Study of the flux and transition time control coefficient profiles in a metabolic system in vitro and the effect of an external stimulator.
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ABSTRACT:
SUBMITTER: Torres NV
PROVIDER: S-EPMC1138742 | biostudies-other | 1989 Jun
REPOSITORIES: biostudies-other
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Project description:BackgroundAdvances in modern high-throughput techniques of molecular biology have enabled top-down approaches for the estimation of parameter values in metabolic systems, based on time series data. Special among them is the recent method of dynamic flux estimation (DFE), which uses such data not only for parameter estimation but also for the identification of functional forms of the processes governing a metabolic system. DFE furthermore provides diagnostic tools for the evaluation of model validity and of the quality of a model fit beyond residual errors. Unfortunately, DFE works only when the data are more or less complete and the system contains as many independent fluxes as metabolites. These drawbacks may be ameliorated with other types of estimation and information. However, such supplementations incur their own limitations. In particular, assumptions must be made regarding the functional forms of some processes and detailed kinetic information must be available, in addition to the time series data.ResultsThe authors propose here a systematic approach that supplements DFE and overcomes some of its shortcomings. Like DFE, the approach is model-free and requires only minimal assumptions. If sufficient time series data are available, the approach allows the determination of a subset of fluxes that enables the subsequent applicability of DFE to the rest of the flux system. The authors demonstrate the procedure with three artificial pathway systems exhibiting distinct characteristics and with actual data of the trehalose pathway in Saccharomyces cerevisiae.ConclusionsThe results demonstrate that the proposed method successfully complements DFE under various situations and without a priori assumptions regarding the model representation. The proposed method also permits an examination of whether at all, to what degree, or within what range the available time series data can be validly represented in a particular functional format of a flux within a pathway system. Based on these results, further experiments may be designed to generate data points that genuinely add new information to the structure identification and parameter estimation tasks at hand.
| S-EPMC3495652 | biostudies-literature
Project description:Organisms have to continuously adapt to changing environmental conditions or undergo developmental transitions. To meet the accompanying change in metabolic demands, the molecular mechanisms of adaptation involve concerted interactions which ultimately induce a modification of the metabolic state, which is characterized by reaction fluxes and metabolite concentrations. These state transitions are the effect of simultaneously manipulating fluxes through several reactions. While metabolic control analysis has provided a powerful framework for elucidating the principles governing this orchestrated action to understand metabolic control, its applications are restricted by the limited availability of kinetic information. Here, we introduce structural metabolic control as a framework to examine individual reactions' potential to control metabolic functions, such as biomass production, based on structural modeling. The capability to carry out a metabolic function is determined using flux balance analysis (FBA). We examine structural metabolic control on the example of the central carbon metabolism of Escherichia coli by the recently introduced framework of functional centrality (FC). This framework is based on the Shapley value from cooperative game theory and FBA, and we demonstrate its superior ability to assign "share of control" to individual reactions with respect to metabolic functions and environmental conditions. A comparative analysis of various scenarios illustrates the usefulness of FC and its relations to other structural approaches pertaining to metabolic control. We propose a Monte Carlo algorithm to estimate FCs for large networks, based on the enumeration of elementary flux modes. We further give detailed biological interpretation of FCs for production of lactate and ATP under various respiratory conditions.
| S-EPMC3868538 | biostudies-literature
Project description:We have applied Metabolic Control Analysis (MCA) in an attempt to determine the distribution of glycolytic flux control between the steps of glycolysis in aged disks of potato tuber under aerobic conditions, using concentrations of glycolytic metabolites in tuber tissue from a range of transgenic potato plants and published enzyme kinetic data. We modelled the substrate and effector kinetics of potato tuber phosphofructokinase (PFK) by reanalysing published results. Despite the scarcity of reliable kinetic data, our results are in agreement with experimental findings namely that, under the conditions described, PFK has little control over glycolytic flux. Furthermore our analysis predicts that under these conditions far more control lies in the dephosphorylation of phosphoenolpyruvate and/or in the steps beyond. We have validated the results of our analysis in two ways. First, predictions based on calculated concentration control coefficients from the analysis show generally good agreement with observed metabolite deviation indices discussed in the preceding paper [Thomas, Mooney, Burrell, and Fell (1997) Biochem. J. 322, 111-117]. Second, sensitivity analysis of our results shows that the calculated control coefficients are robust to errors in the elasticities used in the analysis, of which relatively few need to be known accurately. Experimental and control analysis results agree with previous predictions of MCA that strong co-operative feedback inhibition of enzymes serves to move flux control downstream of the inhibiting metabolite. We conclude that MCA can successfully model the outcome of experiments in the genetic manipulation of enzyme amounts.
| S-EPMC1218166 | biostudies-other
Project description:A method for determining Control Coefficients is proposed for systems studied in vitro and applied to a model pathway. Rat liver extract, which converts glucose into glycerol 3-phosphate, was used with the addition to the incubation mixture of fructose-bisphosphate aldolase, triose-phosphate isomerase and glycerol-3-phosphate dehydrogenase as 'auxiliary' enzymes, which leaves all the control on the first three enzymes. The flux of the metabolic pathway was recorded by assaying NADH decay. Flux Control Coefficients (CJE) of hexokinase, glucose-6-phosphate isomerase and phosphofructokinase were calculated by titration of the system with increasing quantities of extraneous enzymes. It is shown that the summation property is fulfilled. The applicability of this procedure to study the control in any metabolic pathway is discussed. Possible relevance of the method to conditions in vivo and its limitations are considered.
| S-EPMC1146541 | biostudies-other
Project description:One of the marvels of biology is the phenotypic plasticity of microorganisms. It allows them to maintain high growth rates across conditions. Studies suggest that cells can express metabolic enzymes at tuned concentrations through adjustment of gene expression. The associated transcription factors are often regulated by intracellular metabolites. Here we study metabolite-mediated regulation of metabolic-gene expression that maximises metabolic fluxes across conditions. We developed an adaptive control theory, qORAC (for 'Specific Flux (q) Optimization by Robust Adaptive Control'), and illustrate it with several examples of metabolic pathways. The key feature of the theory is that it does not require knowledge of the regulatory network, only of the metabolic part. We derive that maximal metabolic flux can be maintained in the face of varying N environmental parameters only if the number of transcription-factor binding metabolites is at least equal to N. The controlling circuits appear to require simple biochemical kinetics. We conclude that microorganisms likely can achieve maximal rates in metabolic pathways, in the face of environmental changes.
| S-EPMC6168163 | biostudies-literature
Project description:BackgroundBacteria have evolved the ability to efficiently and resourcefully adapt to changing environments. A key means by which they optimize their use of available nutrients is through adjustments in gene expression with consequent changes in enzyme activity. We report a new method for drawing environmental inferences from gene expression data. Our method prioritizes a list of candidate carbon sources for their compatibility with a gene expression profile using the framework of flux balance analysis to model the organism's metabolic network.Principal findingsFor each of six gene expression profiles for Escherichia coli grown under differing nutrient conditions, we applied our method to prioritize a set of eighteen different candidate carbon sources. Our method ranked the correct carbon source as one of the top three candidates for five of the six expression sets when used with a genome-scale model. The correct candidate ranked fifth in the remaining case. Additional analyses show that these rankings are robust with respect to biological and measurement variation, and depend on specific gene expression, rather than general expression level. The gene expression profiles are highly adaptive: simulated production of biomass averaged 94.84% of maximum when the in silico carbon source matched the in vitro source of the expression profile, and 65.97% when it did not.ConclusionsInferences about a microorganism's nutrient environment can be made by integrating gene expression data into a metabolic framework. This work demonstrates that reaction flux limits for a model can be computed which are realistic in the sense that they affect in silico growth in a manner analogous to that in which a microorganism's alteration of gene expression is adaptive to its nutrient environment.
| S-EPMC3351459 | biostudies-literature
Project description:Genome-scale metabolic network models can be reconstructed for well-characterized organisms using genomic annotation and literature information. However, there are many instances in which model predictions of metabolic fluxes are not entirely consistent with experimental data, indicating that the reactions in the model do not match the active reactions in the in vivo system. We introduce a method for determining the active reactions in a genome-scale metabolic network based on a limited number of experimentally measured fluxes. This method, called optimal metabolic network identification (OMNI), allows efficient identification of the set of reactions that results in the best agreement between in silico predicted and experimentally measured flux distributions. We applied the method to intracellular flux data for evolved Escherichia coli mutant strains with lower than predicted growth rates in order to identify reactions that act as flux bottlenecks in these strains. The expression of the genes corresponding to these bottleneck reactions was often found to be downregulated in the evolved strains relative to the wild-type strain. We also demonstrate the ability of the OMNI method to diagnose problems in E. coli strains engineered for metabolite overproduction that have not reached their predicted production potential. The OMNI method applied to flux data for evolved strains can be used to provide insights into mechanisms that limit the ability of microbial strains to evolve towards their predicted optimal growth phenotypes. When applied to industrial production strains, the OMNI method can also be used to suggest metabolic engineering strategies to improve byproduct secretion. In addition to these applications, the method should prove to be useful in general for reconstructing metabolic networks of ill-characterized microbial organisms based on limited amounts of experimental data.
| S-EPMC1487183 | biostudies-literature
Project description:BACKGROUND:Flux analyses, such as Metabolic Flux Analysis (MFA), Flux Balance Analysis (FBA), Flux Variability Analysis (FVA) or similar methods, can provide insights into the cellular metabolism, especially in combination with experimental data. The most common integration of extracellular concentration data requires the estimation of the specific fluxes (/rates) from the measured concentrations. This is a time-consuming, mathematically ill-conditioned inverse problem, raising high requirements for the quality and quantity of data. METHOD:In this contribution, a time integrated flux analysis approach is proposed which avoids the error-prone estimation of specific flux values. The approach is adopted for a Metabolic time integrated Flux Analysis and (sparse) time integrated Flux Balance/Variability Analysis. The proposed approach is applied to three case studies: (1) a simulated bioprocess case studying the impact of the number of samples (experimental points) and measurements' noise on the performance; (2) a simulation case to understand the impact of network redundancies and reaction irreversibility; and (3) an experimental bioprocess case study, showing its relevance for practical applications. RESULTS:It is observed that this method can successfully estimate the time integrated flux values, even with relatively low numbers of samples and significant noise levels. In addition, the method allows the integration of additional constraints (e.g., bounds on the estimated concentrations) and since it eliminates the need for estimating fluxes from measured concentrations, it significantly reduces the workload while providing about the same level of insight into the metabolism as classic flux analysis methods.
| S-EPMC6955888 | biostudies-literature
Project description:Engineered microbes can be used for producing value-added chemicals from renewable feedstocks, relieving the dependency on nonrenewable resources such as petroleum. These microbes often are composed of synthetic metabolic pathways; however, one major problem in establishing a synthetic pathway is the challenge of precisely controlling competing metabolic routes, some of which could be crucial for fitness and survival. While traditional gene deletion and/or coarse overexpression approaches do not provide precise regulation, cis-repressors (CRs) are RNA-based regulatory elements that can control the production levels of a particular protein in a tunable manner. Here, we describe a protocol for a generally applicable fluorescence-activated cell sorting technique used to isolate eight subpopulations of CRs from a semidegenerate library in Escherichia coli, followed by deep sequencing that permitted the identification of 15 individual CRs with a broad range of protein production profiles. Using these new CRs, we demonstrated a change in production levels of a fluorescent reporter by over two orders of magnitude and further showed that these CRs are easily ported from E. coli to Pseudomonas putida. We next used four CRs to tune the production of the enzyme PpsA, involved in pyruvate to phosphoenolpyruvate (PEP) conversion, to alter the pool of PEP that feeds into the shikimate pathway. In an engineered P. putida strain, where carbon flux in the shikimate pathway is diverted to the synthesis of the commodity chemical cis,cis-muconate, we found that tuning PpsA translation levels increased the overall titer of muconate. Therefore, CRs provide an approach to precisely tune protein levels in metabolic pathways and will be an important tool for other metabolic engineering efforts.
| S-EPMC9594778 | biostudies-literature
Project description:For almost 30 years, keratinocyte differentiation has been studied in numerous cell models including keratinocyte primary culture with various supplemented culture media. In this respect, it has become quite difficult to draw comparisons between studies using such a variety of culture conditions. Serum-free condition with low calcium has been used to culture basal proliferating cells, though differentiation is induced by various procedures. These latter include the addition of calcium at mM concentration and a concomitant addition of serum and calcium. Lowering the incubation temperature of cells has also been reported to induce a premature differentiation of keratinocytes in organotypic skin culture. This effect of temperature on keratinocyte differentiation has been poorly depicted, although average human skin temperature has been shown to be about 32 °C. However, studying differentiation and quantifying shifts in the differentiation rate of a cell population implies to precisely know i) the proportion of differentiated cells in the whole population, and ii) to which extent and to which level of expression, the induction of a gene or a protein might be considered as a marker of differentiation. This lack has rarely been taken into consideration and has surely led to over-interpretations of single protein induction and to consequent extrapolations to real differentiation processes. By means of paralleled analyses with immunocytofluorescence, flow cytometry, and with multiple differentiation markers quantify by qPCR and western-blot, we studied the paradoxical connection between calcium, serum, multilayer culture and incubation temperature on the differentiation of in vitro keratinocytes. Conversely to previous reports, we have shown that calcium switch is indeed a potent model for inducing calcium-dependent genes, but is not an efficient procedure when one wishes to assess the keratinocyte differentiation rate. Moreover, we have demonstrated that a synergic stimulation by calcium, serum, confluence and lower incubation temperature amplified the differentiation rate.
| S-EPMC3792032 | biostudies-literature