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Combining genetics and computers to develop n -

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HideRobyn Williams: A few weeks ago I spoke to the president of the Royal Society of London Sir Paul Nurse, who gave us five big ideas in biology. The fifth was information and numbers, just what this week's PhD Sam Forster is crunching at Monash. Here's how:

Sam Forster: For most of us, computers are an integral part of our everyday lives. If you can define a question, a computer can usually provide you with an answer. Today medical research questions are occupying the processing time of some of the world's most powerful computers. This is where my PhD at Monash University comes in. I combine my training in computer science with my training in genetics to work in a field called bioinfomatics. That is, using computers to solve medical research problems.

We're working on the basic premise that to treat infection and disease we need to first understand how the normal healthy immune system works. If we can understand this condition it will give us insights into the mechanisms of how an infection occurs and improve our understandings of other immune related cellular processes such as cancers and autoimmune diseases.

To achieve this aim we need to understand how this response is controlled naturally and how it is a modified, both in the disease condition and through therapeutic intervention. This will allow us to develop new treatments and improve the quality of existing medicine.

To do this research we used cutting-edge technology called next-generation sequencing. Many people are familiar with sequencing from the human genome project. This was a worldwide collaboration completed in 2003 that took 13 years and cost approximately $3 billion. The biological equivalent of the Apollo Moon mission, this project succeeded in mapping a sequence of 3 billion As, Cs, Ts and Gs, the DNA code, that contained the instruction for how to make an individual person.

While this was a monumental task at the time, the technology has advanced so quickly that it is now possible to perform the same task in two weeks at the cost of approximately $1,000. To put this in perspective, if space travel had improved this quickly after the Apollo missions, it would now be possible to take your whole family for an afternoon on the Moon for under $100.

In my work we use this sequencing technology not to measure the genome but the expression of that genome, called the transcriptome. While the genome contains the instructions for all the ways a cell might respond, the transcriptome is the response to the environment that is actually occurring within the cell.

The response we're interested in measuring is the innate immune response. The innate immune response represents the body's first line of defence against infection. It works by a cell recognising a danger signal, then releasing a whole series of signalling molecules to warn the surrounding cells that this danger is imminent. One of the key signalling molecules associated with this process is called interferon. Interferon was discovered over 50 years ago and is used widely to treat diseases as diverse as chronic viral infections like hepatitis, multiple sclerosis and numerous cancers.

What we are trying to do is understand the complete response of a cell when interferon is present. This allows us to map the type of responses the interferon warning signal will generate and understand how this can protect the cell against infection and impact in cancers or autoimmune diseases. Using the sequencing technology to measure the genetic response of the cell allows us to examine how the cell responds when given interferon, but this is not a simple undertaking.

Within the string of 3 billion As, Ts, Cs, and Gs there are approximately 28,000 regions or genes that could be expressed within the cell. When an infection occurs, approximately 10% of these genes will be switched on or off in a carefully orchestrated, finely controlled but interdependent pattern. When we sequence the response, an experiment will normally result in more than 1 billion measurements of these genes. To give you an idea how much data is involved, if we were to print these results from just one experiment, we would end up with a pile of paper almost 4 kilometres high. Obviously very powerful computers are required to analyse this sort of data.

When we perform this analysis we can start to generate a picture of the complete response to interferon and understand how the subtleties of any particular component of the response is controlled. With this understanding we can then examine the disease condition and begin to predict which component has gone wrong. For example, by comparing the immune response that is actually occurring during an infection to the complete response that we know can occur, it is possible to identify the key component of the response that the infectious agent is manipulating. This manipulation gives us clues and provides insights into how the disease process is occurring. Through this understanding we can start to design treatments that specifically target the problem, thus minimising side-effects and improving treatment outcome. This combination of cutting-edge technology with powerful computational analysis represents a new era in medical research.

By applying this technology in my PhD project, we're just beginning to understand the innate immune system, infections and other disease processes at a level never before possible. This understanding will allow us to start to generate medicines for different conditions where we can replicate and improve the beneficial effects of current generation medicines or fundamentally alter our approaches to disease treatment, all this through the power of computational analysis.

Robyn Williams: PhD student Sam Forster at Monash University in Melbourne.