Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Modeling Challenge Data to Quantify Endogenous Lactate Production.

ABSTRACT: With the intention of isolating the susceptibility of modeling methodology to influence our investigation of the infusion data, we used three kinetic approaches to our models: a simple approach, a unit approach, and a novel approach. The simple approach used exclusively built-in modeling features of the software in terms of units of the infusion dilution (mmol/L), as well as in terms of the precision of switching the infusion on and off. The unit approach used the same switching mechanism as the simple approach, but the units were modeled in those of the infusion (e.g., mmol/kg). Thirdly with the novel approach, we used an automated approach to controlling the infusion, in the sense that as the modeling mechanism sensed the slowdown of the infusion, it was gradually turned off. The units of the analysis for the novel approach were exactly the same as those deployed in the unit approach. Our objective here was to see if common pharmacokinetic parameters were seriously impacted by the particular modeling method.

SUBMITTER: Stefanovski D

PROVIDER: S-EPMC8277460 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Modeling Challenge Data to Quantify Endogenous Lactate Production.

Stefanovski Darko D Wilkins Pamela A PA Boston Raymond C RC

Frontiers in endocrinology 20210628

With the intention of isolating the susceptibility of modeling methodology to influence our investigation of the infusion data, we used three kinetic approaches to our models: a simple approach, a unit approach, and a novel approach. The simple approach used exclusively built-in modeling features of the software in terms of units of the infusion dilution (mmol/L), as well as in terms of the precision of switching the infusion on and off. The unit approach used the same switching mechanism as the ...[more]

PMID: 34267726

Similar Datasets

Modeling of cell cultivation for monoclonal antibody production processes considering lactate metabolic shifts.

Project description:Demand for monoclonal antibodies (mAbs) is rapidly increasing. To achieve higher productivity, there have been improvements to cell lines, operating modes, media, and cultivation conditions. Representative mathematical models are needed to narrow down the growing number of process alternatives. Previous studies have proposed mechanistic models to depict cell metabolic shifts (e.g., lactate production to consumption). However, the impacts of variations of some operating conditions have not yet been fully incorporated in such models. This paper offers a new mechanistic model considering variations in dissolved oxygen and glutamine depletion on cell metabolism applied to a novel Chinese hamster ovary (CHO) cell line. Expressions for the specific rates of lactate production, glutamine consumption, and mAb production were formulated for stirred and shaken-tank reactors. A deeper understanding of lactate metabolic shifts was obtained under different combinations of experimental conditions. Lactate consumption was more pronounced in conditions with higher DO and low glutamine concentrations. The model offers mechanistic insights that are useful for designing advanced operation strategies. It can be used in design space generation and process optimization for better productivity and product quality.

| S-EPMC11659809 | biostudies-literature

Experimental Data and PBPK Modeling Quantify Antibody Interference in PEGylated Drug Carrier Delivery.

Project description:Physiologically-based pharmacokinetic (PBPK) modeling is a popular drug development tool that integrates physiology, drug physicochemical properties, preclinical data, and clinical information to predict drug systemic disposition. Since PBPK models seek to capture complex physiology, parameter uncertainty and variability is a prevailing challenge: there are often more compartments (e.g., organs, each with drug flux and retention mechanisms, and associated model parameters) than can be simultaneously measured. To improve the fidelity of PBPK modeling, one approach is to search and optimize within the high-dimensional model parameter space, based on experimental time-series measurements of drug distributions. Here, we employ Latin Hypercube Sampling (LHS) on a PBPK model of PEG-liposomes (PL) that tracks biodistribution in an 8-compartment mouse circulatory system, in the presence (APA+) or absence (naïve) of anti-PEG antibodies (APA). Near-continuous experimental measurements of PL concentration during the first hour post-injection from the liver, spleen, kidney, muscle, lung, and blood plasma, based on PET/CT imaging in live mice, are used as truth sets with LHS to infer optimal parameter ranges for the full PBPK model. The data and model quantify that PL retention in the liver is the primary differentiator of biodistribution patterns in naïve versus APA+ mice, and spleen the secondary differentiator. Retention of PEGylated nanomedicines is substantially amplified in APA+ mice, likely due to PL-bound APA engaging specific receptors in the liver and spleen that bind antibody Fc domains. Our work illustrates how applying LHS to PBPK models can further mechanistic understanding of the biodistribution and antibody-mediated clearance of specific drugs.

| S-EPMC8576315 | biostudies-literature

The effects of the exogenous provision of lactate and the endogenous production of lactate on protein synthesis in the heart.

Project description:We have investigated the effects of exogenous addition of lactate and of the stimulation of endogenous production of lactate on protein synthesis in the anterogradely perfused rat heart. In the absence of exogenous lactate, hearts release lactate into the perfusate. At lactate concentrations of 0.2 mM and greater, the heart takes up lactate. The best fit for lactate uptake plotted against exogenous lactate concentration is a rectangular hyperbola with a maximal rate of 220 mumol/2 h per heart (wet wt. about 1 g). Uptake is half-maximal at about 1.3 mM-lactate. The stimulation of protein synthesis also exhibits a rectangular-hyperbolic dependence on exogenous lactate concentration, with maximal stimulation being about 38%. Half-maximal stimulation occurs at about 0.9 mM-lactate. We stimulated endogenous lactate production by perfusion with 2-cyanocinnamate (an inhibitor of mitochondrial pyruvate transport) at concentrations up to 70 microM. Cardiac outputs, intracellular pH and the concentrations of phosphocreatine and the adenine nucleotides were not altered. Atrial protein-synthesis rates were unchanged, but ventricular rates were decreased. We conclude that endogenous lactate production is unlikely to stimulate protein synthesis and that the stimulation of protein synthesis by exogenous lactate is related to its uptake.

| S-EPMC1130649 | biostudies-other

Brain lactate kinetics: Modeling evidence for neuronal lactate uptake upon activation.

Project description:A critical issue in brain energy metabolism is whether lactate produced within the brain by astrocytes is taken up and metabolized by neurons upon activation. Although there is ample evidence that neurons can efficiently use lactate as an energy substrate, at least in vitro, few experimental data exist to indicate that it is indeed the case in vivo. To address this question, we used a modeling approach to determine which mechanisms are necessary to explain typical brain lactate kinetics observed upon activation. On the basis of a previously validated model that takes into account the compartmentalization of energy metabolism, we developed a mathematical model of brain lactate kinetics, which was applied to published data describing the changes in extracellular lactate levels upon activation. Results show that the initial dip in the extracellular lactate concentration observed at the onset of stimulation can only be satisfactorily explained by a rapid uptake within an intraparenchymal cellular compartment. In contrast, neither blood flow increase, nor extracellular pH variation can be major causes of the lactate initial dip, whereas tissue lactate diffusion only tends to reduce its amplitude. The kinetic properties of monocarboxylate transporter isoforms strongly suggest that neurons represent the most likely compartment for activation-induced lactate uptake and that neuronal lactate utilization occurring early after activation onset is responsible for the initial dip in brain lactate levels observed in both animals and humans.

| S-EPMC1297516 | biostudies-literature

Computational modeling to determine key regulators of hypoxia effects on the lactate production in the glycolysis pathway.

Project description:In solid tumors, hypoxia can trigger aberrant expression of transcription factors and genes, resulting in abnormal biological functions such as altered energetic pathways in cancer cells. Glucose metabolism is an important part of this phenomenon, which is associated with changes in the functional expression of transporters and enzymes involved in the glycolysis pathway. The latter phenomenon can finally lead to the lactate accumulation and pH dysregulation in the tumor microenvironment and subsequently further invasion and metastasis of cancer cells. Having capitalized on the computational modeling, in this study, for the first time, we aimed to investigate the effects of hypoxia-induced factor-1 (HIF-1) mediated hypoxia on the magnitude of functional expression of all the enzymes and transporters involved in the glycolysis process. The main objective was to establish a quantitative relationship between the hypoxia intensity and the intracellular lactate levels and determine the key regulators of the glycolysis pathway. This model clearly showed an increase in the lactate concentration during the oxygen depletion. The proposed model also predicted that the phosphofructokinase-1 and phosphoglucomutase enzymes might play the most important roles in the regulation of the lactate production.

| S-EPMC7280308 | biostudies-literature

Determination of physiological parameters for endogenous glucose production in individuals using diurnal data.

Project description:BackgroundTriple tracer meal experiments used to investigate organ glucose-insulin dynamics, such as endogenous glucose production (EGP) of the liver are labor intensive and expensive. A procedure was developed to obtain individual liver related parameters to describe EGP dynamics without the need for tracers.ResultsThe development used an existing formula describing the EGP dynamics comprising 4 parameters defined from glucose, insulin and C-peptide dynamics arising from triple meal studies. The method employs a set of partial differential equations in order to estimate the parameters for EGP dynamics. Tracer-derived and simulated data sets were used to develop and test the procedure. The predicted EGP dynamics showed an overall mean R 2 of 0.91.ConclusionsIn summary, a method was developed for predicting the hepatic EGP dynamics for healthy, pre-diabetic, and type 2 diabetic individuals without applying tracer experiments.

| S-EPMC7422590 | biostudies-literature

Chromobodies to Quantify Changes of Endogenous Protein Concentration in Living Cells.

Project description:Understanding cellular processes requires the determination of dynamic changes in the concentration of genetically nonmodified, endogenous proteins, which, to date, is commonly accomplished by end-point assays in vitro Molecular probes such as fluorescently labeled nanobodies (chromobodies, CBs) are powerful tools to visualize the dynamic subcellular localization of endogenous proteins in living cells. Here, we employed the dependence of intracellular levels of chromobodies on the amount of their endogenous antigens, a phenomenon, which we termed antigen-mediated CB stabilization (AMCBS), for simultaneous monitoring of time-resolved changes in the concentration and localization of native proteins. To improve the dynamic range of AMCBS we generated turnover-accelerated CBs and demonstrated their application in visualization and quantification of fast reversible changes in antigen concentration upon compound treatment by quantitative live-cell imaging. We expect that this broadly applicable strategy will enable unprecedented insights into the dynamic regulation of proteins, e.g. during cellular signaling, cell differentiation, or upon drug action.

| S-EPMC6283300 | biostudies-literature

Lactate production yield from engineered yeasts is dependent from the host background, the lactate dehydrogenase source and the lactate export.

Project description:BackgroundMetabolic pathway manipulation for improving the properties and the productivity of microorganisms is becoming a well established concept. For the production of important metabolites, but also for a better understanding of the fundamentals of cell biology, detailed studies are required. In this work we analysed the lactate production from metabolic engineered Saccharomyces cerevisiae cells expressing a heterologous lactate dehydrogenase (LDH) gene. The LDH gene expression in a budding yeast cell introduces a novel and alternative pathway for the NAD+ regeneration, allowing a direct reduction of the intracellular pyruvate to lactate, leading to a simultaneous accumulation of lactate and ethanol.ResultsFour different S. cerevisiae strains were transformed with six different wild type and one mutagenised LDH genes, in combination or not with the over-expression of a lactate transporter. The resulting yield values (grams of lactate produced per grams of glucose consumed) varied from as low as 0,0008 to as high as 0.52 g g-1. In this respect, and to the best of our knowledge, higher redirections of the glycolysis flux have never been obtained before without any disruption and/or limitation of the competing biochemical pathways.ConclusionIn the present work it is shown that the redirection of the pathway towards the lactate production can be strongly modulated by the genetic background of the host cell, by the source of the heterologous Ldh enzyme, by improving its biochemical properties as well as by modulating the export of lactate in the culture media.

| S-EPMC1373645 | biostudies-literature

Mathematical modeling of the glucagon challenge test.

Project description:A model for the homeostasis of glucose through the regulating hormones glucagon and insulin is described. It contains a subsystem that models the internalization of the glucagon receptor. Internalization is a mechanism in cell signaling, through which G-protein coupled receptors are taken from the surface of the cell to the endosome. The model is used to interpret data from a glucagon challenge test in which subjects have been under treatment with a novel glucagon receptor anti-sense drug which is aimed at reducing the number of receptors in the liver. It is shown how the receptor internalization results in tolerance of the blood glucose concentration to glucagon-induced hyperglycemia. We quantify the reduction of the number of receptors using the model and the data before and after treatment.

| S-EPMC6868112 | biostudies-literature

Improving Protein-Ligand Interaction Modeling with cryo-EM Data, Templates, and Deep Learning in 2021 Ligand Model Challenge.

Project description:Elucidating protein-ligand interaction is crucial for studying the function of proteins and compounds in an organism and critical for drug discovery and design. The problem of protein-ligand interaction is traditionally tackled by molecular docking and simulation, which is based on physical forces and statistical potentials and cannot effectively leverage cryo-EM data and existing protein structural information in the protein-ligand modeling process. In this work, we developed a deep learning bioinformatics pipeline (DeepProLigand) to predict protein-ligand interactions from cryo-EM density maps of proteins and ligands. DeepProLigand first uses a deep learning method to predict the structure of proteins from cryo-EM maps, which is averaged with a reference (template) structure of the proteins to produce a combined structure to add ligands. The ligands are then identified and added into the structure to generate a protein-ligand complex structure, which is further refined. The method based on the deep learning prediction and template-based modeling was blindly tested in the 2021 EMDataResource Ligand Challenge and was ranked first in fitting ligands to cryo-EM density maps. These results demonstrate that the deep learning bioinformatics approach is a promising direction for modeling protein-ligand interactions on cryo-EM data using prior structural information.

| S-EPMC9855343 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data