Mathematical Biology:Mathematics for understanding living systems

Mathematical Foundations

Core mathematics required for biological modelling.

Foundations of Mathematical Biology

How biological questions are translated into mathematical models.

Population Dynamics

Mathematical models of population growth, structure and extinction.

Interactions Between Species

Competition, predation, mutualism and interacting populations.

Dynamical Systems in Biology

Equilibria, stability, phase-plane analysis and biological dynamics.

Epidemiological Modelling

Mathematical models of infectious disease transmission and control.

Stochastic Processes for Biology

Random processes, Markov models and stochastic biological events.

Stochastic Differential Equations

Continuous stochastic models, Brownian motion and moment methods.

Spatial Mathematical Biology

Diffusion, spatial populations, travelling waves and pattern formation.

Cell and Molecular Biology Models

Mathematical models of biochemical, genetic and cellular processes.

Physiological and Medical Modelling

Models of drugs, tumours, immunity and physiological systems.

Evolutionary Mathematics

Selection, genetic drift, population genetics and evolutionary dynamics.

Networks in Biology

Graph-based models of contact, genetic and ecological networks.

Data and Parameter Estimation

Connecting mathematical models with biological observations and data.

Statistics for Mathematical Biology

Statistical methods for analysing biological data and uncertainty.

Computational Mathematical Biology

Numerical modelling, simulation and scientific computing with Python.

Machine Learning and Mathematical Biology

Data-driven learning combined with mechanistic biological models.

Decision Making and Prediction

Forecasting, risk, intervention and decisions under uncertainty.

Worked Biological Models

Complete worked examples connecting theory, computation and biology.

Reference Library

Notation, terminology, formulas and computational references.

About MathsBio

MathsBio is a progressive guide to mathematical biology. The learning structure moves from mathematical foundations to biological models, computation and applications.