Talk:Biological networks

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 Definition Networks having no human designer, having emerged from nature by organic evolutionary processes, its foundational system a biological cell, the cell a computationally-enabled information-processing bio-computer, designed basically to live and reproduce itself, self-assembling and self-organizing, autonomous, capable of cooperating with other cells to generate multicellular structures that can intelligently design other networks. [d] [e]

Create start of new article on biological networks

Create start of new article: Biological networks. Anthony.Sebastian 04:26, 12 January 2010 (UTC)

Interesting topic

You've actually touched on one of my areas of interest. As you know, I've spent many years working with computers in medicine, but, especially in security and reliability, I've had personal interests in applying biomedical concepts to computer networks.

When the first Internet worm propagated in (umm) 1987?, I watched a lot of very smart people running around reinventing a number of concepts of epidemiology. At one point, I asked one of the best security people around if he had considered the work of John Snow, and got the response of "who?"

If you look at Virtual Router Redundancy Protocol, one familiar with both can imagine the backup router acting as an AV node backing up the SA node. I've applied epidemiological contact tracing principles to try to look for the patterns characteristic of secondary infection, of multiple infection sources, etc., but gotten relatively little interest. A few network security engineers think of "quarantine", most commonly citing SARS, but don't go into anything like the hierarchy of immunologic responses. Yet, I see parallels in the way we deal initially with distributed denial of service and a nonspecific macrophage response, while we divert the suspicious flow to analysis and start to develop the equivalent of immunoglobulins (e.g., blackhole routes). Too bad I can't find anyone interested in sponsoring an interdisciplinary team in this area. --Howard C. Berkowitz 05:09, 12 January 2010 (UTC)

Wow! "...applying biomedical concepts to computer networks..." If you could give every computer on the network a sophisticated effective immune system, with Darwinian evolution built in, you'd get a ticket to Stockholm.
I started Biological networks while reading Dennis Bray's Wetware: A Computer in Every Cell. I'd played with bionets in working on Systems biology. There I cited:
Barabási AL (2002) Linked: The New Science of Networks. Cambridge, Mass: Perseus Pub. ISBN 0-7382-0667-9
Watts DJ (2007) A twenty-first century science. Nature 445:489
Alon U (2003) Biological networks: the tinkerer as an engineer. Science 301:1866-7 PMID 14512615
Alon U (2007) Simplicity in biology. Nature 446:497
Prill RJ et al.(2004) Dynamic properties of network motifs contribute to biological network organization. PLoS Biol 3: e343
Sporns O, Kotter R (2004) Motifs in brain networks. PLoS Biol 2: e369
Alon U (2007) An Introduction to Systems Biology: Design Principles of Biological Circuits. Boca Raton: Chapman and Hall/CRC
I also wrote about bionets in Life and further in mLife/Draft. Seemed like biological networks merited its own article. Anthony.Sebastian 03:00, 13 January 2010 (UTC)

The line blurs

As man-machine interfaces get more intuitive, as well as machine-machine, it gets interesting in drawing the line. If one looks, for example, of mobile ad hoc networking, I'm at least vaguely reminded of a local inflammatory/immune response, with automatic failover of dead or migrated cells. It's one thing to think of telescopes, radars, and other sensors that increase range, but the soldier-level local area network, as in the intra-squad radio, is what I might call "connectionist" evolution.

To turn it around, it's easier to turn off an electronic network than a biological one. I can see evolutionary reasons for the autonomic response to pain that causes prostaglandin release and stiffness, but I'm not sure if that's as adaptive as when we were in caves. A lot of modern electronic networking is filtering, removing artifacts, and eventually majority vendor logic.

Again this may be getting too far afield, but consider the analogies between brain fusion of multiple sources, Blue Force Tracker, and the common operational picture. There are classes of instrumentation such as network intrusion detection systems which, arguably, send somewhat processed antigens for amplification. Howard C. Berkowitz 05:22, 14 January 2010 (UTC)

Applying biomedical concepts to computer networks

Howard, section title your words. Made me think of 'genetic algorithms', where the computer's solution to a given task evolves from random candidate input algorithms, fitness evaluations, selection of fittest, copying with 'mutations', repeat process until you get the algorithm that performs the task (e.g., defrags a hard drive; maximizes broadband use efficiency).

Also, widespread use of neural networks. Anthony.Sebastian 16:14, 15 January 2010 (UTC)


Cardiac output and Transmission Control Protocol

Cardiac output = rate * stroke volume * peripheral resistance

Throughput = window size * transmission rate * error/loss rate

Autonomous computing/virtualization

See, in part, cloud computing. Very strong trend these days to run applications and operating system on virtual machine images. Just as a real chip doesn't care about its running LINUX or WINDOWS, does a B-lymphocyte really care what immunoglobulins it's making, as long as it has the surface receptors?

Distributed denial of service/inflammatory-immune response

The hardest hacks to recognize are the botnets that throw small attacks at many, many places (see also swarming (military). What analogies for cytokine accumulation triggering major hormonal release? Howard C. Berkowitz 19:19, 15 January 2010 (UTC)

Presynaptic reuptake vs. degenerative feedback from receivers

In multicast networks, such as one-to-many television distribution, there is no requirement for explicit source-destination connections, but the destinations can send reception quality messages to the source such that the source adjusts its parameters of transmission. --Howard C. Berkowitz 13:44, 16 January 2010 (UTC)

"Biological networks differ from such man-made networks"

Is this always true, given man-made networks (see mobile ad hoc networking, automatic identification system, TACAN, Blue Force Tracker) are increasingly self-organizing so that even a human designer does not have prior knowledge of who will use the network?

Both human and biological networks still can have "anonymous" capacity limits. A biological network might be limited by the number of cellular receptors, while the human network uses connection admission control to limit the number of new participants in the network -- either an explicit connection process, an inability to find a clear time slot as in AIS, or excessive exponential backoff in Ethernet. --Howard C. Berkowitz 13:40, 16 January 2010 (UTC)

Howard, very technically articulated comments, from my perspective.
When I wrote, "Biological networks differ from such man-made networks", I specified such to refer to the network characterization in the preceding paragraph, reflecting my desire to convey to the average reader the characteristics of many of the types of man-made networks the average reader might recognize as networks, such as the World Wide Web. I think I understand the gist of your query, though.
What significance do you attach to having, or not having, "prior knowledge of who will use the network"? I ask because you seem to relate not having "prior knowledge of who will use the network" with networks becoming "increasingly self-organizing", as if becoming "increasingly self-organizing" characterized biological networks. Do you equate "who will use the [man-made] network" with "what might signal a biological network"? Perhaps I will understand better when I study the links you gave.
In any case, I will revisit the lede in light of your remarks. Anthony.Sebastian 22:52, 16 January 2010 (UTC)