Blood glucose (BG) regulation in type 1 diabetic patients has been investigated by researchers for a long time. Many mathematical models mimicking the physiological behavior of diabetic patients have been developed to predict BG variations. Models characterizing meal absorption and physical activities have also been developed in the literature, as they play a significant role in altering BG levels. Hence, existing glucose-insulin dynamic models dating back from early 1960s are reviewed along with an overview of meal absorption and exercise effect models. The available knowledge-driven BG models have been classified into different families based on their origin for development. Also, five knowledge-driven BG models (with at least one model from a family) have been analyzed by either varying basal insulin or meal ingestion. The available meal absorption models have also been simulated to compare and analyze them for different meal sizes. The major objective of the analysis is to study the BG dynamics of different models at their nominal parameter values, under varying basal insulin doses and meal ingestion. Similar analysis has been performed on 10 adult patient models in a recent benchmark simulator for comparison. These results will be useful for understanding the responses of different BG models at their nominal parameter values and for preliminary selection of a suitable treatment model(s) for a patient.

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The need for first principles based models for chemical and biological processes has led to the development of techniques for model-based design of experiments (MBDOE). These techniques help in speeding up the parameter estimation efforts and typically lead to improved parameter precision with a relatively short experimental campaign. In the case of complex kinetic networks involving parallel and/or consecutive reactions, correlation among model parameters makes the inverse problem of parameter estimation very difficult. It is therefore important to develop experimental design techniques that not only increase information content about the system to facilitate precise parameter estimation but also reduce the correlation among parameters. This article presents a multiobjective optimization (MOO) based framework for experimental design, where, in addition to the traditional objective of eliciting maximally informative data for parameter estimation, an explicit objective to reduce correlation among parameters is included. The proposed MOO based framework is tested on two case studies, and results are compared with the traditional alphabetical designs. The approach provides a pictorial representation of trade-off between system information and correlation among parameters in the form of Pareto-optimal front, which offers the experimentalist the freedom to choose experimental design(s) that are most suitable to implement on the experimental system and realize the benefits of such experiments.

Anesthesia implies maintaining a triad of hypnosis, analgesia, and neuromuscular blockade by infusing several drugs which specifically act on each of the above aspects. This work focuses on controlling the hypnosis by automatic infusion of propofol using bispectral index (BIS) as the primary controlled variable. A fourthorder nonlinear pharmacokinetic (PK)-pharmacodynamic (PD) representation is used for the hypnosis dynamics of patients. A reliable PK-PD model with associated parameters is obtained from the literature and the closed-loop responses of four types of control strategies (model predictive control, internal model control, controller with modeling error compensation, and proportional-integral-derivative (PID) control) are compared. Robust performance of the four controllers is tested for a broad range of patients by considering variability in PK-PD parameters. Also, the relative performance of the four controllers is studied for different setpoints, noise, and disturbances in BIS signal. Numerical simulations show that all the advanced controllers performed better than the PID controller with the model predictive controller showing the best performance.

Data classification algorithms applied for class prediction in computational biology literature are data specific and have shown varying degrees of performance. Different classes cannot be distinguished solely based on interclass distances or decision boundaries. We propose that inter-relations among the features be exploited for separating observations into specific classes. A new variable predictive model based class discrimination (VPMCD) method is described here. Three well established and proven data sets of varying statistical and biological significance are utilized as benchmark. The performance of the new method is compared with advanced classification algorithms. The new method performs better during different tests and shows higher stability and robustness. The VPMCD is observed to be a potentially strong classification approach and can be effectively extended to other data mining applications involving biological systems.