Inflammation is one of the vital mechanisms through which the immune system responds to harmful stimuli. During inflammation, proinflammatory and anti-inflammatory cytokines interplay to orchestrate fine-tuned and dynamic immune responses. The cytokine interplay governs switches in the inflammatory response and dictates the propagation and development of the inflammatory response. Molecular pathways underlying the interplay are complex, and time-resolved monitoring of mediators and cytokines is necessary as a basis to study them in detail. Our understanding can be advanced by mathematical models that enable to analyze the system of interactions and their dynamical interplay in detail. We, therefore, used a mathematical modeling approach to study the interplay between prominent proinflammatory and anti-inflammatory cytokines with a focus on tumor necrosis factor and interleukin 10 (IL-10) in lipopolysaccharide-primed primary human monocytes. Relevant time-resolved data were generated by experimentally adding or blocking IL-10 at different time points. The model was successfully trained and could predict independent validation data and was further used to perform simulations to disentangle the role of IL-10 feedbacks during an acute inflammatory event. We used the insight to obtain a reduced predictive model including only the necessary IL-10-mediated feedbacks. Finally, the validated reduced model was used to predict early IL-10–tumor necrosis factor switches in the inflammatory response. Overall, we gained detailed insights into fine-tuning of inflammatory responses in human monocytes and present a model for further use in studying the complex and dynamic process of cytokine-regulated acute inflammation. © 2023 The Authors

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.

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.

We are currently in the middle of a major shift in biomedical research: unprecedented and rapidly growing amounts of data may be obtained today, from in vitro, in vivo and clinical studies, at molecular, physiological and clinical levels. To make use of these large-scale, multi-level datasets, corresponding multi-level mathematical models are needed, i.e. models that simultaneously capture multiple layers of the biological, physiological and disease-level organization (also referred to as quantitative systems pharmacology-QSP-models). However, today's multi-level models are not yet embedded in end-usage applications, neither in drug research and development nor in the clinic. Given the expectations and claims made historically, this seemingly slow adoption may seem surprising. Therefore, we herein consider a specific example-type 2 diabetes-and critically review the current status and identify key remaining steps for these models to become mainstream in the future. This overview reveals how, today, we may use models to ask scientific questions concerning, e.g., the cellular origin of insulin resistance, and how this translates to the whole-body level and short-term meal responses. However, before these multi-level models can become truly useful, they need to be linked with the capabilities of other important existing models, in order to make them 'personalized' (e.g. specific to certain patient phenotypes) and capable of describing long-term disease progression. To be useful in drug development, it is also critical that the developed models and their underlying data and assumptions are easily accessible. For clinical end-usage, in addition, model links to decision-support systems combined with the engagement of other disciplines are needed to create user-friendly and cost-efficient software packages.

Insulin resistance is a major aspect of type 2 diabetes (T2D), which results from impaired insulin signaling in target cells. Signaling to regulate forkhead box protein O1 (FOXO1) may be the most important mechanism for insulin to control transcription. Despite this, little is known about how insulin regulates FOXO1 and how FOXO1 may contribute to insulin resistance in adipocytes, which are the most critical cell type in the development of insulin resistance. We report a detailed mechanistic analysis of insulin control of FOXO1 in human adipocytes obtained from non-diabetic subjects and from patients with T2D. We show that FOXO1 is mainly phosphorylated through mTORC2-mediated phosphorylation of protein kinase B at Ser(473) and that this mechanism is unperturbed in T2D. We also demonstrate a cross-talk from the MAPK branch of insulin signaling to stimulate phosphorylation of FOXO1. The cellular abundance and consequently activity of FOXO1 are halved in T2D. Interestingly, inhibition of mTORC1 with rapamycin reduces the abundance of FOXO1 to the levels in T2D. This suggests that the reduction of the concentration of FOXO1 is a consequence of attenuation of mTORC1, which defines much of the diabetic state in human adipocytes. We integrate insulin control of FOXO1 in a network-wide mathematical model of insulin signaling dynamics based on compatible data from human adipocytes. The diabetic state is network-wide explained by attenuation of an mTORC1-to-insulin receptor substrate-1 (IRS1) feedback and reduced abundances of insulin receptor, GLUT4, AS160, ribosomal protein S6, and FOXO1. The model demonstrates that attenuation of the mTORC1-to-IRS1 feedback is a major mechanism of insulin resistance in the diabetic state.

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.

In metabolic diseases such as Type 2 Diabetes and Non-Alcoholic Fatty Liver Disease, the systemic regulation of postprandial metabolite concentrations is disturbed. To understand this dysregulation, a quantitative and temporal understanding of systemic postprandial metabolite handling is needed. Of particular interest is the intertwined regulation of glucose and non-esterified fatty acids (NEFA), due to the association between disturbed NEFA metabolism and insulin resistance. However, postprandial glucose metabolism is characterized by a dynamic interplay of simultaneously responding regulatory mechanisms, which have proven difficult to measure directly. Therefore, we propose a mathematical modelling approach to untangle the systemic interplay between glucose and NEFA in the postprandial period. The developed model integrates data of both the perturbation of glucose metabolism by NEFA as measured under clamp conditions, and postprandial time-series of glucose, insulin, and NEFA. The model can describe independent data not used for fitting, and perturbations of NEFA metabolism result in an increased insulin, but not glucose, response, demonstrating that glucose homeostasis is maintained. Finally, the model is used to show that NEFA may mediate up to 30-45% of the postprandial increase in insulin-dependent glucose uptake at two hours after a glucose meal. In conclusion, the presented model can quantify the systemic interactions of glucose and NEFA in the postprandial state, and may therefore provide a new method to evaluate the disturbance of this interplay in metabolic disease.

The response to insulin is impaired in type 2 diabetes. Much information is available about insulin signaling, but understanding of the cellular mechanisms causing impaired signaling and insulin resistance is hampered by fragmented data, mainly obtained from different cell lines and animals. We have collected quantitative and systems-wide dynamic data on insulin signaling in primary adipocytes and compared cells isolated from healthy and diabetic individuals. Mathematical modeling and experimental verification identified mechanisms of insulin control of the MAPKs ERK1/2. We found that in human adipocytes, insulin stimulates phosphorylation of the ribosomal protein S6 and hence protein synthesis about equally via ERK1/2 and mTORC1. Using mathematical modeling, we examined the signaling network as a whole and show that a single mechanism can explain the insulin resistance of type 2 diabetes throughout the network, involving signaling both through IRS1, PKB, and mTOR and via ERK1/2 to the nuclear transcription factor Elk1. The most important part of the insulin resistance mechanism is an attenuated feedback from the protein kinase mTORC1 to IRS1, which spreads signal attenuation to all parts of the insulin signaling network. Experimental inhibition of mTORC1 using rapamycin in adipocytes from non-diabetic individuals induced and thus confirmed the predicted network-wide insulin resistance.

Recent clinical studies suggest sustained treatment effects of interleukin-1β (IL-1β)-blocking therapies in type 2 diabetes mellitus. The underlying mechanisms of these effects, however, remain underexplored. Using a quantitative systems pharmacology modeling approach, we combined ex vivo data of IL-1β effects on β-cell function and turnover with a disease progression model of the long-term interactions between insulin, glucose, and β-cell mass in type 2 diabetes mellitus. We then simulated treatment effects of the IL-1 receptor antagonist anakinra. The result was a substantial and partly sustained symptomatic improvement in β-cell function, and hence also in HbA1C, fasting plasma glucose, and proinsulin-insulin ratio, and a small increase in β-cell mass. We propose that improved β-cell function, rather than mass, is likely to explain the main IL-1β-blocking effects seen in current clinical data, but that improved β-cell mass might result in disease-modifying effects not clearly distinguishable until >1 year after treatment.

Type 2 diabetes is characterized by raised blood glucose levels caused by an insufficient insulin control of glucose homeostasis. This lack of control is expressed both through insufficient release of insulin by the pancreatic beta-cells, and through insulin resistance in the insulin-responding tissues. We find insulin resistance of the adipose tissue particularly interesting since it appears to influence other insulin-responding tissues, such as muscle and liver, to also become insulin resistant.

The insulin signaling network is highly complex with cross-interacting intermediaries, positive and negative feedbacks, etc. To facilitate the mechanistic understanding of this network, we obtain dynamic, information-rich data and use model-based analysis as a tool to formally test different hypotheses that arise from the experimental observations. With dynamic mathematical models, we are able to combine knowledge and experimental data into mechanistic hypotheses, and draw conclusions such as rejection of hypotheses and prediction of outcomes of new experiments.

We aim for an increased understanding of adipocyte insulin signaling and the underlying mechanisms of the insulin resistance that we observe in adipocytes from subjects diagnosed with type 2 diabetes. We also aim for a complete picture of the insulin signaling network in primary human adipocytes from normal and diabetic subjects with a link to relevant clinical parameters: plasma glucose and insulin. Such a complete picture of insulin signaling has not been presented before. Not for adipocytes and not for other types of cells.

In this thesis, I present the development of the first comprehensive insulin signaling model that can simulate both normal and diabetic data from adipocytes – and that is linked to a whole-body glucose-insulin model. In the linking process we conclude that at least two glucose uptake parameters differ between the in vivo and in vitro conditions (Paper I). We also perform a model analysis of the early insulin signaling dynamics in rat adipocytes and conclude that internalization is important for an apparent reversed order of phosphorylation seen in these cells (Paper II). In the development of the first version of the comprehensive insulin signaling model, we introduce a key parameter for the diabetic state – an attenuated feedback (Paper III). We finally continue to build on the comprehensive model and include signaling to nuclear transcription via ERK and report substantial crosstalk in the insulin signaling network (Paper IV).