Biology subjects

Simonsson, C.

Publications and source records attributed to Simonsson, C..

12 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↗

bioRxiv2026

Fixed-dose streptozotocin combined with high-fat diet induces a stable type 2 diabetes-like phenotype in C57BL/6J mice

High-fat diet (HFD) combined with streptozotocin (STZ) is widely used to model type 2 diabetes (T2D) in rodents, but is often associated with high mortality, non-responders, and inconsistent outcomes. STZ is conventionally administered using body weight-adjusted dosing (mg/kg), despite evidence that heavier animals, including HFD-fed mice, exhibit more severe glycaemic responses. Here, we performed metabolic phenotyping in chow- and HFD-fed C57BL/6J mice treated with low or high fixed doses (mg instead of mg/kg) of anomer-equilibrated STZ. HFD combined with low-dose STZ induced a stable T2D-like phenotype characterized by sustained obesity, moderate hyperglycaemia, insulin resistance, and partial {beta}-cell loss, with low inter-individual variability. In contrast, high-dose STZ induced a T1D-like phenotype with extensive {beta}-cell loss. A semi-mechanistic mathematical model was developed and validated against independent experimental data, reproducing the observed dynamics of fasting glucose in response to fixed-dose STZ. The model further predicted that weight-adjusted (mg/kg) dosing could introduce variability in glycaemic responses, particularly in HFD-fed mice. Together, these results demonstrate that fixed-dose, anomer-equilibrated STZ induces a stable T2D-like phenotype, providing an alternative to conventional weight-adjusted dosing in HFD-fed mice.

physiology↗

bioRxiv2026

An Integrated Analysis of GLP-1R Agonist Mechanisms: Addressing Study Variations in Heterogeneous Cell Systems

Experimental cell systems support the development of pharmacological therapies such as glucagon-like peptide-1 receptor agonists (GLP-1RAs). However, their utility in drug discovery is limited due to study variability, which complicates formation of unified conclusions based on all available data. To address this, we conducted a comprehensive analysis of the GLP-1RA exenatide, incorporating 16 new and five pre-existing mono- or co-culture studies of human liver and pancreatic models. We employed a new pragmatic model-based approach designed to handle the common situation of heterogeneous in vitro datasets with few replicates per condition. All studies are jointly explained (disagreement<{chi}{superscript 2}-limit; 542<732), thereby providing a unified conclusion based on all studies. This work links in vitro biology to clinically relevant mechanisms, such as exenatides effect on glucose-insulin interplay, and predicts previously undescribed inter-study variabilities. Independent validation confirms predictive performance (64<83). Our new integrative approach enhances the utility of experimental cell systems in preclinical drug discovery.

systems biology↗

bioRxiv2026

Exploring a mathematical framework for quantifying cell size- dependent glucose uptake in adipocytes

Insulin-stimulated glucose uptake (ISGU) in adipocytes is central to maintain systemic glucose homeostasis. Understanding how ISGU relates to adipocyte traits, such as cell-size, is critical for elucidating pressing questions related to metabolic dysfunction connected to adipose hypertrophy and hyperplasia. Cell size is considered a central trait reflecting multiple aspects of adipocyte metabolism. However, a robust quantitative approach to estimate ISGU for a specific cell size is currently missing. Here, in an attempt to move towards such a method, we have formulated an approach using a mathematical framework. The framework consists of a linear equation: the product of the known number of cells (calculated using coulter counter-based cell-size distributions) and the unknown ISGU/cell, compared to the absolute ISGU (measured using 14C-glucose tracer assays). To solve this equation, we formulate a minimization problem which is optimized to find the unknown ISGU/cell for the best solution. Using different formulations of the equation we can investigate the need for either cell size-dependent or independent ISGU/cell, to describe varying glucose uptake in a cell sample of various cell sizes. While the framework needs further refinement, we demonstrate that cell size-dependent uptake slightly improved the agreement between model and experimental data for some groups. Together, with further validation this could serve as a useful tool to resolve long-standing questions regarding size-dependent characteristics like adipocyte size and cellular function. Key findingsHerein we explore a method to quantify cell size-dependent glucose uptake in adipocytes

cell 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↗

bioRxiv2025

M4 drug discovery: human drug predictions from integrated pre-clinical insights exemplified with a GLP1-R agonist

A major recent breakthrough in the treatment of type 2 diabetes has been the development of glucagon-like peptide-1 receptor agonists (GLP-1RAs). However, current translational frameworks struggle to predict the clinical outcomes of these drugs from preclinical data. There are several reasons for this struggle, which are generic for many drugs: GLP-1RAs act through multi-timescale mechanisms in which short-term effects propagate into long-term changes; no single preclinical system can capture all their effects in humans; and mechanistic extrapolation requires modelling numerous whole-body biological processes. To address this gap, we present a new extrapolation approach, M4 drug discovery, and retrospectively apply it to the GLP-1RA exenatide in a manner that is generalisable to other drugs. The method integrates: Multi-level data (cellular to whole-body), Multi-timescale data (minutes to months), Multi-species data (e.g., rodents to humans), and Mechanistic knowledge. In this study, we integrate human cell and animal data with drug-free human studies to successfully predict human pharmacokinetics (cost < {chi}2, p=0.05; 64 < 97) and the outcomes of a 30-week clinical trial (36 < 45). We found that integrating information across the four M4 axes improved predictive performance and physiological relevance: multi-species data inform pharmacokinetics, human cell data provide human population- and donor-specific potency estimates, animal data reveal additional drug effects not observable in cell cultures, and the multi-timescale mathematical modelling enables short-term effects of exenatide and meals to inform long-term changes in insulin sensitivity. This work provides new tools for drug extrapolations, supporting the community towards safer and more informed preclinical-to-clinical drug extrapolations.

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↗

bioRxiv2023

A multi-scale digital twin for adiposity-driven insulin resistance in humans: diet and drug effects

AimsThe increased prevalence of insulin resistance is one of the major health risks in society today. Insulin resistance involves both short-term dynamics, such as altered meal responses, and long-term dynamics, such as development of type 2 diabetes. Insulin resistance also occurs on different physiological levels, ranging from disease phenotypes to organ-organ communication and intracellular signaling. To better understand the progression of insulin resistance, an analysis method is needed that can combine different timescales and physiological levels. One such method is digital twins, consisting of combined mechanistic multi-scale and multi-level mathematical models. We have previously developed a multi-level model for short-term glucose homeostasis and intracellular insulin signaling, and there exists long-term weight regulation models. However, no one has combined these kinds of models into an interconnected, multi-level and multi-timescale digital twin model. Herein, we present a first such multi-scale digital twin for the progression of insulin resistance in humans. MethodsThe model is based on ordinary differential equations representing biochemical and physiological processes, in which unknown parameters were fitted to data using a MATLAB toolbox. ResultsThe connected twin correctly predicts independent data from a weight increase study, both for weight-changes, for fasting plasma insulin and glucose levels, as well as for intracellular insulin signaling. Similarly, the model can predict independent weight-change data in a weight loss study, involving diet and the weight loss drug topiramate. These independent validation tests are confirmed by a chi-square test (() = 4.8 < 21 = 2, (12,0.05)). In both these cases, the model can also predict non-measured variables, such as activity of intracellular intermediaries, glucose tolerance responses, and organ fluxes. ConclusionsWe present a first multi-level and multi-timescale model, describing dynamics on the whole-body, organ and cellular levels, ranging from minutes to years. This model constitutes the basis for a new digital twin technology, which in the future could potentially be used to aid medical pedagogics and increase motivation and compliance and thus aid in prevention and treatment of insulin resistance.

systems biology↗

bioRxiv2021

Digital twin predicting diet response before and after long-term fasting

Today, there is great interest in diets proposing new combinations of macronutrient compositions and fasting schedules. Unfortunately, there is little consensus regarding the impact of these different diets, since available studies measure different sets of variables in different populations, thus only providing partial, non-connected insights. We lack an approach for integrating all such partial insights into a useful and interconnected big picture. Herein, we present such an integrating tool. The tool uses a novel mathematical model that describes mechanisms regulating diet-response and fasting metabolic fluxes, both for organ-organ crosstalk, and inside the liver. The tool can mechanistically explain and integrate data from several clinical studies, and correctly predict new independent data, including data from a new clinical study. Using this model, we can predict non-measured variables, e.g. hepatic glycogen and gluconeogenesis, and we can quantify personalized expected differences in outcome for any diet. This constitutes a new digital twin technology.

systems biology↗

bioRxiv2021

A multi-scale in silico mouse model for insulin resistance and humanoid type 2 diabetes

Insulin resistance (IR) causes compensatory insulin production, which in humans eventually progresses to beta-cell failure and type 2 diabetes (T2D). This disease progression involves multi-scale processes, ranging from intracellular signaling to organ-organ and whole-body level regulations, on timescales from minutes to years. T2D progression is commonly studied using overfed and genetically modified rodents. However, rodents do not exhibit human T2D progression, with IR-driven beta-cell failure, and available multi-scale data is too complex to fully comprehend using traditional analysis. To help resolve these issues, we here present an in silico mouse model. This is the first mathematical model that simultaneously explains multi-scale mouse IR data on all three levels - cells, organs, body - ranging from minutes to months. The model correctly predicts new independent multi-scale validation data and provides insights into non-measured processes. Finally, we present a humanoid in silico mouse exhibiting disease progression from IR to IR-driven T2D.

systems biology↗

bioRxiv2020

A systems biology analysis of adrenergically stimulated adiponectin exocytosis in white adipocytes

Circulating levels of the adipocyte hormone adiponectin are typically reduced in obesity and this deficiency has been linked to metabolic diseases. It is thus important to understand the mechanisms controlling adiponectin exocytosis. This understanding is hindered by the high complexity of the data and the underlying signaling network. To handle this complexity, we here analyze the data using systems biology mathematical modelling. Previously, we have developed a mathematical model for how different intracellular concentrations of Ca2+, cAMP and ATP affect adiponectin exocytosis (measured as increase in membrane capacitance). However, recent work has shown that adiponectin exocytosis is physiologically triggered via signaling pathways involving adrenergic {beta}3 receptors ({beta}3ARs). Therefore, we have herein developed a more comprehensive model that also includes adiponectin exocytosis stimulated by extracellularly applied epinephrine or the {beta}3AR agonist CL 316,243. Our model can explain all previous patch-clamp data, as well as new data consisting of a combination of the intracellular mediators and extracellular adrenergic stimuli. Without changing the parameters, the model can accurately predict independent validation data with other combinations of patch-clamp pipette solutions and external stimuli. Finally, we use the model to perform new in silico experiments examining situations where corresponding wet lab experiments are difficult to perform. By this approach, we simulated adiponectin exocytosis in single cells, in response to the reduction of {beta}3ARs that is observed in adipocytes from animals with obesity-induced diabetes. Our work brings us one step closer to understanding the intricate regulation of adiponectin exocytosis.

systems biology↗

bioRxiv2020

Mechanisms of a sustained anti-inflammatory drug response in alveolar macrophages unraveled with mathematical modeling

Both initiation and suppression of inflammation are hallmarks of the immune response. If not balanced, the inflammation may cause extensive tissue damage, which is associated with common diseases, e.g. asthma and atherosclerosis. Anti-inflammatory drugs often come with severe side effects driven by high and fluctuating drug concentrations. To remedy this, drugs with sustained anti-inflammatory responses are desirable. However, we still lack a quantitative mechanistic understanding of such sustained effects. Here, we study the anti-inflammatory response to a common glucocorticoid drug, Dexamethasone. We find a sustained response 22 hours after drug removal. With hypothesis testing using mathematical modeling, we unravel the underlying mechanism - a slow release of Dexamethasone from the receptor-drug complex. The developed model is in agreement with time-resolved training and testing data, and is used to simulate hypothetical treatment schemes. This work opens up for a more knowledge-driven drug development, to find sustained anti-inflammatory responses and fewer side effects.

systems biology↗