Functional Symmetry

I am intrigued with conservation and symmetry and general optimization properties as they pertain to biological systems. Well, perhaps simplicity also comes into play, in considerations of general aesthetics.

But there’s something profoundly beautiful about observations of the old biology tenet, “form follows function.” In an investigation of ion channels in biological membranes, particularly of neurons, one often notices a few things. First, a cell’s membrane is rather impermeable to ionic “stuff.” So in order for transport of ions, for instance, across a membrane, there have to be holes in the membrane.

As it turns out, there are many types of holes, including these ion channels, which are often selective for a particular ion. By and large, only that ion can go through its channel, and there are various ways of regulating this. Within the inside of the channels, called the pore (center of figure above), there are at least two factors involved in selectivity of ions – on one hand there is the physical size of the pore, and there is also what’s called a selectivity filter. A selectivity filter is essentially a binding site for the ion that enables it to continue along its pathway from one side of the membrane to the other. (There’s generally some sort of weak hydrogen-type bonding that occurs at that site, I think.)

So consider this – let’s say the ion, say a K⁺, is moving from the inside to the outside of the cell across a membrane. The selectivity would be such that you’d only want K⁺ leaving through the channel and not Ca²⁺ or Na⁺. But in the reverse direction – from out to in – the selectivity ought to be equally efficient.

This implies that the observed transverse symmetry in the protein channel is necessary for the bidirectional selectivity either by size of the ion or using a chemical segment as a filter! Quite fascinating, if you ask me!

“Magnetic Resonance Spectroscopy Identifies Neural Progenitor Cells in the Live Human Brain”

Using widely available techniques, Manganas et al. have been able to characterize a marker to quantify the presence of neural progenitor cells (NPCs) in rodent and human brains, in vivo. They are actually utilizing much of the same technique that takes two distinct functional forms; at the end of the day it’s all nuclear magnetic resonance imaging, but the commonly used NMR in organic chemistry is employed for characterizing chemicals, while the more familiar (to the neurosciences) magnetic resonance imaging (MRI) is utilized for visualization of the brains in vivo. According to Manganas et al., “Our findings thus open the possibility of investigating the role of NPCs and neurogenesis in a wide variety of human brain disorders.”

In regard to the plasticity of adult brains, though known for several years now, it often takes a long time for dogma in sciences to change. It certainly does not occur on the scale of developmental processes, and it’s largely a coping mechanism to compensate for various injuries. However, the evidence for dynamism within the adult brain is clear — it’s actually hard to imagine proper neural function within our daily lives without it, as every new “thing” we learn has got to have a neural correlate.

The article appears in this week’s issue of Science Magazine.

Arbitrary Version Numbering

So with all software, no matter how small, it seems like it’s a good idea to have version numbers. Mine are pretty arbitrary. I try and keep whole numbers as milestones. Like when the code is running in a more or less feature complete form, it’ll be a 1.0. There’s a lot of 1.0s running around.

The primary benefit of this is that, when you look back at your working directory, you’ll be able to have some semblance of the MESS of files that are lying around, all with the same names from copying and modifying files.

Another useful tip is revision numbers in the beginning comments. I can’t understate the importance of this! Otherwise you’ll go nuts later. A simple header like the following is so useful:

function fData = frequency(input1,input2)

% FDATA = FREQUENCY(INPUT1,INPUT2)

% will return the frequency of firing for

% a given input1 and input2 blah blah, etc.

%

% Version 0.9

% Rev. 2007-08-25

Simple and you’ll thank yourself in 6 months when you’re writing your paper and revisiting your code, trying to figure out what on earth you had done!

Another useful tool in determining file differences (UNIX/Linux/Mac folks) is diff. If you have two files for comparison that are the same name (!), then you can use diff to figure out line by line differences. The program is pretty smart and quite simple to use:

diff ~/origdir/frequency.m ~/someotherdir/frequency.m

will give a simple output to the screen with < and > lines marked to note file differences. < means that the line was included in the left file (in origdir) and missing in the right file, and vice versa with >.

Brainbow imaging

Admittedly, molecular biology reads somewhat like the Post-Modern Prometheus, complete with green glowing rabbits and the like. The power to manipulate genetics has been harnessed in many ways in the last couple of decades to give tremendous insight into the complex world of biology. Recently, the Lichtman lab at Harvard University published their results regarding yet another new technology that may just live up to the promise.

The method harnesses a well known Cre/lox strategy of genetic recombination with a clever alteration, which enables the system to naturally create a large variety of permutations that are manifested as distinct fluorescent colors. Thus each cell will express its own color and can be viewed much more distinctly in the absolute mess of cells that constitutes this organ.

Where modern cellular imaging methods, such as fluorescence or electron microscopy, and “ancient” (but still employed) techniques of Golgi silver staining and Nissl staining are often plagued by either problems of sparseness or completeness in their ability to help visualize neurons, by the very nature of this technique, cells can be followed and more or less uniquely identified.

The article can be found in the journal Nature.

Synfire chains, supersynapses, and STDP

An idea proposed in the early 80s called synfire chains proposes that self-assembling networks of neurons can form in which small clusters or groups of neurons communicate with each other, possibly in a sequential linear fashion, more than other neurons. This concept is somewhat interesting if it can play a role in explaining how small networks of neurons spontaneously choose to communicate selectively with each other and possibly form cortical ensembles.

In a paper entitled “Development of Neural Circuitry for Precise Temporal Sequences through Spontaneous Activity, Axon Remodeling, and Synaptic Plasticity” by Jun and Jin, they explore the formation of these synfire chains with a computational model that utilizes leaky integrate and fire neurons along with two activity dependent learning rules. The more well known of these rules is the Hebbian type spike timing dependent plasticity (STDP), a phenomenological model that takes into account the relative timing between pre- and postsynaptic pairs of spikes.

Additionally, employing various thresholds of synaptic potentiation, the second rule they employ caps the number and strength of axonal remodeling — that is to say that when their neurons make strong connections with certain synapses (so-called supersynapses), they prune away other, weaker connections. It is this additional contribution that allows their model to spontaneously exhibit the synfire chain like behavior.

The article appeared in Aug. 2007 online issue of PLoS One, which is a progressive online journal offering free access. You can grab the article at http://www.plosone.org/.