The Mathematics and Life Sciences Division bridges mathematics and biology, using mathematical and computational methods to address key challenges in life sciences. From understanding disease dynamics to analyzing ecosystems and neural networks, this division leverages data analytics and modeling to generate insights that drive advancements in public health, environmental management, and personalized medicine.

• Biological Network Modeling: Investigating gene regulation, protein interactions, and metabolic pathways to understand cellular behavior and disease mechanisms.
• Ecosystem Dynamics and Biodiversity: Using models to analyze species interactions, predict environmental impacts, and promote biodiversity conservation.
• Disease Dynamics and Control: Modeling infectious diseases to inform prevention and control strategies, including applications for pandemic preparedness.
• Neural and Cognitive Modeling: Applying mathematical models to study brain dynamics and neurological disorders, contributing to fields such as cognitive science and mental health.

• Cellular and Molecular Modeling: Developing mathematical models to simulate cellular processes and understand their implications for health and disease.
• Predictive Modeling for Public Health: Using epidemiological models to predict disease outbreaks and guide public health responses.
• Ecological Impact Assessment: Modeling ecosystem responses to environmental changes, supporting biodiversity and sustainability.
• Genomic Data Analysis: Employing computational techniques to analyze genomic and proteomic data for applications in personalized medicine and drug discovery.
• Neurological System Analysis: Understanding brain functions and disorders through computational models and simulations that can lead to innovative treatments.