Gunnar Cedersund

This paper develops a mathematical model of chylomicron dynamics incorporating the second meal effect, showing it can predict postprandial lipid responses across different BMI values.

The paper introduces M4 drug discovery, a method that integrates multi-level, multi-timescale, and multi-species data with mechanistic knowledge to predict clinical outcomes of drugs like GLP-1RAs from preclinical data.

This paper integrates GLP-1RA exenatide studies across heterogeneous cell systems to address variability, providing a unified conclusion and predicting inter-study variabilities.

This paper develops a mathematical model integrating multiple alcohol biomarkers to reconstruct past drinking events and provides an interactive web tool for forensic analysis.

This paper introduces a method to calculate uncertainty in model-derived biomarkers from non-normal hemodynamic data, identifying reliable biomarkers like ventricular relaxation rate.

The paper assesses the reproducibility of cardiovascular model parameters derived from 4D Flow MRI and cuff pressure measurements, finding variability ranges between 2.5% and 41% across different conditions.

The paper presents a model-driven meta-analysis showing inhibitory neurons dominate BOLD-fMRI responses, with excitatory neurons contributing less than 20%.

The paper presents a physiologically-based digital twin model that simulates real-life alcohol consumption and connects short-term drinking to long-term PEth levels, enabling personalized predictions and eHealth applications.

The paper develops a multi-scale digital twin model to simulate insulin resistance progression, validating it with diet and drug intervention data.

The paper uses mathematical models to study IL-10 feedback mechanisms in human monocytes during inflammation, revealing their role in regulating TNF dynamics.

The paper develops a method to build a comprehensive adipocyte signaling model by iteratively adding phosphosites with confidence levels, achieving high predictive accuracy for inhibitor data.

The paper measures nuclear transport rates in single yeast cells using FRAP and identifies variability in nuclear pore complexes and other factors as sources of cell-to-cell differences.

The paper develops a mechanistic model integrating neurovascular and neurometabolic couplings to describe and predict metabolic and BOLD responses to visual stimuli.

The paper introduces a digital twin model that predicts diet response and metabolic changes before and after fasting by integrating clinical data and mechanistic models.

This paper presents a hybrid digital twin model combining mechanistic and machine learning approaches to simulate ischemic stroke risk based on physiological factors over time.

The paper develops a mathematical model linking in vitro and in vivo lipolysis data to explain fatty acid release mechanisms and simulate insulin clamp scenarios in type 2 diabetes.

The paper develops a systems biology model to simulate adrenergic stimulation of adiponectin exocytosis in white adipocytes and validates it with experimental data.

The paper develops a computational model of a liver-islet microphysiological system to predict glucose metabolism and translate experimental results to humans.

The paper updates a multi-level mathematical model of glucose homeostasis to better reflect organ-specific glucose uptake, dynamics, and the impact of blood flow on insulin-stimulated glucose uptake.

The paper presents an in silico mouse model that simulates multi-scale insulin resistance and type 2 diabetes progression across cells, organs, and the whole body.

The paper develops a quantitative model that integrates multiple neurovascular coupling data types to predict and explain hemodynamic responses across species.

The paper uses mathematical modeling to uncover the mechanism of a sustained anti-inflammatory response to Dexamethasone in alveolar macrophages, identifying slow receptor-drug complex release as the key factor.

LASSIM infers genome-wide gene regulatory networks using mechanistic ODE models, demonstrating its effectiveness in modeling Th2 differentiation with integrated data and siRNA experiments.