This module provides an integrative perspective on the role of systems biology and underpinning experimental systems in understanding and managing diseases, with a particular focus on cancer.
The aim of this Module is to provide the student with (i) A critical awareness of the contribution of integrative systems approaches to disease diagnosis and management (ii) The ability to appraise the role of high throughput data generation in advancing knowledge of diseases.
By the end of this module the student should be able to:
1. Appraise the role of systems biology in advancing understanding of therapeutic intervention
2. Evaluate the role of statistical image analysis in disease diagnosis and prognosis
3. Critically discuss experimental techniques relevant to systems biology
1 Systems biology concepts
Fundamental concepts in systems biology and the relationship among in silico, in vitro and in vivo systems.
2 Imaging and statistical analysis of tumours
Imaging systems, imaging software, databases, sampling challenges, analysing patterns in space.
3 Process-based, data-driven modelling
Process-based models: formulation, data requirements, uses and limitations. Data-driven models: formulation, data requirements, uses and limitations. Perspectives for hybrid models.
4 Agent-based modelling
The intuitive appeal of agent-based models, modelling systems with agents, simulation, visualisation and interpretation.
5 Qualitative and quantitative data streams
Appraisal of qualitative, quantitative and 'semi- quantitative' data; mixing data streams; accuracy and precision in modelling.
6 High throughput data
Techniques for high throughput data generation, the relationship among data, information and knowledge, bioinformatics and data storage.
7 Novel measurement systems
Next generation measurement opportunities, miniturisation of equipment and the benefits
8 Drug discovery and clinical trials
The role of systems biology in the drug discovery process and its potential impact on clinical trials.
Statement on Teaching, Learning and Assessment
All 12 lectures will be delivered in the first 6 weeks. Five laboratory sessions will develop skills in statistical analysis of imaging data in weeks 2-6. This will contribute to Unit 1. Five practical activities/ guest lectures/ demonstrations will develop awareness of state of the art experimental systems. Week 7 will explore student understanding of the range of material covered with structured discussions on key aspects of the material. From weeks 8-12 students will be met in small tutorial groups to support their independent research for the report and viva (Unit 2).
Teaching and Learning Work Loads
|Supervised Practical Activity||10|
|Unsupervised Practical Activity||0|
Credit Value – The total value of SCQF credits for the module. 20 credits are the equivalent of 10 ECTS credits. A full-time student should normally register for 60 SCQF credits per semester.
We make every effort to ensure that the information on our website is accurate but it is possible that some changes may occur prior to the academic year of entry. The modules listed in this catalogue are offered subject to availability during academic year 2018/19 , and may be subject to change for future years.