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That isn't very realistic. Data collection is still the major driving force in life science discovery. Good IT infrastructure enables large screens, but only in conjuction with robotics, microfluidics, sweat, and a lot of disposable plastic, including petri dishes.

Modeling biological systems is a difficult task. As Hiroaki Kitano points out, "[in biological systems] large numbers of functionally diverse, and frequently multifunctional, sets of elements interact selectively and nonlinearly to produce coherant rather than complex behaviours". There are still a huge number of elements and relationships to discover.

Get open source bioinformatics tools from:
bioinformatics.org
bioperl.org
biojava.org
and even www.cvbig.org for a talk on bioinformatics with PHP/Ming

"Perhaps he [the biologist] has a mathematition [your spelling] there somewhere too to help out with the algorithms, but in the end he does no 'real' work himself except to come up with the idea."

So in your opinion, coming up with the idea isn't real work? I could not disagree more. Biology domain knowlege drives bioinformatics. Your project may be "specific", but I'll bet it is specific only because of a comprehensive understanding of the problem by at least one biologist.

As far as using CS expertise, that depends on the problem and the applicable skills of the biologist. I have a doctorate in biology, but no formal training in CS and yet I have been able to write my own code and/or modify open source tools which run in a high throughput, clustered linux environment constructed by ... me. I have also developed (an admittedly shallow, but growing) working knowlege of classic algorithms and associated data structures. Does my implementation constitute real work?

Do I value CS, math, and statistics professionals? Of course! I rely on open source tools and the linux movement as much as the biology community. I don't pretend that I invented domain knowlege in biology, computer science, or math. I suppose my best "skill" is to put my pride aside and ask questions, read, and learn from multiple coommunities. Depending on the community, I ask as a newbie, novice, or expert. I believe in the value of sharing to such an extent that I started a bioinformatics interest group.

I say this as a bioinformatician working in industry who spent two winters of his career stuck in a remote part of Switzerland with nothing but a laptop and an early release of slackware. ;-)

My research team uses a high throughput mouse model system to identify mouse oncogenes and their human orthologues. We are fortunate enough to have access to both the public and Celera mouse/human genomes and have made detailed gene structure comparisons between hundreds of mouse and human genes and synteny comparisons between thousands of genes. Gene regulation and alternative splicing theories aside (both valid), there are certainly enough structural difference between mouse and human genes to obviate the need to invoke "missing genes". A better argument would be to focus on the known limitations of gene finding algorithms and the imperfections in EST and genome assemblies.

Are there missing genes in the public and private databases? Certainly. Until there is a full length clone, each gene call is a hypothesis. Ongoing curation will continue to sort out both false negatives and false positives.

CVBIG!

I recently gave a presentation for the Central Valley Bioinformatics Users Group (www.cvbig.org) titled "Clustering BLAST with Linux, Perl, PHP, and Postgres". Some of the slides are available here. I say some, because I just started the process of open sourcing my work. Management at Sagres Discovery (my employer) has been very receptive to releasing my code. And why not? My involvement in the open source community (and CVBIG in particular) taps the company into large reservoir of skills and experience (not to mention potential employees). Besides, open sourcing the tools does not mean open sourcing discoveries. Bioinformatics will continue to benefit from the open source movement. Fortunately, many in academia and industry recognize its value.

I recently gave a presentation for the Central Valley Bioinformatics Users Group (www.cvbig.org) titled "Clustering BLAST with Linux, Perl, PHP, and Postgres". Some of the slides are available here. I say some, because I just started the process of open sourcing my work. Management at Sagres Discovery (my employer) has been very receptive to releasing my code. And why not? My involvement in the open source community (and CVBIG in particular) taps the company into large reservoir of skills and experience (not to mention potential employees). Besides, open sourcing the tools does not mean open sourcing discoveries. Bioinformatics will continue to benefit from the open source movement. Fortunately, many in academia and industry recognize its value.