Durham University
Programme and Module Handbook

Postgraduate Programme and Module Handbook 2017-2018 (archived)

Module MATH40615: Systems Biology and Bayesian Inference

Department: Mathematical Sciences

MATH40615: Systems Biology and Bayesian Inference

Type Open Level 4 Credits 15 Availability Not available in 2017/18 Module Cap None.
Tied to


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Excluded Combination of Modules

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  • To provide students with an introduction to Bayesian Statistical Methodology and its application to stochastic models in Systems Biology.


  • Bayesian Paradigm, conditional independence and conjugacy.
  • Bayesian inference and Markov Chain Monte Carlo.
  • Systems Biology reaction networks, biochemical kinetics, stochastic simulation of networks.
  • The diffusion approximation and Bayesian Inference for rate parameters in stochastic kinetic models.

Learning Outcomes

Subject-specific Knowledge:
  • Students will have an understanding of the specialised mathematical theory together with mastery of a coherent body of knowledge of Bayesian statistics and Stochastic Processes.
Subject-specific Skills:
  • Students will be able to solve complex and specialised problems, draw conclusions and deploy abstract reasoning and mathematical intuition.
  • They will develop their mathematical self-sufficiency and be able to read and understand advanced mathematics independently, in subjects relevant for applications in Biology.
Key Skills:
  • Problem solving.
  • Self-organisation, self-discipline and self-knowledge.
  • Ability to learn actively and reflectively and to develop intuition, the ability to tackle material which is given both unfamiliar and complex.

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

  • Lectures will provide the means to give concise, focussed presentation of the relevant subject matter of the module. They will be supported by reference to suitable text books and where appropriate by the distribution of written material or through links on DUO (on-line learning resource).
  • Workshops based on the concepts presented will be used as support teaching.
  • Problem sheets will be given regularly in lectures to help students gain an understanding of the concepts presented. These will be assessed formatively.
  • Student performance will be assessed summatively through examination.
  • Formative assessments will provide the means for the student to demonstrate their acquisition of subject knowledge and the development of their problem solving skills. The tests will also provide opportunities for feedback, for students to gauge their progress, and for the Management Committee to monitor progress throughout the duration of the module.

Teaching Methods and Learning Hours

Activity Number Frequency Duration Total/Hours
Lectures 15 1 1 15
Workshops 5 0.5 1 5
Self Study 130
Total 150

Summative Assessment

Component: Examination Component Weighting: 70%
Element Length / duration Element Weighting Resit Opportunity
Examination 1.5 100% Y
Component: Mathematical Essay Component Weighting: 30%
Element Length / duration Element Weighting Resit Opportunity
Essay 10 100% Y

Formative Assessment:

Problem sheets distributed in lectures.

Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University