Freelancing science

What you can read in here is a set of my loose notes on complex systems and biology. I want to learn about the topic as fast as I can, so if I’m wrong anywhere, please point that to me. This post is an overview and indication of issues I’d like to cover.

Image source: http://commons.wikimedia.org/wiki/File:Biocomplexity_spiral.jpg

Complex adaptive systems (CAS) are the heart of many phenomenas we observe every day, such as global trade, ecosystems, human body, immune system, internet and even language. Complexity of CAS does not equall to amount of information, rather it’s a indication of complex, positive and negative interactions of its components. All CAS feature a common set of dualisms:

• distinct/connected – CAS are built of a large number of agents that interact simultaneously and independently but all together become tightly regulated system (other names: individual/system or distributed/collective)
• robust/sensitive – CAS are pretty robust, yet at the same time are quite sensitive to initial conditions and some signals (see butterfly effect); both features are unpredictable
• local/global – protein is a CAS, protein network is a CAS, cell is a CAS, tissue is a CAS, organism is a CAS, society is a CAS; agents of a CAS, can be CAS themselves
• adaptive/evolving – CAS is able to adapt as a system and usually its agents are also mutually adaptive, and at the same time CAS is evolving; even if local landscape prefers simpler solutions (adaptation) CAS usually evolve toward bigger complexity

These dualisms are in some sense as artificial as wave-particle dualism. Complex system has all these features at the same time – their visibility depends only on design of a experiment. As a result, CAS present a common set of features: they are self-organizing, coherent, emergent and non-linear.

Probably the best so far representation of CAS is a network, which has a number of important features: it is scale-free (distribution of links in the network tends to follow power law), clustered (“friend of my friend is likely my friend too”) and small-world-like (diameter of a network is small, aka “six degrees of separation”). Such representation has been applied to biological complex systems, such as metabolic networks, or protein-protein interaction networks with a great success. However please remember that it’s only representation and many times people argued that scale-free networks may not be the best approximation of natural networks (see for example this recent paper).

Scale-free or not, network representation doesn’t address all dualities mentioned above, especially last two. Naturally emerging levels of organisation and relation between adaptation and evolution of complex systems are rarely studied from biological point of view, probably because we don’t have a clear idea how to reduce these phenomenas to something measurable.

In the next posts, I will try to cover other CAS representations and computational approaches to CAS modeling.

Few days ago I had a chance to speak about Science 2.0 at the Institute of Biochemistry and Biophysics of Polish Academy of Sciences (the one I’m affiliated with). Compared to the seminar on the same topic I gave at the same place (but for much smaller audience) 4 years ago, I had much more stories to tell, way more real-life examples and better idea of where the whole “2.0″ meme is leading us. I also got better at speaking (4 years ago some of my colleagues literally slept on my seminar). So, message got clearer, and messenger had improved.

But given wide interest in the topic from inside and outside of academic environment already before the seminar I think two things had happened in Poland in the last 4 years. First, internet got recognized as a game changing technology, and people simply are interested in any new way they can use this tool (yes, I know it’s 2009 – if you live on the nets it’s hard to realize how slow adoption rate is outside of virtual worlds). Second thing is, that internet as a tool is also recognized as important – for example people had ideas to include Science 2.0 topics into program of PhD studies (I will follow up on this topic in a week or two). Getting popular, important… Only wide adoption is what we need .

Last year I bought a domain name AllTwoPointZero.com. Initially I had an idea to launch a huge portal around “2.0″ meme – essentially tracking changes in communication methods across various areas. I wanted to quit science and start a consulting career in helping people to communicate more efficiently (new channels and tools, efficient visual communication, etc.). However, a market for such services in Poland is nonexistent, and I didn’t have a mood for relocation, so I’ve turned to other opportunities (and as effect, I’ve stayed in science). Neverthess, I still had a domain but no clear idea what to use it for.

So, with only a little time left, the next option I took was a tracker/aggregator. In theory, once done, it didn’t need much maintenance. There’s quite a lot of services for such purpose out there, but they didn’t necessarily allowed for certain things I wanted to have, so I had to code my own script. As I didn’t have much time, the resulting site is a little rough (it cannot compete with wonderful sites Euan is coding, such as recently released preview of Streamosphere). However, you should get an idea what I’m aiming for. Currently it tracks blog posts and conversations in areas of Science 2.0, Health 2.0 and Culture 2.0 (with Enterprise and Government to follow). Because within these types I sort all entries by date, I had to remove some bloggers from “Key People” list, as their high-speed blogging did not allow others to appear in the box at all.

At this stage, the set of sources is far from perfect – outside of science, conversations seem to be highly homogenous. When I improve the sources (maybe will use Twitter and custom FriendFeed searches), I plan to add some kind of visual summary to the tracked conversations to see if I can find some patterns that will let me establish a connection between disciplines. Let’s see…

While I was collecting links, I’ve found one interesting thing: you can find people interested in these three areas both over at FriendFeed and over at Twine. However, it seems that only scientists are actively talking with each other at these services – where are other groups storing their discussions?