Metabolic control analysis: a survey of its theoretical and experimental development

Control and Regulation of Substrate Selection in Cytoplasmic and Mitochondrial Catabolic Networks. A Systems Biology Analysis

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.

Thermodynamic constraints on the regulation of metabolic fluxes

Nutrition and metabolism are fundamental to cellular function. Metabolic activity (i.e. rates of flow, most commonly referred to as flux) is constrained by thermodynamics and regulated by the activity of enzymes. The general principles that relate biological and physical variables to metabolic control are incompletely understood. Using metabolic control analysis and computer simulations in several models of simplified metabolic pathways, we derive analytical expressions that define relationships between thermodynamics, enzyme activity, and flux control. The relationships are further analyzed in a mathematical model of glycolysis as an example of a complex biochemical pathway. We show that metabolic pathways that are very far from equilibrium are controlled by the activity of upstream enzymes. However, in general, regulation of metabolic fluxes by an enzyme has a more adaptable pattern, which relies more on distribution of free energy among reaction steps in the pathway than on the thermodynamic properties of the given enzyme. These findings show how the control of metabolic pathways is shaped by thermodynamic constraints of the given pathway.

Delving deeper: Relating the behaviour of a metabolic system to the properties of its components using symbolic metabolic control analysis

High-level behaviour of metabolic systems results from the properties of, and interactions between, numerous molecular components. Reaching a complete understanding of metabolic behaviour based on the system’s components is therefore a difficult task. This problem can be tackled by constructing and subsequently analysing kinetic models of metabolic pathways since such models aim to capture all the relevant properties of the system components and their interactions. Symbolic control analysis is a framework for analysing pathway models in order to reach a mechanistic understanding of their behaviour. By providing algebraic expressions for the sensitivities of system properties, such as metabolic flux or steady-state concentrations, in terms of the properties of individual reactions it allows one to trace the high level behaviour back to these low level components. Here we apply this method to a model of pyruvate branch metabolism in Lactococcus lactis in order to explain a previously observed negative flux response towards an increase in substrate concentration. With this method we are able to show, first, that the sensitivity of flux towards changes in reaction rates (represented by flux control coefficients) is determined by the individual metabolic branches of the pathway, and second, how the sensitivities of individual reaction rates towards their substrates (represented by elasticity coefficients) contribute to this flux control. We also quantify the contributions of enzyme binding and mass-action to enzyme elasticity separately, which allows for an even finer-grained understanding of flux control. These analytical tools allow us to analyse the control properties of a metabolic model and to arrive at a mechanistic understanding of the quantitative contributions of each of the enzymes to this control. Our analysis provides an example of the descriptive power of the general principles of symbolic control analysis.

Noise propagation in an integrated model of bacterial gene expression and growth

In bacterial cells, gene expression, metabolism, and growth are highly interdependent and tightly coordinated. As a result, stochastic fluctuations in expression levels and instantaneous growth rate show intricate cross-correlations. These correlations are shaped by feedback loops, trade-offs and constraints acting at the cellular level; therefore a quantitative understanding requires an integrated approach. To that end, we here present a mathematical model describing a cell that contains multiple proteins that are each expressed stochastically and jointly limit the growth rate. Conversely, metabolism and growth affect protein synthesis and dilution. Thus, expression noise originating in one gene propagates to metabolism, growth, and the expression of all other genes. Nevertheless, under a small-noise approximation many statistical quantities can be calculated analytically. We identify several routes of noise propagation, illustrate their origins and scaling, and establish important connections between noise propagation and the field of metabolic control analysis. We then present a many-protein model containing >1000 proteins parameterized by previously measured abundance data and demonstrate that the predicted cross-correlations between gene expression and growth rate are in broad agreement with published measurements.

A metabolic control analysis approach to introduce the study of systems in biochemistry: the glycolytic pathway in the red blood cell

Metabolic control analysis (MCA) is a promising approach in biochemistry aimed at understanding processes in a quantitative fashion. Here the contribution of enzymes and transporters to the control of a given pathway flux and metabolite concentrations is determined and expressed quantitatively by means of numerical coefficients. Metabolic flux can be influenced by a wide variety of modulators acting on one or more metabolic steps along the pathway. We describe a laboratory exercise to study metabolic regulation of human erythrocytes (RBCs). Within the framework of MCA, students use these cells to determine the sensitivity of the glycolytic flux to two inhibitors (iodoacetic acid: IA, and iodoacetamide: IAA) known to act on the enzyme glyceraldehyde-3-phosphate-dehydrogenase. Glycolytic flux was estimated by determining the concentration of extracellular lactate, the end product of RBC glycolysis. A low-cost colorimetric assay was implemented, that takes advantage of the straightforward quantification of the absorbance signal from the photographic image of the multi-well plate taken with a standard digital camera. Students estimate flux response coefficients for each inhibitor by fitting an empirical function to the experimental data, followed by analytical derivation of this function. IA and IAA exhibit qualitatively different patterns, which are thoroughly analyzed in terms of the physicochemical properties influencing their action on the target enzyme. IA causes highest glycolytic flux inhibition at lower concentration than IAA. This work illustrates the feasibility of using the MCA approach to study key variables of a simple metabolic system, in the context of an upper level biochemistry course. © 2018 International Union of Biochemistry and Molecular Biology, 46(5):502-515, 2018.

A Review of Dynamic Modeling Approaches and Their Application in Computational Strain Optimization for Metabolic Engineering

Mathematical modeling is a key process to describe the behavior of biological networks. One of the most difficult challenges is to build models that allow quantitative predictions of the cells' states along time. Recently, this issue started to be tackled through novel in silico approaches, such as the reconstruction of dynamic models, the use of phenotype prediction methods, and pathway design via efficient strain optimization algorithms. The use of dynamic models, which include detailed kinetic information of the biological systems, potentially increases the scope of the applications and the accuracy of the phenotype predictions. New efforts in metabolic engineering aim at bridging the gap between this approach and other different paradigms of mathematical modeling, as constraint-based approaches. These strategies take advantage of the best features of each method, and deal with the most remarkable limitation—the lack of available experimental information—which affects the accuracy and feasibility of solutions. Parameter estimation helps to solve this problem, but adding more computational cost to the overall process. Moreover, the existing approaches include limitations such as their scalability, flexibility, convergence time of the simulations, among others. The aim is to establish a trade-off between the size of the model and the level of accuracy of the solutions. In this work, we review the state of the art of dynamic modeling and related methods used for metabolic engineering applications, including approaches based on hybrid modeling. We describe approaches developed to undertake issues regarding the mathematical formulation and the underlying optimization algorithms, and that address the phenotype prediction by including available kinetic rate laws of metabolic processes. Then, we discuss how these have been used and combined as the basis to build computational strain optimization methods for metabolic engineering purposes, how they lead to bi-level schemes that can be used in the industry, including a consideration of their limitations.

Four Key Steps Control Glycolytic Flux in Mammalian Cells

Altered glycolysis is a hallmark of diseases including diabetes and cancer. Despite intensive study of the contributions of individual glycolytic enzymes, systems-level analyses of flux control through glycolysis remain limited. Here, we overexpress in two mammalian cell lines the individual enzymes catalyzing each of the 12 steps linking extracellular glucose to excreted lactate, and find substantial flux control at four steps: glucose import, hexokinase, phosphofructokinase, and lactate export (and not at any steps of lower glycolysis). The four flux-controlling steps are specifically upregulated by the Ras oncogene: optogenetic Ras activation rapidly induces the transcription of isozymes catalyzing these four steps and enhances glycolysis. At least one isozyme catalyzing each of these four steps is consistently elevated in human tumors. Thus, in the studied contexts, flux control in glycolysis is concentrated in four key enzymatic steps. Upregulation of these steps in tumors likely underlies the Warburg effect.

Redox mechanism of levobupivacaine cytostatic effect on human prostate cancer cells

Anti-cancer effects of local anesthetics have been reported but the mode of action remains elusive. Here, we examined the bioenergetic and REDOX impact of levobupivacaine on human prostate cancer cells (DU145) and corresponding non-cancer primary human prostate cells (BHP). Levobupivacaine induced a combined inhibition of glycolysis and oxidative phosphorylation in cancer cells, resulting in a reduced cellular ATP production and consecutive bioenergetic crisis, along with reactive oxygen species generation. The dose-dependent inhibition of respiratory chain complex I activity by levobupivacaine explained the alteration of mitochondrial energy fluxes. Furthermore, the potency of levobupivacaine varied with glucose and oxygen availability as well as the cellular energy demand, in accordance with a bioenergetic anti-cancer mechanism. The levobupivacaine-induced bioenergetic crisis triggered cytostasis in prostate cancer cells as evidenced by a S-phase cell cycle arrest, without apoptosis induction. In DU145 cells, levobupivacaine also triggered the induction of autophagy and blockade of this process potentialized the anti-cancer effect of the local anesthetic. Therefore, our findings provide a better characterization of the REDOX mechanisms underpinning the anti-effect of levobupivacaine against human prostate cancer cells.

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Local anesthetics reduce cancer recurrence in prostate cancer.

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Metabolic reprogramming in a hallmark of cancer.

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Complex I inhibition is a potential anti-cancer bioenergetic therapeutic strategy.

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Levobupivacaine inhibits complex I activity and mitochondrial respiration.

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Autophagy blocker combined with levobupivacaine induces cytostasis in prostate cancer.

Structure versus time in the evolutionary diversification of avian carotenoid metabolic networks

Historical associations of genes and proteins are thought to delineate pathways available to subsequent evolution; however, the effects of past functional involvements on contemporary evolution are rarely quantified. Here, we examined the extent to which the structure of a carotenoid enzymatic network persists in avian evolution. Specifically, we tested whether the evolution of carotenoid networks was most concordant with phylogenetically structured expansion from core reactions of common ancestors or with subsampling of biochemical pathway modules from an ancestral network. We compared structural and historical associations in 467 carotenoid networks of extant and ancestral species and uncovered the overwhelming effect of pre-existing metabolic network structure on carotenoid diversification over the last 50 million years of avian evolution. Over evolutionary time, birds repeatedly subsampled and recombined conserved biochemical modules, which likely maintained the overall structure of the carotenoid metabolic network during avian evolution. These findings explain the recurrent convergence of evolutionary distant species in carotenoid metabolism and weak phylogenetic signal in avian carotenoid evolution. Remarkable retention of an ancient metabolic structure throughout extensive and prolonged ecological diversification in avian carotenoid metabolism illustrates a fundamental requirement of organismal evolution - historical continuity of a deterministic network that links past and present functional associations of its components.

Metabolite secretion in microorganisms: the theory of metabolic overflow put to the test

INTRODUCTION

Microbial cells secrete many metabolites during growth, including important intermediates of the central carbon metabolism. This has not been taken into account by researchers when modeling microbial metabolism for metabolic engineering and systems biology studies.

MATERIALS AND METHODS

The uptake of metabolites by microorganisms is well studied, but our knowledge of how and why they secrete different intracellular compounds is poor. The secretion of metabolites by microbial cells has traditionally been regarded as a consequence of intracellular metabolic overflow.

CONCLUSIONS

Here, we provide evidence based on time-series metabolomics data that microbial cells eliminate some metabolites in response to environmental cues, independent of metabolic overflow. Moreover, we review the different mechanisms of metabolite secretion and explore how this knowledge can benefit metabolic modeling and engineering.

A Predictive Model for Selective Targeting of the Warburg Effect through GAPDH Inhibition with a Natural Product

Targeted cancer therapies that use genetics are successful, but principles for selectively targeting tumor metabolism that is also dependent on the environment remain unknown. We now show that differences in rate-controlling enzymes during the Warburg effect (WE), the most prominent hallmark of cancer cell metabolism, can be used to predict a response to targeting glucose metabolism. We establish a natural product, koningic acid (KA), to be a selective inhibitor of GAPDH, an enzyme we characterize to have differential control properties over metabolism during the WE. With machine learning and integrated pharmacogenomics and metabolomics, we demonstrate that KA efficacy is not determined by the status of individual genes, but by the quantitative extent of the WE, leading to a therapeutic window in vivo. Thus, the basis of targeting the WE can be encoded by molecular principles that extend beyond the status of individual genes.

In Vitro Metabolic Engineering of Amorpha-4,11-diene Biosynthesis at Enhanced Rate and Specific Yield of Production

In vitro metabolic engineering is an alternative approach to cell-based biosynthesis. It offers unprecedented flexibility for the study of biochemical pathways, thus providing useful information for the rational design and assembly of reaction modules. Herein, we took the advantage of in vitro metabolic engineering to initially gain insight into the regulatory network of a reconstituted amorpha-4,11-diene (AD) synthetic pathway. Guided by lin-log approximation, we rapidly identified the hitherto unrecognized inhibition of adenosine triphosphate (ATP) on both farnesyl pyrophosphate synthase (FPPS) and amorpha-4,11-diene synthase (ADS). Furthermore, the byproduct, pyrophosphate (PPi), potently inhibits ADS, but not FPPS. To lower the inhibition, an ATP recycling system and pyrophosphatase were used and resulted in a significant (∼3-fold) enhancement in the rate of AD production (∼5.7 μmol L^-1 min^-1). A coimmobilized multienzyme reaction system was then developed to recycle the enzymes. When inhibitory metabolites concentrations were reduced, the specific enzymatic yield of AD was further enhanced (>6-fold). This study demonstrated that mitigating the accumulation of inhibitory metabolites can result in higher yields of AD production by in vitro multienzymatic reactions.

Engineering biological systems using automated biofoundries

Engineered biological systems such as genetic circuits and microbial cell factories have promised to solve many challenges in the modern society. However, the artisanal processes of research and development are slow, expensive, and inconsistent, representing a major obstacle in biotechnology and bioengineering. In recent years, biological foundries or biofoundries have been developed to automate design-build-test engineering cycles in an effort to accelerate these processes. This review summarizes the enabling technologies for such biofoundries as well as their early successes and remaining challenges.

Parametric Sensitivity Analysis of Oscillatory Delay Systems with an Application to Gene Regulation

A parametric sensitivity analysis for periodic solutions of delay-differential equations is developed. Because phase shifts cause the sensitivity coefficients of a periodic orbit to diverge, we focus on sensitivities of the extrema, from which amplitude sensitivities are computed, and of the period. Delay-differential equations are often used to model gene expression networks. In these models, the parametric sensitivities of a particular genotype define the local geometry of the evolutionary landscape. Thus, sensitivities can be used to investigate directions of gradual evolutionary change. An oscillatory protein synthesis model whose properties are modulated by RNA interference is used as an example. This model consists of a set of coupled delay-differential equations involving three delays. Sensitivity analyses are carried out at several operating points. Comments on the evolutionary implications of the results are offered.

No Evidence That Protein Noise-Induced Epigenetic Epistasis Constrains Gene Expression Evolution

Changes in gene expression can affect phenotypes and therefore both its level and stochastic variability are frequently under selection. It has recently been proposed that epistatic interactions influence gene expression evolution: gene pairs where simultaneous knockout is more deleterious than expected should evolve reduced expression noise to avoid concurrent low expression of both proteins. In apparent support, yeast genes with many epistatic partners have low expression variation both among isogenic individuals and between species. However, the specific predictions and basic assumptions of this verbal model remain untested. Using bioinformatics analysis, we first demonstrate that the model's predictions are unsupported by available large-scale data. Based on quantitative biochemical modeling, we then show that epistasis between expression reductions (epigenetic epistasis) is not expected to aggravate the fitness cost of stochastic expression, which is in sharp contrast to the verbal argument. This nonintuitive result can be readily explained by the typical diminishing return of fitness on gene activity and by the fact that expression noise not only decreases but also increases the abundance of proteins. Overall, we conclude that stochastic variation in epistatic partners is unlikely to drive noise minimization or constrain gene expression divergence on a genomic scale.

ADAPTATION TO FERMENTING RESOURCES IN DROSOPHILA MELANOGASTER: ETHANOL AND ACETIC ACID TOLERANCES SHARE A COMMON GENETIC BASIS

Ethanol and acetic acid tolerances were compared in a French, highly tolerant population, and in a Congolese, very sensitive population. For both tolerances, chromosome substitutions demonstrated a major effect on chromosome 3, a lesser effect on chromosome 2, and no effect on chromosome 1, except in interactions. Directional selection experiments led to significant increases of tolerance to both toxics. Of greater interest, a strong correlated response was observed in each line: increased ethanol tolerance was accompanied by higher acetic acid tolerance and vice versa. A high genetic correlation (average value r = 0.77) was found between the two traits. These data suggest that alcohol dehydrogenase (ADH) activity does not play a major role in explaining the physiological differences known between Afrotropical and European populations. The metabolic flux permitting the detoxification of ethanol and acetic acid seems to be mainly controlled by acetyl-coA synthetase (ACS) at least in adult flies. Acetic acid adaptation could be as important as ethanol adaptation in the ecology of Drosophila melanogaster and other Drosophila species.

High-resolution respirometry of fine-needle muscle biopsies in pre-manifest Huntington's disease expansion mutation carriers shows normal mitochondrial respiratory function

Alterations in mitochondrial respiration are an important hallmark of Huntington's disease (HD), one of the most common monogenetic causes of neurodegeneration. The ubiquitous expression of the disease causing mutant huntingtin gene raises the prospect that mitochondrial respiratory deficits can be detected in skeletal muscle. While this tissue is readily accessible in humans, transgenic animal models offer the opportunity to cross-validate findings and allow for comparisons across organs, including the brain. The integrated respiratory chain function of the human vastus lateralis muscle was measured by high-resolution respirometry (HRR) in freshly taken fine-needle biopsies from seven pre-manifest HD expansion mutation carriers and nine controls. The respiratory parameters were unaffected. For comparison skeletal muscle isolated from HD knock-in mice (HdhQ111) as well as a broader spectrum of tissues including cortex, liver and heart muscle were examined by HRR. Significant changes of mitochondrial respiration in the HdhQ knock-in mouse model were restricted to the liver and the cortex. Mitochondrial mass as quantified by mitochondrial DNA copy number and citrate synthase activity was stable in murine HD-model tissue compared to control. mRNA levels of key enzymes were determined to characterize mitochondrial metabolic pathways in HdhQ mice. We demonstrated the feasibility to perform high-resolution respirometry measurements from small human HD muscle biopsies. Furthermore, we conclude that alterations in respiratory parameters of pre-manifest human muscle biopsies are rather limited and mirrored by a similar absence of marked alterations in HdhQ skeletal muscle. In contrast, the HdhQ111 murine cortex and liver did show respiratory alterations highlighting the tissue specific nature of mutant huntingtin effects on respiration.

Robust identification of metabolic control for microbial l-methionine production following an easy-to-use puristic approach

The identification of promising metabolic engineering targets is a key issue in metabolic control analysis (MCA). Conventional approaches make intensive use of model-based studies, such as exploiting post-pulse metabolic dynamics after proper perturbation of the microbial system. Here, we present an easy-to-use, purely data-driven approach, defining pool efflux capacities (PEC) for identifying reactions that exert the highest flux control in linear pathways. Comparisons with linlog-based MCA and data-driven substrate elasticities (DDSE) showed that similar key control steps were identified using PEC. Using the example of l-methionine production with recombinant Escherichia coli, PEC consistently and robustly identified main flux controls using perturbation data after a non-labeled ¹²C-l-serine stimulus. Furthermore, the application of full-labeled ¹³C-l-serine stimuli yielded additional insights into stimulus propagation to l-methionine. PEC analysis performed on the ¹³C data set revealed the same targets as the ¹²C data set. Notably, the typical drawback of metabolome analysis, namely, the omnipresent leakage of metabolites, was excluded using the ¹³C PEC approach.

The Enigma of the Respiratory Chain Supercomplex

Respiratory chain dysfunction plays an important role in human disease and aging. It is now well established that the individual respiratory complexes can be organized into supercomplexes, and structures for these macromolecular assemblies, determined by electron cryo-microscopy, have been described recently. Nevertheless, the reason why supercomplexes exist remains an enigma. The widely held view that they enhance catalysis by channeling substrates is challenged by both structural and biophysical information. Here, we evaluate and discuss data and hypotheses on the structures, roles, and assembly of respiratory-chain supercomplexes and propose a future research agenda to address unanswered questions.

The promiscuous enzyme medium-chain 3-keto-acyl-CoA thiolase triggers a vicious cycle in fatty-acid beta-oxidation

Mitochondrial fatty-acid beta-oxidation (mFAO) plays a central role in mammalian energy metabolism. Multiple severe diseases are associated with defects in this pathway. Its kinetic structure is characterized by a complex wiring of which the functional implications have hardly been explored. Repetitive cycles of reversible reactions, each cycle shortening the fatty acid by two carbon atoms, evoke competition between intermediates of different chain lengths for a common set of 'promiscuous' enzymes (enzymes with activity towards multiple substrates). In our validated kinetic model of the pathway, substrate overload causes a steep and detrimental flux decline. Here, we unravel the underlying mechanism and the role of enzyme promiscuity in it. Comparison of alternative model versions elucidated the role of promiscuity of individual enzymes. Promiscuity of the last enzyme of the pathway, medium-chain ketoacyl-CoA thiolase (MCKAT), was both necessary and sufficient to elicit the flux decline. Subsequently, Metabolic Control Analysis revealed that MCKAT had insufficient capacity to cope with high substrate influx. Next, we quantified the internal metabolic regulation, revealing a vicious cycle around MCKAT. Upon substrate overload, MCKAT's ketoacyl-CoA substrates started to accumulate. The unfavourable equilibrium constant of the preceding enzyme, medium/short-chain hydroxyacyl-CoA dehydrogenase, worked as an amplifier, leading to accumulation of upstream CoA esters, including acyl-CoA esters. These acyl-CoA esters are at the same time products of MCKAT and inhibited its already low activity further. Finally, the accumulation of CoA esters led to a sequestration of free CoA. CoA being a cofactor for MCKAT, its sequestration limited the MCKAT activity even further, thus completing the vicious cycle. Since CoA is also a substrate for distant enzymes, it efficiently communicated the 'traffic jam' at MCKAT to the entire pathway. This novel mechanism provides a basis to explore the role of mFAO in disease and elucidate similar principles in other pathways of lipid metabolism.

Discovery and Characterization of the 3-Hydroxyacyl-ACP Dehydratase Component of the Plant Mitochondrial Fatty Acid Synthase System

We report the characterization of the Arabidopsis (Arabidopsis thaliana) 3-hydroxyacyl-acyl carrier protein dehydratase (mtHD) component of the mitochondrial fatty acid synthase (mtFAS) system, encoded by AT5G60335. The mitochondrial localization and catalytic capability of mtHD were demonstrated with a green fluorescent protein transgenesis experiment and by in vivo complementation and in vitro enzymatic assays. RNA interference (RNAi) knockdown lines with reduced mtHD expression exhibit traits typically associated with mtFAS mutants, namely a miniaturized morphological appearance, reduced lipoylation of lipoylated proteins, and altered metabolomes consistent with the reduced catalytic activity of lipoylated enzymes. These alterations are reversed when mthd-rnai mutant plants are grown in a 1% CO₂ atmosphere, indicating the link between mtFAS and photorespiratory deficiency due to the reduced lipoylation of glycine decarboxylase. In vivo biochemical feeding experiments illustrate that sucrose and glycolate are the metabolic modulators that mediate the alterations in morphology and lipid accumulation. In addition, both mthd-rnai and mtkas mutants exhibit reduced accumulation of 3-hydroxytetradecanoic acid (i.e. a hallmark of lipid A-like molecules) and abnormal chloroplastic starch granules; these changes are not reversible by the 1% CO₂ atmosphere, demonstrating two novel mtFAS functions that are independent of photorespiration. Finally, RNA sequencing analysis revealed that mthd-rnai and mtkas mutants are nearly equivalent to each other in altering the transcriptome, and these analyses further identified genes whose expression is affected by a functional mtFAS system but independent of photorespiratory deficiency. These data demonstrate the nonredundant nature of the mtFAS system, which contributes unique lipid components needed to support plant cell structure and metabolism.

Traditional and novel tools to probe the mitochondrial metabolism in health and disease

Mitochondrial metabolism links energy production to other essential cellular processes such as signaling, cellular differentiation, and apoptosis. In addition to producing adenosine triphosphate (ATP) as an energy source, mitochondria are responsible for the synthesis of a myriad of important metabolites and cofactors such as tetrahydrofolate, α-ketoacids, steroids, aminolevulinic acid, biotin, lipoic acid, acetyl-CoA, iron-sulfur clusters, heme, and ubiquinone. Furthermore, mitochondria and their metabolism have been implicated in aging and several human diseases, including inherited mitochondrial disorders, cardiac dysfunction, heart failure, neurodegenerative diseases, diabetes, and cancer. Therefore, there is great interest in understanding mitochondrial metabolism and the complex relationship it has with other cellular processes. A large number of studies on mitochondrial metabolism have been conducted in the last 50 years, taking a broad range of approaches. In this review, we summarize and discuss the most commonly used tools that have been used to study different aspects of the metabolism of mitochondria: ranging from dyes that monitor changes in the mitochondrial membrane potential and pharmacological tools to study respiration or ATP synthesis, to more modern tools such as genetically encoded biosensors and trans-omic approaches enabled by recent advances in mass spectrometry, computation, and other technologies. These tools have allowed the large number of studies that have shaped our current understanding of mitochondrial metabolism. WIREs Syst Biol Med 2017, 9:e1373. doi: 10.1002/wsbm.1373 For further resources related to this article, please visit the WIREs website.

Construction and simulation of the Bradyrhizobium diazoefficiens USDA110 metabolic network: a comparison between free-living and symbiotic states

The first genome-scale metabolic network forBradyrhizobiumwas constructed and the metabolic properties were compared between the free-living and symbiotic physiological states.

Direct Evidence for Metabolon Formation and Substrate Channeling in Recombinant TCA Cycle Enzymes

Supramolecular assembly of enzymes into metabolon structures is thought to enable efficient transport of reactants between active sites via substrate channeling. Recombinant versions of porcine citrate synthase (CS), mitochondrial malate dehydrogenase (mMDH), and aconitase (Aco) were found to adopt a homogeneous native-like metabolon structure in vitro. Site-directed mutagenesis performed on highly conserved arginine residues located in the positively charged channel connecting mMDH and CS active sites led to the identification of CS(R65A) which retained high catalytic efficiency. Substrate channeling between the CS mutant and mMDH was severely impaired and the overall channeling probability decreased from 0.99 to 0.023. This work provides direct mechanistic evidence for the channeling of reaction intermediates, and disruption of this interaction would have important implications on the control of flux in central carbon metabolism.

Rate control in yeast protein synthesis at the population and single-cell levels

Yeast commits approximately 76% of its energy budget to protein synthesis and the efficiency and control of this process are accordingly critical to organism growth and fitness. We now have detailed genetic, biochemical and biophysical knowledge of the components of the eukaryotic translation machinery. However, these kinds of information do not, in themselves, give us a satisfactory picture of how the overall system is controlled. This is where quantitative system analysis can enable a step-change in our understanding of biological resource management and how this relates to cell physiology and evolution. An important aspect of this more system-oriented approach to translational control is the inherent heterogeneity of cell populations that is generated by gene expression noise. In this short review, we address the fact that, although the vast majority of our knowledge of the translation machinery is based on experimental analysis of samples that each contain hundreds of millions of cells, in reality every cell is unique in terms of its composition and control properties. We have entered a new era in which research into the heterogeneity of cell systems promises to provide answers to many (previously unanswerable) questions about cell physiology and evolution.

The metabolome 18 years on: a concept comes of age

BACKGROUND

The term 'metabolome' was introduced to the scientific literature in September 1998.

AIM AND KEY SCIENTIFIC CONCEPTS OF THE REVIEW

To mark its 18-year-old 'coming of age', two of the co-authors of that paper review the genesis of metabolomics, whence it has come and where it may be going.

Evaluation of rate law approximations in bottom-up kinetic models of metabolism

BACKGROUND

The mechanistic description of enzyme kinetics in a dynamic model of metabolism requires specifying the numerical values of a large number of kinetic parameters. The parameterization challenge is often addressed through the use of simplifying approximations to form reaction rate laws with reduced numbers of parameters. Whether such simplified models can reproduce dynamic characteristics of the full system is an important question.

RESULTS

In this work, we compared the local transient response properties of dynamic models constructed using rate laws with varying levels of approximation. These approximate rate laws were: 1) a Michaelis-Menten rate law with measured enzyme parameters, 2) a Michaelis-Menten rate law with approximated parameters, using the convenience kinetics convention, 3) a thermodynamic rate law resulting from a metabolite saturation assumption, and 4) a pure chemical reaction mass action rate law that removes the role of the enzyme from the reaction kinetics. We utilized in vivo data for the human red blood cell to compare the effect of rate law choices against the backdrop of physiological flux and concentration differences. We found that the Michaelis-Menten rate law with measured enzyme parameters yields an excellent approximation of the full system dynamics, while other assumptions cause greater discrepancies in system dynamic behavior. However, iteratively replacing mechanistic rate laws with approximations resulted in a model that retains a high correlation with the true model behavior. Investigating this consistency, we determined that the order of magnitude differences among fluxes and concentrations in the network were greatly influential on the network dynamics. We further identified reaction features such as thermodynamic reversibility, high substrate concentration, and lack of allosteric regulation, which make certain reactions more suitable for rate law approximations.

CONCLUSIONS

Overall, our work generally supports the use of approximate rate laws when building large scale kinetic models, due to the key role that physiologically meaningful flux and concentration ranges play in determining network dynamics. However, we also showed that detailed mechanistic models show a clear benefit in prediction accuracy when data is available. The work here should help to provide guidance to future kinetic modeling efforts on the choice of rate law and parameterization approaches.

The Landscape of Evolution: Reconciling Structural and Dynamic Properties of Metabolic Networks in Adaptive Diversifications

The network of the interactions among genes, proteins, and metabolites delineates a range of potential phenotypic diversifications in a lineage, and realized phenotypic changes are the result of differences in the dynamics of the expression of the elements and interactions in this deterministic network. Regulatory mechanisms, such as hormones, mediate the relationship between the structural and dynamic properties of networks by determining how and when the elements are expressed and form a functional unit or state. Changes in regulatory mechanisms lead to variable expression of functional states of a network within and among generations. Functional properties of network elements, and the magnitude and direction of evolutionary change they determine, depend on their location within a network. Here, we examine the relationship between network structure and the dynamic mechanisms that regulate flux through a metabolic network. We review the mechanisms that control metabolic flux in enzymatic reactions and examine structural properties of the network locations that are targets of flux control. We aim to establish a predictive framework to test the contributions of structural and dynamic properties of deterministic networks to evolutionary diversifications.

Constructing kinetic models of metabolism at genome-scales: A review

Constraint-based modeling of biological networks (metabolism, transcription and signal transduction), although used successfully in many applications, suffer from specific limitations such as the lack of representation of metabolite concentrations and enzymatic regulation, which are necessary for a complete physiologically relevant model. Kinetic models conversely overcome these shortcomings and enable dynamic analysis of biological systems for enhanced in silico hypothesis generation. Nonetheless, kinetic models also have limitations for modeling at genome-scales chiefly due to: (i) model non-linearity; (ii) computational tractability; (iii) parameter identifiability; (iv) estimability; and (v) uncertainty. In order to support further development of kinetic models as viable alternatives to constraint-based models, this review presents a brief description of the existing obstacles towards building genome-scale kinetic models. Specific kinetic modeling frameworks capable of overcoming these obstacles are covered in this review. The tractability and physiological feasibility of these models are discussed with the objective of using available in vivo experimental observations to define the model parameter space. Among the different methods discussed, Monte Carlo kinetic models of metabolism stand out as potentially tractable methods to model genome scale networks while also addressing in vivo parameter uncertainty.

Stability of Ensemble Models Predicts Productivity of Enzymatic Systems

Stability in a metabolic system may not be obtained if incorrect amounts of enzymes are used. Without stability, some metabolites may accumulate or deplete leading to the irreversible loss of the desired operating point. Even if initial enzyme amounts achieve a stable steady state, changes in enzyme amount due to stochastic variations or environmental changes may move the system to the unstable region and lose the steady-state or quasi-steady-state flux. This situation is distinct from the phenomenon characterized by typical sensitivity analysis, which focuses on the smooth change before loss of stability. Here we show that metabolic networks differ significantly in their intrinsic ability to attain stability due to the network structure and kinetic forms, and that after achieving stability, some enzymes are prone to cause instability upon changes in enzyme amounts. We use Ensemble Modelling for Robustness Analysis (EMRA) to analyze stability in four cell-free enzymatic systems when enzyme amounts are changed. Loss of stability in continuous systems can lead to lower production even when the system is tested experimentally in batch experiments. The predictions of instability by EMRA are supported by the lower productivity in batch experimental tests. The EMRA method incorporates properties of network structure, including stoichiometry and kinetic form, but does not require specific parameter values of the enzymes.

Redirection of metabolite biosynthesis from hydroxybenzoates to volatile terpenoids in green hairy roots of Daucus carota

A metabolic shift in green hairy root cultures of carrot from phenylpropanoid/benzenoid biosynthesis toward volatile isoprenoids was observed when compared with the metabolite profile of normal hairy root cultures. Hairy roots cultures of Daucus carota turned green under continuous illumination, while the content of the major phenolic compound p-hydroxybenzoic acid (p-HBA) was reduced to half as compared to normal hairy roots cultured in darkness. p-Hydroxybenzaldehyde dehydrogenase (HBD) activity was suppressed in the green hairy roots. However, comparative volatile analysis of 14-day-old green hairy roots revealed higher monoterpene and sesquiterpene contents than found in normal hairy roots. Methyl salicylate content was higher in normal hairy roots than in green ones. Application of clomazone, an inhibitor of 1-deoxy-D-xylulose 5-phosphate synthase (DXS), reduced the amount of total monoterpenes and sesquiterpenes in green hairy roots compared to normal hairy roots. However, methyl salicylate content was enhanced in both green and normal hairy roots treated with clomazone as compared to their respective controls. Because methyl-erythritol 4-phosphate (MEP) and phenylpropanoid pathways, respectively, contribute to the formation of monoterpenes and phenolic acids biosynthesis, the activities of enzymes regulating those pathways were measured in terms of their in vitro activities, in both green and normal hairy root cultures. These key enzymes were 1-deoxy-D-xylulose 5-phosphate reductoisomerase (DXR), an early regulatory enzyme of the MEP pathway, pyruvate kinase (PK), an enzyme of primary metabolism related to the MEP pathway, shikimate dehydrogenase (SKDH) which is involved in biosynthesis of aromatic amino acids, and phenylalanine ammonia-lyase (PAL) that catalyzes the first step of phenylpropanoid biosynthesis. Activities of DXR and PK were higher in green hairy roots as compared to normal ones, whereas the opposite trend was observed for SKDH and PAL activities. Gene expression analysis of DXR and PAL showed trends similar to those for the respective enzyme activities. Based on these observations, we suggest a possible redirection of metabolites from the primary metabolism toward isoprenoid biosynthesis, limiting the phenolic biosynthetic pathway in green hairy roots grown under continuous light.

A Dormant Microbial Component in the Development of Preeclampsia

Preeclampsia (PE) is a complex, multisystem disorder that remains a leading cause of morbidity and mortality in pregnancy. Four main classes of dysregulation accompany PE and are widely considered to contribute to its severity. These are abnormal trophoblast invasion of the placenta, anti-angiogenic responses, oxidative stress, and inflammation. What is lacking, however, is an explanation of how these themselves are caused. We here develop the unifying idea, and the considerable evidence for it, that the originating cause of PE (and of the four classes of dysregulation) is, in fact, microbial infection, that most such microbes are dormant and hence resist detection by conventional (replication-dependent) microbiology, and that by occasional resuscitation and growth it is they that are responsible for all the observable sequelae, including the continuing, chronic inflammation. In particular, bacterial products such as lipopolysaccharide (LPS), also known as endotoxin, are well known as highly inflammagenic and stimulate an innate (and possibly trained) immune response that exacerbates the inflammation further. The known need of microbes for free iron can explain the iron dysregulation that accompanies PE. We describe the main routes of infection (gut, oral, and urinary tract infection) and the regularly observed presence of microbes in placental and other tissues in PE. Every known proteomic biomarker of "preeclampsia" that we assessed has, in fact, also been shown to be raised in response to infection. An infectious component to PE fulfills the Bradford Hill criteria for ascribing a disease to an environmental cause and suggests a number of treatments, some of which have, in fact, been shown to be successful. PE was classically referred to as endotoxemia or toxemia of pregnancy, and it is ironic that it seems that LPS and other microbial endotoxins really are involved. Overall, the recognition of an infectious component in the etiology of PE mirrors that for ulcers and other diseases that were previously considered to lack one.

Theoretical approaches for the dynamics of complex biological systems from information of networks

Modern biology has provided many examples of large networks describing the interactions between multiple species of bio-molecules. It is believed that the dynamics of molecular activities based on such networks are the origin of biological functions. On the other hand, we have a limited understanding for dynamics of molecular activity based on networks. To overcome this problem, we have developed two structural theories, by which the important aspects of the dynamical properties of the system are determined only from information on the network structure, without assuming other quantitative details. The first theory, named Linkage Logic, determines a subset of molecules in regulatory networks, by which any long-term dynamical behavior of the whole system can be identified/controlled. The second theory, named Structural Sensitivity Analysis, determines the sensitivity responses of the steady state of chemical reaction networks to perturbations of the reaction rate. The first and second theories investigate the dynamical properties of regulatory and reaction networks, respectively. The first theory targets the attractors of the regulatory network systems, whereas the second theory applies only to the steady states of the reaction network systems, but predicts their detailed behavior. To demonstrate the utility of our methods several biological network systems, and show they are practically useful to analyze behaviors of biological systems.

Dynamics of ADAM17-Mediated Shedding of ACE2 Applied to Pancreatic Islets of Male db/db Mice

Angiotensin-converting enzyme 2 (ACE2) gene therapy aimed at counteracting pancreatic ACE2 depletion improves glucose regulation in two diabetic mouse models: db/db mice and angiotensin II-infused mice. A disintegrin and metalloproteinase 17 (ADAM17) can cause shedding of ACE2 from the cell membrane. The aim of our studies was to determine whether ADAM17 depletes ACE2 levels in pancreatic islets and β-cells. Dynamics of ADAM17-mediated ACE2 shedding were investigated in 832/13 insulinoma cells. Within a wide range of ACE2 expression levels, including the level observed in mouse pancreatic islets, overexpression of ADAM17 increases shed ACE2 and decreases cellular ACE2 levels. We provide a mathematical description of shed and cellular ACE2 activities as a function of the ADAM17 activity. The effect of ADAM17 on the cellular ACE2 content was relatively modest with an absolute control strength value less than 0.25 and approaching 0 at low ADAM17 activities. Although we found that ADAM17 and ACE2 are both expressed in pancreatic islets, the β-cell is not the major cell type expressing ACE2 in islets. During diabetes progression in 8-, 12-, and 15-week-old db/db mice, ACE2 mRNA and ACE2 activity levels in pancreatic islets were not decreased over time nor significantly decreased compared with nondiabetic db/m mice. Levels of ADAM17 mRNA and ADAM17 activity were also not significantly changed. Inhibiting basal ADAM17 activity in mouse islets failed to affect ACE2 levels. We conclude that whereas ADAM17 has the ability to shed ACE2, ADAM17 does not deplete ACE2 from pancreatic islets in diabetic db/db mice.

PreProPath: An Uncertainty-Aware Algorithm for Identifying Predictable Profitable Pathways in Biochemical Networks

Pathway analysis is a powerful approach to enable rational design or redesign of biochemical networks for optimizing metabolic engineering and synthetic biology objectives such as production of desired chemicals or biomolecules from specific nutrients. While experimental methods can be quite successful, computational approaches can enhance discovery and guide experimentation by efficiently exploring very large design spaces. We present a computational algorithm, Predictably Profitable Path (PreProPath), to identify target pathways best suited for engineering modifications. The algorithm utilizes uncertainties about the metabolic networks operating state inherent in the underdetermined linear equations representing the stoichiometric model. Flux Variability Analysis is used to determine the operational flux range. PreProPath identifies a path that is predictable in behavior, exhibiting small flux ranges, and profitable, containing the least restrictive flux-limiting reaction in the network. The algorithm is computationally efficient because it does not require enumeration of pathways. The results of case studies show that PreProPath can efficiently analyze variances in metabolic states and model uncertainties to suggest pathway engineering strategies that have been previously supported by experimental data.

Controlling E. coli Gene Expression Noise

Intracellular protein copy numbers show significant cell-to-cell variability within an isogenic population due to the random nature of biological reactions. Here we show how the variability in copy number can be controlled by perturbing gene expression. Depending on the genetic network and host, different perturbations can be applied to control variability. To understand more fully how noise propagates and behaves in biochemical networks we developed stochastic control analysis (SCA) which is a sensitivity-based analysis framework for the study of noise control. Here we apply SCA to synthetic gene expression systems encoded on plasmids that are transformed into Escherichia coli. We show that (1) dual control of transcription and translation efficiencies provides the most efficient way of noise-versus-mean control. (2) The expressed proteins follow the gamma distribution function as found in chromosomal proteins. (3) One of the major sources of noise, leading to the cell-to-cell variability in protein copy numbers, is related to bursty translation. (4) By taking into account stochastic fluctuations in autofluorescence, the correct scaling relationship between the noise and mean levels of the protein copy numbers was recovered for the case of weak fluorescence signals.

Increasing Redundancy Exponentially Reduces Error Rates during Algorithmic Self-Assembly

While biology demonstrates that molecules can reliably transfer information and compute, design principles for implementing complex molecular computations in vitro are still being developed. In electronic computers, large-scale computation is made possible by redundancy, which allows errors to be detected and corrected. Increasing the amount of redundancy can exponentially reduce errors. Here, we use algorithmic self-assembly, a generalization of crystal growth in which the self-assembly process executes a program for growing an object, to examine experimentally whether redundancy can analogously reduce the rate at which errors occur during molecular self-assembly. We designed DNA double-crossover molecules to algorithmically self-assemble ribbon crystals that repeatedly copy a short bitstring, and we measured the error rate when each bit is encoded by 1 molecule, or redundantly encoded by 2, 3, or 4 molecules. Under our experimental conditions, each additional level of redundancy decreases the bitwise error rate by a factor of roughly 3, with the 4-redundant encoding yielding an error rate less than 0.1%. While theory and simulation predict that larger improvements in error rates are possible, our results already suggest that by using sufficient redundancy it may be possible to algorithmically self-assemble micrometer-sized objects with programmable, nanometer-scale features.

Extensive decoupling of metabolic genes in cancer

Tumorigenesis requires the re-organization of metabolism to support malignant proliferation. We examine how the altered metabolism of cancer cells is reflected in the rewiring of co-expression patterns among metabolic genes. Focusing on breast and clear-cell kidney tumors, we report the existence of key metabolic genes which act as hubs of differential co-expression, showing significantly different co-regulation patterns between normal and tumor states. We compare our findings to those from classical differential expression analysis, and counterintuitively observe that the extent of a gene's differential co-expression only weakly correlates with its differential expression, suggesting that the two measures probe different features of metabolism. Focusing on this discrepancy, we use changes in co-expression patterns to highlight the apparent loss of regulation by the transcription factor HNF4A in clear cell renal cell carcinoma, despite no differential expression of HNF4A. Finally, we aggregate the results of differential co-expression analysis into a Pan-Cancer analysis across seven distinct cancer types to identify pairs of metabolic genes which may be recurrently dysregulated. Among our results is a cluster of four genes, all components of the mitochondrial electron transport chain, which show significant loss of co-expression in tumor tissue, pointing to potential mitochondrial dysfunction in these tumor types.

A Complete Pathway Model for Lipid A Biosynthesis in Escherichia coli

Lipid A is a highly conserved component of lipopolysaccharide (LPS), itself a major component of the outer membrane of Gram-negative bacteria. Lipid A is essential to cells and elicits a strong immune response from humans and other animals. We developed a quantitative model of the nine enzyme-catalyzed steps of Escherichia coli lipid A biosynthesis, drawing parameters from the experimental literature. This model accounts for biosynthesis regulation, which occurs through regulated degradation of the LpxC and WaaA (also called KdtA) enzymes. The LpxC degradation signal appears to arise from the lipid A disaccharide concentration, which we deduced from prior results, model results, and new LpxK overexpression results. The model agrees reasonably well with many experimental findings, including the lipid A production rate, the behaviors of mutants with defective LpxA enzymes, correlations between LpxC half-lives and cell generation times, and the effects of LpxK overexpression on LpxC concentrations. Its predictions also differ from some experimental results, which suggest modifications to the current understanding of the lipid A pathway, such as the possibility that LpxD can replace LpxA and that there may be metabolic channeling between LpxH and LpxB. The model shows that WaaA regulation may serve to regulate the lipid A production rate when the 3-deoxy-D-manno-oct-2-ulosonic acid (KDO) concentration is low and/or to control the number of KDO residues that get attached to lipid A. Computation of flux control coefficients showed that LpxC is the rate-limiting enzyme if pathway regulation is ignored, but that LpxK is the rate-limiting enzyme if pathway regulation is present, as it is in real cells. Control also shifts to other enzymes if the pathway substrate concentrations are not in excess. Based on these results, we suggest that LpxK may be a much better drug target than LpxC, which has been pursued most often.

Parallelized small-scale production of uniformly (13)C-labeled cell extract for quantitative metabolome analysis

The need for quantitative intracellular metabolome information is central to modern applied biotechnology and systems biology. In most cases, sample preparation and metabolite analysis result in degradation of metabolites and signal suppression due to metabolite instability and matrix effects during LC-MS analysis. Therefore the application of uniformly (U) (13)C-labeled cell extract as an internal standard has gained interest in recent years. In this study a multiple-step protocol has been developed for efficient preparation of U-(13)C-labeled Escherichia coli cell extracts in stirred-tank bioreactors on a milliliter scale with a minimal supply of costly (13)C-labeled substrate. Significant reduction of fermentation medium salt concentration in the U-(13)C-labeled cell extract was achieved to reduce ion-suppression effects during mass-spectrometric analysis. Additionally, variation of reaction conditions in parallel-operated stirred-tank bioreactors on a milliliter scale enables the simultaneous preparation of U-(13)C-labeled cell extracts with varying metabolite concentrations, which is shown by an example of the labeled phosphoenolpyruvate level in E. coli.

'Idealized' state 4 and state 3 in mitochondria vs. rest and work in skeletal muscle

A computer model of oxidative phosphorylation (OXPHOS) in skeletal muscle is used to compare state 3, intermediate state and state 4 in mitochondria with rest and work in skeletal muscle. 'Idealized' state 4 and 3 in relation to various 'experimental' states 4 and 3 are defined. Theoretical simulations show, in accordance with experimental data, that oxygen consumption (V'O2), ADP and Pi are higher, while ATP/ADP and Δp are lower in rest than in state 4, because of the presence of basal ATP consuming reactions in the former. It is postulated that moderate and intensive work in skeletal muscle is very different from state 3 in isolated mitochondria. V'O2, ATP/ADP, Δp and the control of ATP usage over V'O2 are much higher, while ADP and Pi are much lower in the former. The slope of the phenomenological V'O2-ADP relationship is much steeper during the rest-work transition than during the state 4-state 3 transition. The work state in intact muscle is much more similar to intermediate state than to state 3 in isolated mitochondria in terms of ADP, ATP/ADP, Δp and metabolic control pattern, but not in terms of V'O2. The huge differences between intact muscle and isolated mitochondria are proposed to be caused by the presence of the each-step activation (ESA) mechanism of the regulation of OXPHOS in intact skeletal muscle. Generally, the present study suggests that isolated mitochondria (at least in the absence of Ca2+) cannot serve as a good model of OXPHOS regulation in intact skeletal muscle.

Integrative methods for studying cardiac energetics

The more recent studies of human pathologies have essentially revealed the complexity of the interactions involved at the different levels of integration in organ physiology. Integrated organ thus reveals functional properties not predictable by underlying molecular events. It is therefore obvious that current fine molecular analyses of pathologies should be fruitfully combined with integrative approaches of whole organ function. It follows an important issue in the comprehension of the link between molecular events in pathologies, and whole organ function/dysfunction is the development of new experimental strategies aimed at the study of the integrated organ physiology. Cardiovascular diseases are a good example as heart submitted to ischemic conditions has to cope both with a decreased supply of nutrients and oxygen, and the necessary increased activity required to sustain whole body-including the heart itself-oxygenation.By combining the principles of control analysis with noninvasive (31)P NMR measurement of the energetic intermediates and simultaneous measurement of heart contractile activity, we developed MoCA (for Modular Control and Regulation Analysis), an integrative approach designed to study in situ control and regulation of cardiac energetics during contraction in intact beating perfused isolated heart (Diolez et al., Am J Physiol Regul Integr Comp Physiol 293(1):R13-R19, 2007). Because it gives real access to integrated organ function, MoCA brings out a new type of information-the "elasticities," referring to internal responses to metabolic changes-that may be a key to the understanding of the processes involved in pathologies. MoCA can potentially be used not only to detect the origin of the defects associated with the pathology, but also to provide the quantitative description of the routes by which these defects-or also drugs-modulate global heart function, therefore opening therapeutic perspectives. This review presents selected examples of the applications to isolated intact beating heart and a wider application to cardiac energetics under clinical conditions with the direct study of heart pathologies.

Computational strategies for a system-level understanding of metabolism

Cell metabolism is the biochemical machinery that provides energy and building blocks to sustain life. Understanding its fine regulation is of pivotal relevance in several fields, from metabolic engineering applications to the treatment of metabolic disorders and cancer. Sophisticated computational approaches are needed to unravel the complexity of metabolism. To this aim, a plethora of methods have been developed, yet it is generally hard to identify which computational strategy is most suited for the investigation of a specific aspect of metabolism. This review provides an up-to-date description of the computational methods available for the analysis of metabolic pathways, discussing their main advantages and drawbacks. In particular, attention is devoted to the identification of the appropriate scale and level of accuracy in the reconstruction of metabolic networks, and to the inference of model structure and parameters, especially when dealing with a shortage of experimental measurements. The choice of the proper computational methods to derive in silico data is then addressed, including topological analyses, constraint-based modeling and simulation of the system dynamics. A description of some computational approaches to gain new biological knowledge or to formulate hypotheses is finally provided.