Disease Modelling
Diabetes
Creating a diabetes risk profile and analyzing long-term progression using advanced clustering techniques
Diabetes is a chronic metabolic disorder characterized by the body’s inability to process glucose, resulting in hyperglycemia and organ damage. The International Diabetes Federation projects that by 2045, 784 million people will be affected, an increase from 10.5% in 2021. India, known as the diabetes capital of the world, sees Type 2 Diabetes (T2D) developing at younger ages and with thinner phenotypes. Thus, effective, cost-efficient interventions to prevent or delay T2DM progression are crucial for public health.
High-risk factors for diabetes can be identified through advanced machine learning techniques, particularly clustering algorithms applied to longitudinal cohort data. This method groups individuals based on similar health characteristics, facilitating the identification of phenotypes associated with increased diabetes risk. Based on the extracted features and parameters, a model can be developed to analyze diabetes progression and the interactions of risk factors over time. The resulting insights can guide healthcare providers in making informed, data-driven decisions and developing personalized treatment plans.
Quantifying the pathogenesis of obesity using comprehensive network models
The World Health Organization (WHO) classifies obesity as a pandemic, affecting a substantial portion of adults and children. One in every ten adults and one in every five children below 18 are said to be obese (Body Mass Index > 30kg/m2). Obesity significantly increases the risk of numerous diseases, including type 2 diabetes (T2DM), and it worsened COVID-19 outcomes.
This research will construct Boolean network models of obesity and T2DM biological pathways to identify key molecular interactions driving disease progression. Integrating these Boolean networks with ordinary differential equation models and longitudinal data will provide insights into disease dynamics and multi-factor interactions, surpassing the limitations of experimental data alone. Future research will focus on staging disease progression and identifying points of no return.
ACLF(Acute on Chronic Liver Failure)
Developing predictive tools for ACLF
Liver disease is a significant global health challenge characterized by inflammation of liver cells, which can cause various complications. It typically progresses through five reversible stages, emphasizing the need for early intervention. A key concern is ACLF, which can develop gradually but may lead to sudden severe inflammation that worsens liver function, complicating treatment options for clinicians. The unpredictable nature of ACLF necessitates timely intervention to improve patient outcomes.
Our research addresses this gap by developing a comprehensive model integrating immune response and metabolic pathway dynamics to predict the likelihood of individuals with a history of liver disease developing ACLF. We aim to design interventions to prevent severe inflammation associated with ACLF or provide timely treatment strategies to mitigate its effects. This approach enhances our understanding of liver disease progression and provides practical tools for early intervention and improved clinical outcomes.
A Network Based Discrete Dynamic Modeling Framework for Understanding NAFLD Progression
NAFLD is a spectrum of liver diseases ranging from simple steatosis to non-alcoholic steatohepatitis (NASH), fibrosis, cirrhosis, and hepatocellular carcinoma (HCC). It is defined as hepatic fat accumulation in more than 5% of hepatocytes, without significant alcohol intake. NAFLD affects 25–30% of the world’s population, making it a major global health burden. The healthcare and economic impact is significant due to hospitalizations, complications, and long-term management. Therefore, early detection and intervention with lifestyle or therapeutic approaches can prevent disease progression.
The aim of this project is to develop a Boolean model of Non-Alcoholic Fatty Liver Disease (NAFLD) that captures the key molecular and metabolic interactions driving disease progression. A Boolean model of NAFLD simplifies the complex metabolic and signaling networks into logical interactions, making the disease mechanisms easier to study. It helps identify key regulatory nodes driving progression from steatosis to NASH and fibrosis. Such models can predict outcomes of interventions and guide therapeutic strategies where experimental data are limited. This model will help in understanding critical regulatory nodes, simulating disease dynamics, and exploring potential therapeutic targets.
