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Computational biology

Computational biology is just one of a number of terms used to describe work at the computer science/life science interface; other terms used include bioinformatics, biocomputing, biohealth informatics and systems biology. Here we briefly outline the skills required for work in this exciting interdisciplinary area, and then provide examples of a few current research projects at this interface. If you would like to more about our courses for professional development in biology/computer science, please choose a tab above to link to course information. There are more examples of research projects on the projects and the publications pages.

While different projects will require different sets of specific skills, a successful project requires people with the core skills that will enable them to work together as a team. People with a background in the biosciences will need to develop skills in computer science, and computer scientists will need to understand biological data.

Biological data
The high-throughput methods are producing huge amounts of data on the genome, the transcriptome, the proteome and the metabolome. Computer scientists need to understand how life scientists describe their data. Life scientists need to understand how data should be formatted for efficient processing, and also the importance of the capture of metadata.
Understanding the basics of unix
The amount of data being produced, particularly by the high throughput sequencers, means that efficient file management is critical.
Computer programming
While not all life scientists need to become proficient programmers, an understanding of programming concepts will make it much easier for a life scientist to work in an interdisciplinary project. Proficiency in one language will lower the barrier to proficiency in another programming languages.

Applications of bioinformatics

The projects described below give just a few examples of current work in computational biology.

African Bovine Sleeping Sickness

The University of Manchester’s Bioinformatics research team has developed a groundbreaking work-flow system that is helping scientists worldwide develop a cure for African Bovine Sleeping Sickness.

The team, who are based in the School of Computer Science, have created an online environment called Taverna that lets scientists share their research findings.

The work-flow system allows scientists in different locations to communicate their research with one another, meaning that they can work collaboratively, and that previous research findings can gain new value.

As a result, significant breakthroughs are being made to combat African trypanosomiasis, also known as ‘sleeping sickness’, which causes livestock to develop flu-like symptoms.

The disease is spread in the saliva of flies that carry parasites, which then breed in the blood of contaminated cattle. This causes them to develop symptoms including fever, lethargy and joint pain, often resulting in death during sleep.

The sociological implications for communities who depend on agriculture to survive can be devastating, but some cattle have a resistance to the disease.

Andrew Brass, Professor of Bioinformatics from the School of Computer Science, said: “Some African cattle have a unique resistance. We are interested in categorising that resistance.

“The work-flow system significantly aids us in recording research with the cattle on the ground. Previously, during a drought lions could actually eat our research.

“The work-flow system is also helping us in hypothesis generation. We have a huge information management system, that’s where Taverna is important.

“In the past it was easy to be biased. You tended to look where you expected to find results.  With the work-flow system in place we can be agnostic in hypothesis generation.

“We can now look more widely, and in doing that we have spotted things we might otherwise have missed.”

Network biology

As more genome, transcriptome, proteome and interaction data flows from the wet labs, computational biologists are seeking new ways to integrate and mine the data. Biological networks provide important new frameworks within which we can make progress in analysing and visualising the data.

A current theme is the analysis of cancer data, to see how transcription factor networks change with different types of cancer.

There are links to previous projects in network biology from the projects page.

Distance learning in computational biology Courses in computational biology