quinta-feira, 24 de março de 2011
Biologia computacional favorece mais qual teoria? Design Inteligente ou Design Não Inteligente?
HT/TC: Bradford Read more here/Leia mais aqui: Telic Thoughts
NOTA DESTE BLOGGER:
Qual referencial teórico é mais favorecido pela biologia computacional -- Design Inteligente ou Design Inteligente? Pelo andar da carruagem epistêmica, a teoria da evolução através da seleção natural de Darwin leva uma goleada da teoria do Design Inteligente.
Mãos à obra, oops, ao contexto de justificação teórica da biologia computacional. Quem foi mesmo que disse que o DNA é como um programa de software, mas muito mais complexo do que os até aqui inventados? Bill Gates!!! Gente, eu acho que vocês são telepatas.
E ainda têm a cara de pau de dizer que a TDI impede o avanço da ciência. Qual ciência caras-pálidas???
Published online 25 November 2009
Nature 462, 408-410 (2009)
Computational biology: Biological logic
An intuitive approach to computer modelling could reveal paths to discovery, finds Lucas Laursen.
Grabbing one of the three laptops in her office at Microsoft Research in Cambridge, UK, Jasmin Fisher flips open the lid and starts to describe how she and her collaborators used an approach from computer science to make a discovery in molecular biology.
Fisher glances across her desk to where her collaborator, Nir Piterman of Imperial College London, is watching restlessly. "I know you could do this faster," she says to Piterman, who is also her husband. "But you are a computer scientist and I am a biologist and we must be patient."
After a few moments, patience is rewarded: Fisher pulls up a screen of what looks like programming code. Pointing to a sequence of lines highlighted in red, she explains that it is a warning generated by software originally developed for finding flaws in microchip circuitry. In 2007, she, Piterman and their colleagues found a similar alert in a simulation they had devised for signalling pathways in the nematode worm Caenorhabditis elegans. Using that as a clue, they predicted and then experimentally verified the existence of a mutation that disrupts normal cell growth1.
'Executable biology', as Fisher calls what she's demonstrating, is an emerging approach to biological modelling that, its proponents say, could make simulations of cells and their components easier for researchers to build, understand and verify experimentally.
The screen full of code doesn't look especially intuitive to a non-programmer. But Fisher toggles to another window that shows the same C. elegans simulation expressed graphically. It now looks much more like the schematic diagrams of cell–cell interactions and cellular pathways that biologists often sketch on white boards, in notebooks or even on cocktail napkins. One big goal of executable biology is to make model-building as easy as sketching. Fisher explains that each piece of biological knowledge pictured on the screen, such as the fact that the binding of one protein complex to another is necessary to activate a certain signal, corresponds to a programming statement on the first screen. Likewise, the diagram as a whole — illustrating, say, a regulatory pathway — corresponds to a sequence of statements that collectively function as a computer simulation.
Ultimately, she says, this kind of software should develop to a point at which researchers can draw a hypothetical pathway or interaction on the screen in exactly the way they're already used to doing, and have the computer automatically convert their drawing into a working simulation. The results of that simulation would then show the researchers whether or not their hypothesis corresponds to actual cell behaviour, and perhaps — as happened in the 2007 work — make predictions that suggest fruitful new experiments.
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