🧬 Biomathematics

Course Code: 29006-AUX
Duration: 8 Weeks | 2 Sessions per Week (Theory + Practical)

---

📚 Introduction:

Biomathematics is the interdisciplinary field where mathematics meets biology.
It provides the mathematical frameworks and tools needed to model, simulate, and analyze biological systems — from the spread of diseases to the dynamics of populations and genetic sequencing.

---

✨ Course Description:

This course explores how mathematical models are formulated and applied in biological contexts.
Students will learn about population dynamics, epidemiology, and bioinformatics, gaining a solid understanding of how mathematics drives progress in modern biology and healthcare.

---

🎯 Course Objectives:

• Understand the fundamental mathematical models used in biological sciences.

• Apply mathematical techniques to study population growth, spread of diseases, and biological data analysis.

• Develop skills in interpreting biological phenomena through mathematical lenses.

---

🎯 Target Audience:

• Advanced undergraduate and graduate students in Mathematics, Biology, Biotechnology, and Health Sciences.

• Professionals and researchers interested in quantitative biological modeling.

• Anyone with a background in Differential Equations and Statistics.

---

🛠️ Materials and Resources:

• Software: MATLAB, Python (NumPy, SciPy, BioPython libraries).

• Reading Material: Research papers, biomathematics textbooks, online biological datasets.

• Tools: Simulation environments and epidemiological modeling platforms.

---

🧑‍🏫 Instruction Method:

• 1 Theory Session per week (Concepts, Models, Case Studies).

• 1 Practical Session per week (Simulations, Software Usage, Project Work).

• Assignments and exercises based on real-world biological data.

• Group projects and discussions on contemporary biological challenges.

---

🗂️ What You Will Learn:

✅ How to create and analyze population models (growth, interaction, extinction).
✅ How to model epidemics and predict disease spread (SIR models and beyond).
✅ Basics of bioinformatics, including sequence alignment and biological data analysis.
✅ Application of differential equations, probability, and statistics in biological systems.

---

🗺️ Detailed Course Outline:

📅 Week 1:

Introduction to Biomathematics

• Overview of mathematical biology.

• The role of modeling in modern biology.

📅 Week 2:

Population Dynamics I

• Exponential and logistic growth models.

• Mathematical description of competition and predation.

📅 Week 3:

Population Dynamics II

• Advanced population models (age-structured, spatial models).

• Applications in ecology and conservation.

📅 Week 4:

Introduction to Epidemiology

• Basic concepts: infection rates, recovery rates, immunity.

• Introduction to the SIR (Susceptible-Infected-Recovered) model.

📅 Week 5:

Advanced Epidemiological Models

• SEIR models (adding exposed stage).

• Vaccination strategies and modeling disease control.

📅 Week 6:

Introduction to Bioinformatics

• Biological sequences: DNA, RNA, and proteins.

• Sequence alignment and basic database searches.

📅 Week 7:

Data Analysis in Biomathematics

• Statistical methods in biology.

• Handling and interpreting biological data.

📅 Week 8:

Final Projects and Presentations

• Develop and present a model related to population dynamics, epidemiology, or bioinformatics.

• Real-world biological problem-solving.

---

🏆 Final Outcome:

By the end of this course, you will be able to:

• Build mathematical models to describe biological systems.

• Analyze and simulate biological processes like epidemics and population changes.

• Use computational tools to manage and interpret biological data.

• Integrate mathematical reasoning into biological research and applications.

---