| 1. |
- Lövfors, William, 1991-
(author)
-
A comprehensive dynamic model of the adipocyte
- 2022
-
Doctoral thesis (other academic/artistic)abstract
- The adipose tissue contributes to energy homeostasis by storing excess energy as triglycerides when the energy status is high, and by releasing fatty acids when the energy status is low. In addition to the involvement in energy homeostasis, the adipose tissue also has a function of hormonal control exerted by the release of adipokines such as adiponectin. Dysregulations in the signaling pathways of these two functions are involved in the development of type 2 diabetes and related complications such as cardiovascular disease. These signaling pathways are too complex to be fully unraveled without a systematic framework, such as mathematical modeling. Previous modeling works have investigated the insulin signaling pathways leading to glucose uptake in primary human adipocytes in response to insulin stimulation. Furthermore, experimental works have investigated how adrenergic stimuli and varying concentrations of intracellular mediators triggers the release of adiponectin from 3T3‐L1 adipocytes, and how insulin and adrenergic stimuli influence lipolysis in primary human adipocytes. Additionally, large‐scale phosphoproteomic data for insulin signaling in 3T3‐L1 adipocytes have become available. However, these experimental data had not been systematically investigated using mathematical modeling. In this thesis, I have used mathematical modeling to study three aspects of the adipocyte: 1) adiponectin release, 2) lipolysis, and 3) intracellular crosstalk between the pathways of glucose uptake, lipolysis, and adiponectin release. Finally, I have developed a new method for automatic model expansion.In Paper I, we used mathematical modeling to test a hypothesis of the mechanisms controlling adiponectin exocytosis in 3T3‐L1 cells. We found that the hypothesis had to be revised in order to be in agreement with the available experimental data. We used the revised model to quantify the balance between the exocytosis and the endocytosis, and to predict the amount of released adiponectin in response to additional experiments.In Paper II, we extended the adiponectin exocytosis model from Paper I with mechanisms for how extracellular adrenergic stimulation trigger adiponectin exocytosis. We also used the model to quantify the effect of a decreased amount of β3‐adrenergic receptors on the adrenergically stimulated adiponectin exocytosis.In Paper III, we tested a hypothesis of the impact of, and crosstalk between, insulin and adrenergic stimulation on the lipolysis. We used the model to test three different actions by insulin on the lipolysis, and to predict fatty acid release in vivo in response to stimulations with epinephrine and insulin.In Paper IV, we combined the models from Paper I‐III with a previously published model for glucose uptake. We then used the connected model as a core model to which additional signaling data could be added using a new method for automatic model expansion. This new method incorporates prior‐knowledge and large‐scale data to expand a core model with thousands of additional phosphosites into a comprehensive model of the adipocyte. The comprehensive expanded model can propagate the effect of type 2 diabetes from the core model to a substantial part of the phosphoproteome, and could thus facilitate the finding of new drug targets or treatment regimens for type 2 diabetic patients.
|
|
| 2. |
- Lövfors, William, 1991-, et al.
(author)
-
A comprehensive mechanistic model of adipocyte signaling with layers of confidence
- 2022
-
Other publication (other academic/artistic)abstract
- 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.Competing Interest StatementThe authors have declared no competing interest.
|
|
| 3. |
- Lövfors, William, 1991-, et al.
(author)
-
A comprehensive mechanistic model of adipocyte signaling with layers of confidence
- 2023
-
In: npj Systems Biology and Applications. - : Springer Nature. - 2056-7189. ; 9:1
-
Journal article (peer-reviewed)abstract
- Adipocyte signaling, normally and in type 2 diabetes, is far from fully understood. We have earlier developed detailed dynamic mathematical models for several well-studied, 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 and systems level knowledge on protein interactions are key. However, methods to combine detailed dynamic models with large-scale data, using information about the confidence of included interactions, are lacking. We have developed a method to first establish a core model by connecting existing models of adipocyte cellular signaling for: (1) lipolysis and fatty acid release, (2) glucose uptake, and (3) the release of adiponectin. Next, we use publicly available phosphoproteome data for the insulin response in adipocytes together with prior knowledge on protein interactions, to identify phosphosites downstream of the core model. In a parallel pairwise approach with low computation time, we test whether identified phosphosites can be added to the model. We iteratively collect accepted additions into layers and continue the search for phosphosites downstream of these added layers. For the first 30 layers with the highest confidence (311 added phosphosites), the model predicts independent data well (70–90% correct), and the predictive capability gradually decreases when we add layers of decreasing confidence. In total, 57 layers (3059 phosphosites) can be added to the model with predictive ability kept. Finally, our large-scale, layered model enables dynamic simulations of systems-wide alterations in adipocytes in type 2 diabetes.
|
|
| 4. |
- Lövfors, William, 1991-, et al.
(author)
-
A systems biology analysis of lipolysis and fatty acid release from adipocytes in vitro and from adipose tissue in vivo
- 2021
-
In: PLOS ONE. - San Fransisco, United States : Public Library of Science. - 1932-6203. ; 16:12
-
Journal article (peer-reviewed)abstract
- 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.
|
|
| 5. |
- Nyman, Elin, et al.
(author)
-
Mathematical modeling improves EC50 estimations from classical dose–response curves
- 2015
-
In: The FEBS Journal. - : Wiley. - 1742-464X .- 1742-4658. ; 282:5, s. 951-962
-
Journal article (peer-reviewed)abstract
- The beta-adrenergic response is impaired in failing hearts. When studying beta-adrenergic function in vitro, the half-maximal effective concentration (EC50) is an important measure of ligand response. We previously measured the in vitro contraction force response of chicken heart tissue to increasing concentrations of adrenaline, and observed a decreasing response at high concentrations. The classical interpretation of such data is to assume a maximal response before the decrease, and to fit a sigmoid curve to the remaining data to determine EC50. Instead, we have applied a mathematical modeling approach to interpret the full dose–response curvein a new way. The developed model predicts a non-steady-state caused by a short resting time between increased concentrations of agonist, which affect the dose–response characterization. Therefore, an improved estimate of EC50 may be calculated using steady-state simulations of the model. The model-based estimation of EC50 is further refined using additional time resolved data to decrease the uncertainty of the prediction. The resulting model-based EC50 (180–525 nM) is higher than the classically interpreted EC50 (46–191 nM). Mathematical modeling thus makes it possible to reinterpret previously obtained datasets, and to make accurate estimates of EC50 even when steady-state measurements are not experimentally feasible.
|
|
| 6. |
- Podéus, Henrik, et al.
(author)
-
A physiologically-based digital twin for alcohol consumption-predicting real-life drinking responses and long-term plasma PEth
- 2024
-
In: npj Digital Medicine. - : Springer Nature. - 2398-6352. ; 7:1
-
Journal article (peer-reviewed)abstract
- 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 χ2 -test (187.0 < Tχ2 = 226.4) and independent validation data (70.8 < Tχ2 = 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.
|
|
| 7. |
- Simonsson, Christian, 1992-
(author)
-
Mathematical Modelling of MASLD ‐ Towards Digital Twins in Liver Disease
- 2024
-
Doctoral thesis (other academic/artistic)abstract
- Unhealthy dieting and a sedentary lifestyle are causing an increased prevalence of obesity related complications. One such complication is metabolic dysfunction-associated steatotic liver disease (MASLD) the manifestation of metabolic dysregulation and insulin resistance in the liver. Today, MASLD effect a third of the world’s population. One of the main characteristics of MASLD is accumulation of ectopic lipids in the liver, also denoted steatosis. Steatosis is not inherently dangerous but is an indication of metabolic dysregulation, and long-term MASLD can progress into severe conditions such as chronic hepatic inflammation denoted metabolic dysfunction-associated steatohepatitis (MASH), liver scarring (cirrhosis), and primary liver cancer (hepatocellular carcinoma, HCC). Moreover, these conditions can be further aggravated by alcohol consumption. The increase in potential patients with MASLD will have an enormous burden on future healthcare. Thus, future healthcare has a need for innovative solutions to lessen this burden. Such solutions should be capable of personalized and preventive measures, cost-effective high throughput screening methods, and frameworks integrating all available patient data, for all stages of MASLD. Today, some of these methodologies already exist, however there is still a need for ways to integrate different liver biomarkers into a user-friendly framework, with strong personalization and predictive capabilities. For this purpose, data-driven mathematical modelling is of use. Data-driven mathematical models has proven useful for such integration in other disease areas such as stroke. In this thesis, I have created and explored several mathematical models aimed at exploring different aspects of MASLD, as well as developed several models using data from example: our own collected magnetic resonance imaging (MRI) data from patients suffering from chronic liver disease or HCC, and pre-clinical mouse data of insulin resistance progression. The studies presented in this thesis investigate diet-driven insulin resistance development, steatosis development and screening, as well as lifestyle interventions for alcohol and dietary habits, and liver function evaluation at late-stage liver disease. Thus, this thesis presents a possible fundament to create a so-called digital twin of MASLD – a highly personalized model capable of making predictions based on lifestyle.
|
|
