The Metabolic Syndrome (MetS) is a complex, multifactorial disorder that develops slowly over time presenting itself with large differences among MetS patients. We applied a systems biology approach to describe and predict the onset and progressive development of MetS, in a study that combined in vivo and in silico models. A new data-driven, physiological model (MINGLeD: Model INtegrating Glucose and Lipid Dynamics) was developed, describing glucose, lipid and cholesterol metabolism. Since classic kinetic models cannot describe slowly progressing disorders, a simulation method (ADAPT) was used to describe longitudinal dynamics and to predict metabolic concentrations and fluxes. This approach yielded a novel model that can describe long-term MetS development and progression. This model was integrated with longitudinal in vivo data that was obtained from male APOE*3-Leiden.CETP mice fed a high-fat, high-cholesterol diet for three months and that developed MetS as reflected by classical symptoms including obesity and glucose intolerance. Two distinct subgroups were identified: those who developed dyslipidemia, and those who did not. The combination of MINGLeD with ADAPT could correctly predict both phenotypes, without making any prior assumptions about changes in kinetic rates or metabolic regulation. Modeling and flux trajectory analysis revealed that differences in liver fluxes and dietary cholesterol absorption could explain this occurrence of the two different phenotypes. In individual mice with dyslipidemia dietary cholesterol absorption and hepatic turnover of metabolites, including lipid fluxes, were higher compared to those without dyslipidemia. Predicted differences were also observed in gene expression data, and consistent with the emergence of insulin resistance and hepatic steatosis, two well-known MetS co-morbidities. Whereas MINGLeD specifically models the metabolic derangements underlying MetS, the simulation method ADAPT is generic and can be applied to other diseases where dynamic modeling and longitudinal data are available.

Results: We collected a large range of postprandial glucose and insulin dynamics for 53 common food products and mixed meals. Currently available glycemic measures were found to be inadequate to describe the heterogeneity in postprandial dynamics. By estimating model parameters from glucose and insulin data, the physiology-based dynamic model accurately describes the measured data whilst adhering to physiological constraints. Conclusions: The physiology-based dynamic model provides a systematic framework to analyze postprandial glucose and insulin profiles. By changing parameter values the model can be adjusted to simulate impaired glucose tolerance and insulin resistance.

One contribution of 12 to a theme issue 'The Human Physiome: a necessary key to the creative destruction of medicine'. 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 decisionsupport systems combined with the engagement of other disciplines are needed to create user-friendly and cost-efficient software packages.

Diabetes is a serious and life-threatening condition that reduces the quality of life of the patient and is also costly, both in medical costs and in lost work-hours. 1 The incidence and severity of the complications of diabetes can considerably be reduced if patients develop a lifestyle that leads to good glycemic control. 2,3 Research has shown that diabetes education can reduce HbA1c over a longer period, 4,5 resulting in a lower risk of complications. 6,7 Education is therefore a fundamental part of diabetes care. It is currently provided in several 1-on-1 or group sessions with a physician, diabetes nurse, dietician, or podiatrist. This is time-consuming and costly.A major part of diabetes education is learning how to adjust insulin injections based on carbohydrate intake, exercise and factors like stress or illness. However, possibilities for the patient to safely practice with this newly acquired knowledge are limited to trying different strategies on his own body. This gives a considerable risk of hypo-or hyperglycemia. Here, we describe the development and verification of the physiological model for healthy subjects that forms the basis of the Eindhoven Diabetes Education Simulator (E-DES). E-DES shall provide diabetes patients with an individualized virtual practice environment incorporating the main factors that influence glycemic control: food, exercise, and medication.

Cancer immunotolerance can be reversed by checkpoint blockade immunotherapy in some patients, but response prediction remains a challenge. CD4+ T cells play an important role in activating adaptive immune responses against cancer. Conversion to an immune suppressive state impairs the anti-cancer immune response and is mainly effected by CD4+ Treg cells. A number of signal transduction pathways activate and control functions of CD4+ T cell subsets. As previously described, assays have been developed which enable quantitative measurement of the activity of signal transduction pathways (e.g. TGFβ, NFκB, PI3K-FOXO, JAK-STAT1/2, JAK-STAT3, Notch) in a cell or tissue sample. Using these assays, pathway activity profiles for various CD4+ T cell subsets were defined and cellular mechanisms underlying breast cancer-induced immunotolerance investigated in vitro. Results were used to measure the immune response state in a clinical breast cancer study.MethodsSignal transduction pathway activity scores were measured on Affymetrix expression microarray data of resting, immune-activated, and immune-activated CD4+ T cells incubated with breast cancer tissue supernatants, and of CD4+ Th1, Th2, and Treg cells, and in a clinical study in which CD4+ T cells were derived from blood, lymph node and cancer tissue from primary breast cancer patients (n=10).ResultsIn vitro CD4+ T cell activation induced PI3K, NFκB, JAK-STAT1/2, and JAK-STAT3 pathway activity. Simultaneous incubation with primary cancer supernatant reduced PI3K and NFκB, and partly reduced JAK-STAT3, pathway activity, while simultaneously increasing TGFβ pathway activity; characteristic of an immune tolerant state. CD4+ Th1, Th2, and Treg cells all had a specific pathway activity profile, with activated immune suppressive Treg cells characterized by NFκB, JAK-STAT3, TGFβ, and Notch pathway activity. An immune tolerant pathway profile was identified in CD4+ T cells from tumor infiltrate of a subset of primary breast cancer patients which could be contributed to activated Treg cells. A Treg pathway profile was also identified in blood samples.ConclusionSignaling pathway assays can be used to quantitatively measure the functional immune response state of lymphocyte subsets in vitro and in vivo. Clinical results suggest that in primary breast cancer the adaptive immune response of CD4+ T cells has frequently been replaced by immunosuppressive Treg cells, potentially causing resistance to checkpoint inhibition. In vitro study results suggest that this effect is mediated by soluble factors from cancer tissue (e.g. TGFβ). Signaling pathway activity analysis on TIL and/or blood samples is expected to improve predicting and monitoring response to checkpoint inhibitor immunotherapy.