Biology subjects

Podeus, H.

Publications and source records attributed to Podeus, H..

4 recordsLinked to original sources

bioRxiv2026

A Mathematical Model of Dietary Lipid Absorption and Postprandial Chylomicron Dynamics

Obesity and related conditions such as dyslipidemia impose an increasing burden on healthcare systems worldwide. These conditions are associated with altered postprandial chylomicron (CM) metabolism, the elusive and critical first step in lipid metabolism. This step remains elusive because it is governed by large interindividual variations and a complex set of intestinal processes. In particular, the second meal effect (SME) implies that enterocytes release previously stored fat during subsequent meals. To deal with this complexity, CM and lipid metabolism have previously been explored using mathematical modeling. However, existing models primarily describe TAG dynamics following a single meal or are too complex for practical personalization across datasets. Herein, we address these limitations by presenting a small-scale mathematical model of CM dynamics that incorporates the SME. The presented model successfully describes data from six clinical studies of both single and repeated meal interventions. Model performance was further evaluated by predicting independent datasets using a BMI-dependent calibration. Finally, to demonstrate model applicability, we simulated full-day responses consisting of three sequential meals in individuals with varying BMI values, with qualitative agreement to clinical observations. This work supports our understanding of the SME, person-specific CM postprandial responses, and mechanisms underlying obesity.

Systems Biology↗

bioRxiv2025

Predicting Real-life Drinking Scenarios through a Physiological Digital Twin Incorporating Secondary Alcohol Markers

Alcohol consumption poses significant societal challenges, necessitating accurate tools for detecting at-risk drinking. Various biomarkers reflect alcohol intake over different timeframes. Blood alcohol concentration (BAC) and breath alcohol concentration (BrAC) are commonly used for short-term detection, particularly in forensic contexts such as driving under the influence of alcohol (DUIA) cases. Moreover, the rapid kinetics of BAC and BrAC limit their utility in determining the precise timing of intake--an essential factor in legal cases involving defenses like the hipflask argument. To address this, secondary metabolites including ethyl glucuronide (EtG), ethyl sulphate (EtS), and urine alcohol concentration (UAC) offer slower, more time-sensitive profiles. Combining these markers could enable a more accurate reconstruction of past alcohol consumption events. Traditionally, mathematical models have been used to help extract information from the dynamics of alcohol-related markers. Existing mathematical models typically focus on primary markers or single secondary markers in isolation. In this study, we present an extended mathematical model that integrates all markers; BAC, EtG, EtS, and UAC into a unified framework, expanding our previous physiological twin model. Our updated model enables personalized simulations of alcohol metabolism and intake timing, which in turn creates the fundament for enhancing forensic assessments and supporting applications where accurate temporal analysis of alcohol consumption is critical. To facilitate accessibility and practical use of this analysis, we have implemented and provided an interactive web application.

systems biology↗

bioRxiv2023

A Physiologically Based Digital Twin for Alcohol Consumption - Predicting Real-life Drinking Responses and Long-term Plasma PEth

Alcohol consumption is associated with a wide variety of preventable health complications and is a major risk factor for all-cause mortality in the age group 15-47 years. To reduce dangerous drinking behavior, eHealth applications have shown promise. A particularly interesting potential lies in the combination of eHealth apps with mathematical models. However, existing mathematical models do not consider real-life situations, such as combined intake of meals and beverages, and do not connect drinking to clinical markers, such as phosphatidylethanol (PEth). Herein, we present such a model which can simulate real-life situations and connect drinking to long-term markers. The new model can accurately describe both estimation data according to a {chi}2-test (187.0 < T{chi}2 = 226.4) and independent validation data (70.8 < T{chi}2 =93.5). The model can also be personalized using anthropometric data from a specific individual and can thus be used as a physiologically-based digital twin. This twin is also able to connect short-term consumption of alcohol to the long-term dynamics of PEth levels in the blood, a clinical biomarker of alcohol consumption. Here we illustrate how connecting short-term consumption to long-term markers allows for a new way to determine patient alcohol consumption from measured PEth levels. An additional use case of the twin could include the combined evaluation of patient-reported AUDIT forms and measured PEth levels. Finally, we integrated the new model into an eHealth application, which could help guide individual users or clinicians to help reduce dangerous drinking.

systems biology↗

bioRxiv2021

A multi-data based quantitative model for neurovascular coupling in the brain.

The neurovascular coupling (NVC) forms the foundation for functional imaging techniques of the brain, since NVC connects neural activity with observable hemodynamic changes. Many aspects of the NVC have been studied both experimentally and with mathematical models: various combinations of blood volume and flow, electrical activity, oxygen saturation measures, blood oxygenation level-dependent (BOLD) response, and optogenetics have been measured and modeled in rodents, primates, or humans. We now present a first inter-connected mathematical model that describes all such data types simultaneously. The model can predict independent validation data not used for training. Using simulations, we show for example how complex bimodal behaviors appear upon stimulation. These simulations thus demonstrate how our new quantitative model, incorporating most of the core aspects of the NVC, can be used to mechanistically explain each of its constituent datasets.

systems biology↗