Drug Discovery Engines

[A princípio este espaço será, até certo ponto, bílingue, com alguns poucos textos em inglês, em geral construídos no contexto de minha insistente participação no Everything2; a partir de agora postarei alguns deles. Fica o aviso de que, se meu estilo já pode ser confuso na língua pátria, a qualidade da prosa nesse segundo idioma vai de ruim a sofrível.]

Early attempts to recreate (micro-)life in silicohave begun around 15 years ago. A useful model would suggest ahypothesis that forces the model builder to do an experiment. Take theearly effort of Drew Endy of University of California at Berkeley andJohn Yin of University of Wisconsin-Madison as a landmark. Their computational model incorporates everything we know about the way the T7 bacteriophage virus infects the infamous Escherichia coli.The seemingly impressive simulation includes how all T7’s 56 genestranslates into 59 proteins, how those subverts the host cell, and evenhow the viruses would evolve resistance to various RNA-based drugs.Despite including measures from near two decades of experiments, earlymodels fail miserably in that there are still a huge number of degreesof freedom so that they can be tweaked to produce almost any behavior.These models are then just sketchy caricatures based on the traditionalgene => RNA => protein basic sequence.

The billions of dollars initially invested in technologies such assequencing, combinatorial chemistry and robotics haven’t paid off ashoped because of the naive idea that you can redirect the cell in adesired way just by sending in a drug that inhibits only one protein.Indeed, you could draw a map of all the components of the simplestsingle-celled microorganism and put all the connecting arrows and stillhave absolutely no ability to predict anything.

Since around 10 years ago, some more mathematically-mindedbiologists have been putting forth an effort to use computersimulations to search for some unifying principles that could order thefacts, rather than search for a pretentious single model. I.e., apurely reductionist, top-down approach to simulating cells.For instance, from the currently more sophisticated cell simulationsone can argue that robustness is a good candidate to be one of theseconjectured emerging universal properties. Knowingly, to survive andprosper (i.e., (self-)reproduce), cells must have backup systems andbiological networks that tolerate interferences such as dramatictemperature swings, food supply changes, and toxic chemicals assaults.In this all-important context, virtual experiments run with thejapanese E-Cell model, a single-celled “microbe” mostly built from genes borrowed from Mycoplasma genitalium– the smallest genome yet discovered in a self-reproducing life-form -indicate that even with a drastic change in the magnitudes of variousgenes expressions a cell’s behavior can remain practically unchanged.Experiments in which the researcher adjust virtual cells to reflect theactivity of a specific drug are revealing that the resulting dramaticchanges in cellular state can lead to a very little efficacy on theunderlying disease condition.

It has been vividly argued that what most strongly affects how acell behaves in response to a drug or disease is not any manipulationof a particular gene or protein, but how all the genes and proteinsinteract dynamically – i.e., the story emerges from the links, which shift over time.As we know hardly anything about most biochemical systems, somemodellers are taking an engineering approach by figuring out the basiclaws the cell’s behavior must obey. Perhaps the most famous example ofthis approach, pioneered by computer science demigod John R. Koza,is that of a set of programs genetically evolving to match entireactual reaction networks. As measured data on how cells processchemicals over time are piling up, this evolutionary approach could oneday be used even to deduce the convoluted paths by which cells turnfood into energy, growth and waste.

Other modellers are mathematically reconstructing biochemicalnetworks from first-principles, subjecting them to required mass,electrical and thermodynamical constraints, and then predicting optimalsolutions within the remaining (physically viable) ones. For example, aresearch group at the University of California at San Diego has predicted that Escherichia coli is optimized for growth, not energy production.

The observation that many biochemical problems most likely have anoptimal answer has led some modellers to predict a near future withquantitative models of cell function, organ function and eventuallywhole-animal function, perfect drug discovery engines.


Deixe um comentário

Preencha os seus dados abaixo ou clique em um ícone para log in:

Logotipo do WordPress.com

Você está comentando utilizando sua conta WordPress.com. Sair / Alterar )

Imagem do Twitter

Você está comentando utilizando sua conta Twitter. Sair / Alterar )

Foto do Facebook

Você está comentando utilizando sua conta Facebook. Sair / Alterar )

Foto do Google+

Você está comentando utilizando sua conta Google+. Sair / Alterar )

Conectando a %s