Biology subjects

Kaltenbach, H.-M.

Publications and source records attributed to Kaltenbach, H.-M..

6 recordsLinked to original sources

bioRxiv2023

ENKIE: A package for predicting enzyme kinetic parameter values and their uncertainties

Relating metabolite and enzyme abundances to metabolic fluxes requires reaction kinetics, core elements of dynamic and enzyme cost models. However, kinetic parameters have been measured only for a fraction of all known enzymes, and the reliability of the available values is unknown. The ENzyme KInetics Estimator (ENKIE) uses Bayesian Multilevel Models to predict value and uncertainty of KM and kcat parameters. Our models use five categorical predictors and achieve prediction performances comparable to deep learning approaches that use sequence and structure information. They provide accurate uncertainty predictions and interpretable insights into the main sources of uncertainty. We expect our tool to simplify the construction of priors for Bayesian kinetic models of metabolism.

systems biology↗

bioRxiv2022

New model of glucose-insulin regulation characterizes effects of physical activity and facilitates personalized treatment evaluation in children and adults with type 1 diabetes

Accurate treatment adjustment to physical activity (PA) remains a challenging problem in type 1 diabetes (T1D) management. Exercise-driven effects on glucose metabolism depend strongly on duration and intensity of the activity, and are highly variable between patients. In-silico evaluation can support the development of improved treatment strategies, and can facilitate personalized treatment optimization. This requires models of the glucose-insulin system that capture relevant exercise-related processes. We developed a model of glucose-insulin regulation that describes changes in glucose metabolism for moderate- to high-intensity PA of short and prolonged duration. In particular, we incorporated the insulin-independent increase in glucose uptake and production, including glycogen depletion, and the prolonged rise in insulin sensitivity. The model further includes meal absorption and insulin kinetics, allowing simulation of everyday scenarios. The model accurately predicts glucose dynamics for varying PA scenarios in a range of independent validation data sets, and full-day simulations with PA of different timing, duration and intensity agree with clinical observations. We personalized the model on data from a multi-day free-living study of children with T1D by adjusting a small number of model parameters to each child. To assess the use of the personalized models for individual treatment evaluation, we compared subject-specific treatment options for PA management in replay simulations of the recorded data with altered meal, insulin and PA inputs. Author summaryExercise represents a cornerstone of diabetes management. Yet, many people with type 1 diabetes restrain from exercising, since it increases the risk for hypoglycemia and requires adjusted insulin treatment. The effect of exercise on blood glucose levels depends on exercise duration and intensity, but also varies strongly between individuals, making accurate adjustment a challenge. Mathematical models can help to better understand exercise physiology and to devise new treatment strategies. Here, we propose a model of glucose-insulin regulation that captures the effects of exercise on glucose metabolism and personalize it to individual children with type 1 diabetes, allowing subject-specific treatment assessment.

systems biology↗

bioRxiv2021

A Comprehensive Model of Glucose-Insulin Regulation Including Acute and Prolonged Effects of Physical Activity in Type 1 Diabetes

ObjectiveFor type 1 diabetic patients, accurate adjustment of insulin treatment to physical activity (PA) is a challenging open problem. Glucose uptake by the exercising muscles increases acutely, causing increased hepatic glucose production to maintain glucose homeostasis. Meanwhile, insulin sensitivity is elevated for a prolonged period to drive glycogen repletion during recovery. These processes strongly depend on PA duration and intensity, making their combined effects difficult to predict accurately. In this work, we develop a model of glucose-insulin regulation that captures PA from low to high intensity including acute and prolonged effects on glucose metabolism. MethodsWe extended an existing minimal model of glucose-insulin regulation to capture PA-driven changes in glucose metabolism. We incorporated the insulin-independent increase in glucose uptake and production, including the effects of glycogen depletion and of high-intensity PA on production. The model also captures the prolonged increase in insulin sensitivity. ResultsThe model accurately predicts glucose dynamics of published data during short and prolonged PA of moderate to high intensity and during subsequent recovery. In-silico full-day studies elucidate the effects of timing, duration and intensity of PA and of insulin bolus reduction on glucose levels during and after the activity. ConclusionThe proposed model captures the blood glucose dynamics during all main PA regimes. SignificanceMathematical models of glucose-insulin regulation are critical components of closed-loop insulin delivery and clinical decision support systems for achieving good glycemic control. The presented model shows potential for the development and assessment of algorithms targeting treatment adjustment to PA.

bioengineering↗

bioRxiv2020

Probabilistic Thermodynamic Analysis of Metabolic Networks

Random sampling of metabolic fluxes can provide an unbiased description of the capabilities of a metabolic network. However, current sampling approaches do not model thermodynamics explicitly, leading to inaccurate predictions of an organisms potential or actual metabolic operations. We present a probabilistic framework combining thermodynamic quantities with steady-state flux constraints to analyze the properties of a metabolic network. It includes methods for probabilistic metabolic optimization and for joint sampling of thermodynamic and flux spaces. Applied to a model of E. coli, we use the methods to reveal known and novel mechanisms of substrate channeling, and to accurately predict reaction directions and metabolite concentrations. Interestingly, predicted flux distributions are multimodal, leading to discrete hypotheses on E. coli s metabolic capabilities. C++ source code with MATLAB interface available at https://gitlab.com/csb.ethz/pta.

systems biology↗

bioRxiv2019

Development and Optimisation of a Defined High Cell Density Yeast Medium

Saccharomyces cerevisiae cells grown in a small volume of a defined media neither reach the desired cell density nor grow at a fast enough rate to scale down the volume and increase the sample number of classical biochemical assays, as the detection limit of the readout often requires a high number of cells as an input. To ameliorate this problem, we developed and optimised a new high cell density (HCD) medium for S. cerevisiae. Starting from a widely-used synthetic medium composition, we systematically varied the concentrations of all components without the addition of other compounds. We used response surface methodology (RSM) to develop and optimise the five components of the medium: glucose, yeast nitrogen base, amino acids, mono-sodium glutamate and inositol. We monitored growth, cell number and cell size to ensure that the optimisation was towards a greater density of cells rather than just towards an increase in biomass (i.e larger cells). Cells grown in the final medium, HCD, exhibit growth more similar to the complex medium YPD than to the synthetic medium SD, while the final cell density prior to the diauxic shift is increased about three- and tenfold, respectively. We found normal cell-cycle behaviour throughout the growth phases by monitoring DNA content and protein expression using fluorescent reporters. We also ensured that HCD media could be used with a variety of strains and that they allow selection for all common yeast auxotrophic markers.

microbiology↗

bioRxiv2018

A simple and flexible computational framework for inferring sources of heterogeneity from single-cell dynamics

The availability of high-resolution single-cell data makes data analysis and interpretation an important open problem, for example, to disentangle sources of cell-to-cell and intra-cellular variability. Nonlinear mixed effects models (NLMEs), well established in pharmacometrics, account for such multiple sources of variations, but their estimation is often difficult. Single-cell analysis is an even more challenging application with larger data sets and models that are more complicated. Here, we show how to leverage the quality of time-lapse microscopy data with a simple two-stage method to estimate realistic dynamic NLMEs accurately. We demonstrate accuracy by benchmarking with a published model and dataset, and scalability with a new mechanistic model and corresponding dataset for amino acid transporter endocytosis in budding yeast. We also propose variation-based sensitivity analysis to identify time-dependent causes of cell-to-cell variability, highlighting important sub-processes in endocytosis. Generality and simplicity of the approach will facilitate customized extensions for analyzing single-cell dynamics.

systems biology↗