For those who contemplate the day when we can say the workings of the brain are fully understood and solved, this article (Building a Virtual Microbe, Gene by Gene by Gene - New York Times) about the consortium trying to do the same for the simple bacterium E. Coli is humbling.
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Have You Heard? Gossip Turns Out to Serve a Purpose - New York Times
From the article:
Gossip not only helps clarify and enforce the rules that keep people working well together, studies suggest, but it circulates crucial information about the behavior of others that cannot be published in an office manual. As often as it sullies reputations, psychologists say, gossip offers a foothold for newcomers in a group and a safety net for group members who feel in danger of falling out.
Avida is an open-source software laboratory, which uses evolving computer programs to study evolution. Here’s an interesting article called Testing Darwin from Discover magazine, which is about interviews with the Digital Evolution Laboratory at Michigan State University (which develops Avida).
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“The researchers…have studied transcription factors, the signalling molecules inside cells that activate or deactivate genes. They found that the strength of the signal is less important than the dynamic frequency pattern that is used.
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‘The timing of the repeating signal is essential for its interpretation. It seems that cells may read the oscillations in level of transcription factors in a similar way to Morse code.’
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Links:
almost offtopic, but neat; here’s a course on “DNA and Molecular Computation”, with a list of papers such as “Chemical Kinetics is Turing Universal”. I didn’t know so much work had been done on this stuff already! cool!
http://www.cs.caltech.edu/courses/cns288/
(btw, if you’re interested in reading one of the actual articles, here’s Chemical implementation of finite-state machines, which goes into more (computational) detail than “Chemical Kinetics is Turing Universal” actually does).
interesting research summaries of recent research in neuromorphic engineering: here’s a link to the newsletter.
Today, I attended an interesting CBA talk on the MIT Registry of Standard Biological Parts. The basic premise is that molecular biology has elucidated enough about particular genes, proteins, promoters, etc. that we can start to make an inventory of these various “parts”. More than just a database, the Registry promotes the idea of reliable engineering with combinable components (think resistors and capacitors put together into larger circuits). This all sounds very ambitious and a bit idealized (especially if you’re a biologist) until you realize that they’ve actually done this already and built some neat, complex things (like a synchronized oscillator and a bullseye).
Though not directly related to neuroscience, this combination of biology and computation is very interesting. I encourage all of you to check out parts.mit.edu. It looks like they’re trying to make it as open source as possible, too.
For discussion: Is this the right approach to engineering with biology? What can be made more easily with biological substrates than with silicon?
This week’s Science has a nice introductory article (but with some mathematical detail) on using probabilistic graphical models to model cellular networks. Even for those of you who already know the formalisms (Bayesian networks, HMMs, etc.), you might find the recent biological applications discussed interesting.
Also, there are several other mathematical biology articles in the issue, including a review on evolutionary game theory.
Plants use distributed computation to decide how to open and close their stomata in order to take in as much CO2 as possible while losing the least amount of water.
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