Biology subjects

Lövfors, W.

Publications and source records attributed to Lövfors, W..

5 recordsLinked to original sources

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↗

bioRxiv2022

A comprehensive mechanistic model of adipocyte signaling with layers of confidence

Adipocyte cellular signaling, normally and in type 2 diabetes, is far from fully studied. We have earlier developed detailed dynamic mathematical models for some well-studied, and partially overlapping, signaling pathways in adipocytes. Still, these models only cover a fraction of the total cellular response. For a broader coverage of the response, large-scale phosphoproteomic data is key. There exists such data for the insulin response of adipocytes, as well as prior knowledge on possible protein-protein interactions associated with a confidence level. However, methods to combine detailed dynamic models with large-scale data, using information about the confidence of included interactions, are lacking. In our new method, we first establish a core model by connecting our partially overlapping models of adipocyte cellular signaling with focus on: 1) lipolysis and fatty acid release, 2) glucose uptake, and 3) the release of adiponectin. We use the phosphoproteome data and prior knowledge to identify phosphosites adjacent to the core model, and then try to add the adjacent phosphosites to the model. The additions of the adjacent phosphosites is tested in a parallel, pairwise approach with low computation time. We then iteratively collect the accepted additions into a layer, and use the newly added layer to find new adjacent phosphosites. We find that the first 15 layers (60 added phosphosites) with the highest confidence can correctly predict independent inhibitor-data (70-90 % correct), and that this ability decrease when we add layers of decreasing confidence. In total, 60 layers (3926 phosphosites) can be added to the model and still keep predictive ability. Finally, we use the comprehensive adipocyte model to simulate systems-wide alterations in adipocytes in type 2 diabetes. This new method provide a tool to create large models that keeps track of varying confidence.

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 multi-level model analysis of lipolysis and fatty acid release from adipocytes in vitro and from adipose tissue invivo

Lipolysis and the release of fatty acids to supply energy fuel to other organs, such as between meals, during exercise, and starvation, are fundamental functions of the adipose tissue. The intracellular lipolytic pathway in adipocytes is activated by adrenaline and noradrenaline, and inhibited by insulin. Circulating fatty acids are elevated in type 2 diabetic individuals. The mechanisms behind this elevation are not fully known, and to increase the knowledge a link between the systemic circulation and intracellular lipolysis is key. However, data on lipolysis and knowledge from in vitro systems have not been linked to corresponding in vivo data and knowledge in vivo. Here, we use mathematical modelling to provide such a link. We examine mechanisms of insulin action by combining in vivo and in vitro data into an integrated mathematical model that can explain all data. Furthermore, the model can describe independent data not used for training the model. We show the usefulness of the model by simulating new and more challenging experimental setups in silico, e.g. the extracellular concentration of fatty acids during an insulin clamp, and the difference in such simulations between individuals with and without type 2 diabetes. Our work provides a new platform for model-based analysis of adipose tissue lipolysis, under both non-diabetic and type 2 diabetic conditions.

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↗