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Metabolic fluxes and their control mechanisms are fundamental in cellular metabolism, offering insights for the study of biological systems and biotechnological applications. However, quantitative and predictive understanding of controlling biochemical reactions in microbial cell factories, especially at the system level, is limited. In this work, we present ARCTICA, a computational framework that integrates constraint-based modelling with machine learning tools to address this challenge. Using the model cyanobacterium Synechocystis sp. PCC 6803 as chassis, we demonstrate that ARCTICA effectively simulates global-scale metabolic flux control. Key findings are that (i) the photosynthetic bioproduction is mainly governed by enzymes within the Calvin–Benson–Bassham (CBB) cycle, rather than by those involve in the biosynthesis of the end-product, (ii) the catalytic capacity of the CBB cycle limits the photosynthetic activity and downstream pathways and (iii) ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO) is a major, but not the most, limiting step within the CBB cycle. Predicted metabolic reactions qualitatively align with prior experimental observations, validating our modelling approach. ARCTICA serves as a valuable pipeline for understanding cellular physiology and predicting rate-limiting steps in genome-scale metabolic networks, and thus provides guidance for bioengineering of cyanobacteria. • A workflow for metabolic flux control analysis in Synechocystis sp. PCC 6803. • Machine learning with features derived from genome-scale metabolic modelling. • Identification of potential key reactions for metabolic adaptations and cell bioengineering. [ABSTRACT FROM AUTHOR]

L-cysteine is an amino acid with relevance to the pharmaceutical, food, feed, and cosmetic industry. The environmental and societal impact of its chemical production has led to the development of more sustainable fermentative L-cysteine production processes with engineered E. coli based on glucose and thiosulfate as sulphur source. Still, most of the published processes show low yields. For the identification of further metabolic engineering targets, engineered E. coli cells were withdrawn from a fed-batch production process, followed by in vivo metabolic control analysis (MCA) based on the data of short-term perturbation experiments, metabolomics (LC–MS), and thermodynamic flux analysis (TFA). In vivo MCA indicated that the activities of the L-cysteine synthases of the cells withdrawn from the production process might be limiting, and we hypothesised that the L-cysteine precursor O-acetylserine (OAS) might be exported from the cells faster than it took to transform OAS into L-cysteine. By increasing the expression of the L-cysteine synthases, either sulfocysteine synthase or L-cysteine synthase, which transform OAS into L-cysteine, an improvement of up to 70% in specific L-cysteine productivity and up to 47% in the final L-cysteine concentration was achieved in standardised fed-batch processes thereby increasing the yield on glucose by more than 85 to 9.2% (w/w). Key points: • Metabolic control analysis was applied to analyse L-cysteine production with E. coli • OAS export was faster than its transformation to L-cysteine • Overexpression of L-cysteine synthases improved L-cysteine productivity and yield [ABSTRACT FROM AUTHOR]

Dynamic variables in the non-equilibrium systems of life are determined by catalytic activities. These relate to the expression of the genome. The extent to which such a variable depends on the catalytic activity defined by a gene has become more and more important in view of the possibilities to modulate gene expression or intervene with enzyme function through the use of medicinal drugs. With all the complexity of cellular systems biology, there are still some very simple principles that guide the control of variables such as fluxes, concentrations, and half-times. Using time-unit invariance we here derive a multitude of laws governing the sums of the control coefficients that quantify the control of multiple variables by all the catalytic activities. We show that the sum of the control coefficients of any dynamic variable over all catalytic activities is determined by the control of the same property by time. When the variable is at a maximum, minimum or steady, this limits the sums to simple integers, such as 0, −1, 1, and −2, depending on the variable under consideration. Some of the implications for biological control are discussed as is the dependence of these results on the precise definition of control. [ABSTRACT FROM AUTHOR]

Abstract Background Although efficient l-tryptophan production using engineered Escherichia coli is established from glucose, the use of alternative carbon sources is still very limited. Through the application of glycerol as an alternate, a more sustainable substrate (by-product of biodiesel preparation), the well-studied intracellular glycolytic pathways are rerouted, resulting in the activity of different intracellular control sites and regulations, which are not fully understood in detail. Metabolic analysis was applied to well-known engineered E. coli cells with 10 genetic modifications. Cells were withdrawn from a fed-batch production process with glycerol as a carbon source, followed by metabolic control analysis (MCA). This resulted in the identification of several additional enzymes controlling the carbon flux to l-tryptophan. Results These controlling enzyme activities were addressed stepwise by the targeted overexpression of 4 additional enzymes (trpC, trpB, serB, aroB). Their efficacy regarding l-tryptophan productivity was evaluated under consistent fed-batch cultivation conditions. Although process comparability was impeded by process variances related to a temporal, unpredictable break-off in l-tryptophan production, process improvements of up to 28% with respect to the l-tryptophan produced were observed using the new producer strains. The intracellular effects of these targeted genetic modifications were revealed by metabolic analysis in combination with MCA and expression analysis. Furthermore, it was discovered that the E. coli cells produced the highly toxic metabolite methylglyoxal (MGO) during the fed-batch process. A closer look at the MGO production and detoxification on the metabolome, fluxome, and transcriptome level of the engineered E. coli indicated that the highly toxic metabolite plays a critical role in the production of aromatic amino acids with glycerol as a carbon source. Conclusions A detailed process analysis of a new l-tryptophan producer strain revealed that several of the 4 targeted genetic modifications of the E. coli l-tryptophan producer strain proved to be effective, and, for others, new engineering approaches could be derived from the results. As a starting point for further strain and process optimization, the up-regulation of MGO detoxifying enzymes and a lowering of the feeding rate during the last third of the cultivation seems reasonable.

Cell-free systems are useful tools for prototyping metabolic pathways and optimizing the production of various bioproducts. Mechanistically-based kinetic models are uniquely suited to analyze dynamic experimental data collected from cell-free systems and provide vital qualitative insight. However, to date, dynamic kinetic models have not been applied with rigorous biological constraints or trained on adequate experimental data to the degree that they would give high confidence in predictions and broadly demonstrate the potential for widespread use of such kinetic models. In this work, we construct a large-scale dynamic model of cell-free metabolism with the goal of understanding and optimizing butanol production in a cell-free system. Using a combination of parameterization methods, the resultant model captures experimental metabolite measurements across two experimental conditions for nine metabolites at timepoints between 0 and 24 h. We present analysis of the model predictions, provide recommendations for butanol optimization, and identify the aldehyde/alcohol dehydrogenase as the primary bottleneck in butanol production. Sensitivity analysis further reveals the extent to which various parameters are constrained, and our approach for probing valid parameter ranges can be applied to other modeling efforts. • A kinetic model captures timecourse data, including butanol production, in two experimental conditions in cell-free extracts. • Model hypothesizes metabolic mechanisms to explain experimentally observed behaviors between conditions. • Strategies to increase butanol production are predicted, the most promising of which is experimentally validated. • Analyses reveal how model parameters are constrained between many possible parameter sets within an ensemble. [ABSTRACT FROM AUTHOR]

Metabolic models are typically characterized by a large number of parameters. Traditionally, metabolic control analysis is applied to differential equation-based models to investigate the sensitivity of predictions to parameters. A corresponding theory for constraint-based models is lacking, due to their formulation as optimization problems. Here, we show that optimal solutions of optimization problems can be efficiently differentiated using constrained optimization duality and implicit differentiation. We use this to calculate the sensitivities of predicted reaction fluxes and enzyme concentrations to turnover numbers in an enzyme-constrained metabolic model of Escherichia coli. The sensitivities quantitatively identify rate limiting enzymes and are mathematically precise, unlike current finite difference based approaches used for sensitivity analysis. Further, efficient differentiation of constraint-based models unlocks the ability to use gradient information for parameter estimation. We demonstrate this by improving, genome-wide, the state-of-the-art turnover number estimates for E. coli. Finally, we show that this technique can be generalized to arbitrarily complex models. By differentiating the optimal solution of a model incorporating both thermodynamic and kinetic rate equations, the effect of metabolite concentrations on biomass growth can be elucidated. We benchmark these metabolite sensitivities against a large experimental gene knockdown study, and find good alignment between the predicted sensitivities and in vivo metabolome changes. In sum, we demonstrate several applications of differentiating optimal solutions of constraint-based metabolic models, and show how it connects to classic metabolic control analysis. • Solutions of convex optimization problems can be efficiently differentiated. • Metabolic control analysis can be applied to constraint-based models. • Differentiable constraint-based models can be used for parameter estimation. • Automatic differentiation can be leveraged to find sensitivities of complex models. [ABSTRACT FROM AUTHOR]

Background: Although efficient l-tryptophan production using engineered Escherichia coli is established from glucose, the use of alternative carbon sources is still very limited. Through the application of glycerol as an alternate, a more sustainable substrate (by-product of biodiesel preparation), the well-studied intracellular glycolytic pathways are rerouted, resulting in the activity of different intracellular control sites and regulations, which are not fully understood in detail. Metabolic analysis was applied to well-known engineered E. coli cells with 10 genetic modifications. Cells were withdrawn from a fed-batch production process with glycerol as a carbon source, followed by metabolic control analysis (MCA). This resulted in the identification of several additional enzymes controlling the carbon flux to l-tryptophan. Results: These controlling enzyme activities were addressed stepwise by the targeted overexpression of 4 additional enzymes (trpC, trpB, serB, aroB). Their efficacy regarding l-tryptophan productivity was evaluated under consistent fed-batch cultivation conditions. Although process comparability was impeded by process variances related to a temporal, unpredictable break-off in l-tryptophan production, process improvements of up to 28% with respect to the l-tryptophan produced were observed using the new producer strains. The intracellular effects of these targeted genetic modifications were revealed by metabolic analysis in combination with MCA and expression analysis. Furthermore, it was discovered that the E. coli cells produced the highly toxic metabolite methylglyoxal (MGO) during the fed-batch process. A closer look at the MGO production and detoxification on the metabolome, fluxome, and transcriptome level of the engineered E. coli indicated that the highly toxic metabolite plays a critical role in the production of aromatic amino acids with glycerol as a carbon source. Conclusions: A detailed process analysis of a new l-tryptophan producer strain revealed that several of the 4 targeted genetic modifications of the E. colil-tryptophan producer strain proved to be effective, and, for others, new engineering approaches could be derived from the results. As a starting point for further strain and process optimization, the up-regulation of MGO detoxifying enzymes and a lowering of the feeding rate during the last third of the cultivation seems reasonable. [ABSTRACT FROM AUTHOR]

The evolution of operons has puzzled evolutionary biologists since their discovery, and many theories exist to explain their emergence, spreading, and evolutionary conservation. In this work, we suggest that DNA replication introduces a selective force for the clustering of functionally related genes on chromosomes, which we interpret as a preliminary and necessary step in operon formation. Our reasoning starts from the observation that DNA replication produces copy number variations of genomic regions, and we propose that such changes perturb metabolism. The formalization of this effect by exploiting concepts from metabolic control analysis suggests that the minimization of such perturbations during evolution could be achieved through the formation of gene clusters and operons. We support our theoretical derivations with simulations based on a realistic metabolic network, and we confirm that present-day genomes have a degree of compaction of functionally related genes, which is significantly correlated to the proposed perturbations introduced by replication. The formation of clusters of functionally related genes in microbial genomes has puzzled microbiologists since their first discovery. Here, we suggest that replication, and the copy number variations due to the replisome passage, might play a role in the process through a perturbation in metabolite homeostasis. We provide theoretical support to this hypothesis, and we found that both simulations and genomic analysis support our hypothesis., Importance: The formation of clusters of functionally related genes in microbial genomes has puzzled microbiologists since their discovery. Here, we suggest that replication, and the copy number variations due to the replisome passage, might play a role in the process through a perturbation in metabolite homeostasis. We provide theoretical support to this hypothesis, and we found that both simulations and genomic analysis support our hypothesis.

Abstract Background Significant efforts have been made in building large-scale kinetic models of cellular metabolism in the past two decades. However, most kinetic models published to date, remain focused around central carbon pathways or are built around ad hoc reduced models without clear justification on their derivation and usage. Systematic algorithms exist for reducing genome-scale metabolic reconstructions to build thermodynamically feasible and consistently reduced stoichiometric models. However, it is important to study how network complexity affects conclusions derived from large-scale kinetic models built around consistently reduced models before we can apply them to study biological systems. Results We reduced the iJO1366 Escherichia Coli genome-scale metabolic reconstruction systematically to build three stoichiometric models of different size. Since the reduced models are expansions around the core subsystems for which the reduction was performed, the models are nested. We present a method for scaling up the flux profile and the concentration vector reference steady-states from the smallest model to the larger ones, whilst preserving maximum equivalency. Populations of kinetic models, preserving similarity in kinetic parameters, were built around the reference steady-states and their metabolic sensitivity coefficients (MSCs) were computed. The MSCs were sensitive to the model complexity. We proposed a metric for measuring the sensitivity of MSCs to these structural changes. Conclusions We proposed for the first time a workflow for scaling up the size of kinetic models while preserving equivalency between the kinetic models. Using this workflow, we demonstrate that model complexity in terms of networks size has significant impact on sensitivity characteristics of kinetic models. Therefore, it is essential to account for the effects of network complexity when constructing kinetic models. The presented metric for measuring MSC sensitivity to structural changes can guide modelers and experimentalists in improving model quality and guide synthetic biology and metabolic engineering. Our proposed workflow enables the testing of the suitability of a kinetic model for answering certain study-specific questions. We argue that the model-based metabolic design targets that are common across models of different size are of higher confidence, while those that are different could be the objective of investigations for model improvement.

Dynamic variables in the non-equilibrium systems of life are determined by catalytic activities. These relate to the expression of the genome. The extent to which such a variable depends on the catalytic activity defined by a gene has become more and more important in view of the possibilities to modulate gene expression or intervene with enzyme function through the use of medicinal drugs. With all the complexity of cellular systems biology, there are still some very simple principles that guide the control of variables such as fluxes, concentrations, and half-times. Using time-unit invariance we here derive a multitude of laws governing the sums of the control coefficients that quantify the control of multiple variables by all the catalytic activities. We show that the sum of the control coefficients of any dynamic variable over all catalytic activities is determined by the control of the same property by time. When the variable is at a maximum, minimum or steady, this limits the sums to simple integers, such as 0, −1, 1, and −2, depending on the variable under consideration. Some of the implications for biological control are discussed as is the dependence of these results on the precise definition of control.

L-tryptophan production from glycerol with Escherichia coli was analysed by perturbation studies and metabolic control analysis. The insertion of a non-natural shikimate transporter into the genome of an Escherichia coli L-tryptophan production strain enabled targeted perturbation within the product pathway with shikimate during parallelised short-term perturbation experiments with cells withdrawn from a 15 L fed-batch production process. Expression of the shikimate/H+-symporter gene (shiA) from Corynebacterium glutamicum did not alter process performance within the estimation error. Metabolic analyses and subsequent extensive data evaluation were performed based on the data of the parallel analysis reactors and the production process. Extracellular rates and intracellular metabolite concentrations displayed evident deflections in cell metabolism and particularly in chorismate biosynthesis due to the perturbations with shikimate. Intracellular flux distributions were estimated using a thermodynamics-based flux analysis method, which integrates thermodynamic constraints and intracellular metabolite concentrations to restrain the solution space. Feasible flux distributions, Gibbs reaction energies and concentration ranges were computed simultaneously for the genome-wide metabolic model, with minimum bias in relation to the direction of metabolic reactions. Metabolic control analysis was applied to estimate elasticities and flux control coefficients, predicting controlling sites for L-tryptophan biosynthesis. The addition of shikimate led to enhanced deviations in chorismate biosynthesis, revealing a so far not observed control of 3-dehydroquinate synthase on L-tryptophan formation. The relative expression of the identified target genes was analysed with RT-qPCR. Transcriptome analysis revealed disparities in gene expression and the localisation of target genes to further improve the microbial L-tryptophan producer by metabolic engineering. [ABSTRACT FROM AUTHOR]

Microorganisms are widely used to produce valuable compounds. Because thousands of metabolic reactions occur simultaneously and many metabolic reactions are related to target production and cell growth, the development of a rational design method for metabolic pathway modification to optimize target production is needed. In this paper, recent advances in metabolic engineering are reviewed, specifically considering computational pathway modification design and experimental evaluation of metabolic fluxes by 13C-metabolic flux analysis. Computational tools for seeking effective gene deletion targets and recruiting heterologous genes are described in flux balance analysis approaches. A kinetic model and adaptive laboratory evolution were applied to identify and eliminate the rate-limiting step in metabolic pathways. Data science-based approaches for process monitoring and control are described to maximize the performance of engineered cells in bioreactors. [ABSTRACT FROM AUTHOR]

Abstract Background Terpenoids are of high interest as chemical building blocks and pharmaceuticals. In microbes, terpenoids can be synthesized via the methylerythritol phosphate (MEP) or mevalonate (MVA) pathways. Although the MEP pathway has a higher theoretical yield, metabolic engineering has met with little success because the regulation of the pathway is poorly understood. Results We applied metabolic control analysis to the MEP pathway in Escherichia coli expressing a heterologous isoprene synthase gene (ispS). The expression of ispS led to the accumulation of isopentenyl pyrophosphate (IPP)/dimethylallyl pyrophosphate (DMAPP) and severely impaired bacterial growth, but the coexpression of ispS and isopentenyl diphosphate isomerase (idi) restored normal growth and wild-type IPP/DMAPP levels. Targeted proteomics and metabolomics analysis provided a quantitative description of the pathway, which was perturbed by randomizing the ribosome binding site in the gene encoding 1-deoxyxylulose 5-phosphate synthase (Dxs). Dxs has a flux control coefficient of 0.35 (i.e., a 1% increase in Dxs activity resulted in a 0.35% increase in pathway flux) in the isoprene-producing strain and therefore exerted significant control over the flux though the MEP pathway. At higher dxs expression levels, the intracellular concentration of 2-C-methyl-d-erythritol-2,4-cyclopyrophosphate (MEcPP) increased substantially in contrast to the other MEP pathway intermediates, which were linearly dependent on the abundance of Dxs. This indicates that 4-hydroxy-3-methylbut-2-en-1-yl diphosphate synthase (IspG), which consumes MEcPP, became saturated and therefore limited the flux towards isoprene. The higher intracellular concentrations of MEcPP led to the efflux of this intermediate into the growth medium. Discussion These findings show the importance of Dxs, Idi and IspG and metabolite export for metabolic engineering of the MEP pathway and will facilitate further approaches for the microbial production of valuable isoprenoids.

The fermentation system is a metabolic system that can be considered as a complex system, since it involves metabolites linked by different reactions. Some outputs of the reaction act as input in the other reactions and they control the behaviour of the system. Glucose as the main carbon source of the yeast cells plays an important role in the cell’s reproduction cycle and product synthesis. Glucose promotes negative feedback to some reactions in the central metabolism of the yeast cell. In this paper we mathematically study the effects of the negative feedback (inhibition) by glucose and other metabolites on the dynamics of the fermentation system. We also study the sensitivity of the flux in the presence of inhibition. We found that high inhibition by glucose affects the concentration of acetyl-CoA, which will lead to the respiration pathway of the yeast cells. High inhibition by acetaldehyde affects the concentration of all metabolites, including the maximal concentration of ethanol. Based on the metabolic control analysis results, we found that glucose can be considered as the external regulating point in increasing the flux of ethanol. Although glucose acts as a negative feedback, it can also be used to promote certain processes in the metabolic pathway since it gives the highest positive control in increasing the flux of ethanol as the desired product. For the internal regulation point, the maximal concentration of ethanol can be increased significantly by regulating the maximal activity of alcohol dehydrogenase and acetaldehyde dehydrogenase simultaneously.

Background: Significant efforts have been made in building large-scale kinetic models of cellular metabolism in the past two decades. However, most kinetic models published to date, remain focused around central carbon pathways or are built around ad hoc reduced models without clear justification on their derivation and usage. Systematic algorithms exist for reducing genome-scale metabolic reconstructions to build thermodynamically feasible and consistently reduced stoichiometric models. However, it is important to study how network complexity affects conclusions derived from large-scale kinetic models built around consistently reduced models before we can apply them to study biological systems. Results: We reduced the iJO1366 Escherichia Coli genome-scale metabolic reconstruction systematically to build three stoichiometric models of different size. Since the reduced models are expansions around the core subsystems for which the reduction was performed, the models are nested. We present a method for scaling up the flux profile and the concentration vector reference steady-states from the smallest model to the larger ones, whilst preserving maximum equivalency. Populations of kinetic models, preserving similarity in kinetic parameters, were built around the reference steady-states and their metabolic sensitivity coefficients (MSCs) were computed. The MSCs were sensitive to the model complexity. We proposed a metric for measuring the sensitivity of MSCs to these structural changes. Conclusions: We proposed for the first time a workflow for scaling up the size of kinetic models while preserving equivalency between the kinetic models. Using this workflow, we demonstrate that model complexity in terms of networks size has significant impact on sensitivity characteristics of kinetic models. Therefore, it is essential to account for the effects of network complexity when constructing kinetic models. The presented metric for measuring MSC sensitivity to structural changes can guide modelers and experimentalists in improving model quality and guide synthetic biology and metabolic engineering. Our proposed workflow enables the testing of the suitability of a kinetic model for answering certain study-specific questions. We argue that the model-based metabolic design targets that are common across models of different size are of higher confidence, while those that are different could be the objective of investigations for model improvement. [ABSTRACT FROM AUTHOR]

Identification of the rate-limiting step in a metabolic pathway is an important challenge in metabolic engineering for enhancing pathway flow. Although specific enzyme activities (V max) provide valuable clues for the identification, it is time-consuming and difficult to measure multiple enzymes in the pathway because different assay protocols are required for each enzyme. In the present study, we propose a method to simultaneously determine the V max values of multiple enzymes using a kinetic model with a time course of the intermediate concentrations through an in vitro experiment. To demonstrate this method, nine glycolysis reactions for converting glucose-6-phosphate (G6P) to pyruvate in Escherichia coli were considered. In a reaction mixture containing G6P and cofactors, glycolysis was initiated by adding a crude cell extract obtained from stationary phase cells. The V max values were optimized to minimize the difference between the measured and simulated time-courses using a kinetic model. Metabolic control analysis using the kinetic model with the estimated V max values revealed that fructose bisphosphate aldolase (FBA) was the rate-limiting step in the upper part of glycolysis. The addition of FBA in the reaction mixture successfully increased the glycolytic flux in vitro. Furthermore, in vivo , the specific glucose consumption rate of an FBA overexpression strain was 1.4 times higher than that of the control strain during the stationary phase. These results confirmed that FBA was the rate-limiting step in glycolysis under the stationary phase. This approach provides V max values of multiple enzymes in a pathway for metabolic control analysis with a kinetic model. [ABSTRACT FROM AUTHOR]

Epistasis is often used to probe functional relationships between genes, and it plays an important role in evolution. However, we lack theory to understand how functional relationships at the molecular level translate into epistasis at the level of whole-organism phenotypes, such as fitness. Here, I derive two rules for how epistasis between mutations with small effects propagates from lower- to higher-level phenotypes in a hierarchical metabolic network with first-order kinetics and how such epistasis depends on topology. Most importantly, weak epistasis at a lower level may be distorted as it propagates to higher levels. Computational analyses show that epistasis in more realistic models likely follows similar, albeit more complex, patterns. These results suggest that pairwise inter-gene epistasis should be common, and it should generically depend on the genetic background and environment. Furthermore, the epistasis coefficients measured for high-level phenotypes may not be sufficient to fully infer the underlying functional relationships.

Metabolism represents the biological level that is most related to the phenotypes of the cell and, alterations or reprogramming of this can (i) affect the production of primary or secondary metabolites in microorganisms of biotechnological interest, (ii) favor or not the inhibition of growth in pathogenic organisms and (iii) developing metabolic disorders such as obesity or diabetes, among others. The study of metabolism, redesign, and redirection of metabolic fluxes has become an important area of research (also known as Metabolic Engineering), as it has allowed the development and design of improved biological processes, the identification of therapeutic targets, the design of therapeutic strategies to cure metabolic disorders and the identification of biomarkers in cancer, among others. Currently, the development of computational methodologies is allowing to study the cell metabolism under different environmental conditions and directing experiments with the model's predictions. The purpose of this review is to highlight the importance of metabolic flux analysis as a general methodology to study metabolic reprogramming in different organisms of biotechnological, medical, and therapeutic interest. This work condenses the theoretical bases and key concepts to understand the analysis of metabolic fluxes, which will be a fundamental input for those who are entering to the world of systems biology or related areas. [ABSTRACT FROM AUTHOR]

Vegetable oils are one of the main agricultural commodities. Demand has been increasing steadily over the last five decades and, with finite land available, it is vital that we increase productivity. My laboratory has focused on the regulation of plant lipid metabolism and, as part of this work, we identified diacylglycerol acyltransferase (DGAT) as important at regulating carbon flux during oil accumulation. This led to collaborations with Randy Weselake's research group when we quantified the importance of DGAT in oilseed rape by using flux control analysis. Later, with David Taylor, we showed that over‐expression of DGAT boosted oil accumulation in field‐grown crops by around 8%. These studies led to a multitude of experiments with different oil crops, such as oil palm and soybean, as well as many rewarding collaborations with Randy. [ABSTRACT FROM AUTHOR]

Multiple insulin-regulated enzymes participate in hepatic glycogen synthesis, and the rate-controlling step responsible for insulin stimulation of glycogen synthesis is unknown. We demonstrate that glucokinase (GCK)-mediated glucose phosphorylation is the rate-controlling step in insulin-stimulated hepatic glycogen synthesis in vivo, by use of the somatostatin pancreatic clamp technique using [13C6]glucose with metabolic control analysis (MCA) in three rat models: 1) regular chow (RC)-fed male rats (control), 2) high fat diet (HFD)-fed rats, and 3) RC-fed rats with portal vein glucose delivery at a glucose infusion rate matched to the control. During hyperinsulinemia, hyperglycemia dosedependently increased hepatic glycogen synthesis. At similar levels of hyperinsulinemia and hyperglycemia, HFD-fed rats exhibited a decrease and portal delivery rats exhibited an increase in hepatic glycogen synthesis via the direct pathway compared with controls. However, the strong correlation between liver glucose-6-phosphate concentration and net hepatic glycogen synthetic rate was nearly identical in these three groups, suggesting that the main difference between models is the activation of GCK. MCA yielded a high control coefficient for GCK in all three groups. We confirmed these findings in studies of hepatic GCK knockdown using an antisense oligonucleotide. Reduced liver glycogen synthesis in lipid-induced hepatic insulin resistance and increased glycogen synthesis during portal glucose infusion were explained by concordant changes in translocation of GCK. Taken together, these data indicate that the rate of insulin-stimulated hepatic glycogen synthesis is controlled chiefly through GCK translocation. [ABSTRACT FROM AUTHOR]

• The intracellular metabolism of cellulosic butanol formation was investigated. • Dynamic model was developed by integrating Omics and fermentation data. • Cell and process regulators were identified via metabolic control analysis. • Rational metabolic engineering strategies were proposed. A consolidated bioprocess for cellulosic n -butanol production has been developed by engineering Clostridium cellulovorans to overexpress a bifunctional aldehyde/alcohol dehydrogenase. Rational metabolic engineering is important to further improve butanol production. This study aimed to investigate intracellular metabolism and identify the key regulators of cellulosic butanol formation in C. cellulovorans via integrated Omics and fermentation kinetics data analysis. First, comparative proteomics and metabolomics analyses of wild type and n -butanol producing mutant strain were conducted, which quantified 624 host cell proteins and 474 primary and secondary metabolites. Compared to wild type, most cellulases in cellulolysis were up-regulated, but three glycolysis enzymes and three enzymes in central pathway were down-regulated in the n -butanol producing strain. Second, a dynamic model integrating Omics and fermentation data was developed to identify key regulators in butanol biosynthesis, which were ranked by further metabolic control analysis. Finally, rational metabolic engineering was performed in C. cellulovorans by overexpressing two genes (thl and hbd) identified as important factors limiting butanol biosynthesis, which improved butanol yield and C4/C2 ratio. This study demonstrated a research approach to integrate multi-Omics and fermentation data of C. cellulovorans and guide its rational metabolic engineering, which can also be applied to other microorganisms. [ABSTRACT FROM AUTHOR]

• Recombinant E. coli strain produces 14.3 g L−1 L-tryptophan within 68 h. • A metabolic analysis allowed insight into metabolism during the process. • Different carbon sources and uptake rates led to varying perturbations of metabolism. • Fluxes and concentrations enable estimation of elasticities and control coefficients. • Precursor supply and steps in L-tryptophan biosynthesis were identified as limiting. E. coli strain NT1259 /pF112 aro FBL kan was able to produce 14.3 g L−1 L-tryptophan within 68 h in a fed-batch process from glycerol on a 15 L scale. To gain detailed insight into metabolism of this E. coli strain in the fed-batch process, a sample of L-tryptophan producing cells was withdrawn after 47 h, was separated rapidly and then resuspended in four parallel stirred-tank bioreactors with fresh media. Four different carbon sources (glucose, glycerol, succinate, pyruvate) were supplied individually with varying feeding rates within 19 min and the metabolic reactions of the cells in the four parallel reactors were analyzed by quantification of extracellular and intracellular substrate, product and metabolite concentrations. Data analysis allowed the estimation of intracellular carbon fluxes and of thermodynamic limitations concerning intracellular concentrations and reaction energies. Carbon fluxes and intracellular metabolite concentrations enabled the estimation of elasticities and flux control coefficients by applying metabolic control analysis making use of a metabolic model considering 48 enzymatic reactions and 56 metabolites. As the flux control coefficients describe connections between enzyme activities and metabolic fluxes, they reveal genetic targets for strain improvement. Metabolic control analysis of the recombinant E. coli cells withdrawn from the fed-batch production process clearly indicated that (i) the supply of two precursors for L-tryptophan biosynthesis, L-serine and phosphoribosyl-pyrophosphate, as well as (ii) the formation of aromatic byproducts and (iii) the enzymatic steps of igps and trps2 within the L-tryptophan biosynthesis pathway have major impact on fed-batch production of L-tryptophan from glycerol and should be the targets for further strain improvements. [ABSTRACT FROM AUTHOR]

Cell‐free systems are growing in importance for the biosynthesis of complex molecules. These systems combine the precision of traditional chemistry with the versatility of biology in creating superior overall processes. Recently, a new synthetic pathway for the biosynthesis of isoprenoids using the substrate isopentenol, dubbed the isopentenol utilization pathway (IUP), was demonstrated to be a promising alternative to the native 2C‐methyl‐d‐erythritol‐4‐phosphate (MEP) and mevalonate (MVA) pathways. This simplified pathway, which contains a minimum of four enzymes to produce basic monoterpenes and only depends on ATP and isopentenol as substrates, allows for a highly flexible approach to the commercial synthesis of isoprenoid products. In this work, we use metabolic reconstitution to characterize this new pathway in vitro and demonstrate its use for the cell‐free synthesis of mono‐, sesquit‐, and diterpenoids. Kinetic modeling and sensitivity analysis were also used to identify the most significant parameters for taxadiene productivity, and metabolic control analysis was employed to elucidate protein‐level interactions within this pathway, which demonstrated that the IUP enzymatic system is primarily controlled by the concentration and kinetics of choline kinase (CK) and not regulated by any pathway intermediates. This is a significant advantage over the natural MEP or MVA pathways as it greatly simplifies future metabolic engineering efforts, both in vitro and in vivo, aiming at improving the kinetics of CK. Finally, we used the insights gathered to demonstrate an in vitro IUP system that can produce 220 mg/L of the diterpene taxadiene, in 9 hr, almost 3‐fold faster than any system reported thus far. [ABSTRACT FROM AUTHOR]

Since its conception some fifty years ago, metabolic control analysis (MCA) aims to understand how cells control their metabolism by adjusting the activity of their enzymes. Here we extend its scope to a whole-cell context. We consider metabolism in the evolutionary context of growth-rate maximisation by optimisation of protein concentrations. This framework allows for the prediction of flux control coefficients from proteomics data or stoichiometric modelling. Since genes compete for finite biosynthetic resources, we treat all protein concentrations as interdependent. We show that elementary flux modes (EFMs) emerge naturally as the optimal metabolic networks in the whole-cell context and we derive their control properties. In the evolutionary optimum, the number of expressed EFMs is determined by the number of protein-concentration constraints that limit growth rate. We use published glucose-limited chemostat data of S. cerevisiae to illustrate that it uses only two EFMs prior to the onset of fermentation and that it uses four EFMs during fermentation. We discuss published enzyme-titration data to show that S. cerevisiae and E. coli indeed can express proteins at growth-rate maximising concentrations. Accordingly, we extend MCA to elementary flux modes operating at an optimal state. We find that the expression of growth-unassociated proteins changes results from classical metabolic control analysis. Finally, we show how flux control coefficients can be estimated from proteomics and ribosome-profiling data. We analyse published proteomics data of E. coli to provide a whole-cell perspective of the control of metabolic enzymes on growth rate. We hope that this paper stimulates a renewed interest in metabolic control analysis, so that it can serve again the purpose it once had: to identify general principles that emerge from the biochemistry of the cell and are conserved across biological species. [ABSTRACT FROM AUTHOR]

Metabolic Control Theory (MCT) and Metabolic Control Analysis (MCA) are the two sides, theoretical and experimental, of the measurement of the sensitivity of metabolic networks in the vicinity of a steady state. We will describe the birth and the development of this theory from the first analyses of linear pathways up to a global mathematical theory applicable to any metabolic network. We will describe how the theory, given the global nature of mitochondrial oxidative phosphorylation, solved the problem of what controls mitochondrial ATP synthesis and then how it led to a better understanding of the differential tissue expression of human mitochondrial pathologies and of the heteroplasmy of mitochondrial DNA, leading to the concept of the threshold effect. [ABSTRACT FROM AUTHOR]

Abstract Background Recent advancements in omics measurement technologies have led to an ever-increasing amount of available experimental data that necessitate systems-oriented methodologies for efficient and systematic integration of data into consistent large-scale kinetic models. These models can help us to uncover new insights into cellular physiology and also to assist in the rational design of bioreactor or fermentation processes. Optimization and Risk Analysis of Complex Living Entities (ORACLE) framework for the construction of large-scale kinetic models can be used as guidance for formulating alternative metabolic engineering strategies. Results We used ORACLE in a metabolic engineering problem: improvement of the xylose uptake rate during mixed glucose–xylose consumption in a recombinant Saccharomyces cerevisiae strain. Using the data from bioreactor fermentations, we characterized network flux and concentration profiles representing possible physiological states of the analyzed strain. We then identified enzymes that could lead to improved flux through xylose transporters (XTR). For some of the identified enzymes, including hexokinase (HXK), we could not deduce if their control over XTR was positive or negative. We thus performed a follow-up experiment, and we found out that HXK2 deletion improves xylose uptake rate. The data from the performed experiments were then used to prune the kinetic models, and the predictions of the pruned population of kinetic models were in agreement with the experimental data collected on the HXK2-deficient S. cerevisiae strain. Conclusions We present a design–build–test cycle composed of modeling efforts and experiments with a glucose–xylose co-utilizing recombinant S. cerevisiae and its HXK2-deficient mutant that allowed us to uncover interdependencies between upper glycolysis and xylose uptake pathway. Through this cycle, we also obtained kinetic models with improved prediction capabilities. The present study demonstrates the potential of integrated “modeling and experiments” systems biology approaches that can be applied for diverse applications ranging from biotechnology to drug discovery.

Appropriate substrate selection between fats and glucose is associated with the success of interventions that maintain health such as exercise or caloric restriction, or with the severity of diseases such as diabetes or other metabolic disorders. Although the interaction and mutual inhibition between glucose and fatty-acids (FAs) catabolism has been studied for decades, a quantitative and integrated understanding of the control and regulation of substrate selection through central catabolic pathways is lacking. We addressed this gap here using a computational model representing cardiomyocyte catabolism encompassing glucose (Glc) utilization, pyruvate transport into mitochondria and oxidation in the tricarboxylic acid (TCA) cycle, β-oxidation of palmitate (Palm), oxidative phosphorylation, ion transport, pH regulation, and ROS generation and scavenging in cytoplasmic and mitochondrial compartments. The model is described by 82 differential equations and 119 enzymatic, electron transport and substrate transport reactions accounting for regulatory mechanisms and key players, namely pyruvate dehydrogenase (PDH) and its modulation by multiple effectors. We applied metabolic control analysis to the network operating with various Glc to Palm ratios. The flux and metabolites’ concentration control were visualized through heat maps providing major insights into main control and regulatory nodes throughout the catabolic network. Metabolic pathways located in different compartments were found to reciprocally control each other. For example, glucose uptake and the ATP demand exert control on most processes in catabolism while TCA cycle activities and membrane-associated energy transduction reactions exerted control on mitochondrial processes namely β-oxidation. PFK and PDH, two highly regulated enzymes, exhibit opposite behavior from a control perspective. While PFK activity was a main rate-controlling step affecting the whole network, PDH played the role of a major regulator showing high sensitivity (elasticity) to substrate availability and key activators/inhibitors, a trait expected from a flexible substrate selector strategically located in the metabolic network. PDH regulated the rate of Glc and Palm consumption, consistent with its high sensitivity toward AcCoA, CoA, and NADH. Overall, these results indicate that the control of catabolism is highly distributed across the metabolic network suggesting that fuel selection between FAs and Glc goes well beyond the mechanisms traditionally postulated to explain the glucose-fatty-acid cycle.

Plant science has become more and more complex. With the introduction of new experimental techniques and technologies, it is now possible to explore the fine details of plant metabolism. Besides steady-state measurements often applied in gas-exchange or metabolomic analyses, new approaches, e.g., based on 13 C labeling, are now available to understand the changes in metabolic concentrations under fluctuating environmental conditions in the field or laboratory. To explore those transient phenomena of metabolite concentrations, kinetic models are a valuable tool. In this chapter, we describe ways to implement and build kinetic models of plant metabolism with the Python software package modelbase. As an example, we use a part of the photorespiratory pathway. Moreover, we show additional functionalities of modelbase that help to explore kinetic models and thus can reveal information about a biological system that is not easily accessible to experiments. In addition, we will point to extra information on the mathematical background of kinetic models to give an impetus for further self-study., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Summary: Lysophosphatidate acyltransferase (LPAAT) catalyses the second step of the Kennedy pathway for triacylglycerol (TAG) synthesis. In this study we expressed Trapaeolum majus LPAAT in Brassica napus (B. napus) cv 12075 to evaluate the effects on lipid synthesis and estimate the flux control coefficient for LPAAT.We estimated the flux control coefficient of LPAAT in a whole plant context by deriving a relationship between it and overall lipid accumulation, given that this process is a exponential.Increasing LPAAT activity resulted in greater TAG accumulation in seeds of between 25% and 29%; altered fatty acid distributions in seed lipids (particularly those of the Kennedy pathway); and a redistribution of label from 14C‐glycerol between phosphoglycerides.Greater LPAAT activity in seeds led to an increase in TAG content despite its low intrinsic flux control coefficient on account of the exponential nature of lipid accumulation that amplifies the effect of the small flux increment achieved by increasing its activity. We have also developed a novel application of metabolic control analysis likely to have broad application as it determines the in planta flux control that a single component has upon accumulation of storage products. [ABSTRACT FROM AUTHOR]

Summary: Sulfur assimilation is central to the survival of plants and has been studied under different environmental conditions. Multiple studies have been published trying to determine rate‐limiting or controlling steps in this pathway. However, the picture remains inconclusive with at least two different enzymes proposed to represent such rate‐limiting steps.Here, we used computational modeling to gain an integrative understanding of the distribution of control in the sulfur assimilation pathway of Arabidopsis thaliana. For this purpose, we set up a new ordinary differential equation (ODE)‐based, kinetic model of sulfur assimilation encompassing all biochemical reactions directly involved in this process. We fitted the model to published experimental data and produced a model ensemble to deal with parameter uncertainties. The ensemble was validated against additional published experimental data.We used the model ensemble to subsequently analyse the control pattern and robustly identified a set of processes that share the control in this pathway under standard conditions.Interestingly, the pattern of control is dynamic and not static, that is it changes with changing environmental conditions. Therefore, while adenosine‐5′‐phosphosulfate reductase (APR) and sulfite reductase (SiR) share control under standard laboratory conditions, APR takes over an even more dominant role under sulfur starvation conditions. [ABSTRACT FROM AUTHOR]

Highlights • Glycolysis in Entamoeba uses pyrophosphate as high-energy phospho group donor. • Pyrophosphate derived from anabolism is insufficient for a PPi-dependent glycolysis. • E. histolytica produces 2–3.5 ATP, instead of 5 ATP per glucose catabolized. • The main controlling steps are glucose transport, glycogen metabolism, PGAM and ADHE. • The main flux-controlling reactions are the most adequate therapeutic targets. Abstract Entamoeba histolytica has neither Krebs cycle nor oxidative phosphorylation activities; therefore, glycolysis is the main pathway for ATP supply and provision of carbon skeleton precursors for the synthesis of macromolecules. Glucose is metabolized through fermentative glycolysis, producing ethanol as its main end-product as well as some acetate. Amoebal glycolysis markedly differs from the typical Embden-Meyerhof-Parnas pathway present in human cells: (i) by the use of inorganic pyrophosphate, instead of ATP, as the high-energy phospho group donor; (ii) with one exception, the pathway enzymes can catalyze reversible reactions under physiological conditions; (iii) there is no allosteric regulation and sigmoidal kinetic behavior of key enzymes; and (iv) the presence of some glycolytic and fermentation enzymes similar to those of anaerobic bacteria. These peculiarities bring about alternative mechanisms of control and regulation of the PPi-dependent fermentative glycolysis in the parasite in comparison to the ATP-dependent and allosterically regulated glycolysis in many other eukaryotic cells. In this review, the current knowledge of the carbohydrate metabolism enzymes in E. histolytica is analyzed. Thermodynamics and stoichiometric analyses indicate 2 to 3.5 ATP yield per glucose metabolized, instead of the often presumed 5 ATP/glucose ratio. PPi derived from anabolism seems insufficient for PPi-glycolysis; hence, alternative ways of PPi supply are also discussed. Furthermore, the underlying mechanisms of control and regulation of the E. histolytica carbohydrate metabolism, analyzed by applying integral and systemic approaches such as Metabolic Control Analysis and kinetic modeling, contribute to unveiling alternative and promising drug targets. [ABSTRACT FROM AUTHOR]

Abstract Leishmaniasis is one of the major health issue in developing countries. The current therapeutic regimen for this disease is less effective with lot of adverse effects thereby warranting an urgent need to develop not only new and selective drug candidates but also identification of effective drug targets. Here we present subtractive genomics procedure for identification of putative drug targets in Leishmania. Comprehensive druggability analysis has been carried out in the current work for identified metabolic pathways and drug targets. We also demonstrate effective metabolic simulation methodology to pinpoint putative drug targets in threonine biosynthesis pathway. Metabolic simulation data from the current study indicate that decreasing flux through homoserine kinase reaction can be considered as a good therapeutic opportunity. The data from current study is expected to show new avenue for designing experimental strategies in search of anti-leishmanial agents. Graphical abstract Unlabelled Image Highlights • Subtractive genomic approach for drug target identification • Durggability assessment of identified pathway • Metabolic Simulation of identified pathway • Effect of in silico inhibition of drug targets on metabolic pathway. [ABSTRACT FROM AUTHOR]

Appropriate substrate selection between fats and glucose is associated with the success of interventions that maintain health such as exercise or caloric restriction, or with the severity of diseases such as diabetes or other metabolic disorders. Although the interaction and mutual inhibition between glucose and fatty-acids (FAs) catabolism has been studied for decades, a quantitative and integrated understanding of the control and regulation of substrate selection through central catabolic pathways is lacking. We addressed this gap here using a computational model representing cardiomyocyte catabolism encompassing glucose (Glc) utilization, pyruvate transport into mitochondria and oxidation in the tricarboxylic acid (TCA) cycle, β-oxidation of palmitate (Palm), oxidative phosphorylation, ion transport, pH regulation, and ROS generation and scavenging in cytoplasmic and mitochondrial compartments. The model is described by 82 differential equations and 119 enzymatic, electron transport and substrate transport reactions accounting for regulatory mechanisms and key players, namely pyruvate dehydrogenase (PDH) and its modulation by multiple effectors. We applied metabolic control analysis to the network operating with various Glc to Palm ratios. The flux and metabolites' concentration control were visualized through heat maps providing major insights into main control and regulatory nodes throughout the catabolic network. Metabolic pathways located in different compartments were found to reciprocally control each other. For example, glucose uptake and the ATP demand exert control on most processes in catabolism while TCA cycle activities and membrane-associated energy transduction reactions exerted control on mitochondrial processes namely β-oxidation. PFK and PDH, two highly regulated enzymes, exhibit opposite behavior from a control perspective. While PFK activity was a main rate-controlling step affecting the whole network, PDH played the role of a major regulator showing high sensitivity (elasticity) to substrate availability and key activators/inhibitors, a trait expected from a flexible substrate selector strategically located in the metabolic network. PDH regulated the rate of Glc and Palm consumption, consistent with its high sensitivity toward AcCoA, CoA, and NADH. Overall, these results indicate that the control of catabolism is highly distributed across the metabolic network suggesting that fuel selection between FAs and Glc goes well beyond the mechanisms traditionally postulated to explain the glucose-fatty-acid cycle. [ABSTRACT FROM AUTHOR]

Knowing how the oxidative phosphorylation (OXPHOS) system in cancer cells operates differently from that of normal cells would help find compounds that specifically paralyze the energy metabolism of cancer cells. The first experiments in the study of mitochondrial respiration using the metabolic control analysis (MCA) method were done with isolated liver mitochondria in the early 80s of the last century. Subsequent studies have shown that the regulation of mitochondrial respiration by ADP in isolated mitochondria differs significantly from a model of mitochondria in situ, where the contacts with components in the cytoplasm are largely preserved. The method of selective permeabilization of the outer membrane of the cells allows the application of MCA to evaluate the contribution of different components of the OXPHOS system to its functioning while mitochondria are in a natural state. In this review, we summarize the use of MCA to study OXPHOS in cancer using permeabilized cells and tissues. In addition, we give examples of how this data fits into cancer research with a completely different approach and methodology. [ABSTRACT FROM AUTHOR]

In Microbiology it is often assumed that growth rate is maximal. This may be taken to suggest that the dependence of the growth rate on every enzyme activity is at the top of an inverse-parabolic function, i.e. that all flux control coefficients should equal zero. This might seem to imply that the sum of these flux control coefficients equals zero. According to the summation law of Metabolic Control Analysis (MCA) the sum of flux control coefficients should equal 1 however. And in Flux Balance Analysis (FBA) catabolism is often limited by a hard bound, causing catabolism to fully control the fluxes, again in apparent contrast with a flux control coefficient of zero. Here we resolve these paradoxes (apparent contradictions) in an analysis that uses the 'Edinburgh pathway', the 'Amsterdam pathway', as well as a generic metabolic network providing the building blocks or Gibbs energy for microbial growth. We review and show that (i) optimization depends on so-called enzyme control coefficients rather than the 'catalytic control coefficients' of MCA's summation law, (ii) when optimization occurs at fixed total protein, the former differ from the latter to the extent that they may all become equal to zero in the optimum state, (iii) in more realistic scenarios of optimization where catalytically inert biomass is compensating or maintenance metabolism is taken into consideration, the optimum enzyme concentrations should not be expected to equal those that maximize the specific growth rate, (iv) optimization may be in terms of yield rather than specific growth rate, which resolves the paradox because the sum of catalytic control coefficients on yield equals 0, (v) FBA effectively maximizes growth yield, and for yield the summation law states 0 rather than 1, thereby removing the paradox, (vi) furthermore, FBA then comes more often to a 'hard optimum' defined by a maximum catabolic flux and a catabolic-enzyme control coefficient of 1. The trade-off between maintenance metabolism and growth is highlighted as worthy of further analysis. [ABSTRACT FROM AUTHOR]

Collagen synthesis is severely diminished in osteoarthritis; thus, enhancing it may help the regeneration of cartilage. Collagen synthesis is submitted to a large procollagen cycle where the greater part of the newly synthesized protein is degraded inside the cell producing a huge waste of material and energy. We have applied the Metabolic Control Analysis approach to study the control of collagen synthesis flux by means of the response coefficients of the flux with respect to glycine, proline and lysine. Our results show that the main cause of the procollagen cycle is a protein misfolding mainly due to glycine scarcity, as well as a moderate deficiency of proline and lysine for collagen synthesis. Thus, increasing these amino acids in the diet (especially glycine) may well be a strategy for helping cartilage regeneration by enhancing collagen synthesis and reducing its huge waste in the procollagen cycle; this possibly contributes to the treatment and prevention of osteoarthritis. [Display omitted] • In osteoarthritis there is a deficiency and degeneration of collagen. • Generalized glycine deficiency in humans prevents the correct synthesis of collagen. • Most of the newly synthesized collagen is discarded in the procollagen cycle. • High glycine and moderate concentrations of proline and lysine promotes collagen synthesis reducing its waste. • Glycine, proline and lysine intake as a nutritional supplements can prevent and fight osteoarthritis. [ABSTRACT FROM AUTHOR]

By analysing a large set of models obtained from the JWS Online and Biomodels databases, we tested to what extent the disequilibrium ratio can be used as an estimator for the flux control of a reaction, a discussion point that was already raised by Kacser and Burns, and Heinrich and Rapoport in their seminal MCA manuscripts. Whereas no functional relation was observed, the disequilibrium ratio can be used as an estimator for the maximal flux control of a reaction step. We extended the original analysis of the relationship by incorporating the overall pathway disequilibrium ratio in the expression, which made it possible to make explicit expressions for flux control coefficients. [ABSTRACT FROM AUTHOR]

Metabolic Control Analysis (MCA) marked a turning point in understanding the design principles of metabolic network control by establishing control coefficients as a means to quantify the degree of control that an enzyme exerts on flux or metabolite concentrations. MCA has demonstrated that control of metabolic pathways is distributed among many enzymes rather than depending on a single rate-limiting step. MCA also proved that this distribution depends not only on the stoichiometric structure of the network but also on other kinetic determinants, such as the degree of saturation of the enzyme active site, the distance to thermodynamic equilibrium, and metabolite feedback regulatory loops. Consequently, predicting the alterations that occur during metabolic adaptation in response to strong changes involving a redistribution in such control distribution can be challenging. Here, using the framework provided by MCA, we illustrate how control distribution in a metabolic pathway/network depends on enzyme kinetic determinants and to what extent the redistribution of control affects our predictions on candidate enzymes suitable as targets for small molecule inhibition in the drug discovery process. Our results uncover that kinetic determinants can lead to unexpected control distribution and outcomes that cannot be predicted solely from stoichiometric determinants. We also unveil that the inference of key enzyme-drivers of an observed metabolic adaptation can be dramatically improved using mean control coefficients and ruling out those enzyme activities that are associated with low control coefficients. As the use of constraint-based stoichiometric genome-scale metabolic models (GSMMs) becomes increasingly prevalent for identifying genes/enzymes that could be potential drug targets, we anticipate that incorporating kinetic determinants and ruling out enzymes with low control coefficients into GSMM workflows will facilitate more accurate predictions and reveal novel therapeutic targets. [ABSTRACT FROM AUTHOR]