Love the Bug!
I had lunch today with the four inaugural students of the joint CS+MBA program at Stanford. When they asked me what I would study if I was with them on campus now, I thought of the textbook that Prof. Drew Endy recently gave me on the design principles of biological circuits.
There is a fascinating section describing how the transcription networks of E.Coli (the common bacteria or “bug” in your gut) robustly build electric flagella motors on demand, and a navigation system that senses food gradients across distances larger than the bacteria itself. It then moves at 30 body lengths per second.
It’s an interesting case study in gene transcription networks. When E.Coli is bathed in nutrients, it focuses energy on cell division (growth) and not movement. With a lack of nutrients, a genetic trigger induces the manufacture of several helical propellers (flagella) to enable it to swim to a better life.
In this diagram, you see the 45nm wide, 100 Hz proton-pump rotary motor, as it is assembled in stages from 30 different proteins (in the text labels above). The motor and flagellum tail are hollow allowing each protein to self-assemble in sequence as they move down the assembly line straw.
I’ll show some of the information networks in the comments below.
The author proposes convergent evolution across many information networks, from genes to protein kinase cascades, to neurons.
The premise that I wrestle with is his claim that these networks are readily understandable. Working from the bottom up, and from the incredibly sparse networks and topologies, I can see why he’s excited. But I wonder if this scales. Jumping to neuronal circuits, the easy modularity is a bit more elusive, and I wonder if the simplified networks of parasitic organisms are a simple tier in the hierarchy of abstractions, just a few steps more complex than codon encoding and epigenetics. Perhaps evolved information networks embed much more accumulated computational complexity and offer fewer pattern matches to our engineered artifacts.
Diagrams from Uri Alon’s textbook on Biological Circuits: http://www.amazon.com/Introduction-Systems-Biology-Mathematical-Computational/dp/1584886420/ref=sr_1_1?ie=UTF8&s=books&qid=1247766134&sr=8-1
Cool stuff from Drew Endy, like digital memory registers embedded in our DNA https://www.flickr.com/photos/jurvetson/3344977501/ Why would researchers want to do this? This could be used to count cell divisions to trace the embryonic development of an organism cell-by-cell. Or more radically, imagine if every cell has a unique ID, clocking at each cell division. Consider the brain. Imagine if that code could express a coded RNA that would migrate to the synapse, where it could bundle with other RNA from connecting neurons. In one destructive readout, you could shotgun sequence all of those RNA bundles and derive the full connectome of the brain in one step.
It’s a very exciting time to be learning anew.
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