A new library of HEMET model: Insulin effects on hepatic metabolism

Next-generation, personalised, model-based critical care medicine: a state-of-the art review of in silico virtual patient models, methods, and cohorts, and how to validation them

Critical care, like many healthcare areas, is under a dual assault from significantly increasing demographic and economic pressures. Intensive care unit (ICU) patients are highly variable in response to treatment, and increasingly aging populations mean ICUs are under increasing demand and their cohorts are increasingly ill. Equally, patient expectations are growing, while the economic ability to deliver care to all is declining. Better, more productive care is thus the big challenge. One means to that end is personalised care designed to manage the significant inter- and intra-patient variability that makes the ICU patient difficult. Thus, moving from current "one size fits all" protocolised care to adaptive, model-based "one method fits all" personalised care could deliver the required step change in the quality, and simultaneously the productivity and cost, of care. Computer models of human physiology are a unique tool to personalise care, as they can couple clinical data with mathematical methods to create subject-specific models and virtual patients to design new, personalised and more optimal protocols, as well as to guide care in real-time. They rely on identifying time varying patient-specific parameters in the model that capture inter- and intra-patient variability, the difference between patients and the evolution of patient condition. Properly validated, virtual patients represent the real patients, and can be used in silico to test different protocols or interventions, or in real-time to guide care. Hence, the underlying models and methods create the foundation for next generation care, as well as a tool for safely and rapidly developing personalised treatment protocols over large virtual cohorts using virtual trials. This review examines the models and methods used to create virtual patients. Specifically, it presents the models types and structures used and the data required. It then covers how to validate the resulting virtual patients and trials, and how these virtual trials can help design and optimise clinical trial. Links between these models and higher order, more complex physiome models are also discussed. In each section, it explores the progress reported up to date, especially on core ICU therapies in glycemic, circulatory and mechanical ventilation management, where high cost and frequency of occurrence provide a significant opportunity for model-based methods to have measurable clinical and economic impact. The outcomes are readily generalised to other areas of medical care.

In silico models for dynamic connected cell cultures mimicking hepatocyte-endothelial cell-adipocyte interaction circle

The biochemistry of a system made up of three kinds of cell is virtually impossible to work out without the use of in silico models. Here, we deal with homeostatic balance phenomena from a metabolic point of view and we present a new computational model merging three single-cell models, already available from our research group: the first model reproduced the metabolic behaviour of a hepatocyte, the second one represented an endothelial cell, and the third one described an adipocyte. Multiple interconnections were created among these three models in order to mimic the main physiological interactions that are known for the examined cell phenotypes. The ultimate aim was to recreate the accomplishment of the homeostatic balance as it was observed for an in vitro connected three-culture system concerning glucose and lipid metabolism in the presence of the medium flow. The whole model was based on a modular approach and on a set of nonlinear differential equations implemented in Simulink, applying Michaelis-Menten kinetic laws and some energy balance considerations to the studied metabolic pathways. Our in silico model was then validated against experimental datasets coming from literature about the cited in vitro model. The agreement between simulated and experimental results was good and the behaviour of the connected culture system was reproduced through an adequate parameter evaluation. The developed model may help other researchers to investigate further about integrated metabolism and the regulation mechanisms underlying the physiological homeostasis.

CREPE: mathematical model for crosstalking of endothelial cells and hepatocyte metabolism

The liver shows a close coexistence between endothelial cells and hepatocytes (HepG2). Endothelial cells' main purpose is to protect (HepG2) from blood vessel shear stress, acting as a barrier, but experimental evidence suggests that they could also play a role in regulating (HepG2) glucose metabolism. A well-known singular effect in hepatocyte-endothelial co-cultures is the reduction of glucose consumption respect to (HepG2) in single culture. (HepG2) were shown to reduce their glucose consumption supporting energy needs of endothelial cells. Monti have studied the effects of endothelin-1 (Et-1) on Glucokinase activity in adult rat (HepG2). They observed a reduction in hepatocytes Glucokinase catalytic rate, which is dependent on Et-1 concentration. We developed crosstalking of endothelial cells and hepatocyte metabolism (CREPE) that is a mathematical model of the endothelin-1 mediated crosstalk between HepG2 and endothelial cells (human umbilical vein endothelial cells) in a traditional static co-culture system. CREPE was validated against experimental data, showing good agreement with them. CREPE can be a starting point to develop predictive tools on complex and highly interconnected environments.