Analysis and characterization of transition states in metabolic systems. Transition times and the passivity of the output flux.
Ontology highlight
ABSTRACT:
SUBMITTER: Torres NV
PROVIDER: S-EPMC1151169 | biostudies-other | 1991 May
REPOSITORIES: biostudies-other
Similar Datasets
Project description:The transition time, tau, of a metabolic system is defined as the ratio of the metabolite concentrations in the system, sigma, to the steady-state flux, J. Its value reflects a temporal characteristic of the system as it relaxes towards the steady state. Like other systemic properties, the value of tau will be a function of the enzyme activities in the system. The influence of a particular enzyme activity on tau can be quantified by a Control Coefficient, C tau ei. We show that it is possible to derive a Summation Theorem sigma ni = 1 C tau ei = -1 and a Connectivity Theorem sigma ni = 1 C tau ei.epsilon viSk = -Sk/sigma. We establish a 'sign rule' that predicts the order of positive and negative Control Coefficients in a sequence.
| S-EPMC1136630 | biostudies-other
Project description:Cellular metabolic fluxes are determined by enzyme activities and metabolite abundances. Biochemical approaches reveal the impact of specific substrates or regulators on enzyme kinetics but do not capture the extent to which metabolite and enzyme concentrations vary across physiological states and, therefore, how cellular reactions are regulated. We measured enzyme and metabolite concentrations and metabolic fluxes across 25 steady-state yeast cultures. We then assessed the extent to which flux can be explained by a Michaelis-Menten relationship between enzyme, substrate, product, and potential regulator concentrations. This revealed three previously unrecognized instances of cross-pathway regulation, which we biochemically verified. One of these involved inhibition of pyruvate kinase by citrate, which accumulated and thereby curtailed glycolytic outflow in nitrogen-limited yeast. Overall, substrate concentrations were the strongest driver of the net rates of cellular metabolic reactions, with metabolite concentrations collectively having more than double the physiological impact of enzymes.
| S-EPMC5414049 | biostudies-literature
Project description:Whole-cell modelling is a newly expanding field that has many applications in lab experiment design and predictive drug testing. Although whole-cell model output contains a wealth of information, it is complex and high dimensional and thus hard to interpret. Here, we present an analysis pipeline that combines machine learning, dimensionality reduction, and network analysis to interpret and visualise metabolic reaction fluxes from a set of single gene knockouts simulated in the Mycoplasma genitalium whole-cell model. We found that the reaction behaviours show trends that correlate with phenotypic classes of the simulation output, highlighting particular cellular subsystems that malfunction after gene knockouts. From a graphical representation of the metabolic network, we saw that there is a set of reactions that can be used as markers of a phenotypic class, showing their importance within the network. Our analysis pipeline can support the understanding of the complexity of in silico cells without detailed knowledge of the constituent parts, which can help to understand the effects of gene knockouts and, as whole-cell models become more widely built and used, aid genome design.
| S-EPMC8718694 | biostudies-literature
Project description:BackgroundThe importance and power of isotope-based metabolic flux analysis and its contribution to understanding the metabolic network is increasingly recognized. Its application is, however, still limited partly due to computational inefficiency. 13C metabolic flux analysis aims to compute in vivo metabolic fluxes in terms of metabolite balancing extended by carbon isotopomer balances and involves a nonlinear least-squares problem. To solve the problem more efficiently, improved numerical optimization techniques are necessary.ResultsFor flux computation, we developed a gradient-based hybrid optimization algorithm. Here, independent flux variables were compactified into [0, 1)-ranged variables using a single transformation rule. The compactified parameters could be discriminated between non-identifiable and identifiable variables after model linearization. The developed hybrid algorithm was applied to the central metabolism of Bacillus subtilis with only succinate and glutamate as carbon sources. This creates difficulties caused by symmetry of succinate leading to limited introduction of 13C labeling information into the system. The algorithm was found to be superior to its parent algorithms and to global optimization methods both in accuracy and speed. The hybrid optimization with tolerance adjustment quickly converged to the minimum with close to zero deviation and exactly re-estimated flux variables. In the metabolic network studied, some fluxes were found to be either non-identifiable or nonlinearly correlated. The non-identifiable fluxes could correctly be predicted a priori using the model identification method applied, whereas the nonlinear flux correlation was revealed only by identification runs using different starting values a posteriori.ConclusionThis fast, robust and accurate optimization method is useful for high-throughput metabolic flux analysis, a posteriori identification of possible parameter correlations, and also for Monte Carlo simulations to obtain statistical qualities for flux estimates. In this way, it contributes to future quantitative studies of central metabolic networks in the framework of systems biology.
| S-EPMC2333969 | biostudies-literature
Project description:Quantitative knowledge of intracellular fluxes in metabolic networks is invaluable for inferring metabolic system behavior and the design principles of biological systems. However, intracellular reaction rates can not often be calculated directly but have to be estimated; for instance, via 13C-based metabolic flux analysis, a model-based interpretation of stable carbon isotope patterns in intermediates of metabolism. Existing software such as FiatFlux, OpenFLUX or 13CFLUX supports experts in this complex analysis, but requires several steps that have to be carried out manually, hence restricting the use of this software for data interpretation to a rather small number of experiments. In this paper, we present Flux-P as an approach to automate and standardize 13C-based metabolic flux analysis, using the Bio-jETI workflow framework. Exemplarily based on the FiatFlux software, it demonstrates how services can be created that carry out the different analysis steps autonomously and how these can subsequently be assembled into software workflows that perform automated, high-throughput intracellular flux analysis of high quality and reproducibility. Besides significant acceleration and standardization of the data analysis, the agile workflow-based realization supports flexible changes of the analysis workflows on the user level, making it easy to perform custom analyses.
| S-EPMC3901227 | biostudies-literature
Project description:MOTIVATION:Metabolic flux balance analysis (FBA) is a standard tool in analyzing metabolic reaction rates compatible with measurements, steady-state and the metabolic reaction network stoichiometry. Flux analysis methods commonly place model assumptions on fluxes due to the convenience of formulating the problem as a linear programing model, while many methods do not consider the inherent uncertainty in flux estimates. RESULTS:We introduce a novel paradigm of Bayesian metabolic flux analysis that models the reactions of the whole genome-scale cellular system in probabilistic terms, and can infer the full flux vector distribution of genome-scale metabolic systems based on exchange and intracellular (e.g. 13C) flux measurements, steady-state assumptions, and objective function assumptions. The Bayesian model couples all fluxes jointly together in a simple truncated multivariate posterior distribution, which reveals informative flux couplings. Our model is a plug-in replacement to conventional metabolic balance methods, such as FBA. Our experiments indicate that we can characterize the genome-scale flux covariances, reveal flux couplings, and determine more intracellular unobserved fluxes in Clostridium acetobutylicum from 13C data than flux variability analysis. AVAILABILITY AND IMPLEMENTATION:The COBRA compatible software is available at github.com/markusheinonen/bamfa. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.
| S-EPMC6612884 | biostudies-other
Project description:Motivation:In the analysis of metabolism, two distinct and complementary approaches are frequently used: Principal component analysis (PCA) and stoichiometric flux analysis. PCA is able to capture the main modes of variability in a set of experiments and does not make many prior assumptions about the data, but does not inherently take into account the flux mode structure of metabolism. Stoichiometric flux analysis methods, such as Flux Balance Analysis (FBA) and Elementary Mode Analysis, on the other hand, are able to capture the metabolic flux modes, however, they are primarily designed for the analysis of single samples at a time, and not best suited for exploratory analysis on a large sets of samples. Results:We propose a new methodology for the analysis of metabolism, called Principal Metabolic Flux Mode Analysis (PMFA), which marries the PCA and stoichiometric flux analysis approaches in an elegant regularized optimization framework. In short, the method incorporates a variance maximization objective form PCA coupled with a stoichiometric regularizer, which penalizes projections that are far from any flux modes of the network. For interpretability, we also introduce a sparse variant of PMFA that favours flux modes that contain a small number of reactions. Our experiments demonstrate the versatility and capabilities of our methodology. The proposed method can be applied to genome-scale metabolic network in efficient way as PMFA does not enumerate elementary modes. In addition, the method is more robust on out-of-steady steady-state experimental data than competing flux mode analysis approaches. Availability and implementation:Matlab software for PMFA and SPMFA and dataset used for experiments are available in https://github.com/aalto-ics-kepaco/PMFA. Supplementary information:Supplementary data are available at Bioinformatics online.
| S-EPMC6041797 | 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:Deciphering the mechanisms of regulation of metabolic networks subjected to perturbations, including disease states and drug-induced stress, relies on tracing metabolic fluxes. One of the most informative data to predict metabolic fluxes are 13C based metabolomics, which provide information about how carbons are redistributed along central carbon metabolism. Such data can be integrated using 13C Metabolic Flux Analysis (13C MFA) to provide quantitative metabolic maps of flux distributions. However, 13C MFA might be unable to reduce the solution space towards a unique solution either in large metabolic networks or when small sets of measurements are integrated. Here we present parsimonious 13C MFA (p13CMFA), an approach that runs a secondary optimization in the 13C MFA solution space to identify the solution that minimizes the total reaction flux. Furthermore, flux minimization can be weighted by gene expression measurements allowing seamless integration of gene expression data with 13C data. As proof of concept, we demonstrate how p13CMFA can be used to estimate intracellular flux distributions from 13C measurements and transcriptomics data. We have implemented p13CMFA in Iso2Flux, our in-house developed isotopic steady-state 13C MFA software. The source code is freely available on GitHub (https://github.com/cfoguet/iso2flux/releases/tag/0.7.2).
| S-EPMC6750616 | biostudies-literature
Project description:Epithelial-mesenchymal transition (EMT) is an important contributor to the invasion and metastasis of epithelial-derived cancers. While considerable effort has focused in the regulators involved in the transition process, we have focused on consequences of EMT to prosurvival signaling. Changes in distinct metastable and 'epigentically-fixed' EMT states were measured by correlation of protein, phosphoprotein, phosphopeptide and RNA transcript abundance. The assembly of 1167 modulated components into functional systems or machines simplified biological understanding and increased prediction confidence highlighting four functional groups: cell adhesion and migration, metabolism, transcription nodes and proliferation/survival networks. A coordinate metabolic reduction in a cluster of 17 free-radical stress pathway components was observed and correlated with reduced glycolytic and increased oxidative phosphorylation enzyme capacity, consistent with reduced cell cycling and reduced need for macromolecular biosynthesis in the mesenchymal state. An attenuation of EGFR autophosphorylation and a switch from autocrine to paracrine-competent EGFR signaling was implicated in the enablement of tumor cell chemotaxis. A similar attenuation of IGF1R, MET and RON signaling with EMT was observed. In contrast, EMT increased prosurvival autocrine IL11/IL6-JAK2-STAT signaling, autocrine fibronectin-integrin α5β1 activation, autocrine Axl/Tyro3/PDGFR/FGFR RTK signaling and autocrine TGFβR signaling. A relatively uniform loss of polarity and cell-cell junction linkages to actin cytoskeleton and intermediate filaments was measured at a systems level. A more heterogeneous gain of ECM remodeling and associated with invasion and migration was observed. Correlation to stem cell, EMT, invasion and metastasis datasets revealed the greatest similarity with normal and cancerous breast stem cell populations, CD49f(hi)/EpCAM(-/lo) and CD44(hi)/CD24(lo), respectively.
| S-EPMC3040305 | biostudies-literature