SYSTEMS BIOLOGY - 2017/8
Module code: BMS3072
School of Biosciences and Medicine
LAING EE Dr (Biosc & Med)
Number of Credits
FHEQ Level 6
Module cap (Maximum number of students)
Overall student workload
Workshop Hours: 12
Independent Study Hours: 100
Lecture Hours: 17
|Assessment type||Unit of assessment||Weighting|
|Coursework||BRIEF (LESS THAN 500 WORDS) REPORT OF A BIOINFORMATICS ANALYSIS PERFORMED WITHIN THE MODULE||10|
|Coursework||BRIEF (LESS THAN 500 WORDS) REPORT OF A MATHEMATICAL MODEL IMPLEMENTED WITHIN THE MODULE||10|
|Coursework||BRIEF (LESS THAN 500 WORDS) REPORT OF A METABOLIC MODELLING APPROACH IMPLEMENTED WITHIN THE MODULE||10|
|Coursework||RESEARCH GRANT PROPOSAL : DESIGN A NOVEL SYSTEMS BIOLOGY RESEARCH PROJECT||70|
Prerequisites / Co-requisites
BMS2036 Methods in Molecular Biology and Genetics
Systems biology is widely accepted as a major future direction of biological research. The ethos of systems biology is to generate, analyse and integrate multiple data sets for understanding and modelling a biological system. We want to know the components (molecules) of the system, how they work/interact, and, ideally, have some quantitation: the abundance of a particular component and/or the rates of action/interaction; due to technological advances within molecular biology we are now able to obtain quantitative information about molecules within a biological system on a large-scale.
The purpose of this module is to introduce students to the basic concepts of systems biology. The module includes work relevant to prokaryotic and eukaryotic systems and is thus suitable for all bioscience students. Learning methods include: Lectures, computational practical sessions, seminars, readings, workshops and research and problem solving during computer based investigations.
To introduce the field of Systems Biology and the key topics within it
To understand the high-throughput technologies ("-omics" : genomics, phenomics, proteomics, transcriptomics, metabolomics, metagenomics and metatranscriptomics) employed to measure parameters at a systems level.
To achieve practical understanding of computer simulations of living systems, computational analysis of experimental data and how to interpret the results
|Understand the basic quantitative techniques used within the field of Systems Biology||KPT|
|Analyse high-throughput data sets using current available softwares/web-tools||CP|
|Use key bioinformatic tools for interpreting your results.||CPT|
|Use software tools for computer simulations of molecular interaction networks.||CPT|
|Integrate diverse data sets to understand organisms at a systems level||CPT|
|Present your ideas in a concise and cohesive style of a research grant proposal||CPT|
|Present your ideas in an oral presentation||PT|
|Work as an effective team member||PT|
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Indicative content includes:
Founding ‘good research practice’ concepts
An introduction to Systems biology
How to design a systems biology experiment.
How to [critically] read a paper
Research proposal “surgery” sessions where students can discuss their proposal with peers and academics.
Databases, tools and statistical analysis supporting high-throughput data comprising:
Genomics : Sequence analysis
Metagenomics and metatranscriptomics: Application of high-throughput sequencing to investigate microbial communities.
Transcriptomics: Microarrays and RNA-seq: Which genes are expressed? Functional analysis of genes.
Phenomics & Proteomics : Identifying substrate utilisation, proteins in the system
Section will include a research seminar discussing current bioinformatics research and applications in systems biology.
Founding concepts in mathematical modelling
Conducting simulations of cellular processes using software
Randomness of single cell behaviour
Section will begin with a research seminar discussing current mathematical modelling research and applications in systems biology.
Metabolomics: Quantitative physiology.
Metabolic control analysis : Determination of control coefficients. Control in linear and branched pathways.
Modelling genome scale metabolic networks.
Section will begin with a research seminar discussing current metabolic modelling research and applications in systems biology.
Methods of Teaching / Learning
The learning and teaching strategy is designed to:
Give students understanding and awareness of systems biology approaches to enable independent and creative application of these approaches to answer research hypotheses and global challenges. We will provide basic skills of computational data analysis and mathematical modelling. Assessment is focussed on critical knowledge evaluation and ability to formulate independent research ideas.
The learning and teaching methods include:
Lectures, including online lectures (1 hour each week)
Research seminars (4 hours)
Research proposal “surgery” workshop (Entire day)
Computer practicals (online; supported using online resources and in-class support seminars, 4 hours each week)
Seminars on designing experiments and critical review (3 hours)
Presentation of original ideas in the form of grant proposal: 60 hours
The assessment strategy is designed to provide students with the opportunity to demonstrate
An ability to understand and accurately report the outputs of bioinformatics analysis and mathematical and metabolic models.
An ability to select the most appropriate methods for answering their own independent research question(s).
An awareness of cutting-edge research in Systems Biology.
Group discussions between students, and lead by academics in seminar sessions, will further demonstrate breath of the field and its role in contemporary life sciences.
The grant proposal assessment is key to providing students with the opportunity of demonstrating their ability to formulate independent research ideas involving application of systems biology approaches.
Thus, the summative assessment for this module consists of:
Three brief (less than 500 words) reports describing the application and results of 1) bioinformatics approaches, 2) mathematical models, 3) metabolic models. These reports will be based on the computational practicals set within the module and are supported through seminar discussions. Students are able to select which of the methods to report. Reports to be submitted at the end of the module (week 36).
Presentation of student’s original idea as a grant proposal (week 37).
Formative assessment and feedback
The students will receive formative feedback from their peers and academics during the seminars. The feedback given here will directly support the development of a research proposal. We provide extensive feedback on research grant proposals during the scheduled proposal workshop sessions, before submission
Reading list for SYSTEMS BIOLOGY : http://aspire.surrey.ac.uk/modules/bms3072
Programmes this module appears in
|Biological Sciences BSc (Hons)||2||Optional||A weighted aggregate mark of 40% is required to pass the module|
|Biochemistry BSc (Hons)||2||Optional||A weighted aggregate mark of 40% is required to pass the module|
|Biotechnology BSc (Hons)||2||Compulsory||A weighted aggregate mark of 40% is required to pass the module|
|Microbiology BSc (Hons)||2||Optional||A weighted aggregate mark of 40% is required to pass the module|
|Biomedical Science BSc (Hons)||2||Optional||A weighted aggregate mark of 40% is required to pass the module|
|Microbiology (Medical) BSc (Hons)||2||Optional||A weighted aggregate mark of 40% is required to pass the module|
|Biomedicine with Data Science BSc (Hons)||2||Compulsory||A weighted aggregate mark of 40% is required to pass the module|
|Biomedicine with Electronic Engineering BSc (Hons)||2||Optional||A weighted aggregate mark of 40% is required to pass the module|
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2017/8 academic year.