MT5855 Stochastic Dynamics in Biology

Academic year

2024 to 2025 Semester 1

Further information on which modules are specific to your programme.

Key module information

SCOTCAT credits

15

The Scottish Credit Accumulation and Transfer (SCOTCAT) system allows credits gained in Scotland to be transferred between institutions. The number of credits associated with a module gives an indication of the amount of learning effort required by the learner. European Credit Transfer System (ECTS) credits are half the value of SCOTCAT credits.

SCQF level

SCQF level 11

The Scottish Credit and Qualifications Framework (SCQF) provides an indication of the complexity of award qualifications and associated learning and operates on an ascending numeric scale from Levels 1-12 with SCQF Level 10 equating to a Scottish undergraduate Honours degree.

Planned timetable

10am Monday (even weeks), Tuesday, Thursday

This information is given as indicative. Timetable may change at short notice depending on room availability.

Module coordinator

Dr J Kursawe

Dr J Kursawe
This information is given as indicative. Staff involved in a module may change at short notice depending on availability and circumstances.

Module description

This module will provide an introduction to stochastic modelling with a focus on applications in biology. It will introduce and explain key biological phenomena where stochastic effects are important, such as stochastic amplification (the emergence of stochastically-enabled oscillations) and stochastic resonance and focussing, where stochastic dynamics can change systems behaviour due to non-linear interactions. The module will include Bayesian techniques that may be used to infer parameters of stochastic models. Stochastic methods are increasingly used in applied maths and in mathematical biology in particular, both in research and in industrial settings. This module aims to equip students with the skills to understand stochastic dynamical systems and complements other modules in the School where dynamical systems are widely discussed using deterministic descriptions such as ODEs or PDEs. Here students learn how to extend such systems to take stochastic effects into account.

Relationship to other modules

Pre-requisites

BEFORE TAKING THIS MODULE YOU MUST PASS MT2508 AND PASS MT3504

Assessment pattern

Coursework (computing project) = 20%, 2-hour Written Examination = 80%

Re-assessment

Oral examination = 100%

Learning and teaching methods and delivery

Weekly contact

2.5 lectures (x 10 weeks), 1 tutorial (x 10 weeks)

Scheduled learning hours

35

The number of compulsory student:staff contact hours over the period of the module.

Guided independent study hours

116

The number of hours that students are expected to invest in independent study over the period of the module.

Intended learning outcomes

  • Comprehend and explain key concepts in stochastic modelling, including stochastic processes, diffusion processes, and basic inference methods.
  • Construct, analyse, and computationally simulate stochastic processes to describe biological systems, for example chemical reaction networks.
  • Assess the impact of stochastic noise onto deterministic systems by analysing stochastic differential equations, such as the Chemical Langevin Equation.
  • Compare stochastic models to data using basic concepts in Bayesian inference.