E-mail: natasha [at] imperial [dot] ac [dot] uk

Office: 407 C Huxley

Lecture time and location: *Fridays, 9–11h in LT 308, and 14-16h in LT 145*

Office hours: *Fridays after the afternoon class, 407 C Huxley*

The course will cover basic biological concepts related to the issues of sequence analysis and alignment, microarray data analysis, biological networks, fundamental graph theoretic algorithms, computational complexity and challenges in network analysis, existing post-genomic approaches for analyzing, modeling, and comparing biological networks, and applications of these approaches to understanding biological function, disease, and evolution. For more details, see the course syllabus below.

Practical work will be part of the coursework assignment and will include individual work on either: (a) development of code for a bioinformatics algorithm, or (b) data analysis. You will choose which option you will do. Completing only one of these two options will result in full marks. If you do both, both will be marked and you will be given the higher grade of the two.

For information about course goals, topics, organization, grading scheme, textbooks and readings, etc., please see the course introduction.

- Lecture 1: Sequencing and Genomics (Dr. Rice)
- Lecture 2: Sequence Analysis (Dr. Rice).
- Lecture 3: Sequence Analysis (Dr. Rice). (in pdf)
- Tutorial 1 questions; Tutorial 1 answers.
- Lecture 4: Functional Genomics and Microarray Analysis (Dr. Rice). (in pdf)
- Tutorial 2 questions; Tutorial 2 answers.
- Lecture 5: Functional Genomics and Microarray Analysis continued (Dr. Rice). (in pdf)
- Tutorial 3 questions; Tutorial 3 answers.
- Tutorial 4a: NCBI, Uniprot, BLAST; Tutorial 4b: Cytoscape.
- Tutorial 5a questions; Tutorial 5a answers. Tutorial 5b: GraphCrunch Tutorial.
- Tutorial 6 questions; Tutorial 6 answers.
- Tutorial 7 questions and answers.
- Tutorial 8: Coursework model answers.

- Lecture 1: Course overview and Introduction to Biology. (in pdf)
- Lectures 2 and 3: Introduction to graph theory. (in pdf)
- (OPTIONAL: Review of more graph algorithms; after slide 11 is optional reading and will not be assessed.)
- Lectures 4 and 5: Introduction to biological networks. (in pdf)
- Lectures 6, 7 and 8: Network properties. (in pdf)
- Coursework – given out on Feb 14, due on Thursday, March 6, 2014 by 2pm.
- Lectures 9 and 10: Network properties and models. (in pdf)
- Lectures 11 and 12: Network comparisons and alignments. (in pdf)
- Lectures 13 and 14: Protein 3D structure (Dr. Noel Malod-Dognin)
- Lecture 15: Software tools for network analysis and modeling; Course review.
- Optional Lectures 16 and 17: Graph clustering; interplay of network topology and biological function. (in pdf)

- Survey: “Graph-theoretic approaches for studying biological networks“, Tijana Milenkovic, PhD advancement, Univeristy of California, Irvine, 2008.
- Natasa Przulj and Tijana Milenkovic, “Computational Methods for Analyzing and Modeling Biological Networks“, a chapter in “
**Biological Data Mining**”, edited by Jake Chen and Stefano Lonardi, Chapman & Hall/CRC; 1 edition, September 1, 2009. - Sequence alignment (pdf, doc)

- Coursework (Given out on February 14, 2014. Due on March 6, 2014 by 2pm.)
- Sample solution to coursework.
- Exam (Date to be determined.)