphate and l-methionine were not significant for biomass production. The final equation of biomass production obtained after ANOVA analysis in terms of coded factors was:The analysis of the effects of FBA-predicted factors on the biomass production showed that thiamine pyrophosphate, nicotinate, KHBiomass = 1761.61 +1.00 × A − 1.16 × B + 1158.95 × C − 1.25 × D − 0.77 × E +2.70 × F − 1.70 × J + 1.57 × L − 1.18 × M + 1.51 × N − 0.71 × O + 0.63 × P + 1.41 × R + 1.75 × S + 1.52 × T − 1.85 × U − 0.66 × V + 1.14 × W + 3.28 × X.l-tryptophan, l-isoleucine, ergosterol, and thiamine pyrophosphate (effect above the Bonferroni Limit) followed by ethanolamine and (NH4)2SO4 . Although l-asparagine had a positive effect, this effect was not significant. All other compounds had an inhibitory effect on lycopene production. The final equation in terms of coded factors was:Six factors were found to have significant positive effects on lycopene production. The most significant factors were 10 (Lycopene) = 1.10 + 0.055 × A − 0.049 × B − 0.14 × D − 0.056 × E − 0.046 × F + 0.026 × G − 0.15 × J + 0.080 × K + 0.049 × L − 0.11 × M − 0.010 × N − 0.085 × P + 5.397e-004 × Q + 0.14 × R − 0.027 × S + 8.320e-003 × T + 0.095 × U − 0.14 × V − 0.053 × W − 0.077 × X.Logl-isoleucine was found as the most significant factor having positive effect on lycopene content, followed by l-tryptophan, ergosterol, thiamine pyrophosphate, and l-histidine. Ethanolamine and (NH4)2SO4 had negligible positive effects on lycopene content. The large number of factors negatively influenced the lycopene content.The final equation for lycopene content in terms of coded factors was:10 (Lycopene content) = −42.56 + 0.033 × A − 0.025 × B − 28.22 × C − 0.12 × D − 0.035 × E − 0.10 × F + 0.012 × G − 0.12 × J + 0.067 × K + 0.015 × L − 0.073 × M − 0.041 × N + 0.020 × O − 0.10 × P + 0.10 × R − 0.065 × S − 0.017 × T + 0.14 × U − 0.12 × V − 0.072 × W − 0.13 × X.Log4)2SO4, and l-lysine had significant positive effects. The effects of KH2PO4, glucose, and thiamine pyrophosphate were also moderately significantly positive. l-methionine, urea, l-histidine, nicotinate, and hypoxanthine significantly inhibited lipid production. The effects of other components were not significant. The final equation for lycopene content in terms of coded factors was: For lipid production, ergosterol, 2SO4, and l-leucine. l-histidine had no significant positive effect. The rest of predicted components strongly repressed lipid accumulation.Lipid content was positively affected by 2PO4, 0.5 g/L (NH4)2SO4, 0.1 g/L ergosterol, 0.3 g/L ethanolamine, 5 g/L lysine, 5 g/L tryptophan, 10 g/L isoleucine, 20 g/L fructose, and 10 g/L glucose.Based on the above observations and multiple post-optimization experiments, the optimization process gave MP medium constituted of 1 g/L asparagine, 6 g/L KHl-isoleucine, ergosterol and (NH4)2SO4. This suggested the positive correlation between lipid accumulation and lycopene biosynthesis. This relationship between both pathways may be influenced by the composition of the culture medium since we found diverse discrete distribution of the concentration of these compounds in the different culture media. In addition, high lipid content did not necessarily imply high lycopene content.The correlation analysis was carried out using the Pearson correlation analysis method with a two-tailed test of significance. The correlation analysis was applied to the data obtained from the Plackett-Burman design responses obtained with the FBA-predicted factors. We obtained a Pearson correlation of 0.49 with a significance of 0.01. The correlation between both variable was significant at the 0.05 level. Furthermore, we found commonness between both variables when positive factors were taken into account. Specifically, lycopene content and lipid content were both significantly stimulated by l-tryptophan, (NH4)2SO4, KH2PO4, thiamine, and l-isoleucine in this decreasing order. The rest of the components including glucose, l-asparagine, and ethanolamine had moderate negative effects on biomass production. The highest biomass production of 33.58 g/L was obtained with run 10. The elimination of factors with negative effects in the first PBD driven on the FBA predicted components did not significantly affect biomass production, showing that their elimination was not due to errors. Similarly, KH2PO4, fructose, ergosterol, (NH4)2SO4, and l-tryptophan promoted lycopene accumulation. A lycopene production of 4.35 mg/g DCW (76.62 mg/L) was achieved under the coordinated effect of these compounds in shake flasks. Only KH2PO4 had positive effect on lycopene content.Following the first Plackett-Burman design (PBD), we conducted another PBD  in orderThis second tour PBD suggested that the selected components were further optimizable for biomass and lycopene concentration but did not really impact on lycopene content. Furthermore, some of components with positive effects in the first round of screening were found inhibiting lycopene production. This could be explained by the fact that the scale of choice of these components was reduced.YALI0B16126g which is annotated as protein farnesyltransferase/geranylgeranyltransferase type-1 subunit alpha. On the contrary, significant effects were recorded for YALI0C18755g, the gene encoding for -farnesyl pyrophosphate (FPP) specific hexaprenyl-diphosphate synthase. This gene was significantly stimulated by the effects of l-tryptophan, l-lysine, l-isoleucine, l-asparagine, KH2PO4, and fructose. The high number of positive factorial effects corroborated with the fact that FPP was set as the target metabolite in the FBA simulation. Only ergosterol, thiamine, and glucose had inhibitory effects on this gene. In addition, the ditrans, polycis-polyprenyl diphosphate synthase -farnesyl diphosphate) gene YALI0C18799g was activated by l-tryptophan, ergosterol, l-asparagine, and to some extent KH2PO4 while other components displayed negative effects. YALI0D17556g, another gene responsible for -FPP specific ditrans, polycis-polyprenyl diphosphate synthase, was significantly stimulated by ethanolamine but repressed by l-tryptophan. The expression of the gene in charge of protein farnesyltransferase subunit beta, YALI0D14762g, was similarly triggered by l-lysine, l-asparagine, fructose, KH2PO4, and l-tryptophan and to a minor extent by glucose. Only l-tryptophan had a positive effect on YALI0D17050g (geranylgeranyl diphosphate synthase). The rate-limiting gene of the mevalonate pathway governing the expression of hydroxymethylglutaryl-CoA reductase (HMGCR), YALI0E04807g, was significantly activated by factors including l-lysine, l-asparagine, l-tryptophan, KH2PO4, and fructose but significantly inhibited by ergosterol and thiamine. The activation of this gene was necessary for converting HMG-CoA into mevalonic acid and subsequent metabolites of the terpenoids biosynthesis backbone. YALI0E05753g -farnesyl diphosphate synthase) was significantly stimulated by l-tryptophan, KH2PO4 and to a minor extent by L-asparagine. However, the lycopene biosynthesis genes introduced into the engineered strains, although highly expressed, were activated by numerous positive factors. However, the number of positive effects was not as significant as expected. This could be explained by the fact that the FBA was targeted towards FPP synthesis. Specifically, the expression of the crtE, crtB, and crtI genes were all promoted by ethanolamine (significant effect) and l-lysine, l-isoleucine, glucose, (NH4)2SO4, ergosterol, and glucose to a trivial degree.In order to get insights into the effect of selected factors on genes of the terpenoids backbone, we realized quantitative RT-PCR experiments to explore expression levels of these genes. No significant effect was found for zwf and crt genes. ACT1 while all heterologous genes were upregulated. The upregulation of heterologous genes implied that the carbon metabolism was efficiently directed to FPP biosynthesis and subsequently permitted lycopene production. Nevertheless, different amounts of biomass and lycopene yielded on different runs of culture media could be explained by divergences in expression patterns as depicted in Overall, the results showed that FBA-predicted and PBD screened factors differentially affected genes of the terpenoids backbone, Indeed, though some heterologous genes were highly expressed in certain media, the level of produced lycopene did not match with expression levels. We found that some media components were inhibiting genes of the terpenoids backbone at different levels but the inhibition did not necessarily imply low production of lycopene. Each gene of the terpenoids backbone was activated by specific selected factors whose coordinated actions allowed high lycopene production.Designed media were initially used for lycopene production in a 1.5 L fermenter with a working volume of 1 L in batch. The fed-batch operation started at 24 h and consisted in the supplementation of a feeding solution containing 200 g/L glucose and 200 g/L of fructose. The feeding (50 mL) was executed every 24 h. The pH was initially controlled at 5.5 and then controlled at 3 after 48 h when the color began to appear. The results of biomass and lycopene production on different media over the fermentation course are depicted in After 10 days of fermentation, we found a maximum biomass production of 40.2 g/L with a lycopene concentration of 126 mg/L from the medium designed without taking into account the FBA predictions (PM) a. LycopeY. lipolytica, as an oleaginous yeast, is able to accumulate significant quantities of lipid bodies under certain growth conditions. This property provides a reliable storehouse for carotenoids biosynthesis and, consequently, the heterologous production of carotenoids using Y. lipolytica may constitute a promising asset for large-scale operations. The heterologous synthesis of lycopene in Y. lipolytica requires the introduction of the biosynthetic genes  even if the expression of crtE is not necessary in various heterologous hosts including Y. lipolytica, and bioengineering approaches for integrating these genes in Y. lipolytica have been described previously [CrtE is a geranylgeranyl diphosphate synthase that catalyzes the conversion of FPP and IPP to GGPP and CrtB is a phytoene synthase that can condense two molecules of the C20 precursor, GGPP, to yield the first C40 hydrocarbon, the phytoene. This is followed by a series of sequential desaturations of phytoene to produce ζ-carotene, neurosporene, and, finally, lycopene [CrtI). The most advanced lycopene production using Y. lipolytica is the recent publication of Matthäus, Ketelhot, Gatter, and Barth [crtB and crtI of Pantoea ananatis were expressed in Y. lipolytica under the control of the TEF1 promoter of Y. lipolytica. Moreover, these authors undertook the modifications of native metabolic pathways in Y. lipolytica via the overexpression of bottleneck genes in the isoprenoid pathway (GGS1 and HMG1) and the knock-out of POX1 to POX6 as well as GUT2 genes and successfully enhanced lycopene production (16 mg/g DCW). In a previous study, taking into account the fact that the use of expression vectors pINA1269 and pINA1312, carotenoid genes crtE, crtB, and crtI can be successively integrated into the yeast chromosome with improved strain stability, we synchronically introduced combinations of these plasmids carrying lycopene biosynthesis genes and spawned lycopene producing strains. Further characterization of these engineered strains showed discrepancies in terms of lycopene and biomass production among engineered strains and suggested the impact of engineering tactics on lycopene biosynthesis in engineered strains.Relative to other yeasts, eviously . CrtE islycopene . This prnd Barth  in whichY. lipolytica and other yeast species for lycopene and other terpenoids molecules. Additionally, our study gave insights into the comprehension of the effects of some external factors on genes of the terpenoids pathway. Correct monitoring of these factors for media designing, coupled with adequate metabolic engineering tactics, will contribute to the fundamental research and the efficient production of lycopene even at the industrial level.In the present study, we aimed to establish an effective fermentation platform for lycopene production from the most robust engineered strain (Po1f-1312E + 1269IB) using statistical and GSMN modeling tools for screening chemical compounds and designing culture media. Efficient media were employed for fed-batch cultivation. The improvement of cultivation conditions  led to lycopene production of 242 mg/L of lycopene using the medium designed based on the variables screened from the FBA-predicted factors. This concentration of lycopene was approximately the double of that obtained in the medium designed based on the literature. The above results demonstrated the usefulness of FBA and modelling tools for improved production of lycopene in consistency with previous investigations showing that FBA simulations can be efficiently applied for experimental validation and improvement of the production of objectives production or biological processes ,28. The Y. lipolytica in order to obtain a dependable lycopene producing strain. The bioengineering work has been published in a local Chinese journal. The strain designated as Po1f-1312E + 1269IB, representing the recombinant strain obtained from the introduction of plasmid pINA1312 (carrying gene crtE) and pINA1269 (carrying genes crtI and crtB) into the host strain Y. lipolytica Po1f, was used in this study given that it displayed noteworthy growth and lycopene production behavior.Prior to cultivation improvement experiments, we first proceeded to the bioengineering of in silico Y. lipolytica terpenoids biosynthesis genome-scale metabolic model (GSM) yli v1.7 updated from our previous publication [Flux balance analysis (FBA) is the paramount method for estimating flux distributions and assumes that organisms utilize substrates adequately to maximize a fixed objective of model (biomass or a particular reaction of production). The FBA method can be used to reproduce environmental conditions necessary for achieving the maximization of the objective function of the model and is an integrated element of the COBRA 2.0 Toolbox in Matlab. The FBA was carried out using Matlab R2011a  and the COBRA 2.0 toolbox. We used an lication . The updn+1) runs, wherein n represents the number of modules to be screened. Each component was assigned two levels of concentrations (high concentration and low concentration). Firstly, a set of 23 factors selected in light of the preliminary experiments and literature review (data not shown), were combined in 24 trials for the designing of FBA-independent cultivation media. Following this, the FBA-driven predicted factors (n = 23) were used for conducting a Plackett-Burman design of 24 runs. Numerous post-optimization experiments were equally carried out on the basis of Plackett-Burman design.To identify the vital factors that affect biomass and lycopene production in the engineered strain and make breakthrough improvements, a saturated screening design based on the Plackett-Burman structures incorporated into Design expert software (version 8) was adopted. The total number of trials to be implemented according to Plackett-Burman was  with a working volume of 1 L at 30 °C. The aeration rate was fixed at 1 vvm (volume air per volume per minute) and the agitation rate was set to automatically respond to the dissolved oxygen (DO) concentration so as to maintain it at 50%. Sterile air filters with 0.2 µm pores were used for air transfer into bioreactor. The DO concentration in the culture broth was measured using a pO2 electrode. Silicone was periodically added as an antifoam agent. The fed-batch operation started at 24 h and consisted in the supplementation of a feeding solution containing 200 g/L glucose and 200 g/L of fructose. The feeding (50 mL) was executed every 24 h. The pH was initially controlled at 5.5 and then controlled at 3 after 48 h following the appearance of color.The optimized designed media were directly used for aerobic fermentation of the transgenic After cultivation, 5 mL of cell broth was harvested, centrifuged at 12,000 rpm for 5 min, washed twice with distilled water and dried at 105 °C for 48 h. The dry cell weight (DCW) was determined by gravimetrically measuring the weight of the dry cell pellets. Glucose measurement was monitored by a spectrophotometric procedure by means of a Glucose Reagent Kit  following the vendor’s guidelines. Lipid extraction protocol was as described in our previous publication .Following the cultivation end time, precise volumes of homogenous culture broth were harvested, centrifuged at 12,000 rpm for 10 min at 4 °C and washed twice with deionized water. The supernatant was discarded and the pellet was used for lycopene extraction. Prior to lycopene extraction, pellets were dissolved in 500 μL of dimethyl sulfoxide (DMSO), pre-warmed at 55 °C for 30 min, shook strongly for 30 s, and then maintained for an extra 30 min without shaking. After that, 500 μL of acetone was added to the DMSO extract and homogenized by vortexing for 30 s, followed by incubation at 55 °C for 15 min with intermittent shaking. The mixture was subsequently centrifuged at 12,000 rpm for 10 min, and the colored mixed supernatants were transferred into a new tube. The extraction step was repeated until all visible pigments in the residual cell pellet and supernatant were extracted. The obtained colored supernatants were pooled together and used for lycopene quantification via high-performance liquid chromatography (HPLC) analysis. All operations occurred on ice under dim light to prevent photodegradation, isomerizations, and structural changes of the carotenoids.The lycopene concentration of each extract was quantified by HPLC analysis which was performed on a Shimadzu LC-20A HPLC instrument equipped with a LC solution software program, LC-20AD pump, and a detector with a UV-Vis lamp. Specifically, it was carried out by injecting 10 μL of the crude carotenoid extract into a Zorbax SB-Aq C18-reverse-phase column . The extract was eluted under isocratic condition with a mobile phase containing 80% acetonitrile, 15% methanol and 5% isopropanol at a flow rate of 1 mL/min at 30 °C. Quantitative analysis was accomplished using a calibration curve obtained with a lycopene standard and final lycopene concentrations were obtained by normalization to the total initial volumes used during extraction.The cells were pelleted and total RNA was extracted as previously described . To keepACT1 was utilized as a housekeeping gene. The results were expressed as fractions of gene expression between the target gene and the housekeeping gene, ACT1 [To prevent the variation of starting materials amounts, ne, ACT1 .Y. lipolytica. The findings of the present study could have implications for improved production of lycopene using Y. lipolytica as a fermentation platform and promote terpenoids production at the industrial scale. The FBA and Plackett–Burman screening investigational design empowered the bringing about of significant variables for lycopene production from