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Course Detail

Course Name Biological Systems, Simulation and Modelling
Course Code 24AIM303
Program B.Tech. in Artificial Intelligence (AI) and Data Science (Medical Engineering)
Semester V - Micro-credential courses: Set 4
Credits 3
Campus Coimbatore

Syllabus

Unit 1

Foundations of Systems Biology: Introduction to systems biology and network theory. Principles of mathematical modeling in biology.Study of biological systems at different levels. Integration of omics data for holistic understanding

Unit 2

Mathematical Modeling in Biology: Mathematical principles for biological modeling. ODEs, PDEs, and stochastic modeling in biology. Agent-based modeling for individual-level interactions.

Unit 3

Computational Tools in Systems Biology: Computational systems biology modeling. Bioinformatics applications for data analysis. Simulation platforms and programming languages. High-performance computing for large-scale simulations.

Unit 4

Applications in Biomedical Engineering: Systems biology in drug discovery and personalized medicine. Computational models in healthcare. Case studies of systems biology in disease understanding. Ethical considerations in applying systems biology to biomedical engineering.

Course Objectives and Outcomes

Course Objectives:

  • This course is designed to introduce students to key ideas and mathematical tools of Systems Biology. Concepts of Systems.
  • Students should be able to associate modelling with their origin in Dynamical Systems Theory and associated mathematical developments.
  • Students should be able to use both traditional and high-throughput experimental techniques to explore those concepts and test hypotheses.
  • The course will introduce students to a) deterministic models, b) stochastic models for cellular and molecular processes and c) graph theory-based analysis of biological networks.

Course Outcomes:

After completing this course, students should be able to
CO1: Apply Systems Biology concepts to analyze and model complex biological systems.
CO2: Utilize mathematical principles, including ODEs and stochastic modeling, to proficiently simulate and analyze diverse biological processes.
CO3: Employ computational tools, bioinformatics applications, and programming languages for comprehensive data analysis and large-scale simulations in systems biology.
CO4: Apply acquired knowledge in real-world scenarios, focusing on drug discovery, personalized medicine, and healthcare computational modeling.

CO-PO Mapping

CO/PO PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2 PSO3
CO1 2 3 2 2 1 2 2 2 3 1 2
CO2 2 3 2 2 1 2 2 2 3 1 2
CO3 2 3 2 2 1 2 2 2 3 1 2
CO4 2 3 2 2 1 2 2 2 3 1 2

Textbooks / References

  1. Edda Klipp, Wolfram Liebermeister, Christoph Wierling, Axel Kowald, Hans Lehrach, Ralf Herwig, “Systems Biology: A Textbook,” Latest Edition, Wiley-VCH, 2017.
  2. A.C. Fowler, J. Ockendon, J.R. King, “Mathematical Models in the Applied Sciences,” Revised Edition, Cambridge University Press, 2017.
  3. Andres Kriete, Roland Eils, “Computational Systems Biology,” Latest Edition, Academic Press, 2019.
  4. David W.Mount, “Bioinformatics: Sequence and Genome Analysis,” Latest Edition, Cold Spring Harbor Laboratory Press, 2018.

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