Meal simulation model of the glucose-insulin system

Digital Twins in Type 1 Diabetes: A Systematic Review

Digital twin is a new concept that is rapidly gaining recognition especially in the medical field. Indeed, being a virtual representation of real-world entities and processes, a digital twin can be used to accurately represent the patients' disease, clarify the treatment target, and realize personalized and precise therapies. However, despite being a revolutionary concept, the diffusion of digital twins in type 1 diabetes (T1D) is still limited. In this systematic review, we analyzed structure, operating conditions, and characteristics of digital twins being developed for T1D. Our search covered published documents until March 2024: 220 publications were identified, 37 of which were duplicated entries; in addition, 173 publications were removed after inspection of titles, abstracts, and keywords; and finally, 11 publications were fully reviewed, of which 8 were deemed eligible for inclusion. We found that all eight methodologies are not comprehensive multi-scale virtual replicas of the individual with T1D, but they all focus on describing glucose-insulin metabolism, aiming to simulate glucose concentration resultant from therapeutic interventions. In this review, we will compare and analyze different factors characterizing these digital twins, such as operating principles (mathematical model, twinning procedure, validation and assessment) and the key aspects for practical adoption (inclusion of physical activity, data required for twinning, open-source availability). We will conclude the paper listing which, in our opinion, are the current limitations and future directives of digital twins in T1D, hoping that this article can be helpful to researchers working on diabetes technologies to further develop the use of such an important instrument.

SAAM II: A general mathematical modeling rapid prototyping environment

Simulation Analysis and Modeling II (SAAM II) is a graphical modeling software used in life sciences for compartmental model analysis, particularly, but not exclusively, appreciated in pharmacokinetics (PK) and pharmacodynamics (PD), metabolism, and tracer modeling. Its intuitive "circles and arrows" visuals allow users to easily build, solve, and fit compartmental models without the need for coding. It is suitable for rapid prototyping of models for complex kinetic analysis or PK/PD problems, and in educating students and non-modelers. Although it is straightforward in design, SAAM II incorporates sophisticated algorithms programmed in C to address ordinary differential equations, deal with complex systems via forcing functions, conduct multivariable regression featuring the Bayesian maximum a posteriori, perform identifiability and sensitivity analyses, and offer reporting functionalities, all within a single package. After 26 years from the last SAAM II tutorial paper, we demonstrate here SAAM II's updated applicability to current life sciences challenges. We review its features and present four contemporary case studies, including examples in target-mediated PK/PD, CAR-T-cell therapy, viral dynamics, and transmission models in epidemiology. Through such examples, we demonstrate that SAAM II provides a suitable interface for rapid model selection and prototyping. By enabling the fast creation of detailed mathematical models, SAAM II addresses a unique requirement within the mathematical modeling community.

Tailoring the Padova Type 2 Diabetes Simulator for Treatment Guidance in Target Populations

OBJECTIVE

The Padova type 2 diabetes (T2D) simulator (T2DS) has been recently proposed to optimize T2D treatments including novel long-acting insulins. It consists of a physiological model and an in silico population describing glucose dynamics, derived from early-stage T2D subjects studied with sophisticated tracer-based experimental techniques. This limits T2DS domain of validity to this specific sub-population. Conversely, running simulations in insulin-naïve or advanced T2D subjects, would be more valuable. However, it is rarely possible or cost-effective to run complex experiments in such populations. Therefore, we propose a method for tuning the T2DS to any desired T2D sub-population using published clinical data. As case study, we extended the T2DS to insulin-naïve T2D subjects, who need to start insulin therapy to compensate the reduced insulin function.

METHODS

T2DS model was identified based on literature data of the target population. The estimated parameters were used to generate a virtual cohort of insulin-naïve T2D subjects (inC1). A model of basal insulin degludec (IDeg) was also incorporated into the T2DS to enable basal insulin therapy. The resulting tailored T2DS was assessed by simulating IDeg therapy initiation and comparing simulated vs. clinical trial outcomes. For further validation, this procedure was reiterated to generate a new cohort of insulin-naïve T2D (inC2) assuming inC1 as target population.

RESULTS

No statistically significant differences were found when comparing fasting plasma glucose and IDeg dose, neither in clinical data vs. inC1, nor inC1 vs. inC2.

CONCLUSIONS

The tuned T2DS allowed reproducing the main findings of clinical studies in insulin-naïve T2D subjects.

SIGNIFICANCE

The proposed methodology makes the Padova T2DS usable for supporting treatment guidance in target T2D populations.

Learning Difference Equations With Structured Grammatical Evolution for Postprandial Glycaemia Prediction

People with diabetes must carefully monitor their blood glucose levels, especially after eating. Blood glucose management requires a proper combination of food intake and insulin boluses. Glucose prediction is vital to avoid dangerous post-meal complications in treating individuals with diabetes. Although traditional methods, and also artificial neural networks, have shown high accuracy rates, sometimes they are not suitable for developing personalised treatments by physicians due to their lack of interpretability. This study proposes a novel glucose prediction method emphasising interpretability: Interpretable Sparse Identification by Grammatical Evolution. Combined with a previous clustering stage, our approach provides finite difference equations to predict postprandial glucose levels up to two hours after meals. We divide the dataset into four-hour segments and perform clustering based on blood glucose values for the two-hour window before the meal. Prediction models are trained for each cluster for the two-hour windows after meals, allowing predictions in 15-minute steps, yielding up to eight predictions at different time horizons. Prediction safety was evaluated based on Parkes Error Grid regions. Our technique produces safe predictions through explainable expressions, avoiding zones D (0.2% average) and E (0%) and reducing predictions on zone C (6.2%). In addition, our proposal has slightly better accuracy than other techniques, including sparse identification of non-linear dynamics and artificial neural networks. The results demonstrate that our proposal provides interpretable solutions without sacrificing prediction accuracy, offering a promising approach to glucose prediction in diabetes management that balances accuracy, interpretability, and computational efficiency.

Quantifying the effect of nutritional interventions on metabolic resilience using personalized computational models

The manifestation of metabolic deteriorations that accompany overweight and obesity can differ greatly between individuals, giving rise to a highly heterogeneous population. This inter-individual variation can impede both the provision and assessment of nutritional interventions as multiple aspects of metabolic health should be considered at once. Here, we apply the Mixed Meal Model, a physiology-based computational model, to characterize an individual's metabolic health in silico. A population of 342 personalized models were generated using data for individuals with overweight and obesity from three independent intervention studies, demonstrating a strong relationship between the model-derived metric of insulin resistance (ρ = 0.67, p < 0.05) and the gold-standard hyperinsulinemic-euglycemic clamp. The model is also shown to quantify liver fat accumulation and β-cell functionality. Moreover, we show that personalized Mixed Meal Models can be used to evaluate the impact of a dietary intervention on multiple aspects of metabolic health at the individual level.

An ensemble machine learning approach for the detection of unannounced meals to enhance postprandial glucose control

BACKGROUND

Hybrid automated insulin delivery systems enhance postprandial glucose control in type 1 diabetes, however, meal announcements are burdensome. To overcome this, we propose a machine learning-based automated meal detection approach; METHODS:: A heterogeneous ensemble method combining an artificial neural network, random forest, and logistic regression was employed. Trained and tested on data from two in-silico cohorts comprising 20 and 47 patients. It accounted for various meal sizes (moderate to high) and glucose appearance rates (slow and rapid absorbing). To produce an optimal prediction model, three ensemble configurations were used: logical AND, majority voting, and logical OR. In addition to the in-silico data, the proposed meal detector was also trained and tested using the OhioT1DM dataset. Finally, the meal detector is combined with a bolus insulin compensation scheme; RESULTS:: The ensemble majority voting obtained the best meal detector results for both the in-silico and OhioT1DM cohorts with a sensitivity of 77%, 94%, 61%, precision of 96%, 89%, 72%, F1-score of 85%, 91%, 66%, and with false positives per day values of 0.05, 0.19, 0.17, respectively. Automatic meal detection with insulin compensation has been performed in open-loop insulin therapy using the AND ensemble, chosen for its lower false positive rate. Time-in-range has significantly increased 10.48% and 16.03%, time above range was reduced by 5.16% and 11.85%, with a minimal time below range increase of 0.35% and 2.69% for both in-silico cohorts, respectively, compared to the results without a meal detector; CONCLUSION:: To increase the overall accuracy and robustness of the predictions, this ensemble methodology aims to take advantage of each base model's strengths. All of the results point to the potential application of the proposed meal detector as a separate module for the detection of meals in automated insulin delivery systems to achieve improved glycemic control.

An extended minimal model of OGTT: estimation of α- and β-cell dysfunction, insulin resistance, and the incretin effect

Loss of insulin sensitivity, α- and β-cell dysfunction, and impairment in incretin effect have all been implicated in the pathophysiology of type 2 diabetes (T2D). Parsimonious mathematical models are useful in quantifying parameters related to the pathophysiology of T2D. Here, we extend the minimum model developed to describe the glucose-insulin-glucagon dynamics in the isoglycemic intravenous glucose infusion (IIGI) experiment to the oral glucose tolerance test (OGTT). The extended model describes glucose and hormone dynamics in OGTT including the contribution of the incretin hormones, glucose-dependent insulinotropic polypeptide (GIP), and glucagon-like peptide-1 (GLP-1), to insulin secretion. A new function describing glucose arrival from the gut is introduced. The model is fitted to OGTT data from eight individuals with T2D and eight weight-matched controls (CS) without diabetes to obtain parameters related to insulin sensitivity, β- and α-cell function. The parameters, i.e., measures of insulin sensitivity, a1, suppression of glucagon secretion, k1, magnitude of glucagon secretion, γ2, and incretin-dependent insulin secretion, γ3, were found to be different between CS and T2D with P values < 0.002, <0.017, <0.009, <0.004, respectively. A new rubric for estimating the incretin effect directly from modeling the OGTT is presented. The average incretin effect correlated well with the experimentally determined incretin effect with a Spearman rank test correlation coefficient of 0.67 (P < 0.012). The average incretin effect was found to be different between CS and T2D (P < 0.032). The developed model is shown to be effective in quantifying the factors relevant to T2D pathophysiology.NEW & NOTEWORTHY A new extended model of oral glucose tolerance test (OGTT) has been developed that includes glucagon dynamics and incretin contribution to insulin secretion. The model allows the estimation of parameters related to α- and β-cell dysfunction, insulin sensitivity, and incretin action. A new function describing the influx of glucose from the gut has been introduced. A new rubric for estimating the incretin effect directly from the OGTT experiment has been developed. The effect of glucose dose was also investigated.

Artificial Intelligence and Machine Learning for Improving Glycemic Control in Diabetes: Best Practices, Pitfalls, and Opportunities

OBJECTIVE

Artificial intelligence and machine learning are transforming many fields including medicine. In diabetes, robust biosensing technologies and automated insulin delivery therapies have created a substantial opportunity to improve health. While the number of manuscripts addressing the topic of applying machine learning to diabetes has grown in recent years, there has been a lack of consistency in the methods, metrics, and data used to train and evaluate these algorithms. This manuscript provides consensus guidelines for machine learning practitioners in the field of diabetes, including best practice recommended approaches and warnings about pitfalls to avoid.

METHODS

Algorithmic approaches are reviewed and benefits of different algorithms are discussed including importance of clinical accuracy, explainability, interpretability, and personalization. We review the most common features used in machine learning applications in diabetes glucose control and provide an open-source library of functions for calculating features, as well as a framework for specifying data sets using data sheets. A review of current data sets available for training algorithms is provided as well as an online repository of data sources.

SIGNIFICANCE

These consensus guidelines are designed to improve performance and translatability of new machine learning algorithms developed in the field of diabetes for engineers and data scientists.

drCORRECT: An Algorithm for the Preventive Administration of Postprandial Corrective Insulin Boluses in Type 1 Diabetes Management

BACKGROUND

In type 1 diabetes therapy, precise tuning of postprandial corrective insulin boluses (CIBs) is crucial to mitigate hyperglycemia without inducing dangerous hypoglycemic events. Several heuristic formulas accounting for continuous glucose monitoring (CGM) trend have been proposed in the literature. However, these formulas suggest a lot of quantized CIB adjustments, and they lack personalization.

METHOD

drCORRECT algorithm proposed in this work employs a patient-specific time parameter and the "dynamic risk" (DR) measure to determine postprandial CIB suggestion. The expected benefits include the reduction of time in hyperglycemia, thanks to the preventive action exploited through DR. drCORRECT has been assessed retrospectively vs the literature methods proposed by Aleppo et al (AL), Bruttomesso et al (BR), and Ziegler et al (ZI) using a data set of 49 CGM daily traces recorded in free-living conditions. Retrospective evaluation of the algorithms is made possible by the use of ReplayBG, a digital twin-based tool that allows assessing alternative insulin therapies on already collected glucose data. Efficacy in terms of glucose control was measured by temporal, risk indicators, and dedicated hyperglycemic/hypoglycemic events metrics.

RESULTS

drCORRECT significantly reduces time spent in hyperglycemia when compared with AL and BR (33.52 [24.16, 39.89]% vs 39.76 [22.54, 48.15]% and 36.32 [26.91, 45.93]%, respectively); significantly reduces daily injected insulin (5.97 [3.80, 8.06] U vs 7.5 [5.21, 10.34] U), glycemia risk index (38.78 [26.58, 55.39] vs 40.78 [27.95, 70.30]), and time spent in hypoglycemia (0.00 [0.00, 1.74]% vs 0.00 [0.00, 10.23]%) when compared with ZI, resulting overall in a safer strategy.

CONCLUSIONS

The proposed drCORRECT algorithm allows preventive actions thanks to the personalized timing configuration and the introduction of the innovative DR-based CIB threshold, proving to be a valid alternative to the available heuristic literature methods.

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

BACKGROUND

The 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 the 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 mathematical models. We have previously developed a model for short-term glucose homeostasis and intracellular insulin signaling, and there exist long-term weight regulation models. Herein, we combine these models into a first interconnected digital twin for the progression of insulin resistance in humans.

METHODS

The model is based on ordinary differential equations representing biochemical and physiological processes, in which unknown parameters were fitted to data using a MATLAB toolbox.

RESULTS

The interconnected twin correctly predicts independent data from a weight increase study, both for weight-changes, fasting plasma insulin and glucose levels, and intracellular insulin signaling. Similarly, the model can predict independent weight-change data in a weight loss study with the weight loss drug topiramate. The model can also predict non-measured variables.

CONCLUSIONS

The model presented herein constitutes the basis for a new digital twin technology, which in the future could be used to aid medical pedagogy and increase motivation and compliance and thus aid in the prevention and treatment of insulin resistance.

Individualized Models for Glucose Prediction in Type 1 Diabetes: Comparing Black-Box Approaches to a Physiological White-Box One

OBJECTIVE

Accurate blood glucose (BG) prediction are key in next-generation tools for type 1 diabetes (T1D) management, such as improved decision support systems and advanced closed-loop control. Glucose prediction algorithms commonly rely on black-box models. Large physiological models, successfully adopted for simulation, were little explored for glucose prediction, mostly because their parameters are hard to individualize. In this work, we develop a BG prediction algorithm based on a personalized physiological model inspired by the UVA/Padova T1D Simulator. Then we compare white-box and advanced black-box personalized prediction techniques.

METHODS

A personalized nonlinear physiological model is identified from patient data through a Bayesian approach based on Markov Chain Monte Carlo technique. The individualized model was integrated within a particle filter (PF) to predict future BG concentrations. The black-box methodologies considered are non-parametric models estimated via gaussian regression (NP), three deep learning methods: long-short-term-memory (LSTM), gated recurrent unit (GRU), temporal convolutional networks (TCN), and a recursive autoregressive with exogenous input model (rARX). BG forecasting performances are assessed for several prediction horizons (PH) on 12 individuals with T1D, monitored in free-living conditions under open-loop therapy for 10 weeks.

RESULTS

NP models provide the most effective BG predictions by achieving a root mean square error (RMSE), RMSE = 18.99 mg/dL, RMSE = 25.72 mg/dL and RMSE = 31.60 mg/dL, significantly outperforming: LSTM, GRU (for PH = 30 minutes), TCN, rARX, and the proposed physiological model for PH=30, 45 and 60 minutes.

CONCLUSIONS

Black-box strategies remain preferable for glucose prediction even when compared to a white-box model with sound physiological structure and individualized parameters.

Analysis of the performances of various controllers adopted in the biomedical field for blood glucose regulation: a case study of the type-1 diabetes

Diabetes remains a critical global health concern that necessitates urgent attention. The contemporary clinical approach to closed-loop care, specifically tailored for insulin-dependent patients, aims to precisely monitor blood glucose levels while mitigating the risks of hyperglycaemia and hypoglycaemia due to erroneous insulin dosing. This study seeks to address this life-threatening issue by assessing and comparing the performance of different controllers to achieve quicker settling and convergence rates with reduced steady-state errors, particularly in scenarios involving meal interruptions. The methodology involves the detection of plasma blood glucose levels, delivery of precise insulin doses to the actuator through a control architecture, and subsequent administration of the calculated insulin dosage to patients based on the control signal. Glucose-insulin dynamics were modelled using kinetics and mass balance equations from the Bergman minimal model. The simulation results revealed that the PID controller exhibited superior performance, maintaining blood glucose concentration around the preferred threshold ∼98.8% of the time, with a standard deviation of 2.50. This was followed by RST with a success rate of 98.5% and standard deviation of 5.00, SPC with a success rate of 58% and standard deviation of 2.99, SFC with a success rate of 55% and standard deviation of 10.08, and finally LCFB with a rate of 10% and significantly higher standard deviation of 64.55.

Developing the UVA/Padova Type 1 Diabetes Simulator: Modeling, Validation, Refinements, and Utility

Arguably, diabetes mellitus is one of the best quantified human conditions. In the past 50 years, the metabolic monitoring technologies progressed from occasional assessment of average glycemia via HbA1c, through episodic blood glucose readings, to continuous glucose monitoring (CGM) producing data points every few minutes. The high-temporal resolution of CGM data enabled increasingly intensive treatments, from decision support assisting insulin injection or oral medication, to automated closed-loop control, known as the "artificial pancreas." Throughout this progress, mathematical models and computer simulation of the human metabolic system became indispensable for the technological progress of diabetes treatment, enabling every step, from assessment of insulin sensitivity via the now classic Minimal Model of Glucose Kinetics, to in silico trials replacing animal experiments, to automated insulin delivery algorithms. In this review, we follow these developments, beginning with the Minimal Model, which evolved through the years to become large and comprehensive and trigger a paradigm change in the design of diabetes optimization strategies: in 2007, we introduced a sophisticated model of glucose-insulin dynamics and a computer simulator equipped with a "population" of N = 300 in silico "subjects" with type 1 diabetes. In January 2008, in an unprecedented decision, the Food and Drug Administration (FDA) accepted this simulator as a substitute to animal trials for the pre-clinical testing of insulin treatment strategies. This opened the field for rapid and cost-effective development and pre-clinical testing of new treatment approaches, which continues today. Meanwhile, animal experiments for the purpose of designing new insulin treatment algorithms have been abandoned.

Performance Effect of Adjusting Insulin Sensitivity for Model-Based Automated Insulin Delivery Systems

BACKGROUND

Model predictive control (MPC) has become one of the most popular control strategies for automated insulin delivery (AID) in type 1 diabetes (T1D). These algorithms rely on a prediction model to determine the best insulin dosing every sampling time. Although these algorithms have been shown to be safe and effective for glucose management through clinical trials, managing the ever-fluctuating relationship between insulin delivery and resulting glucose uptake (aka insulin sensitivity, IS) remains a challenge. We aim to evaluate the effect of informing an AID system with IS on the performance of the system.

METHOD

The University of Virginia (UVA) MPC control-based hybrid closed-loop (HCL) and fully closed-loop (FCL) system was used. One-day simulations at varying levels of IS were run with the UVA/Padova T1D Simulator. The AID system was informed with an estimated value of IS obtained through a mixed meal glucose tolerance test. Relevant controller parameters are updated to inform insulin dosing of IS. Performance of the HCL/FCL system with and without information of the changing IS was assessed using a novel performance metric penalizing the time outside the target glucose range.

RESULTS

Feedback in AID systems provides a certain degree tolerance to changes in IS. However, IS-informed bolus and basal dosing improve glycemic outcomes, providing increased protection against hyperglycemia and hypoglycemia according to the individual's physiological state.

CONCLUSIONS

The proof-of-concept analysis presented here shows the potentially beneficial effects on system performance of informing the AID system with accurate estimates of IS. In particular, when considering reduced IS, the informed controller provides increased protection against hyperglycemia compared with the naïve controller. Similarly, reduced hypoglycemia is obtained for situations with increased IS. Further tailoring of the adaptation schemes proposed in this work is needed to overcome the increased hypoglycemia observed in the more resistant cases and to optimize the performance of the adaptation method.

ReplayBG: A Digital Twin-Based Methodology to Identify a Personalized Model From Type 1 Diabetes Data and Simulate Glucose Concentrations to Assess Alternative Therapies

OBJECTIVE

Design and assessment of new therapies for type 1 diabetes (T1D) management can be greatly facilitated by in silico simulations. The ReplayBG simulation methodology here proposed allows "replaying" the scenario behind data already collected by simulating the glucose concentration obtained in response to alternative insulin/carbohydrate therapies and evaluate their efficacy leveraging the concept of digital twin.

METHODS

ReplayBG is based on two steps. First, a personalized model of glucose-insulin dynamics is identified using insulin, carbohydrate, and continuous glucose monitoring (CGM) data. Then, this model is used to simulate the glucose concentration that would have been obtained by "replaying" the same portion of data using a different therapy. The validity of the methodology was evaluated on 100 virtual subjects using the UVa/Padova T1D Simulator (T1DS). In particular, the glucose concentration traces simulated by ReplayBG are compared with those provided by T1DS in five different scenarios of insulin and carbohydrate treatment modifications. Furthermore, we compared ReplayBG with a state-of-the-art methodology for the scope. Finally, two case studies using real data are also presented.

RESULTS

ReplayBG simulates with high accuracy the effect of the considered insulin and carbohydrate treatment alterations, performing significantly better than state-of-art method in almost all considered situations.

CONCLUSION

ReplayBG proved to be a reliable and robust tool to retrospectively explore the effect of new treatments for T1D on the glucose dynamics. It is freely available as open source software at https://github.com/gcappon/replay-bg.

SIGNIFICANCE

ReplayBG offers a new approach to preliminary evaluate new therapies for T1D management before clinical trials.

Digital Twins in Healthcare: Methodological Challenges and Opportunities

One of the most promising advancements in healthcare is the application of digital twin technology, offering valuable applications in monitoring, diagnosis, and development of treatment strategies tailored to individual patients. Furthermore, digital twins could also be helpful in finding novel treatment targets and predicting the effects of drugs and other chemical substances in development. In this review article, we consider digital twins as virtual counterparts of real human patients. The primary aim of this narrative review is to give an in-depth look into the various data sources and methodologies that contribute to the construction of digital twins across several healthcare domains. Each data source, including blood glucose levels, heart MRI and CT scans, cardiac electrophysiology, written reports, and multi-omics data, comes with different challenges regarding standardization, integration, and interpretation. We showcase how various datasets and methods are used to overcome these obstacles and generate a digital twin. While digital twin technology has seen significant progress, there are still hurdles in the way to achieving a fully comprehensive patient digital twin. Developments in non-invasive and high-throughput data collection, as well as advancements in modeling and computational power will be crucial to improve digital twin systems. We discuss a few critical developments in light of the current state of digital twin technology. Despite challenges, digital twin research holds great promise for personalized patient care and has the potential to shape the future of healthcare innovation.

In Silico Investigation of the Clinical Translatability of Competitive Clearance Glucose-Responsive Insulins

The glucose-responsive insulin (GRI) MK-2640 from Merck was a pioneer in its class to enter the clinical stage, having demonstrated promising responsiveness in in vitro and preclinical studies via a novel competitive clearance mechanism (CCM). The smaller pharmacokinetic response in humans motivates the development of new predictive, computational tools that can improve the design of therapeutics such as GRIs. Herein, we develop and use a new computational model, IM3PACT, based on the intersection of human and animal model glucoregulatory systems, to investigate the clinical translatability of CCM GRIs based on existing preclinical and clinical data of MK-2640 and regular human insulin (RHI). Simulated multi-glycemic clamps not only validated the earlier hypothesis of insufficient glucose-responsive clearance capacity in humans but also uncovered an equally important mismatch between the in vivo competitiveness profile and the physiological glycemic range, which was not observed in animals. Removing the inter-species gap increases the glucose-dependent GRI clearance from 13.0% to beyond 20% for humans and up to 33.3% when both factors were corrected. The intrinsic clearance rate, potency, and distribution volume did not apparently compromise the translation. The analysis also confirms a responsive pharmacokinetics local to the liver. By scanning a large design space for CCM GRIs, we found that the mannose receptor physiology in humans remains limiting even for the most optimally designed candidate. Overall, we show that this computational approach is able to extract quantitative and mechanistic information of value from a posteriori analysis of preclinical and clinical data to assist future therapeutic discovery and development.

Evaluating Deep Q-Learning Algorithms for Controlling Blood Glucose in In Silico Type 1 Diabetes

Patients with type 1 diabetes must continually decide how much insulin to inject before each meal to maintain blood glucose levels within a healthy range. Recent research has worked on a solution for this burden, showing the potential of reinforcement learning as an emerging approach for the task of controlling blood glucose levels. In this paper, we test and evaluate several deep Q-learning algorithms for automated and personalized blood glucose regulation in an in silico type 1 diabetes patient with the goal of estimating and delivering proper insulin doses. The proposed algorithms are model-free approaches with no prior information about the patient. We used the Hovorka model with meal variation and carbohydrate counting errors to simulate the patient included in this work. Our experiments compare different deep Q-learning extensions showing promising results controlling blood glucose levels, with some of the proposed algorithms outperforming standard baseline treatment.

The importance of interpreting machine learning models for blood glucose prediction in diabetes: an analysis using SHAP

Machine learning has become a popular tool for learning models of complex dynamics from biomedical data. In Type 1 Diabetes (T1D) management, these models are increasingly been integrated in decision support systems (DSS) to forecast glucose levels and provide preventive therapeutic suggestions, like corrective insulin boluses (CIB), accordingly. Typically, models are chosen based on their prediction accuracy. However, since patient safety is a concern in this application, the algorithm should also be physiologically sound and its outcome should be explainable. This paper aims to discuss the importance of using tools to interpret the output of black-box models in T1D management by presenting a case-of-study on the selection of the best prediction algorithm to integrate in a DSS for CIB suggestion. By retrospectively "replaying" real patient data, we show that two long-short term memory neural networks (LSTM) (named p-LSTM and np-LSTM) with similar prediction accuracy could lead to different therapeutic decisions. An analysis with SHAP-a tool for explaining black-box models' output-unambiguously shows that only p-LSTM learnt the physiological relationship between inputs and glucose prediction, and should therefore be preferred. This is verified by showing that, when embedded in the DSS, only p-LSTM can improve patients' glycemic control.

Glucose Rate-of-Change and Insulin-on-Board Jointly Weighted Zone Model Predictive Control

We present design and evaluation of closed-loop insulin delivery using zone model predictive control (MPC) featuring an adaptive weighting scheme to address prolonged hyperglycemia due to changes in insulin sensitivity, underdelivery from profile mismatch, and meal composition. In the MPC cost function, the penalty on predicted glucose deviation from the upper zone boundary is weighted by a joint function of predicted glucose rate-of-change (ROC) and insulin-on-board (IOB). The asymmetric weighting gradually increases when glucose ROC and IOB were jointly low, independent of glucose magnitude, to limit hyperglycemia while aggressively reduces for negative glucose ROC to avoid hypoglycemia. The proposed controller was evaluated using two simulation scenarios: an induced resistance scenario and a nominal scenario to highlight the performance over a reference zone MPC with glucose ROC weighting only. The continuous adaption scheme resulted in consistent improvement for the entire glucose range without incurring additional risk of hypoglycemia. For the induced resistance and no feedforward bolus scenario, the percent time in 70-180 mg/dL was higher (53.5% versus 48.9%, p<0.001) with larger improvement in the overnight percent time in tighter glucose range 70-140 mg/dL (70.9% versus 52.9%, p<0.001). The results from extensive simulations, as well as clinical validation in three different outpatient studies demonstrate the utility and safety of the proposed zone MPC.

Uncovering personalized glucose responses and circadian rhythms from multiple wearable biosensors with Bayesian dynamical modeling

Wearable biosensors and smartphone applications can measure physiological variables over multiple days in free-living conditions. We measure food and drink ingestion, glucose dynamics, physical activity, heart rate (HR), and heart rate variability (HRV) in 25 healthy participants over 14 days. We develop a Bayesian inference framework to learn personal parameters that quantify circadian rhythms and physiological responses to external stressors. Modeling the effects of ingestion events on glucose levels reveals that slower glucose decay kinetics elicit larger postprandial glucose spikes, and we uncover a circadian baseline rhythm for glucose with high amplitudes in some individuals. Physical activity and circadian rhythms explain as much as 40%-65% of the HR variance, whereas the variance explained for HRV is more heterogeneous across individuals. A more complex model incorporating activity, HR, and HRV explains up to 15% of additional glucose variability, highlighting the relevance of integrating multiple biosensors to better predict glucose dynamics.

A pathway model of glucose-stimulated insulin secretion in the pancreatic β-cell

The pancreas plays a critical role in maintaining glucose homeostasis through the secretion of hormones from the islets of Langerhans. Glucose-stimulated insulin secretion (GSIS) by the pancreatic β-cell is the main mechanism for reducing elevated plasma glucose. Here we present a systematic modeling workflow for the development of kinetic pathway models using the Systems Biology Markup Language (SBML). Steps include retrieval of information from databases, curation of experimental and clinical data for model calibration and validation, integration of heterogeneous data including absolute and relative measurements, unit normalization, data normalization, and model annotation. An important factor was the reproducibility and exchangeability of the model, which allowed the use of various existing tools. The workflow was applied to construct a novel data-driven kinetic model of GSIS in the pancreatic β-cell based on experimental and clinical data from 39 studies spanning 50 years of pancreatic, islet, and β-cell research in humans, rats, mice, and cell lines. The model consists of detailed glycolysis and phenomenological equations for insulin secretion coupled to cellular energy state, ATP dynamics and (ATP/ADP ratio). Key findings of our work are that in GSIS there is a glucose-dependent increase in almost all intermediates of glycolysis. This increase in glycolytic metabolites is accompanied by an increase in energy metabolites, especially ATP and NADH. One of the few decreasing metabolites is ADP, which, in combination with the increase in ATP, results in a large increase in ATP/ADP ratios in the β-cell with increasing glucose. Insulin secretion is dependent on ATP/ADP, resulting in glucose-stimulated insulin secretion. The observed glucose-dependent increase in glycolytic intermediates and the resulting change in ATP/ADP ratios and insulin secretion is a robust phenomenon observed across data sets, experimental systems and species. Model predictions of the glucose-dependent response of glycolytic intermediates and biphasic insulin secretion are in good agreement with experimental measurements. Our model predicts that factors affecting ATP consumption, ATP formation, hexokinase, phosphofructokinase, and ATP/ADP-dependent insulin secretion have a major effect on GSIS. In conclusion, we have developed and applied a systematic modeling workflow for pathway models that allowed us to gain insight into key mechanisms in GSIS in the pancreatic β-cell.

Quantifying postprandial glucose responses using a hybrid modeling approach: Combining mechanistic and data-driven models in The Maastricht Study

Computational models of human glucose homeostasis can provide insight into the physiological processes underlying the observed inter-individual variability in glucose regulation. Modelling approaches ranging from "bottom-up" mechanistic models to "top-down" data-driven techniques have been applied to untangle the complex interactions underlying progressive disturbances in glucose homeostasis. While both approaches offer distinct benefits, a combined approach taking the best of both worlds has yet to be explored. Here, we propose a sequential combination of a mechanistic and a data-driven modeling approach to quantify individuals' glucose and insulin responses to an oral glucose tolerance test, using cross sectional data from 2968 individuals from a large observational prospective population-based cohort, the Maastricht Study. The best predictive performance, measured by R2 and mean squared error of prediction, was achieved with personalized mechanistic models alone. The addition of a data-driven model did not improve predictive performance. The personalized mechanistic models consistently outperformed the data-driven and the combined model approaches, demonstrating the strength and suitability of bottom-up mechanistic models in describing the dynamic glucose and insulin response to oral glucose tolerance tests.

A simple modeling framework for prediction in the human glucose-insulin system

Forecasting blood glucose (BG) levels with routinely collected data is useful for glycemic management. BG dynamics are nonlinear, complex, and nonstationary, which can be represented by nonlinear models. However, the sparsity of routinely collected data creates parameter identifiability issues when high-fidelity complex models are used, thereby resulting in inaccurate forecasts. One can use models with reduced physiological fidelity for robust and accurate parameter estimation and forecasting with sparse data. For this purpose, we approximate the nonlinear dynamics of BG regulation by a linear stochastic differential equation: we develop a linear stochastic model, which can be specialized to different settings: type 2 diabetes mellitus (T2DM) and intensive care unit (ICU), with different choices of appropriate model functions. The model includes deterministic terms quantifying glucose removal from the bloodstream through the glycemic regulation system and representing the effect of nutrition and externally delivered insulin. The stochastic term encapsulates the BG oscillations. The model output is in the form of an expected value accompanied by a band around this value. The model parameters are estimated patient-specifically, leading to personalized models. The forecasts consist of values for BG mean and variation, quantifying possible high and low BG levels. Such predictions have potential use for glycemic management as part of control systems. We present experimental results on parameter estimation and forecasting in T2DM and ICU settings. We compare the model's predictive capability with two different nonlinear models built for T2DM and ICU contexts to have a sense of the level of prediction achieved by this model.

Estimation of glucose rate of appearance in portal vein circulation using a phenomenological-based model

The joint work of the stomach and the small intestine plays a fundamental role in human digestion. In the stomach, food is turned into a semi-fluid mixture that is slowly released into the small intestine, where most enzymatic reactions occur, and nutrients are absorbed as they become available. This whole process is closely related to glucose homeostasis, mainly because of the appearance of glucose in the portal system and the energetic expenditure of the process itself. The current phenomenological-based model describes such effects of the digestive process on blood glucose concentration. It considers enzymatic and mechanical transformations, energetic expenditure, and the impact of macro-nutrients, fiber, and water on overall digestion and glucose absorption. The model estimates the rate of glucose appearance in the portal vein and is intended to be further integrated into existing models for other human organs and used in model-based systems such as an artificial pancreas with automated insulin delivery.

Realistic simulation of time-course measurements in systems biology

In systems biology, the analysis of complex nonlinear systems faces many methodological challenges. For the evaluation and comparison of the performances of novel and competing computational methods, one major bottleneck is the availability of realistic test problems. We present an approach for performing realistic simulation studies for analyses of time course data as they are typically measured in systems biology. Since the design of experiments in practice depends on the process of interest, our approach considers the size and the dynamics of the mathematical model which is intended to be used for the simulation study. To this end, we used 19 published systems biology models with experimental data and evaluated the relationship between model features (e.g., the size and the dynamics) and features of the measurements such as the number and type of observed quantities, the number and the selection of measurement times, and the magnitude of measurement errors. Based on these typical relationships, our novel approach enables suggestions of realistic simulation study designs in the systems biology context and the realistic generation of simulated data for any dynamic model. The approach is demonstrated on three models in detail and its performance is validated on nine models by comparing ODE integration, parameter optimization, and parameter identifiability. The presented approach enables more realistic and less biased benchmark studies and thereby constitutes an important tool for the development of novel methods for dynamic modeling.

E-DES-PROT: A novel computational model to describe the effects of amino acids and protein on postprandial glucose and insulin dynamics in humans

Current computational models of whole-body glucose homeostasis describe physiological processes by which insulin regulates circulating glucose concentrations. While these models perform well in response to oral glucose challenges, interaction with other nutrients that impact postprandial glucose metabolism, such as amino acids (AAs), is not considered. Here, we developed a computational model of the human glucose-insulin system, which incorporates the effects of AAs on insulin secretion and hepatic glucose production. This model was applied to postprandial glucose and insulin time-series data following different AA challenges (with and without co-ingestion of glucose), dried milk protein ingredients, and dairy products. Our findings demonstrate that this model allows accurate description of postprandial glucose and insulin dynamics and provides insight into the physiological processes underlying meal responses. This model may facilitate the development of computational models that describe glucose homeostasis following the intake of multiple macronutrients, while capturing relevant features of an individual's metabolic health.

New model of glucose-insulin regulation characterizes effects of physical activity and facilitates personalized treatment evaluation in children and adults with type 1 diabetes

Accurate treatment adjustment to physical activity (PA) remains a challenging problem in type 1 diabetes (T1D) management. Exercise-driven effects on glucose metabolism depend strongly on duration and intensity of the activity, and are highly variable between patients. In-silico evaluation can support the development of improved treatment strategies, and can facilitate personalized treatment optimization. This requires models of the glucose-insulin system that capture relevant exercise-related processes. We developed a model of glucose-insulin regulation that describes changes in glucose metabolism for aerobic moderate- to high-intensity PA of short and prolonged duration. In particular, we incorporated the insulin-independent increase in glucose uptake and production, including glycogen depletion, and the prolonged rise in insulin sensitivity. The model further includes meal absorption and insulin kinetics, allowing simulation of everyday scenarios. The model accurately predicts glucose dynamics for varying PA scenarios in a range of independent validation data sets, and full-day simulations with PA of different timing, duration and intensity agree with clinical observations. We personalized the model on data from a multi-day free-living study of children with T1D by adjusting a small number of model parameters to each child. To assess the use of the personalized models for individual treatment evaluation, we compared subject-specific treatment options for PA management in replay simulations of the recorded data with altered meal, insulin and PA inputs.

Robust nonlinear control of blood glucose in diabetic patients subject to model uncertainties

Recent advances in the artificial pancreas system provide an emerging treatment option for type 1 diabetes. The performance of the blood glucose regulation directly relies on the accuracy of the glucose-insulin modeling. Sorenson model involves the behavior of different organs and offers precise representation. However, the high complexity of such a model makes the controller design procedure a hard task. Therefore, the high-order nonlinear Sorensen model as a popular high-fidelity physiological model is opted in this paper to analyze the glucose-insulin interactions in great detail, and a new robust nonlinear approach to regulate the blood glucose concentration (BGC) in Type-I diabetic patients is proposed. Inspiring the backstepping technique, for designing an acceptable controller, the model is divided into three main subsystems such that in each subsystem, the virtual control input laws are obtained using both Lyapunov stability and input-to-state theorems. Since the measurement of the parameters in the glucose-insulin system is not accurate, parametric uncertainties are defined in the investigated model. Furthermore, owing to the fact that the only measurable state variable is blood glucose, the estimation of inaccessible state variables is an important issue that is properly considered by the unscented Kalman filter (UKF) estimator. The suggested approach is compared to H_∞, robust H_∞, and linear parameter-varying control approaches. The comparison results on 500 simulated patients imply a remarkable superiority of the proposed controller approach to the compared methods in terms of the BGC tracking and the algorithm robustness in the presence of food intake disturbance patterns.

Adaptive Personalized Prior-Knowledge-Informed Model Predictive Control for Type 1 Diabetes

This work considers the problem of adaptive prior-informed model predictive control (MPC) formulations that explicitly incorporate prior knowledge in the model development and is robust to missing data in the output measurements. The proposed prediction model is based on a latent variables model to extract glycemic dynamics from highly-correlated data and incorporates prior knowledge of exponential stability to improve the prediction ability. Missing data structures are formulated to enable model predictions when output measurements are missing for short periods of time. Based on the latent variables model, the MPC strategy and adaptive rules are developed to automatically tune the aggressiveness of the MPC. The adaptive prior-knowledge-informed MPC is evaluated with computer simulations for the control of blood glucose concentrations in people with Type 1 diabetes (T1D) using simulated virtual patients. Due to the variability among people with T1D, the hyperparameters of the prior-knowledge-informed model are personalized to individual subjects. The percentage of time spent in the target range is 76.48% when there are no missing data and 76.52% when there are missing data episodes lasting up to 30 mins (6 samples). Incorporating the adaptive rules further improves the percentage of time in target range to 84.58% and 84.88% for cases with no missing data and missing data, respectively. The proposed adaptive prior-informed MPC formulation provides robust, effective, and safe regulation of glucose concentration in T1D despite disturbances and missing measurements.

Insulin Signaling Pathway Model in Adipocyte Cells

BACKGROUND

Worldwide, type 2 diabetes mellitus (T2DM) is one of the most pervasive and fastgrowing disorders, bringing long-term adverse effects. T2DM arises from pancreatic β-cells deficiency to produce enough insulin or when the body cannot effectively use the insulin produced by such cells. Accordingly, early diagnosis will decrease the long-term effects and high-healthcare costs of diabetes.

OBJECTIVE

The objective is developing an integrated mathematical model of the insulin signaling network based on Brännmark's model, which can simulate the signaling events more comprehensively with the added key components.

METHODS

In this study, a thorough mathematical model of the insulin signaling network was developed by expanding the previously validated model and incorporating the glycogen synthesis module. Parameters (69 parameters) of the integrated model were evaluated by a genetic algorithm by fitting the model predictions to eighty percent of experimental data from the literature. Twenty percent of the experimental data were used to evaluate the final optimized model.

RESULTS

The time-response curves indicate that the GS phosphorylation reaches its maximum in response to 10^-7 M insulin after 4 min, while the maximum phosphorylated GSK3 is attained within ~50 min. The doseresponse curves for the GSP and GSK3 of the insulin signaling intermediaries in response to the increased concentration of insulin, after 10 min, in the input from 0-100 nM exhibits a decreasing trend, whereas an increasing trend was observed for the GS and GSK3P. The GSK and GS phosphorylation sensitivity was enhanced by increasing the initial insulin concentration level from 0.001 to 100 nM. However, the sensitivity of GSK3 to insulin concentration changes (from 0.001 to 100 nM) was 3-fold higher than GS sensitivity.

CONCLUSION

Considerably, the trends of all signaling components simulated by the expanded model shows high compatibility with experimental data (R2 ≥ 0.9), which approves the accuracy of the proposed model. The proposed mathematical model can be used in many biological systems and combined with the whole-body model of the blood glucose regulation system for a better understanding of the causes and potential treatment of type 2 diabetes. Although, this model is not a complete description of insulin signaling, yet it can make profound contributions to improvements regarding other important components and signaling branches such as epidermal growth factor (EGF) signaling, as well as signaling in other cell types in the model structure of future works.

Research gaps and opportunities in precision nutrition: an NIH workshop report

Precision nutrition is an emerging concept that aims to develop nutrition recommendations tailored to different people's circumstances and biological characteristics. Responses to dietary change and the resulting health outcomes from consuming different diets may vary significantly between people based on interactions between their genetic backgrounds, physiology, microbiome, underlying health status, behaviors, social influences, and environmental exposures. On 11-12 January 2021, the National Institutes of Health convened a workshop entitled "Precision Nutrition: Research Gaps and Opportunities" to bring together experts to discuss the issues involved in better understanding and addressing precision nutrition. The workshop proceeded in 3 parts: part I covered many aspects of genetics and physiology that mediate the links between nutrient intake and health conditions such as cardiovascular disease, Alzheimer disease, and cancer; part II reviewed potential contributors to interindividual variability in dietary exposures and responses such as baseline nutritional status, circadian rhythm/sleep, environmental exposures, sensory properties of food, stress, inflammation, and the social determinants of health; part III presented the need for systems approaches, with new methods and technologies that can facilitate the study and implementation of precision nutrition, and workforce development needed to create a new generation of researchers. The workshop concluded that much research will be needed before more precise nutrition recommendations can be achieved. This includes better understanding and accounting for variables such as age, sex, ethnicity, medical history, genetics, and social and environmental factors. The advent of new methods and technologies and the availability of considerably more data bring tremendous opportunity. However, the field must proceed with appropriate levels of caution and make sure the factors listed above are all considered, and systems approaches and methods are incorporated. It will be important to develop and train an expanded workforce with the goal of reducing health disparities and improving precision nutritional advice for all Americans.

Data assimilation on mechanistic models of glucose metabolism predicts glycemic states in adolescents following bariatric surgery

Type 2 diabetes mellitus is a complex and under-treated disorder closely intertwined with obesity. Adolescents with severe obesity and type 2 diabetes have a more aggressive disease compared to adults, with a rapid decline in pancreatic β cell function and increased incidence of comorbidities. Given the relative paucity of pharmacotherapies, bariatric surgery has become increasingly used as a therapeutic option. However, subsets of this population have sub-optimal outcomes with either inadequate weight loss or little improvement in disease. Predicting which patients will benefit from surgery is a difficult task and detailed physiological characteristics of patients who do not respond to treatment are generally unknown. Identifying physiological predictors of surgical response therefore has the potential to reveal both novel phenotypes of disease as well as therapeutic targets. We leverage data assimilation paired with mechanistic models of glucose metabolism to estimate pre-operative physiological states of bariatric surgery patients, thereby identifying latent phenotypes of impaired glucose metabolism. Specifically, maximal insulin secretion capacity, σ, and insulin sensitivity, S_I, differentiate aberrations in glucose metabolism underlying an individual's disease. Using multivariable logistic regression, we combine clinical data with data assimilation to predict post-operative glycemic outcomes at 12 months. Models using data assimilation sans insulin had comparable performance to models using oral glucose tolerance test glucose and insulin. Our best performing models used data assimilation and had an area under the receiver operating characteristic curve of 0.77 (95% confidence interval 0.7665, 0.7734) and mean average precision of 0.6258 (0.6206, 0.6311). We show that data assimilation extracts knowledge from mechanistic models of glucose metabolism to infer future glycemic states from limited clinical data. This method can provide a pathway to predict long-term, post-surgical glycemic states by estimating the contributions of insulin resistance and limitations of insulin secretion to pre-operative glucose metabolism.

Quantifying the contribution of triglycerides to metabolic resilience through the mixed meal model

Despite the pivotal role played by elevated circulating triglyceride levels in the pathophysiology of cardio-metabolic diseases many of the indices used to quantify metabolic health focus on deviations in glucose and insulin alone. We present the Mixed Meal Model, a computational model describing the systemic interplay between triglycerides, free fatty acids, glucose, and insulin. We show that the Mixed Meal Model can capture deviations in the post-meal excursions of plasma glucose, insulin, and triglyceride that are indicative of features of metabolic resilience; quantifying insulin resistance and liver fat; validated by comparison to gold-standard measures. We also demonstrate that the Mixed Meal Model is generalizable, applying it to meals with diverse macro-nutrient compositions. In this way, by coupling triglycerides to the glucose-insulin system the Mixed Meal Model provides a more holistic assessment of metabolic resilience from meal response data, quantifying pre-clinical metabolic deteriorations that drive disease development in overweight and obesity.

Integrated experimental-computational analysis of a HepaRG liver-islet microphysiological system for human-centric diabetes research

Microphysiological systems (MPS) are powerful tools for emulating human physiology and replicating disease progression in vitro. MPS could be better predictors of human outcome than current animal models, but mechanistic interpretation and in vivo extrapolation of the experimental results remain significant challenges. Here, we address these challenges using an integrated experimental-computational approach. This approach allows for in silico representation and predictions of glucose metabolism in a previously reported MPS with two organ compartments (liver and pancreas) connected in a closed loop with circulating medium. We developed a computational model describing glucose metabolism over 15 days of culture in the MPS. The model was calibrated on an experiment-specific basis using data from seven experiments, where HepaRG single-liver or liver-islet cultures were exposed to both normal and hyperglycemic conditions resembling high blood glucose levels in diabetes. The calibrated models reproduced the fast (i.e. hourly) variations in glucose and insulin observed in the MPS experiments, as well as the long-term (i.e. over weeks) decline in both glucose tolerance and insulin secretion. We also investigated the behaviour of the system under hypoglycemia by simulating this condition in silico, and the model could correctly predict the glucose and insulin responses measured in new MPS experiments. Last, we used the computational model to translate the experimental results to humans, showing good agreement with published data of the glucose response to a meal in healthy subjects. The integrated experimental-computational framework opens new avenues for future investigations toward disease mechanisms and the development of new therapies for metabolic disorders.

Microphysiological systems (MPS) are powerful tools to unravel biological knowledge underlying disease. MPS provide a physiologically relevant, human-based in vitro setting, which can potentially yield better translatability to humans than current animal models and traditional cell cultures. However, mechanistic interpretation and extrapolation of the experimental results to human outcome remain significant challenges. In this study, we confront these challenges using an integrated experimental-computational approach. We present a computational model describing glucose metabolism in a previously reported MPS integrating liver and pancreas. This MPS supports a homeostatic feedback loop between HepaRG/HHSteC spheroids and pancreatic islets, and allows for detailed investigations of mechanisms underlying type 2 diabetes in humans. We show that the computational model captures the complex dynamics of glucose-insulin regulation observed in the system, and can provide mechanistic insight into disease progression features, such as insulin resistance and β-cell dynamics. Furthermore, the computational model can explain key differences in temporal dynamics between MPS and human responses, and thus provides a tool for translating experimental insights into human outcome. The integrated experimental-computational framework opens new avenues for future investigations toward disease mechanisms and the development of new therapies for metabolic disorders.

The principle of 'brain energy on demand' and its predictive power for stress, sleep, stroke, obesity and diabetes

Does the brain actively draw energy from the body when needed? There are different schools of thought regarding energy metabolism. In this study, the various theoretical models are classified into one of two categories: (1) conceptualizations of the brain as being purely passively supplied, which we call 'P-models,' and (2) models understanding the brain as not only passively receiving energy but also actively procuring energy for itself on demand, which we call 'A-models.' One prominent example of such theories making use of an A-model is the selfish-brain theory. The ability to make predictions was compared between the A- and P-models. A-models were able to predict and coherently explain all data examined, which included stress, sleep, caloric restriction, stroke, type-1-diabetes mellitus, obesity, and type-2-diabetes, whereas the predictions of P-models failed in most cases. The strength of the evidence supporting A-models is based on the coherence of accurate predictions across a spectrum of metabolic states. The theory test conducted here speaks to a brain that pulls its energy from the body on-demand.

Universal Minimal Model for Glucose-Insulin Relationship with the Influence of Food Dynamic

We propose a minimal model defining the relationship between glucose and insulin with the added influence of food intakes. The constructed model consists of a system of 3 nonlinear ordinary differential equations (ODEs). The solutions of our model for both normal and diabetic subjects are compared with a minimal model and a maximal model representing the same relationship. We found that the outputs of our model are similar to those from the minimal and maximal models for both normal and diabetic subjects; the R² are 0.9997 and 0.9922, respectively, when compared with the minimal model, and are 0.9995 and 0.9940, respectively, when compared with the maximal model. Moreover, the relative errors between solutions are at most 0.9035% and as low as 1.488 × 10⁻²% on average when compared with the minimal model for normal subjects and at most 1.331% and as low as 0.1159% on average for diabetic subjects. The discrepancy between our model and the maximal model are at most 1.590% and 5.453% for normal and diabetic subjects, respectively, with a relative error averaging 0.2138% and 0.9002% for normal and diabetic subjects, respectively.

Computational modelling in health and disease: highlights of the 6th annual SysMod meeting

Summary

The Community of Special Interest (COSI) in Computational Modelling of Biological Systems (SysMod) brings together interdisciplinary scientists interested in combining data-driven computational modelling, multi-scale mechanistic frameworks, large-scale -omics data and bioinformatics. SysMod’s main activity is an annual meeting at the Intelligent Systems for Molecular Biology (ISMB) conference, a meeting for computer scientists, biologists, mathematicians, engineers and computational and systems biologists. The 2021 SysMod meeting was conducted virtually due to the ongoing COVID-19 pandemic (coronavirus disease 2019). During the 2-day meeting, the development of computational tools, approaches and predictive models was discussed, along with their application to biological systems, emphasizing disease mechanisms. This report summarizes the meeting.

Availability and implementation

All resources and further information are freely accessible at https://sysmod.info.

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 study. Using this model, we can predict non-measured variables, e.g. hepatic glycogen and gluconeogenesis, in response to fasting and different diets. Furthermore, we exemplify how such metabolic responses can be successfully adapted to a specific individual’s sex, weight, height, as well as to the individual’s historical data on metabolite dynamics. This tool enables an offline digital twin technology.

Fasting and diet are central components of prevention against cardiovascular disease. Unfortunately, there is little consensus regarding which diet schemes are optimal. This is partially because different clinical studies contribute with different non-connected pieces of knowledge, which have not been fully integrated into a useful and interconnected big picture. In principle, mathematical models describing meal responses could be used for such an integration. However, today’s models still lack critical mechanisms, such as protein metabolism and a dynamic glycogen regulation. Herein, we present a) a new expanded model structure including these mechanisms; b) a set of parameters which can simultaneously describe a wide array of complementary estimation data, in both healthy and diabetic populations; c) a personalisation-script, which allows these generic parameters to be tuned to an individual/sub-population, using demographics (age, weight, height, diabetes status) and historic metabolic data. We exemplify how this personalisation can be used to predict new independent data, including a new clinical study, where a qualitatively new prediction is validated: that an oral protein tolerance test gives a clear response in plasma glucose, after, but not before, a 48h fasting period. Our combined model, parameters, and fitting script lay the foundation for an offline digital twin.

Minimal and Maximal Models to Quantitate Glucose Metabolism: Tools to Measure, to Simulate and to Run in Silico Clinical Trials

Several models have been proposed to describe the glucose system at whole-body, organ/tissue and cellular level, designed to measure non-accessible parameters (minimal models), to simulate system behavior and run in silico clinical trials (maximal models). Here, we will review the authors' work, by putting it into a concise historical background. We will discuss first the parametric portrait provided by the oral minimal models-building on the classical intravenous glucose tolerance test minimal models-to measure otherwise non-accessible key parameters like insulin sensitivity and beta-cell responsivity from a physiological oral test, the mixed meal or the oral glucose tolerance tests, and what can be gained by adding a tracer to the oral glucose dose. These models were used in various pathophysiological studies, which we will briefly review. A deeper understanding of insulin sensitivity can be gained by measuring insulin action in the skeletal muscle. This requires the use of isotopic tracers: both the classical multiple-tracer dilution and the positron emission tomography techniques are discussed, which quantitate the effect of insulin on the individual steps of glucose metabolism, that is, bidirectional transport plasma-interstitium, and phosphorylation. Finally, we will present a cellular model of insulin secretion that, using a multiscale modeling approach, highlights the relations between minimal model indices and subcellular secretory events. In terms of maximal models, we will move from a parametric to a flux portrait of the system by discussing the triple tracer meal protocol implemented with the tracer-to-tracee clamp technique. This allows to arrive at quasi-model independent measurement of glucose rate of appearance (Ra), endogenous glucose production (EGP), and glucose rate of disappearance (Rd). Both the fast absorbing simple carbs and the slow absorbing complex carbs are discussed. This rich data base has allowed us to build the UVA/Padova Type 1 diabetes and the Padova Type 2 diabetes large scale simulators. In particular, the UVA/Padova Type 1 simulator proved to be a very useful tool to safely and effectively test in silico closed-loop control algorithms for an artificial pancreas (AP). This was the first and unique simulator of the glucose system accepted by the U.S. Food and Drug Administration as a substitute to animal trials for in silico testing AP algorithms. Recent uses of the simulator have looked at glucose sensors for non-adjunctive use and new insulin molecules.

Integrative modeling of the cell

A whole-cell model represents certain aspects of the cell structure and/or function. Due to the high complexity of the cell, an integrative modeling approach is often taken to utilize all available information including experimental data, prior knowledge and prior models. In this review, we summarize an emerging workflow of whole-cell modeling into five steps: (i) gather information; (ii) represent the modeled system into modules; (iii) translate input information into scoring function; (iv) sample the whole-cell model; (v) validate and interpret the model. In particular, we propose the integrative modeling of the cell by combining available (whole-cell) models to maximize the accuracy, precision, and completeness. In addition, we list quantitative predictions of various aspects of cell biology from existing whole-cell models. Moreover, we discuss the remaining challenges and future directions, and highlight the opportunity to establish an integrative spatiotemporal multi-scale whole-cell model based on a community approach.

Subcutaneous insulin administration by deep reinforcement learning for blood glucose level control of type-2 diabetic patients

BACKGROUND

Type-2 diabetes mellitus is characterized by insulin resistance and impaired insulin secretion in the human body. Many endeavors have been made in terms of controlling and reducing blood glucose via the medium of automated controlling tools to increase precision and efficiency and reduce human error. Recently, reinforcement learning algorithms are proved to be powerful in the field of intelligent control, which was the motivation for the current study.

METHODS

For the first time, a reinforcement algorithm called normalized advantage function (NAF) algorithm has been applied as a model-free reinforcement learning method to regulate the blood glucose level of type-2 diabetic patients through subcutaneous injection. The algorithm has been designed and developed in a model-free approach to avoid additional inaccuracies and parameter uncertainty introduced by the mathematical models of the glucoregulatory system. Insulin doses constitute the control action that is designed to be stated directly in clinical language with the unit IU. In this regard, a new environment state is considered in addition to the glucose level to take into account the delayed effect of insulin elimination under the skin. Finally, a simple but practical reward function is developed to be used with the NAF algorithm to correct the glucose level and maintain it in the desired range.

RESULTS

The simulation environment was set up to imitate the basal-bolus process accurately. Results for 30 days of simulation of the designed controller on three different average virtual patients verify the feasibility and effectiveness of the method and reveal our proposed controller's learning ability. Moreover, as the insulin elimination dynamic was taken into account, a more complete and more realistic model than the previously studied models has emerged.

CONCLUSION

NAF has proved a promising control approach, able to successfully regulate and significantly reduce the fluctuation of the blood glucose without meal announcements, compared to standard optimized open-loop basal-bolus therapies. The method and its results, which are directly in the clinical language, are applicable in real-time clinical situations.

A simulator with realistic and challenging scenarios for virtual T1D patients undergoing CSII and MDI therapy

In silico simulations have become essential for the development of diabetes treatments. However, currently available simulators are not challenging enough and often suffer from limitations in insulin and meal absorption variability, which is unable to realistically reflect the dynamics of people with type 1 diabetes (T1D). Additionally, T1D simulators are mainly designed for the testing of continuous subcutaneous insulin infusion (CSII) therapies. In this work, a simulator is presented that includes a generated virtual patient (VP) cohort and both fast- and long-acting Glargine-100 U/ml (Gla-100), Glargine-300 U/ml (Gla-300), and Degludec-100 U/ml (Deg-100) insulin models. Therefore, in addition to CSII therapies, multiple daily injections (MDI) therapies can also be tested. The Hovorka model and its published parameter probability distributions were used to generate cohorts of VPs that represent a T1D population. Valid patients are filtered through restrictions that guarantee that they are physiologically acceptable. To obtain more realistic scenarios, basal insulin profile patterns from the literature have been used to identify variability in insulin sensitivity. A library of mixed meals identified from real data has also been included. This work presents and validates a methodology for the creation of realistic VP cohorts that include physiological variability and a simulator that includes challenging and realistic scenarios for in silico testing. A cohort of 47 VPs has been generated and in silico simulations of both CSII and MDI therapies were performed in open-loop. The simulation outcome metrics were contrasted with literature results.

A Dual-Hormone Multicontroller for Artificial Pancreas Systems

Artificial pancreas (AP) algorithms can be divided into single-hormone (SH) and dual-hormone (DH). SH algorithms regulate glycemia using insulin as their control input. On the other hand, DH algorithms also use glucagon to counteract insulin. While SH-AP systems are already commercially available, DH-AP systems are still in an earlier research phase. DH-AP systems have been questioned since the added complexity of glucagon infusion does not always guarantee hypoglycemia prevention and might significantly raise insulin delivery. In this work, a DH multicontroller is proposed based on a SH linear quadratic gaussian (LQG) algorithm with an additional LQG controller to deliver glucagon. This strategy has a switched structure that allows activating one of the following three controllers when necessary: a conservative insulin LQG controller to modulate basal delivery ( K₁), an aggressive insulin LQG controller to counteract meals ( K₂), or a glucagon LQG controller to avoid imminent hypoglycemia ( K₃). Here, an in silico study of the benefits of incorporating controller K₃ is carried out. Intra-patient variability and mixed meals are considered. Results indicate that the proposed switched, dual-hormone strategy yields to a reduction in hypoglycemia without increasing hyperglycemia, with no significant rise in insulin delivery.

A genetic mammalian proportional-integral feedback control circuit for robust and precise gene regulation

To survive in the harsh environments they inhabit, cells have evolved sophisticated regulatory mechanisms that can maintain a steady internal milieu or homeostasis. This robustness, however, does not generally translate to engineered genetic circuits, such as the ones studied by synthetic biology. Here, we introduce an implementation of a minimal and universal gene regulatory motif that produces robust perfect adaptation for mammalian cells, and we improve on it by enhancing the precision of its regulation.

The processes that keep a cell alive are constantly challenged by unpredictable changes in its environment. Cells manage to counteract these changes by employing sophisticated regulatory strategies that maintain a steady internal milieu. Recently, the antithetic integral feedback motif has been demonstrated to be a minimal and universal biological regulatory strategy that can guarantee robust perfect adaptation for noisy gene regulatory networks in Escherichia coli. Here, we present a realization of the antithetic integral feedback motif in a synthetic gene circuit in mammalian cells. We show that the motif robustly maintains the expression of a synthetic transcription factor at tunable levels even when it is perturbed by increased degradation or its interaction network structure is perturbed by a negative feedback loop with an RNA-binding protein. We further demonstrate an improved regulatory strategy by augmenting the antithetic integral motif with additional negative feedback to realize antithetic proportional–integral control. We show that this motif produces robust perfect adaptation while also reducing the variance of the regulated synthetic transcription factor. We demonstrate that the integral and proportional–integral feedback motifs can mitigate the impact of gene expression burden, and we computationally explore their use in cell therapy. We believe that the engineering of precise and robust perfect adaptation will enable substantial advances in industrial biotechnology and cell-based therapeutics.

A methodical survey of mathematical model-based control techniques based on open and closed loop control approach for diabetes management

Disturbance of blood sugar level is controlled through well-known biomechanical feedback loops: high levels of glucose in blood facilitate to release insulin from the pancreas which accelerates the absorption rate of cellular glucose. Low glucose levels encourage to release pancreatic glucagon which induces glycogen breakdown to glucose in the liver. These bio-control systems do not function properly in diabetic patients. Though the control of disease seems intuitively easy, in real life, due to many differences in structure by diet and fasting, exercise, medications, patient’s profile and other stressors, it is not that easy. The mathematical models of the glucose-insulin regulatory system follow the patient’s physiological conditions which make it difficult to identify and estimate all the model parameters. In this paper, we have given a systematic literature review on mathematical models of the diabetic patients, and various kinds of disease control techniques through the development of open and closed loop insulin deliver command system and optimization of exogenous insulin rate. It demonstrates the open and closed loop type model-based control strategies underlying the assumptions of the concerned models. The combination of mathematical model with control strategies such as genetic algorithm (GA), neural network (NN), sliding mode controller (SMC), model predictive controller (MPC), and fuzzy logic control (FLC) has been considered, which provides an overview of this area, highlighting the control profile over the diabetic model with promising clinical results, outlining key challenges, and identifying needs for the future research. Also, the significance of these control algorithms has been discussed in the presence of the noises, the controller’s robustness and various other disturbances. It provides substantial information on diabetes management through various control techniques.