Estimating relative changes of metabolic fluxes.
Ontology highlight
ABSTRACT:
SUBMITTER: Huang L
PROVIDER: S-EPMC4238958 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Genome-scale metabolic networks can be modeled in a constraint-based fashion. Reaction stoichiometry combined with flux capacity constraints determine the space of allowable reaction rates. This space is often large and a central challenge in metabolic modeling is finding the biologically most relevant flux distributions. A widely used method is flux balance analysis (FBA), which optimizes a biologically relevant objective such as growth or ATP production. Although FBA has proven to be highly useful for predicting growth and byproduct secretion, it cannot predict the intracellular fluxes under all environmental conditions. Therefore, alternative strategies have been developed to select flux distributions that are in agreement with experimental "omics" data, or by incorporating experimental flux measurements. The latter, unfortunately can only be applied to a limited set of reactions and is currently not feasible at the genome-scale. On the other hand, it has been observed that micro-organisms favor a suboptimal growth rate, possibly in exchange for a more "flexible" metabolic network. Instead of dedicating the internal network state to an optimal growth rate in one condition, a suboptimal growth rate is used, that allows for an easier switch to other nutrient sources. A small decrease in growth rate is exchanged for a relatively large gain in metabolic capability to adapt to changing environmental conditions.Here, we propose Maximum Metabolic Flexibility (MMF) a computational method that utilizes this observation to find the most probable intracellular flux distributions. By mapping measured flux data from central metabolism to the genome-scale models of Escherichia coli and Saccharomyces cerevisiae we show that i) indeed, most of the measured fluxes agree with a high adaptability of the network, ii) this result can be used to further reduce the space of feasible solutions iii) this reduced space improves the quantitative predictions made by FBA and contains a significantly larger fraction of the measured fluxes compared to the flux space that was reduced by a uniform sampling approach and iv) MMF can be used to select reactions in the network that contribute most to the steady-state flux space. Constraining the selected reactions improves the quantitative predictions of FBA considerably more than adding an equal amount of flux constraints, selected using a more naïve approach. Our method can be applied to any cell type without requiring prior information.MMF is freely available as a MATLAB plugin at: http://cs.ru.nl/~wmegchel/mmf.
| S-EPMC4601694 | biostudies-literature
Project description:BACKGROUND:Parkinson's disease is a widespread neurodegenerative disorder which affects brain metabolism. Although changes in gene expression during disease are often measured, it is difficult to predict metabolic fluxes from gene expression data. Here we explore the hypothesis that changes in gene expression for enzymes tend to parallel flux changes in biochemical reaction pathways in the brain metabolic network. This hypothesis is the basis of a computational method to predict metabolic flux changes from post-mortem gene expression measurements in Parkinson's disease (PD) brain. RESULTS:We use a network model of central metabolism and optimize the correspondence between relative changes in fluxes and in gene expression. To this end we apply the Least-squares with Equalities and Inequalities algorithm integrated with Flux Balance Analysis (Lsei-FBA). We predict for PD (1) decreases in glycolytic rate and oxygen consumption and an increase in lactate production in brain cortex that correspond with measurements (2) relative flux decreases in ATP synthesis, in the malate-aspartate shuttle and midway in the TCA cycle that are substantially larger than relative changes in glucose uptake in the substantia nigra, dopaminergic neurons and most other brain regions (3) shifts in redox shuttles between cytosol and mitochondria (4) in contrast to Alzheimer's disease: little activation of the gamma-aminobutyric acid shunt pathway in compensation for decreased alpha-ketoglutarate dehydrogenase activity (5) in the globus pallidus internus, metabolic fluxes are increased, reflecting increased functional activity. CONCLUSION:Our method predicts metabolic changes from gene expression data that correspond in direction and order of magnitude with presently available experimental observations during Parkinson's disease, indicating that the hypothesis may be useful for some biochemical pathways. Lsei-FBA generates predictions of flux distributions in neurons and small brain regions for which accurate metabolic flux measurements are not yet possible.
| S-EPMC6135490 | biostudies-literature
Project description:MotivationOne of the challenging questions in modelling biological systems is to characterize the functional forms of the processes that control and orchestrate molecular and cellular phenotypes. Recently proposed methods for the analysis of metabolic pathways, for example, dynamic flux estimation, can only provide estimates of the underlying fluxes at discrete time points but fail to capture the complete temporal behaviour. To describe the dynamic variation of the fluxes, we additionally require the assumption of specific functional forms that can capture the temporal behaviour. However, it also remains unclear how to address the noise which might be present in experimentally measured metabolite concentrations.ResultsHere we propose a novel approach to modelling metabolic fluxes: derivative processes that are based on multiple-output Gaussian processes (MGPs), which are a flexible non-parametric Bayesian modelling technique. The main advantages that follow from MGPs approach include the natural non-parametric representation of the fluxes and ability to impute the missing data in between the measurements. Our derivative process approach allows us to model changes in metabolite derivative concentrations and to characterize the temporal behaviour of metabolic fluxes from time course data. Because the derivative of a Gaussian process is itself a Gaussian process, we can readily link metabolite concentrations to metabolic fluxes and vice versa. Here we discuss how this can be implemented in an MGP framework and illustrate its application to simple models, including nitrogen metabolism in Escherichia coli.Availability and implementationR code is available from the authors upon request.
| S-EPMC4071196 | biostudies-literature
Project description:Synapse density is an essential indicator of development and functioning of the central nervous system. It is estimated indirectly through the accumulation of pre and postsynaptic proteins in tissue sections. 3D reconstruction of the electron microscopic images in serial sections is one of the most definitive means of estimating the formation of active synapses in the brain. It is tedious and highly skill-dependent. Confocal imaging of whole mounts or thick sections of the brain provides a natural alternative for rapid gross estimation of the synapse density in large areas. The optical resolution and other deep-tissue imaging aberrations limit the quantitative scope of this technique.Here we demonstrate a simple sample preparation method that could enhance the clarity of the confocal images of the neuropil regions of the ventral nerve cord of Drosophila larvae, providing a clear view of synapse distributions. We estimated the gross volume occupied by the synaptic junctions using 3D object counter plug-in of Fiji/ImageJ®. It gave us a proportional estimate of the number of synaptic junctions in the neuropil region. The method is corroborated by correlated super-resolution imaging analysis and through genetic perturbation of synaptogenesis in the larval brain.The method provides a significant improvement in the relative estimate of region-specific synapse density in the central nervous system. Also, it reduced artifacts in the super-resolution images obtained using the stimulated emission depletion microscopy technique.
| S-EPMC5956817 | biostudies-other
Project description:Metabolic flux analysis (MFA) deals with the experimental determination of steady-state fluxes in metabolic networks. An important feature of the (13)C MFA method is its capability to generate information on both directions of bidirectional reaction steps given by exchange fluxes. The biological interpretation of these exchange fluxes and their relation to thermodynamic properties of the respective reaction steps has never been systematically investigated. As a central result, it is shown here that for a general class of enzyme reaction mechanisms the quotients of net and exchange fluxes measured by (13)C MFA are coupled to Gibbs energies of the reaction steps. To establish this relation the concept of apparent flux ratios of enzymatic isotope-labeling networks is introduced and some computing rules for these flux ratios are given. Application of these rules reveals a conceptional pitfall of (13)C MFA, which is the inherent dependency of measured exchange fluxes on the chosen tracer atom. However, it is shown that this effect can be neglected for typical biochemical reaction steps under physiological conditions. In this situation, the central result can be formulated as a two-sided inequality relating fluxes, pool sizes, and standard Gibbs energies. This relation has far-reaching consequences for metabolic flux analysis, quantitative metabolomics, and network thermodynamics.
| S-EPMC1959540 | biostudies-literature
Project description: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.
| S-EPMC6314121 | biostudies-literature
Project description:The recognition of carbon sources and the regulatory adjustments to recognized changes are of particular importance for bacterial survival in fluctuating environments. Despite a thorough knowledge base of Escherichia coli's central metabolism and its regulation, fundamental aspects of the employed sensing and regulatory adjustment mechanisms remain unclear. In this paper, using a differential equation model that couples enzymatic and transcriptional regulation of E. coli's central metabolism, we show that the interplay of known interactions explains in molecular-level detail the system-wide adjustments of metabolic operation between glycolytic and gluconeogenic carbon sources. We show that these adaptations are enabled by an indirect recognition of carbon sources through a mechanism we termed distributed sensing of intracellular metabolic fluxes. This mechanism uses two general motifs to establish flux-signaling metabolites, whose bindings to transcription factors form flux sensors. As these sensors are embedded in global feedback loop architectures, closed-loop self-regulation can emerge within metabolism itself and therefore, metabolic operation may adapt itself autonomously (not requiring upstream sensing and signaling) to fluctuating carbon sources.
| S-EPMC2858440 | biostudies-literature
Project description:Genetic robustness refers to phenotypic invariance in the face of mutation and is a common characteristic of life, but its evolutionary origin is highly controversial. Genetic robustness could be an intrinsic property of biological systems, a result of direct natural selection, or a byproduct of selection for environmental robustness. To differentiate among these hypotheses, we analyze the metabolic network of Escherichia coli and comparable functional random networks. Treating the flux of each reaction as a trait and computationally predicting trait values upon mutations or environmental shifts, we discover that 1) genetic robustness is greater for the actual network than the random networks, 2) the genetic robustness of a trait increases with trait importance and this correlation is stronger in the actual network than in the random networks, and 3) the above result holds even after the control of environmental robustness. These findings demonstrate an adaptive origin of genetic robustness, consistent with the theoretical prediction that, under certain conditions, direct selection is sufficiently powerful to promote genetic robustness in cellular organisms.
| S-EPMC5010001 | biostudies-literature
Project description:The co-catabolism of multiple host-derived carbon substrates is required by Mycobacterium tuberculosis (Mtb) to successfully sustain a tuberculosis infection. However, the metabolic plasticity of this pathogen and the complexity of the metabolic networks present a major obstacle in identifying those nodes most amenable to therapeutic interventions. It is therefore critical that we define the metabolic phenotypes of Mtb in different conditions. We applied metabolic flux analysis using stable isotopes and lipid fingerprinting to investigate the metabolic network of Mtb growing slowly in our steady-state chemostat system. We demonstrate that Mtb efficiently co-metabolises either cholesterol or glycerol, in combination with two-carbon generating substrates without any compartmentalisation of metabolism. We discovered that partitioning of flux between the TCA cycle and the glyoxylate shunt combined with a reversible methyl citrate cycle is the critical metabolic nodes which underlie the nutritional flexibility of Mtb. These findings provide novel insights into the metabolic architecture that affords adaptability of bacteria to divergent carbon substrates and expand our fundamental knowledge about the methyl citrate cycle and the glyoxylate shunt.
| S-EPMC8094261 | biostudies-literature
Project description:Parasites are ubiquitous, yet their effects on hosts are difficult to quantify and generalize across ecosystems. One promising metric of parasitic impact uses the metabolic theory of ecology (MTE) to calculate energy flux, an estimate of energy lost to parasites. We investigated the feasibility of using metabolic scaling rules to compare the energetic burden of parasitism among individuals. Specifically, we found substantial sensitivity of energy flux estimates to input parameters used in the MTE equation when using available data from a model host-parasite system (Gasterosteus aculeatus and Schistocephalus solidus). Using literature values, size data from parasitized wild fish, and a respirometry experiment, we estimate that a single S. solidus tapeworm may extract up to 32% of its stickleback host's baseline metabolic energy requirement, and that parasites in multiple infections may collectively extract up to 46%. The amount of energy siphoned from stickleback to tapeworms is large but did not instigate an increase in respiration rate in the current study. This emphasizes the importance of future work focusing on how parasites influence ecosystem energetics. The approach of using the MTE to calculate energy flux provides great promise as a quantitative foundation for such estimates and provides a more concrete metric of parasite impact on hosts than parasite abundance alone.
| S-EPMC10694383 | biostudies-literature