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CMPC2B06 - INTRODUCTION TO COMPUTATIONAL BIOLOGY

Module Code:
CMPC2B06
Department:
Computing Sciences
Credit Value:
20
Level:
2
Organiser:
Dr. Steven Hayward
Computational biology is one of the great growth areas of both computing sciences and biology due to the development of robotic systems that are able churn out vast amounts of biological data. The challenge computational biologists' face involves turning this data into understanding. This data is often in the form of DNA, RNA or protein sequence. Although an introduction to the basics of molecular biology will be given, the module will mainly focus on the computational methods used in computational biology and bioinformatics. Topics will include sequence analysis, structural genomics and protein modelling, phylogenetics and evolution, and biological networks. Lecturers will highlight the relevance of the material to cutting-edge research.

There is no single text but sections of the following books may be helpful:

  • Alberts, B., Johnson, A., Walter,P.,Lewis,J., Raff, M., Roberts, K., Molecular Biology of the Cell, Garland Publishing Inc, US
  • D.Mount, Bioinformatics, Cold Spring Harbor Laboratory Press
  • J.Setubal, J.Meidanis, Introduction to computational molecular biology,  PWS Publishing Company
  • R.Durbin, S.Eddy, A.Krogh, G.Mitchison,  Biological sequence analysis, Cambridge University Press
  • C. Branden, J. Tooze, Introduction to Protein Structure, Garland

Submission:

Written coursework should be submitted by following the standard CMP practice. Students are advised to refer to the Guidelines and Hints on Written Work in CMP.

Deadlines:

If coursework is handed in after the deadline day or an agreed extension:
 

 

Work submitted Marks deducted
After 15:00 on the due date and before 15:00 on the day following the due date 10 marks
After 15:00 on the second day after the due date and before 15:00 on the third day after the due date 20 marks
After 15:00 on the third day after the due date and before 15:00 on the 20th day after the due date.  All the marks the work merits if submitted on time (ie no marks awarded) 
After 20 working days Work will not be marked and a mark of zero will be entered


Saturdays and Sundays will NOT be taken into account for the purposes of calculation of marks deducted.

All extension requests will be managed through the LTS Hub. A request for an extension to a deadline for the submission of work for assessment should be submitted by the student to the appropriate Learning and Teaching Service Hub, prior to the deadline, on a University Extension Request Form accompanied by appropriate evidence. Extension requests will be considered by the appropriate Learning and Teaching Service Manager in those instances where (a) acceptable extenuating circumstances exist and (b) the request is submitted before the deadline. All other cases will be considered by a Coursework Coordinator in CMP.

For more details, including how to apply for an extension due to extenuating circumstances download Submission for Work Assessment (PDF, 39KB)
 

Plagiarism:

Plagiarism is the copying or close paraphrasing of published or unpublished work, including    the work of another student; without due acknowledgement. Plagiarism is regarded a serious offence by the University, and all cases will be investigated. Possible consequences of plagiarism include deduction of marks and disciplinary action, as detailed by UEA's Policy on Plagiarism and Collusion.


Module Objectives

  • To introduce the basics of molecular biology
  • To develop an understanding of the fundamental methods of computational biology
  • To introduce the dynamic programming algorithm
  • To give an overview of the new emerging areas of research in computational biology

Transferable Skills

  • Algorithmic thinking
  • Systematic approach to the solution of complex problems

Learning Outcomes

On completion of this module students should:

  • Have a understanding of the basic concepts of genetics and molecular biology
  • Have an understanding of the dynamic programming algorithm for sequence alignment
  • Understand methods for multiple sequence alignment
  • Understand techniques used in the prediction of RNA structure
  • Understand the basic concepts and algorithms of phylogenetics
  • Grasp the basic concepts used to characterise, compare and model protein structures

Lectures 33 hours + labs

  • Introduction to the basic concepts of molecular biology and genetics, and the basic algorithms of sequence alignment. This will be followed by an introduction to protein structure and the computational methods used to compare and characterise them.
  • RNA is a molecule very similar to DNA except that it is single stranded. For this reason, it can fold up onto itself, resulting in interesting structures that have various functions in the cell.
  • Although 3-dimensional, these structures have a 2-dimensional backbone, or "secondary structure" that can be predicted using techniques in dynamic programming. We will look at some simple algorithms for predicting RNA secondary structures and their applications.
  • Phylogenetics is the reconstruction and analysis of trees and networks to describe and understand the evolution of species, populations and individuals. It is widely used in molecular biology and other areas of classification (such as linguistics), especially in trying to understand the Tree of Life. The basic underlying data structure in phylogenetics is a leaf-labelled tree. We will look at properties of these trees, and some algorithms for their construction.
  • How do you get from a single cell to a fully grown human (or chicken or tree or bacterium etc)?  In this section we will explore the role of genes in embryo development.  How can gene expression confer information about a cells position within a developing embryo and cause cells to adopt different fates depending upon where they are located?  What mechanisms exist to make this a reliable process?

Examination with Coursework or Project