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You make good points but swing the pendulum too far the other way. No, a bunch of smart computer people won't solve problems that have flummoxed biologists for

by Dn_Ab 14y ago

You make good points but swing the pendulum too far the other way. No, a bunch of smart computer people won't solve problems that have flummoxed biologists for long. But a bunch of smart computer, biology, math, physics and chemistry people will. You are right in that ignoring bench work + domain knowledge and thinking one can come in write a bunch of programs and solve cancer is not only an insult but foolish. Not all the problems have flummoxed biologists for long. The problem of sequencing improving faster than computers is a new one. And one where computer people can help. In fact modern sequencing techniques rely on computers to be viable. Modelling phenotype from gene sequences and predicting possible drug interaction will become real soon. Biology is complex yes but some of it is complexity introduced in an attempt to avoid difficult but simplifying concepts like non linear PDEs. Funny that you mention electronics. See what Yuri Lazebnik has to say about this. http://protein.bio.msu.ru/biokhimiya/contents/v69/pdf/bcm_1403.pdf http://protein.bio.msu.ru/biokhimiya/contents/v69/pdf/bcm_14... Computer people can help with tools, modelling, analysis and prediction.

eshvk 14y ago
I am sorry if I came off as disagreeing with your main points because I actually don't. Just to say a couple of things: At least from looking at such cross-functional bio/c.s. teams work here in Grad. school, I have seen two types: 1. Where the bio (in this case wildlife) people have extremely limited ideas about what happens during a simulation or modeling experiment, this leads to ridiculous attempts to say develop multi-agent models for hyena cooperation which if you read the paper make so many ridiculous assumptions that it doesn't really make sure what if any utility can be obtained as inferences from those models. 2. The other much more rarer situation where the bio people have a keen understanding of the basics of both C.S. and Math and are well aware of the limitations of what they are getting into. Such collaborations usually turn into using a C.S./Math (usually grad. student) person in order to delve deep into the technical specifics which the bio guy could understand with some help. Yuri's article is interesting and while I do agree that some cross pollination of ideas (such as is happening in systems biology) is useful. I am not so sure I am that enthusiastic as he is about drawing so much hope from the engineering analogy (because there is an element of silver bulletism to it:).

by Dn_Ab 14y ago

eshvk 14y ago

I am sorry if I came off as disagreeing with your main points because I actually don't. Just to say a couple of things: At least from looking at such cross-functional bio/c.s. teams work here in Grad. school, I have seen two types: 1. Where the bio (in this case wildlife) people have extremely limited ideas about what happens during a simulation or modeling experiment, this leads to ridiculous attempts to say develop multi-agent models for hyena cooperation which if you read the paper make so many ridiculous assumptions that it doesn't really make sure what if any utility can be obtained as inferences from those models. 2. The other much more rarer situation where the bio people have a keen understanding of the basics of both C.S. and Math and are well aware of the limitations of what they are getting into. Such collaborations usually turn into using a C.S./Math (usually grad. student) person in order to delve deep into the technical specifics which the bio guy could understand with some help. Yuri's article is interesting and while I do agree that some cross pollination of ideas (such as is happening in systems biology) is useful. I am not so sure I am that enthusiastic as he is about drawing so much hope from the engineering analogy (because there is an element of silver bulletism to it:).