February saw the publication of the initial sequencing and analysis of the complete human genome - our so-called "book of life". Just as importantly, similar maps of other organisms, both plant and animal, have also been completed. But this is just the first step of our journey to truly understand ourselves and other organisms in a biological sense. This raw data is useless without the data-mining and analysis software to enable the creation of advanced therapeutic drugs, hardier and more bountiful strains of wheat and barley etc. The people who sequenced the genome are like Christopher Columbus rushing back to the old world to tell all about the new - the real work is just beginning, and using computers will be central to it.
Probably the most difficult aspect of writing on bioinformatics is deciding how much knowledge to assume the general reader has. But here's the least amount of molecular biology you will need to know in order to survive this article! It's known as the central dogma and states: "DNA makes RNA makes protein".
Proteins are the molecules that accomplish most of the functions of the living cell, everything from our sensory system, to structural support. If you know the DNA sequences, you can then figure out what protein(s) it codes for. Having that knowledge on tap gives researchers enormous insights into what makes us (and every other living thing) tick at the most basic level. This knowledge then paves the way for revolutionary changes to the way we live and manipulate our environment. Everything from new therapeutic agents that cure a disease rather than treating the symptoms, to strains of wheat that bear twice as many grains and aren't damaged by adverse weather conditions - the possibilities are endless.
Saying that you are a bioinformatician is about as descriptive as saying you're a software engineer. I define bioinformatics as the discipline of obtaining information about genomic or protein sequence data. And that covers a lot. Everything from advanced hardware architectures to faster searches of DNA databases, to user-friendly web front ends for those databases and right up to applying advanced Artificial Intelligence techniques to search, classify and reason about the gathered data.
And who are bioinformaticians (or bionauts as they are also known)? They are a hybrid breed of biologist and computer scientist, primarily interested in using computing techniques to advance their understanding of life at a submolecular level.
The work done so far is overshadowed only by the amount of work that remains to be done. Asking how far along we are is akin to stepping back in time to 1892 and asking what potential the internal combustion engine has.
What has essentially been completed is a map of the human genome (i.e., the DNA sequences). Now a lot of interest is switching to proteomics - finding out all the proteins encoded by the organism's DNA and what exactly they do. Building the map is necessary to move to this phase, and there is still a lot of work to be done there, but the real applications are in understanding how proteins are formed from DNA and using that knowledge to modify an organism's phenotype (see sidebar).
If you sit down and construct a map of the human genome, then you have one of the essential tools to understanding biological processes at a fundamental level. Once these complicated processes are well understood, researchers can move on to figure out how they can go wrong and how to rectify it.
Biotechnology will totally eclipse the Internet in terms of its impact on our daily lives. Films like Gattaca have tried to peer into the future and predict how, but really all we can do is guess at this stage. As we've seen, genetically-modified (GM) food is already available and offers huge potential to reduce the problems of starvation for poorer countries. On the other hand, there are deep concerns that GM foodstuffs have not been adequately tested and could pose a health risk.
On the medical front, there are the same huge potentials for advances, and concerns. On the one hand, this new-found knowledge brings the goal of eliminating disease one step closer, but also brings serious moral and ethical issues with it - issues that have not been ironed out yet.
The computing challenges in this field are immense. Getting to grips with the huge mountain of data spewing out from the various research groups is going to involve every area of computer science, from designing custom parallel processing hardware to employing Artificial Intelligence techniques to try and aid researchers in their work.
And that's only the tip of the iceberg. What many companies are looking at is moving from wet lab activities to "in silico" biology. In the long term, it is far more cost effective to work out a software model of say, metabolic pathways in a cell and how they react than to keep running large-scale experiments in the laboratory.
There are good precedents for the savings that can be accumulated here and the changes that these systems will bring to the industry. Two professions that have first-hand experience of this are architecture and engineering - Computer Aided Design (CAD) changed these fields fundamentally. The same goes for the typing pools that large companies used to have. Then along came the photocopier and word processor and gave one person the same productivity as one hundred. The large-scale application of computer modelling to biology will have the same effect. But the challenges here are immense. Designing a package to help a mechanical engineer to design suspended bridges is one thing - the engineer knows what the package should eventually look like, the life sciences researcher is looking for the computer scientist to assist in the work.
So the next time you read about the technical gurus of the e-commerce world or the gaming wizards behind Quake 3 or Tomb Raider, spare a thought for the bioinformaticians out there. They've been handed a 3.8 billion-year-old copy of the ultimate software package to analyse - without the manual. I'll bet you won't find too many job specs like that on monster.ie!
Next week the second part of this two-part series will examine how bioinformatics will evolve over the next five-10 years and the effects that will have on our society
Humphrey Sheils is CEO of Teogas Systems, www.teogas.com, a Dublin-based bioinfomatics company