A biologia é muito mais fácil do que 2 + 2 = 4

sábado, março 20, 2010

Biology May Not Be So Complex After All, Physicist Finds

ScienceDaily (Mar. 19, 2010) — Centuries ago, scientists began reducing the physics of the universe into a few, key laws described by a handful of parameters. Such simple descriptions have remained elusive for complex biological systems -- until now.


Effects of parameter variation on escape time distribution for the gKPR process. (A) Mean completion time versus ¸ and Æ for L=8. (B) Coefficient of variation CV2gKPR versus ¸ and Æ for L=8. (C,D) the same for L=16. (Credit: Image courtesy of Emory University)

Emory biophysicist Ilya Nemenman has identified parameters for several biochemical networks that distill the entire behavior of these systems into simple equivalent dynamics. The discovery may hold the potential to streamline the development of drugs and diagnostic tools, by simplifying the research models.

The resulting paper, now available online, will be published in the March issue of Physical Biology.

"It appears that the details of the complexity of these biological systems don't matter, as long as some aggregate property, which we've calculated, remains the same," says Nemenman, associate professor of physics and biology. He conducted the analysis with Golan Bel and Brian Munsky of the Los Alamos National Laboratory.

The simplicity of the discovery makes it "a beautiful result," Nemenman says. "We hope that this theoretical finding will also have practical applications."

He cites the air molecules moving about his office: "All of the crazy interactions of these molecules hitting each other boils down to a simple behavior: An ideal gas law. You could take the painstaking route of studying the dynamics of every molecule, or you could simply measure the temperature, volume and pressure of the air in the room. The second method is clearly easier, and it gives you just as much information."
Nemenman wanted to find similar parameters for the incredibly complex dynamics of cellular networks, involving hundreds, or even thousands, of variables among different interacting molecules. Among the key questions: What determines which features in these networks are relevant? And if they have simple equivalent dynamics, did nature choose to make them so complex in order to fulfill a specific biological function? Or is the unnecessary complexity a "fossil record" of the evolutionary heritage?
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The simplicity of completion time distributions for common complex biochemical processesAuthor

Golan Bel 1,3, Brian Munsky 1,3 and Ilya Nemenman 2


Affiliations

1 Center for Nonlinear Studies and the Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA

2 Departments of Physics and Biology and Computational and Life Sciences Strategic Initiative, Emory University, Atlanta, GA 30322, USA

3 Contributed equally

E-mail

golanbel@gmail.com brian.munsky@gmail.com ilya.nemenman@emory.eduJournal

Physical Biology Create an alert RSS this journalIssue

Volume 7, Number 1Citation

Golan Bel et al 2010 Phys. Biol. 7 016003

doi: 10.1088/1478-3975/7/1/016003
Abstract

Biochemical processes typically involve huge numbers of individual reversible steps, each with its own dynamical rate constants. For example, kinetic proofreading processes rely upon numerous sequential reactions in order to guarantee the precise construction of specific macromolecules. In this work, we study the transient properties of such systems and fully characterize their first passage (completion) time distributions. In particular, we provide explicit expressions for the mean and the variance of the completion time for a kinetic proofreading process and computational analyses for more complicated biochemical systems. We find that, for a wide range of parameters, as the system size grows, the completion time behavior simplifies: it becomes either deterministic or exponentially distributed, with a very narrow transition between the two regimes. In both regimes, the dynamical complexity of the full system is trivial compared to its apparent structural complexity. Similar simplicity is likely to arise in the dynamics of many complex multistep biochemical processes. In particular, these findings suggest not only that one may not be able to understand individual elementary reactions from macroscopic observations, but also that such an understanding may be unnecessary.


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