"Do Schools Kill Creativity?" Well, find that out!

I know this has nothing to do with the class but you should watch it anyways. Nafisa and I were surfing on TED.com and we found Sir Ken Robinson's talk on "Do Schools Kill Creativity?" Why should you watch this? He is funny; mentions human ecology all the way at the end; points some fascinating thoughts about "you" and the creativity.

It is weeks six and you need a little break. And of course, you should use your imagination to create a network niche with the topic. Trust me you wont regret! Here is the video. comments are welcomed.

Binomial Coefficients and the Election

I've been following 538.com: Electoral Projections done Right very closely the last few weeks. The blog authors collect polling data from a wide range of sources, weigh it according to their sense of the poll's reliability, and produce aggregate projections. I'm not an expert, but from what I can tell the blog's authors are very good; I've learned a lot from reading the blog and I think their way of handling data makes a lot of sense. If you're interested in the art and mathematics of political polling, or if you just want to follow the build-up to the election, I highly recommend it.

Anyway, just moments ago what's wrong with this picture appeared in my feed reader from 538.com. In it, the author skewers some fishy poll results that were published earlier today. To do so, he makes reference to the binomial distribution. Proof that I wasn't lying when I said in class that binomial distributions are super useful.

Network analysis of the IPCC

Last class we briefly discussed how networks could be applied to a vast array of topics. This term in my Global Environmental Politics I’ve developed a strong interest in the politics of climate change. A few weeks ago I stumble across a paper on the “Network Analysis of the Intergovernmental Panel on Climate Change (IPCC),” in which Travis Frank, Robert Nicol, and Jaemin Song from MIT examine the team structure, network architecture, and other major influences of the IPCC’s Third Assessment Report.

The IPCC–who in 2007 shared the Nobel Peace Prize with Al Gore– is the scientific body in charge of assessing on a comprehensive, objective, open and transparent basis the latest scientific, technical and socio-economic literature produced worldwide relevant to the understanding of the risk of human-induced climate change, its observed and projected impacts and options for adaptation and mitigation.

The hundreds of climate change scientists who were members of the IPCC at that time, their nationality, fields of expertise, team collaboration among other things make this study quite interesting in understanding the politics of climate change within the scientific community.

The calculation of the clustering coefficient shows that the authors in the IPCC report are less connected than Newman’s least connected research field. They also found that the “longest shortest” path in the network was 19 edges. “Centrality betweenness” is used to rank the top 20 authors and shows that developing countries are not represented as well as the developed countries.

I feel that this paper is a clear example of how network theory can be applied to a specific area to prove arguments related to collaboration in climate change.

Treating Food Webs as Undirected Networks

After the discussion of the ecology papers in class on Friday I got curious about whether anyone had attempted to address the tendency of many people studying food web networks to treat them as undirected despite their directed nature. I spent some time doing some additional reading, and I found that the trend was pretty consistent through the papers that I found. The two papers that offered a justification for it (Williams et al., 2002; Dunne et al., 2002) both stated that a directed graph could be treated as undirected, because "effects can propagate through the network in either direction.". It seems to me that whether this is a valid assumption depends heavily on what exactly the network is being used to study. If one was studying the effects on the food web from the removal of certain species then perhaps it would be an okay idea to work under, because the effects of prey and predator removal would have similar effects on organisms throughout the food web. On the other hand if one was looking at transmission of toxins through an ecosystem then one would have to use a directed network to model it, because they are only going to move in one direction. In almost every other aspect of network analysis I would think that the directed nature of the food web is too much a key part of how the web works that it should not be ignored. There are perhaps a few situations in which an undirected food web could be applicable, but it seems to me that none of the articles that I found provided sufficient justification for an undirected food web.

If we're only 6 degrees of separation away from Osama Bin Laden...

If you have 23 spare minutes, I would suggest listening to this, a National Public Radio program, Talk of the Nation, from January 25. It features Judith Kleinfeld, a psychology professor at the University of Alaska, and Steven Strogatz (of Strogatz and Watts, "Collective dynamics of 'small-world' networks."). It's not a rigorous conversation, but interesting none the less - perhaps particularly for those of us encountering networks theory for the first time. The discussion is primarily dedicated to the validity of the findings of Milgram's original "6 degrees" experiment, and modern examples and consequences of social connectedness. I think it is worth listening to if only to hear an informed discourse on networks - and Strogatz keeps the conversation interesting. He makes the point that there are 3 questions to distinguish when looking at the significance of small-worldness in the context of Milgram's experiment:

1. Given 2 people, is there a short path connecting them?
2. If there is a short path, can people find that path?
3. If paths did exist, and people can find them, could they be used to exert influence?
   

Additionally, he makes the point that people must be willing to cooperate in order to make these paths possible - a complication that I hadn't considered. All of the above distinctions would seem to be relevant to similar applied small world networks - networks in which members have the ability to refuse a connection, and in which not all members can see the short paths. This program manages to frame network theory, and its relevance to the real-world, in an interesting (and entertaining) manner. Most memorably, Strogatz poses the title question, "If we're only 6 degrees of separation away from Osama Bin Laden, why can't we find him?"

Power Laws, or maybe not

On numerous occasions I've urged caution and skepticism when reading papers claiming that there are power laws in some empirical data. Right on cue, a dubious power law claim appeared in a paper published a few weeks ago.

The paper in question is Yu, et al, High-Quality Binary Protein Interaction Map of the Yeast Interactome Network from the October 3, 2008 issue of Science. The interactome referred to is a network of protein-protein interactions. The paper claims that the degree distribution of the interactome network is power-law. This claim was critiqued by Aaron Clauset in a recent blog post, poetically titled power laws in the mist.

Specifically, Aaron examines three power-law claims from the original paper. Using maximum-likelihood estimators instead of log-linear regression, he finds strong evidence that one of the "power-laws" is definitely not a power law, one could be, and one probably is. For the real power law, he estimates an exponent that is incompatible with the values published in Science. The full blog entry is well worth reading, Aaron is a good writer and the piece is a nice discussion of the right way to look for power laws in empirical data.

Aaron's piece is an interesting example of the way that blogs are now being used as a form of scientific communication. Aaron writes

A colleague of mine asked me why I didn't write this up as an official "Comment" for Science. My response was basically that I didn't think it would make a bit of difference if I did, and it's probably more useful to do it informally here, anyway. My intention is not to trash the Yu et al. paper, which as I mentioned above has a lot of genuinely good bits in it, but rather to point out that the common practices (i.e., regressions) for analyzing power-law distributions in empirical data are terrible, and that better, more reliable methods both exist and are easy to use. Plus, I haven't blogged in a while, and grumping about power laws is as much a favorite past time of mine as publishing bad power laws apparently is for some people.

It seems to me that science blogging serves as an excellent complement to process of publishing in peer-reviewed journals. Blogs allow for informal comment, discussion, and debate in a way that can't happen in journals. This sort of back-and-forth serves as an important check on flimsy results and as a way to get quick feedback on new ideas. This sort of dialog isn't new; people have been debating and exchanging ideas at seminars, in hallways, at department gatherings, at academic conferences, and so on, for decades if not centuries. What's new about blogs is that they open up the discussion and allow lots of people to observe and participate in the fun.

How Do Cells Function to Keep Organisms Alive?

I am sure most of you know about certain processes that take place in our bodies such as respiration and digestion. But have you ever wondered why we are alive and or how individual cells synchronize to make this possible? Well, I have, and it is very interesting to think about living organisms, especially human beings. Do you think you are alive just because you eat or breath? Metabolism is one of the processes that keep us alive. We die whenever cells stop their metabolic activities.

Metabolism is the set of chemical reactions in cells that allow organisms to grow, reproduce, walk, talk, breath, think, etc. You get the idea. What I am mostly interested in, considering that I am posting this for the Complex Network class, is the growing interest on modeling metabolic pathway networks. There are similarities between the metabolic pathways of most species, even between unicellular bacteria and human beings.David A. Fell and Andreas Wagner analyzed the structure of a unicellular bacteria's, Escherichia coli, core metabolism to identify metabolites that are central to metabolism.

Why should we learn about metabolic pathway networks? One of the reasons, is that we can then begin to compare the evolutionary history and molecular mechanism of living organism by looking at metabolic and genomic information. It is always puzzling to think about evolution and how living organisms come to existence. I am mostly confused when it comes to evolutionist and creationist theories. On one hand, I feel religiously responsible to believe in what the bible dictates. At least that is how I was raised. On the other hand, I need to know about evolutionary theories, not just because I need to pass my Biology class but also because I know that there has been research and evidence to support these theories.

I find the evolutionary theory hard to believe because of its nature of changing and reforming. There are discoveries every year, or even every day, that prove or disprove previous theories and research. But the creationist theory never changes. It is as simple as God created the earth and its inhabitants. I know if any of the evolutionists at COA read what I have written, they will think that I am going crazy. They are probably right.

There is an incredible amount of research showing links between each and every creation on earth that force me to give some credit to scientists such as Darwin. It is because of such discoveries that we become increasingly closer to knowing living creatures' behavior, structure, composition and other complex features. We also need to know how to model metabolic pathway networks to better understand these features. Surely human beings are some of the smartest, perhaps even the smartest creatures!

Usually, only a single metabolic pathway is studied applying radioactive tracers to an organism. Then the information is used to understand and label the pathways. Such an example could be the metabolic pathway of cellular respiration. But this method does not help when it comes to a more complex metabolic pathway such as the metabolism of the whole cell. The reconstruction technique has allowed researchers to construct models of more complex metabolisms. The diagram on the left shows the interaction between 43 proteins and 40 metabolites in Arabidopsis thaliana citric acid cycle. Red nodes are metabolites and enzymes and the black links are the interactions.

In conclusion, in addition to helping us understand metabolic pathways and how cells function, these types of models can be used to classify human diseases into groups that share common proteins or metabolites, which in turn leads to drug discoveries and biochemical research.

The Robustness of Food Webs

“Network Structure and Biodiversity Loss in Food Webs: Robustness Increase with Connectance” (Dunne, Williams, and Martinez 2002) is a paper investigating the robustness of food webs using networks. It looks at 16 different food webs from 15 different places. Each web was tested to see how the removal (extinction) of certain species might affect the overall food web. A food web was said to collapse if over 50% of its species had become extinct. The robustness of food webs is represented in this paper as the fraction of removals it takes to collapse the food web over the total number of trophic species in the food web. Several other properties of the food networks were calculated to see if they correspond to the robustness of food webs including connectance, species richness, omnivory, and the number of links per species.

There were four experiments simulated with the food webs. In the first simulation the most connected species were removed; in the second the most connected species were removed excluding primary producers(like grass etc.); in the third species were randomly removed, and in the last the least connected species were removed. These simulations were conducted to see if any new insights could be discovered about a food webs robustness and to see if there were any particular species which was significantly more important than the others (i.e. a species that if removed would cause a mass second extinction).

Three out of the 16 food networks displayed power law degree distribution and small world properties but no major differences in reactions to the extinctions were found between the small world and non small world food networks. However, small world food networks are shown to be more severely affected by extinctions due to their lack of strong connectance. Out of all the properties of food webs looked at, connectance appears to be the most influential in determining the robustness of the food web. The more connectance the more robust the food web, which seems to make sense in ecological terms because the more options a predator has to feed upon others the less likely it is for them to become extinct due to loss of a prey species.

While this network analysis of food webs makes sense in mathematical terms I would stress the need to review the assumptions this analysis makes in order to critique its relevance in the real world. Since the network analysis on food webs do not take into account the adaptability of species it may be that they are generally irrelevant in testing robustness. It could be that a species will usually eat only one prey if available but if that prey went extinct then it could move on to eating another species. Also food webs will likely never be able to take into account all links between species so there is an artificial cut off point that could render this analysis unhelpful. Links may also be false due to human error or there could be a particular species that is essential in the predator’s diet because it provides some nutrient that the predator can get no other way. Therefore, even though this predator has other food sources it would still go extinct if that particular species was removed from the food web. However, if scientists could provide field evidence that connectance is an important value in the world outside this analysis of food webs could allow us to identify ecosystems that are particularly vulnerable to mass extinctions.

"It's what we swim in"

Last week I found (via Jon Shock), a thought-provoking talk by Clay Shirky about, among other things, "information overload." What I really like about Shirky's view is that he posits information overload as a fact of life. Paraphrasing slightly, he advocates for

a way of seeing the world that assumes that we are to information overload as fishes are to water: it's just what we swim in. Yiztak Rabin... has said that "if you have the same problem for a long time, maybe it's not a problem. Maybe it's a fact." That's information overload. Talking about information overload as if it explains or excuses anything is actually a distraction.... When you feel yourself getting too much information, ... don't say to yourself what happened to the information, but say to yourself what filter just broke? What was I relying on before that stopped functioning?

This is an extremely clear, and much more succinct statement of what I've been feeling for a while and have been struggling to put to words. Information overload is a fact of life. So we need to be smart and proactive about the filters that we use to manage and process this information. Check out Shirky's talk, below, for more discussion. It's only 25 minutes and is packed with interesting observations.

Networks as a Predictor for the Spread of Cancer

In class, Dave has talked about survival rates of cancer patients . Models and networks are cropping up in other useful ways in the medical field around cancer as well. I came across a study by 6 doctors who used an artificial neural network to see if this could be an effective way of predicting patterns in lymph node metastasis. 

An artificial neural network (ANN) is a computational model that can process vast amounts of data and complex relationships and find patterns. An ANN "learns" from the data and changes accordingly. Wikipedia has a page about ANN that does a nice job explaining what they are an ANN is and how it is applied in many different contexts. 

Lymph node metastasis is particularly significant because in certain kinds cancer, namely gastric and esophageal, the lymph system is the first place to which the cancer spreads. So an accurate prediction of this could help catch and effectively treat the cancer in its early stages. 

In this study, the doctors were specifically looking to determine if genetics were a leading factor in the predictability of lymph node metastasis. By using a mass of data collected by the National Cancer Center in Japan the doctors created an artificial neural network, used more data to refine it, and then tested its accuracy. In a separate study in Germany, the same ANN was was able to predict the incidence of lymph node metastasis as accurately as 96% for certain sub-types of cancer. This could mean more accurate, more efficient, and thus more effective surgeries and postoperative care, with fewer side-effects. The way surgeries are done now, lymph nodes that are not cancerous but appear irregular for other reasons are resected. The removal of lymph nodes not only decreases immunity, but can interfere with lymphatic drainage leading to such unpleasant conditions as lymphedema. Perhaps the use of an artificial neural network could prevent damage to the real living network that is the lymphatic system.