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In Silico Modelling and Simulation for Diabetes Therapy

Authors: Darshna M. Joshi, Hardik Bhatt, Himanshu K. Patel

In Silico Modelling and Simulation for Diabetes Therapy

ISBN: 979-8-89881-364-2
eISBN: 979-8-89881-363-5 (Online)

Introduction

In Silico Modelling and Simulation for Diabetes Therapy is an exploratory study positioned at the intersection of pharmacology, systems biology, and biomedical engineering. The book demonstrates how in silico approaches support a better understanding of diabetes pathophysiology, insulin kinetics, and drug–receptor interactions while reducing reliance on costly, time-intensive clinical trials.

The book begins with core concepts of in silico modelling before examining mechanisms of diabetes and existing care technologies, such as glucose monitoring systems, insulin pumps, artificial pancreas systems, and closed-loop control algorithms. It provides practical coverage of computational tools, including MATLAB, Simulink, COPASI, and Cell Designer, supported by case studies on insulin dosing, therapy optimisation, and data-driven modelling. Ethical, regulatory, and patient privacy considerations are discussed alongside emerging trends such as AI-driven modelling, virtual clinical trials, and personalised diabetes management.


Key Features

  • - Explains in silico modelling principles for diabetes research and therapy design.
  • - Provides integrated disease mechanisms, insulin kinetics, and drug–receptor interactions.
  • - Real-world case studies demonstrating the practical use of computational tools.
  • - Addresses regulatory, ethical, and data privacy considerations in digital health.
  • - Highlights future directions, including AI and personalised diabetes care.

Target Readership:

Students, researchers and professionals in pharmacology, systems biology and biomedical engineering.

Foreword

“Insulin is not a cure for diabetes; it is a treatment. It enables the diabetic to burn sufficient carbohydrates, so that proteins and fats may be added to the diet in sufficient quantities to provide energy for the economic burdens of life.” ~ Frederick Grant Banting

Banting, a Canadian pharmacologist, gave the above-mentioned statement back in 1930; however, much has changed over the years. The key to this is “Gene Therapy”. This excellent piece of work by Darshna et al. provides a good source of information for readers. Understanding phenotypes and their genetic determinants for diabetes and Metabolic Syndrome (MetS) has been quite challenging. With the advent of systems bioinformatics approaches, there is a need to decipher methods for the identification and evaluation of the functional role of phenotypic traits associated with complex diseases. There are associated phenotypes, such as monogenic syndromes and lipodystrophies, that have been used to understand the molecular pathophysiology of Insulin Resistance (IR) underlying obesity and diabetes mellitus. Consequently, these associated phenotypes have been accompanied by a varied genetic approach, as well as urbanization, globalization, and, of course, changes in food and dietary patterns, in addition to epigenetic spectrums. However, there has been a global shift in dietary patterns, which has driven the upsurge of diet-related non-communicable diseases such as Type 2 Diabetes Mellitus (T2DM) and other diseases such as obesity, Cardiovascular Diseases (CVD), and cancer.

Over the years, researchers have conducted a diverse spectrum of Genome-wide Association Studies (GWAS) associated with T2DM. Several consortia aimed to delineate distinct signals and fine-map the sub-population diversity using multi-ancestry meta-analysis, wherein risk scores are shown to have clinical significance. Intriguingly, Polygenic Risk Scores (PRS) remain key among individuals in correlating disease markers. As an extension to the Genetic Risk Score (GRS), the heritability of mutations serves as a rich resource for gene therapy.

Taken together, there is promise for managing diabetes using in silico modeling and simulations. "In Silico Modeling and Simulation for Diabetes Therapy" is a resource for scientists, diabetologists, bioinformaticists, genomicists, and, importantly, laymen who need to be educated on data-driven therapy. I congratulate the authors on this wonderful piece.

Long Hail Precision Medicine!

Yours for Science
Prashanth N Suravajhala
Founder, Bioclues.org