Modeling in Biology

Modeling in Biology provides BSE major students with an introduction to mathematical modeling in biology, with a strong emphasis on statistical analysis. In modern biology, theoretical modeling has become an essential tool for understanding complex biological phenomena, enabling researchers to formulate assumptions, generate forecasts, test predictions, and evaluate the effectiveness of biological models. By integrating mathematical and statistical approaches, the course highlights how quantitative methods can be used to investigate and explain biological systems across multiple scales.

Throughout the course, students explore a wide range of applications of modeling, from cellular processes to population dynamics. Through hands-on activities and the analysis of real-world datasets, students develop strong analytical and problem-solving skills while gaining a deeper appreciation for the complexity and variability inherent in biological systems. The course equips students to interpret biological data rigorously, draw informed conclusions, and contribute to innovative research, effectively bridging the gap between theoretical modeling and practical applications in modern biology.

The Modeling in Biology course offers BSE Major students an introduction to mathematical modeling in biology, with a strong focus on statistical analysis. In modern biology, theoretical modeling has become a vital approach, allowing us to understand underlying phenomena, make assumptions, generate forecasts, and test predictions, ultimately enhancing the efficacy of biological models.

Throughout the course, students explore diverse applications of stochastic modeling, from cellular processes to population dynamics. By gaining hands-on experience and working with real-world data, students develop analytical skills and a deeper appreciation for the intricacies of biological systems. This course equips students to navigate the complexities of biological data, make informed interpretations, and contribute to ground-breaking research, bridging the gap between theory and practice in the fascinating world of biology.

By the end of this module, each student will have had the opportunity to:

• Understand the principles of stochastic modeling and its significance in studying biological systems.
• Explore various stochastic modeling approaches, including Markov chains and birth-death processes, to analyze biological processes over time.
• Apply stochastic simulation methods to model and predict complex behaviors in diverse biological phenomena, such as gene expression, population dynamics, and ecological systems.
• Investigate the broad range of applications where stochastic modeling plays a pivotal role, spanning fields like epidemiology, drug pharmacokinetics, immunology, and systems biology networks.
• Develop practical skills in constructing and interpreting stochastic models, enabling effective problem-solving in biological research and fostering interdisciplinary collaboration between biologists, bioinformaticians, and computational scientists.

After successful completion of the modules, students should be able to:

• Understand important notions of mathematical modeling in biology
• How to read and interpret equations to construct and analyze new models.
• Apply theoretical models to generate appropriate models to interpret data
• Demonstrate a comprehensive understanding of stochastic modeling principles and its relevance in the context of biological systems.
• Utilize various stochastic modeling techniques, including Markov chains and birth-death processes, to analyze and predict dynamic behaviors in biological processes.

• Reference material will be provided on LMS for every module
• Textbook: An Introduction to Stochastic Processes with Applications to Biology, Linda JS Allen, Chapman & Hall/CRC, Mathematical and Computational Biology Series.