Short Threads of Reading Queue

Here are some academic papers, articles, and blog posts I have put into my reading queue over the past few weeks that I have found interesting and/or comment-worthy. I have organized them into threads (e.g. streams of consciousness) here:

Short thread on cell biology and genomics:

[1] Xie, J. et.al   Autocrine signaling based selection of combinatorial antibodies that transdifferentiate human stem cells. PNAS, doi:10.1073/pnas.1306263110 (2013).

[2] Williams, R.B.H. et.al   The influence of genetic variation on gene expression. Genome Research, 17, 1707-1716 (2007).

In [1], the researchers use a combination of receptor antibodies to reprogram a cell's fate. Yet more evidence that cellular reprogramming is not only possible, but involves more than just a few transcription factors or a spontaneous transformation. The science in [2] is a pre-RNA-seq study on the effects of standing genome variation on steady-state gene expression. A good early review, although there is now more current/specific work available.

Short thread on economics, markets, and technology:

[1] Yglesias, M.   Who gets rich when robots take our jobs. Moneybox blog, May 13 (2013).

Mr. Spacely from "The Jetsons". He's rich and George Jetson is not.

[2] Falk, A. and Szech, N.   Morals and Markets. Science, 707, 340 (2013).

After reading [1], I come away with the impression that the only thing that can be economically gained from automation is a bolstering of the arbitrary claim (e.g. Russian roulette) to genius (e.g. even patent trolling qualifies). Apparently, it is more relevant (and fleeting) than ever. This is part of a trend that has lead to productivity gains of the last 40 years becoming locked up in corporations and/or an executive elite. Again, automation has helped this trend along, although automation does not always result in this outcome.

In [2], a curious finding is reported. If you are part of a market, you are more likely to let a mouse die for a lower amount of money. A novel addition to the experimental moral philosophy field. Not quite sure if this is an exercise in mutually-assured moral behavior (e.g. bystander effect), or a call to make judgments about economic value in isolation. Is there more than meets the eye to this simple set of experiments? As an aside, how does this relate to the psychology of auctions?

Short thread on subjectivity in the brain:

[1] Wittmann, M. et.al   The neural substrates of subjective time dilation. Frontiers in Human Neuroscience, doi:10.3389/neuro.09.002.2010 (2010).

[2] Schurger, A. et.al   Reproducibility distinguishes conscious from nonconscious neural representations. Science, 327, 97 (2010).

Apparently, 2010 was a good year for investigating subjectivity in the brain. How do we measure engagement with a piece of art or the practice of culture? In [1], changes in activity patterns among the "cognitive control" and "default activity" brain networks mediate subjective responses to visual motion. In [2], neural activity related to conscious, neural correlates of subjectivity must be both of a certain duration and intensity as well as being reproducible. While subjective experiences can be transient and unique, their neural correlates are not.

Happy 50th birthday, Chaos theory!


[1] Arbesman, S.   The Fiftieth Anniversary of Chaos. Social Dimension blog, May 17 (2013).

[2] Lorenz, E.N.   Deterministic Nonperiodic Flow. Journal of Atmospheric Science, 20, 130-141 (1963).

[3] Motter, A.E. and Campbell, D.K.   Chaos at Fifty. Physics Today, May, 27 (2013).


This feature got a pretty decent response on my micro-blog, Tumbld Thoughts: happy 50th birthday to the study of chaos [1]. A worldview first proposed (in formal fashion) by Edward Lorenz in a landmark paper on weather prediction called “Deterministic Nonperiodic Flow” [2]. Later, the field would grow to encompass analytical strategies such as nonperiodic attractors, bifurcation maps, and fractals.

As a new way to describe physical phenomena and complex systems with a high degree of nonlinearity and subtle unpredictabilities (e.g. the butterfly effect), chaos shattered the notion of a clockwork universe [3]. As a paradigm shifting concept, chaos theory has the potential to enrich all areas of science [4].

Image on left is from [1], and image at the right is from [3]. For the latest work in the field, check out the journal “Chaos: an interdisciplinary journal of nonlinear science”.

For examples from brain science, see the following two articles and book:

* Robson, D.   Disorderly genius: How chaos drives the brain. New Scientist, June 29 (2009). YouTube video.

* Kitzbichler, M.G., Smith, M.L., Christensen, S.R., Bullmore, E.   Broadband Criticality of Human Brain Network Synchronization. PLoS Computational Biology, 5(3), e1000314 (2009).

* Freeman, W.J.   Neurodynamics: an exploration in mesoscopic brain dynamics. Springer, Berlin (2006).




Intriguing evolution stuff:

Zimmer, C.   Enlisting a virtual pack, to study canine minds. New York Times, April 22 (2013).

The Dognition website.

This is a story about Dr. Hare, the Anthropologist (the study of humans) interested in canine cognition. Can we throw any more species in there? Oh yes -- apparently dogs are more intelligent than their wolf wild-type cousins (determined by something called the "pointing test"). So to make this assessment more scientific, Dr. Hare came up with a test for dog intelligence. He also founded a company called Dognition, which is collecting data from dogs worldwide. But there's no such thing as a Dog IQ just yet. It will be interesting to see how intelligence corresponds with breed and degree of artificial selection for specific traits.

Evolutionary "gut check":

Burger, O. et.al   Human mortality improvement in evolutionary context. PNAS, doi:10.1073/ pnas.1215627109 (2013).

This is a paper that I could not quite figure out. My gut says that something is not quite right/being accounted for here. Are they using ethnographically-observed hunter gatherer populations to derive an evolutionary baseline? If so, can they truly demonstrate that these populations actually represent such a baseline? Also, it seems to me that increases in life expectancy may involve the elimination of early mortality (due to warfare, violence, and disease) rather than a biological or cultural adaptation (particularly one on the order of those that distinguish between sister taxa, as the one that distinguishes human hunter-gatherers and chimps).

...and, finally, actual robots!

ICRA 2013 Conference website. Held in Karlsruhe, Germany, and sponsored by IEEE.

Erico Guizzo reports for IEEE Spectrum from ICRA (Robotics Conference), and brings us (among many other interesting things) a feature on Entropica:

LEFT: Screenshots of Entropica configurations (social network interactions and a pole balancing task). RIGHT: real-world (e.g. Primate) behaviors (termite dipping/tool use and stock market trading).

Wissner-Gross, A.D. and Freer, C.E.   Causal Entropic Forces. Physical Review Letters, 110, 168702 (2013).

Hewitt, J.   The emergence of complex behaviors through causal entropic forces. Phys.org, April 22 (2013).

Using a robotic model, it can be demonstrated that general intelligence (in the form of causal generalization) may be amplified or otherwise result from entropy maximization. This is related to work done on ant trails, showing that they conform to Fermat's principle of least time.

Lecture to BEACON Center, Michigan State

On May 17th (Friday) at 3:30pm (EST), I will be giving a lecture entitled "Adventures in Quasi-Evolution" [1] to the BEACON Center [2]. The audience is Thrust Group 1 (Genomes, Networks, and Evolvability).

The first half of my talk will be on computational models of cellular reprogramming (e.g. evolutionary modulus, or the engineering on the remnants of evolutionary and developmental variation). The second half is some emerging work I am doing on the cultural evolution of economic value (e.g. evolutionary through the looking glass, or how evolutionary models [3] may explain current economic puzzles).

NOTES:

[1] "Adventures in Quasi-Evolution". Figshare, doi:10.6084/m9.figshare.701463 (2013). I define quasi-evolution as "changes over time not due to reproductive fitness or generational inheritance".

[2] For those who are unfamiliar, the BEACON Center (an NSF-funded center) is a multi-disciplinary, multi-University group interested in the intersection of biology and engineering with relevance to evolution. Catch the lecture at one of these locations:

Michigan State University: Biomedical and Physical Sciences Building, Room 1441 (BEACON seminar room), 3:30pm.

North Carolina A and T University: McNair Hall, Lecture Room 4, 3:30pm.

University of Idaho: Life Sciences South (LSS), Room 144, 12:30pm

University of Texas, Austin: Service Building (SER), Room 321H, 2:30pm.

University of Washington: UW Hutchinson Cancer Center, Building PAA Room 023D, 12:30pm

[3] The cultural evolutionary models used in my research are called Contextual Geometric Structures (CGSs). Contextual Geometric Structures (CGS) will never play chess well, or perhaps at all. They will never beat Ken Jennings on Jeopardy. That's not the point. They exist as soft or fuzzy (e.g. possibilistic, non-transitive) classifiers that capture (or at least reproduce) the structural features of cultural behavior. This is quite different from the dual inheritance models that are common in studies that focus on the geneaology of traits.

The "structure" of culture has been observed and pondered by many cultural anthropologists, from Claude Levi-Strauss to Pierre Bourdieu. Bourdieu used a construct called the "habitus" to characterize the relationship between individuals in a single generation and cultural structures that exist across multiple generations. CGSs provide a measure of computational precision (e.g. a kernel function)  to this and other conceptions of "cultural logic".

As a quasi-evolutionary phenomenon, CGSs are designed to capture neither the dual inheritance of genes and culture nor the connectionism of a traditional cognitive model. The outcomes of CGS agents are not geared towards optimal decision-making. Rather, they classify natural phenomena according to a set of discrete oppositions (or categories).

Some of these are based on premises (e.g. historically-determined preferences), while others are based on biological features of the organism. The CGS agents then use the classificatory state of other agents as a cue to either follow (conform) or disperse (dissent). What results is a form of social learning that can be used to update (or obliterate) the space between the lower-level categories.

CGS simulations can be run either in an liquid-like simulation, or independently. In the original conception, which maps CGSs to geographic and other spatial phenomena, a hybrid model was proposed. In experiments geared towards understanding the social construction of economic value, populations of CGS kernels and their agents are static with respect to spatial position. However, they exchange items and attach value to those items based on repeated interaction.

For more information, please see:
Alicea, B.  Contextual Geometric Structures: modeling the fundamental components of cultural behavior. Proceedings of Artificial Life, 13, 147-154 (2012).

Celebrity Recurrence, Professional Graphs, and Nano-Self-Expression

This has been cross-posted to my micro-blog, Tumbld Thoughts:

First up, it’s time for internet memes to meet mathematical concepts. On the left is a conceptual piece I am calling “The Cage Recurrence”. This is based on the Nick Cage vampire urban legend [1]. The transformations in the image are based on the Poincare recurrence (inset A), originally developed by Henri Poincare [2].

A Poincare recurrence occurs when the state of a volume-preserving flow map (e.g. 2-D image) returns to a close approximation of the initial condition after a period of time (usually very long). Theoretically, this is related to ergodic theory and the evolution of Hamiltonian systems [3]. As such, the dynamics of a Poincare recurrence can also be illustrated as a chaotic Poincare map (inset B, left) or a deterministic Baker’s map (inset B, right).

Next up, LinkedIn has a new feature that allows you to visualize your professional social network. It plots all of your first-order connections on the basis of shared connections and other attributes.

The network tends to cluster by workplace/community, but my network includes a lot of people that do not fall into any one category (as do most, I would imagine). InMaps uses programming tools such as Hadoop for handling large datasets and Processing for visualization. Fun!

Finally, here is a feature on nanoscale movies. In 1990, Don Eigler and other researchers at IBM [4] were able to assemble the letters “I-B-M” out of Xenon atoms using scanning tunneling microscopy (STM) [5].

Now, similar techniques have been used to make the world’s first atomic-scale animated short film “A Boy and His Atom” [6]. A “Star Trek” logo is also possible [7]. 

NOTES:

[1] Google “Nick Cage vampire” for more information.

[2] Image/illustration is courtesy of the Max Planck institute for Complex Systems and Crutchfield, J. et.al   Chaos. Scientific American, December (1986).

[3] it may also be the mechanism behind deja vu in “The Matrix” trilogy.


[4] Eigler, D.M. and Schweizer, E.K.   Positioning single atoms with a scanning tunnelling microscope. Nature, 344, 524-526 (1990).

[5] Johnson, D.   IBM Makes Smallest Movie Ever. IEEE Nanoclast Blog, May 1 (2013).

[6] Pachal, P.   IBM Manipulates Atoms to Create the World’s Smallest Movie. Mashable, May 1 (2013).

IBM Research, “A Boy and His Atom”. YouTube, April 30 (2013).

[7] Kramer, M.  IBM Warps Atoms into Crazy “Star Trek” Art. Space.com, May 3 (2013).

The significance of influence metrics: some fun with Klout and Google Scholar

 

Here is the main Klout interface with my personal Klout score and its trend over time. Klout is (please pick the one that is most applicable):

a) a popularity time-series, like brain electrical activity or stock market performance, and is probably (in an indirect fashion) related to each [1].

b) based on what exactly? I know, this puzzled me initially as well. Find out more here.

c) an excellent way to parlay your Facebook playtime into professorial tenure [2]. Not really. But it would not be at all surprising to me, given the almost-reflexive reverence paid to social media by business and journalistic culture.

d) a way to determine you personal worth, if your life is all about social media. And, really, what person’s life isn’t these days. It’s the new religion. Praise Zuckerberg [3]!

e) all of the above. Hope this raises my Klout score!

UPDATE: Since cross-posting this to Tumbld Thoughts, my Klout score has indeed increased by 8 (it is 40 as of May 5th). Looks like it is due to an increase in Facebook activity (or perhaps more targeted Facebook activity -- suggesting that the Klout score could be incredibly easy to manipulate). World domination, here I come!

Of course, this says nothing about my "official" academic research footprint. Or does it? Perhaps we can learn more about these types of metrics by looking at Google Scholar. Holly Dunsworth at the blog Mermaid's Tale has looked at what exactly constitutes her h-index measurement. In short, just because papers are cited does not mean that they are cited for the same reasons, and thus do not have a uniform degree of influence across citations [4].

My h-index [5] is 1 (across 27 papers -- some being Figshare documents), and is highly asymmetrical. A single Nature Reviews Neuroscience paper accounts for most of the citations taken into account. I'm actually not sure what database they are using to calculate citations (and thus influence), since it is not taking into account a number of peer-reviewed conference papers and book chapters (and blog posts, for that matter - [6]).

Does this dude abide? Apparently, I've only been influencing people in a significant manner since 2011......

Okay, I have not reached a definitive opinion about this as of yet. It's just food for thought, so please discuss.

NOTES:

[1] Here is a fun paper (for theoretical physicists, at least) on this topic from Marcus Raichle's group: He, B.J., Zempel, J.M., Snyder, A.Z., Marcus E. Raichle, M.E.   The temporal structures and functional significance of scale-free brain activity. Neuron, 66(3), 353-369 (2011).

[2] a very apt April Fools' joke deftly executed by C. Titus Brown on his blog Living in an Ivory Basement.

[3] the classic "Microsoft buys the Catholic Church" internet meme is probably appropriate here.

[4] Here is another critical assessment of citation statistics: Adler, R., Ewing, J., and Taylor, P.   Citation Statistics. arXiv:0910.3529 (2010).

In addition, Audrey Watters at Hack (Higher) Education blog (hosted by Inside Higher Ed) has a post on the problems she's experienced with Google Scholar.

[5] Of course, the h-index is just one possible way to measure research output. But caveats of the h-index and then some apply to all alternative methods.

[6] This was a problem for Jonathan Eisen (Tree of Life blog) as well. At least Google tries to be accomodating in these cases.......

In general, "The Secret History of Rock" by Roni Sarig might enlighen this discussion a bit. In many cases, relatively (or in some cases absolutely) obscure bands such as the Dead Kennedys have served to influence much more popular (but perhaps less influential) musicians and bands. Influence networks serve as the mechanism for absolute vs. relative influence. While the h-index does not capture this phenomenon well, the Klout socre might be better at uncovering this.

Evolving the Tardis: Carnival of Evolution #59

This has been cross-posted to my micro-blog, Tumbld Thoughts:

I've passed the CoE torch yet again. Dirk Steinke from the University of Guelph and blog DNA Barcoding brings us this month’s “Carnival of Evolution” (#59). Continuing with the futuristic/fantasy theme of last month’s carnival, the theme this month is: Dr. Who, evolutionary biologist. Who knows, perhaps he can make sense of Dalek diversity. This edition features the recent Synthetic Daisies post "Replication, Model Organisms, and Evolutionary Signatures".

An analytics update to Carnival of Evolution #58: As of May 3 (since April 1), this version has received 630 hits. Not bad for an academically-oriented blog carnival. Enjoy this month's installment (it should get a fair number of hits as well), and if you wish to host a future edition, please contact Bjorn Ostman.

Freely-associated Earth Day and Outdoor Adventure-related Content

This is being cross-posted to my micro-blog, Tumbld Thoughts.

Here is a recent story on how 70,000 ladybugs have been released in the Mall of America to combat aphid infestations of the interior foliage. At 4.2 million square feet, the Mall of America has developed an incipient ecosystem [1]. The dynamics of this ecosystem will interesting to observe, particularly in light of the work that has been done in the field of biospherics (e.g. Biosphere 2, which is sponsored by the University of Arizona) [2]. Speaking of self-sustaining ecosystems.......

Nice ecosystem animation for Earth Day, courtesy of Google Doodle. Surf the web to find the true meaning of Earth Day, I guess. Speaking of surfing.....

Why it pays to Surf Michigan. No, really! There is a thriving (albeit obscure) surf culture in the US state of Michigan [3]. Lake Michigan surfing (inset on the left, picture from the St. Joseph Pier on Lake Michigan) has been going on for years, usually in the Fall and Spring when gales present waves large enough for surfing on.

When there is significant rainfall (such as during the past few weeks) or Spring snowmelt, the inland rivers (inset on the right, picture from the Red Cedar River in front of the Administration Building at Michigan State University) allow for interesting surf conditions.

Rincon Beach Park, Santa Barbara County, CA. There is also beachfront right across the street from the UCSB campus.

Surfing at Michigan State is a bit like a cold UC Santa Barbara with ducks on a river (odd mental image, I'm sure). Speaking of academically-oriented surfing [4], here is a profile on the "Physics of Surfing" class offered at UC San Diego [5]. Happy outdoor adventuring!

Scenes from the "Physics of Surfing" class, which combines lessons in instrumentation, oceanography, and of course surfing.

NOTES:

[1] this has parallels to ecosystems that emerge due to climatic changes such as the retreat of glaciers.

[2] Allen, J.   People Challenges in Biospheric Systems for Long-Term Habitation in Remote Areas, Space Stations, Moon, and Mars Expeditions. Life Support and Biosphere Science, 8, 67-70 (2002).

[3] Pictures of wetsuit adventurers in interesting conditions courtesy of Matuli Surf Company (Matulis brothers, Midland, MI).

[4] Here is a list of the top 10 surf colleges from Surfer magazine. Michigan State (nor any other Michigan University) is on it. The only odd duck here is NYU, which offers you the opportunity to surf Long Island (and perhaps the sewers). I might add Florida Atlantic University (FAU) to the list, at least during hurricane season.

[5] A few more links about those skeptical of the academic value of studying surfing: 1) a video on the physics of surfing by Kevin Stahl and friends, 2) a white paper called "The Physics of Ocean Waves" by Michael Twardos (courtesy of Snake Gabrielson's Surflibrary.org), 3) a story by John Jeka at the University of Maryland in the "Neuromorphic Engineer" called "Light touch-contact: not just for surfers". The article profiles the role of touch (e.g. somatosensory information) in helping people and other animals keep their balance when moving across a surface.

Replication, Model Organisms, and the Role of Evolutionary Signatures

The following slides and commentary focus on an open problem that involves the difference in perspective between medical researchers and evolutionary biologists. By perspective, I mean the types of explanatory frameworks one uses to understand a set of results.

Notice that I could have used the word "theory", but it actually has more to do with the cultural premises of one's discipline and formal training [1], especially in cases where there is a lack of good theory.

These slides are the second part of a talk of mine called "If your results are unpredictable, does it make them any less true? (posted to Figshare), which is a follow-up on the HTDE 2012 Workshop.

This set of slides was inspired by an in-lab discussion about a news article, that lead me to a recent PNAS paper on sepsis research in mice and humans. While mice are the accepted model organism for studying sepsis [2], it turns out that the physiological response (e.g. microarray studies and gene expression correlations) to sepsis in humans is very different than in mice. 

This result is interesting from an evolutionary standpoint. While there is phylogenetic distance between mice and humans, they are both mammals and certainly share many physiological and genomic characteristics. Furthermore, can these differences be explained using evolutionary theory? Has there been evolution in the sepsis response between mice and humans, or are these differences due to a highly variable response that can vary widely between species (and perhaps even between individuals in the same species)?

The variation in pathway activation and physiological responses seems to be quite common in medical research. When a certain experimental manipulation is done to multiple species [3], there is a range of possible outcomes, from a common response to a widely varying responses. We will return to this later. 

For now, let's consider why such massive differences might exist between humans and mice for a single physiological response. This is where we must return to the issue of premises. Given your background and preferences, you might choose a single explanatory framework. 

I have presented three in the slide below:  black box, complexity, and noise. Each of these may find support depending on the measures used and physiological components assayed. Yet each of them used in isolation may not be particularly satisfying, nor even explain very much of the data by themselves. This is why good, unified theories are of such value.

Another important aspect of understanding this variable response is to rule out alternative hypotheses. In the slide below, I consider three potential artifacts that could unduly influence the animal model results: standardization of environmental conditions, artificial selection on the model organism population due to selective breeding, and the tendency of the experimenter to put more weight on features of the experimental design or analysis that allow for greater experimental replication within a particular species. Particularly in the case of the first and last point, the lesson is that standardization of the experimental setting may actually do more harm than good and introduce ecological validity problems.

Now I present my interpretation of what is going on with the sepsis result. This consists of two hypotheses that can be applied to each species (human and mouse). The first is that the physiological response to sepsis is exact, which utilizes the same pathways and same patterns of gene expression across most conspecifics but only within a single species. This might require mutational distance and other evolutionary changes among the genes that explain the sepsis phenotype. 

The alternate hypothesis says that the physiological response to sepsis is variational, which means that there is potentially great variation is mechanism across most conspecifics of the same species. This variation need not be due to heritable mutation, but simply a lack of specificity in the molecular pathways and other associated mechanisms. In this case, there would be differences between human and mouse far greater than a consensus phylogeny might suggest.

What is a variational response? The term "variational" [4] is taken (perhaps loosely) from the mathematics and physics literature, and is generally used to describe a system with many potential solutions. In this context, the goal of the variational method is to approximate potential solutions based on optimizing their properties. 

One example can be shown in the slide below: two alternate routes from Toronto to Vancouver. Each route is the "shortest" route using two pathway criterion. One pathway is tightly restricted to the Trans-Canadian highway, while the other allows for an alternate route along a number of US interstates (e.g. 5, 90, 94). Both routes are about the same number of kilometers in length (e.g. number of steps in a physiological pathway). Yet they might be alternately used due to the incapacitation of one pathway or the other [5].

The slide below shows these hypotheses in a phylogenetic context. As a contingency table, we consider the exact and variational scenarios for both conserved and divergent mechanisms. In the case of a conserved mechanism, there is very little mutational change to the underlying genes or pathway. For a divergent mechanism, the opposite is true.

To further understand what is meant by evolutionary conservation (and how it affects the consistency of physiological responses across species), I will now discuss two examples from the literature: the regulation of stress and aging, and the use of zebrafish as a human analogue. This will hopefully put my evolutionary speculations in context.

In aging research, phylogenetically-divergent species such as yeast and flatworms are used to understand the substrate of interventions such as caloric restriction and the activity of pathways related to stress resistance. As Longo and Fabrizio [6] demonstrate using aggregated data (see below slide), the associated pathway architectures are quite invariant across yeast, flatworms, and humans. However, this may not involve the same genes form species to species. In cases where conserved genes are known to be involved, it is not clear whether this conservation of mechanism components extends to a conserved mechanism itself.

A recent set of papers [7] focuses on comparing the genomes and proteomes of zebrafish with humans. As zebrafish and humans diverged around 440 million years ago [8], we would expect there to be vast differences in both function and genomics. However, there are occasionally greater differences among zebrafish than between zebrafish and humans. Another puzzle similar to the sepsis story, excpet that now we have extensive characterization of the genome and proteome to work from.

In the slide below (taken from Figure 3 of the Howe et.al paper), we can see how orthologues are shared by zebrafish and human as well as the relationship between ohnologues in the zebrafish genome. Data such as these may provide good future estimates on how and why differences exist when evaluating variation related to basic physiological functions with and between zebrafish and humans.

So what can be learn from the big picture? Particularly when distinguishing between the homogeneity expected from experimental replication and the heterogeneity posed by natural variation? Perhaps we can treat experimental replication as a generative model, where the basic experiment is expected to reveal a range of likely outcomes. Like generative models in machine learning, the goal of analysis is to pick the best model (or in this case, the set of data that provide the closest match to what we know about the underlying natural phenomenon). 

This is a tricky proposition, because both the possible set of experimental and natural outcomes are incompletely known. Nevertheless, as in the case of understanding physiological processes and outcomes as variational processes, we can make good approximations that provide high explanatory power [9] without over-relying on the replication of results.

NOTES: 

[1] cultural premises are also known as "point of view". See this Tumbld Thoughts post for a detailed review.

From two wildly different premises: a painting entitled "Picasso and Dali Paint an Egg" (Artist unknown).

[2] model organisms are used conduct experiments that are either unethical or impossible to engage in with human subjects. Here is the full NIH list of model organisms. The accepted human analogues range from fruit flies (Drosophila) to mice (Mus musculus) and round worms (C. elegans) and zebrafish (Danio rerio). A newer trend is to use domesticated animals (e.g. sheep, cows, goats, pigs) as (non-traditional) model organisms.

Please see the following papers for more information on cross-species comparisons of model organisms with relevance to disease:

Golstein, P., Aubry, L., and Levraud, J.P.   Cell-death alternative model organisms: why and which? Nature Reviews Molecular and Cell Biology, 4(10), 798-807 (2003).

Goldstein, P.   Cell death in unusual but informative and beautiful model organisms. Seminars in Cancer Biology, 17(2), 91–93 (2007).

[3] this effect can be observed (usually understood via anecdotal reporting methods) both in vivo and in vitro (cell culture models). 

[4] the variational principle is widely used in quantum physics and engineering to arrive at solutions in very large, complex systems. Why not a version of this idea for physiological systems analysis?

[5] this suggests a role for mechanisms such as robustness, evolvability, and degeneracy.

[6] Longo, V.D. and Fabrizio, P.   Regulation of longevity and stress resistance: a molecular strategy conserved from yeast to humans? CMLS: Cellular and molecular life sciences, 59(6), 903-908 (2002).

[7] Here are a host of relevant papers (including a recent feature article in Nature):

a. Varshney, G.K. et.al   A large-scale zebrafish gene knockout resource for the genome-wide study of gene function. Genome Research, 23, 727-735 (2013).

b. Kettleborough, R.N.W. et.al   A systematic genome-wide analysis of zebrafish protein-coding gene function. Nature, doi:10.1038/nature11992 (2013).

c. Schier, A.F.   Zebrafish earns its stripes. Nature, doi:10.1038/nature12094 (2013).

d. Howe, K.   The zebrafish reference genome sequence and its relationship to the human genome. Nature, doi:10.1038/nature12111 (2013).

e. Barbazuk, W.B.   The Syntenic Relationship of the Zebrafish and Human Genomes. Genome Research, 10, 1351-1358 (2000).

[8] data derived from multiple consensus phylogenies (a meta-meta analysis) curated by Timetree.org. 

[9] This was cross-posted to my micro-blog, Tumbld Thoughts:

A new paper by Button et.al [a] featured in Wired Science claims that research in the Neurosciences are plagued with low statistical power (e.g. explanatory capacity of significant results), which is based on an 2005 paper by John Ioannidis [b] that applies a measure called positive predictive value (PPV) for determining the reliability of results in a particular scientific field (top image). While Ioannidis originally focused on results in Psychology, in later papers he has extended this line of inquiry to Computational Biology (e.g. microarray analysis) [c].

This reliability can be compromised by something called the proteus phenomenon [d], which deals with drawing a consensus from a series of datasets that exhibit similar biases. Two potential examples of this can be seen in a meta-meta analysis of the Psychological literature (Figure 3) from [a], and the Social Psychology literature. In the case of the latter, a paper from Vul et.al [e] investigates the exceedingly high correlations between brain activity data yielded from neuroimaging data and personality (e.g. self-reported) measures. Does this mean that there truly IS a high correlation, or is a subtle bias at work here?

Whether these concerns are overblown is up for debate, and it may be an artifact of the way we test for significance (e.g. NHST) rather than inherent problems with the method of experimental replication [f]. Fortunately, people are trying to address some of these issues (bottom image). Examples include the Equator Network [g] and the reproducibility project [h], both of which advocate open science. And, of course, there are more philosophically-oriented issues that I have started to address with the Hard-to Define Events (HTDE) approach.

NOTES:

[a] Button, K.S., Ioannidis, J.P.A., Mokrysz, C., Nosek, B.A., Flint, J., Robinson, E.S.J., and Munafo, M.R.   Power failure: why small sample size undermines the reliability of neuroscience. Nature Reviews Neuroscience, doi:10.1038/nrn3475 (2013).

[b] Ioannidis, J. P.   Why most published research findings are false. PLoS Medicine, 2, e124 (2005).

[c] Ioannidis, J. P. et.al   Repeatability of published microarray gene expression analyses. Nature Genetics, 41, 149–155 (2009).

[d] Pfeiffer, T., Bertram, L. & Ioannidis, J. P. Quantifying selective reporting and the Proteus phenomenon for multiple datasets with similar bias. PLoS ONE 6, e18362 (2011).

"The chances for non-significant studies going in the same direction as the initial result are estimated to be lower than the chances for non-significant studies opposing the initial result"

[e] Vul, E., Harris, C., Winkielman, P.,  and Pashler, H.   Puzzlingly High Correlations in fMRI Studies of Emotion, Personality, and Social Cognition. Perspectives on Psychological Science, 4, 274 (2009). Also see Ed Vul's site on "voodoo correlations".

[f] for more information on the BEST test (an alternative to tests of the null hypothesis), please see: Kruschke, J.K.   Bayesian estimation supersedes the t test. Journal of Experimental Psychology: (2012).

[g] Simera, I. et.al   Transparent and accurate reporting increases reliability, utility, and impact of your research: reporting guidelines and the EQUATOR Network. BMC Medicine 8, 24 (2010). 

Other (more specialized) consortia geared toward this end include the Consolidated Standards of Reporting Trials (CONSORT), the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), and the Collaborative Approach to Meta-Analysis and Review of Animal Data in Experimental Stroke (CAMARADES).

[h] Open-Science-Collaboration. An open, large-scale, collaborative effort to estimate the reproducibility of psychological science. Perspectives in Psychological Science, 7, 657–660 (2012).

Google Doodle -- Leonhard Euler

This has been cross-posted to my micro-blog, Tumbld Thoughts.

Today's Google Doodle (still shot of animation shown here) is in celebration of Leonhard Euler's posthumous 309th birthday. He may be dead in the flesh, but his ideas live on [1]. He had an incredible output of coherent (and long-lived) ideas, some of which are used in technologies as diverse as aircraft design, global positioning systems, the design of virtual (computer) interfaces, and holographic design [2].

NOTES:

[1] For those who are unfamiliar with his work, he's postage stamp worthy (see above image). This used to be a big deal. Perhaps in Europe it still is.

[2] Mathematical and physical tools such as Euler angles and Euler's disks (here is video demo of Euler angles in the context of a phenomenon called gimbal lock and Euler disks in the context of holographs).

Richard Gordon, Transmogrifying from Virtual to Physical, Brought us Bits of Embryogenesis

I was honored to be able to bring Dr. Richard (Dick) Gordon to the Michigan State campus for a seminar on April 9 (see video on Vimeo). Currently at the Gulf Specimen Marine Laboratory (and retired from the University of Manitoba), Dick is a theoretical development biologist of the highest caliber [1]. He gave a talk entitled "Cause and Effect in the Interaction between Embryogenesis and the Genome" [2]. He even brought toys [3] to illustrate his theory of cellular differentiation.

Dick's virtual world avatar (Paleo Darwin) is seated in the middle picture.

Dick Gordon, master collaborator.

The theoretical model he presented suggests that differentiation waves [4] pulse through the embryo during development, which set up spatially-restricted gene expression and differentiation into distinct cellular types. According to this view, each cell's differentiation is a binary and recursive process (e.g. one "decision" point building upon another), and is contingent upon the cell's position and environment. In this sense, higher-level organization (e.g. modules) are not caused by gene expression. Rather, gene expression changes that lead to observable phenotypic modules [5] and other patterns are caused by the extracellular environment of a developing organism.

An example of a Wurfel toy, taken from a slide in his talk. A fine example of Canadian innovation.

There were many profound moments in this lecture. An overarching theme of the talk was how candidate ideas (e.g. hypotheses) are tested, implemented, and critically examined in the course of doing science. One of these was the "organizer" experiments of Hans Spemann [6], in which a piece of tissue transplanted to an embryo can induce the formation of a second animal. Subsequent experiments have shown that while transplanted tissue accomplishes this, other transplanted materials (even some which are non-organic) can induce this response as well. Perhaps the effect is not due to the tissue itself, but the hydrophobicity or hydrophilicity of the materials transplanted. This might be characterized as a special case of Type I error due to incomplete experimental information [7].

Picture of Hans Spemann (inducers).

Another candidate idea presented was Alan Turing's notion of "morphogens". Morphogens are hypothetical molecules proposed by Turing to drive pattern formation in a developing organism [8]. According to the talk, morphogens are not the causal factor for morphogenesis, nor are genetic regulatory cascades. Instead, they are both driven by expansion and contraction waves that course through the embryo. These waves (which have been observed) also trigger the mechanisms of differentiation (e.g. signaling molecules and gene expression changes) in cells. A good example of the problems related to establishing causality in a complex systems.

Picture of Alan Turing (morphogens).

Time-course (and illustration) of differentiation waves moving across an embryo from the talk.

After the talk, Dick and I discussed the possible role of differentiation wave-like activity in the process of in vitro (or perhaps even in vivo) cellular reprogramming (the controlled phenotypic transformation of a cell from one phenotype to another). Interesting stuff, and as always, you are welcome to participate in the Embryo Physics course [9], which is made possible by a fine group of people. Please contact myself or Dick if you are interested in presenting.

The scene of the crime, so to speak. Some quiet moments before my virtual lecture (Scenes from a Graphical, Parallel Biological World) given in April, 2012.

NOTES:

[1] He was originally trained in chemical physics at the University of Oregon (home of the Oregonator). See his Google Scholar profile for more information. According to their records, he has a h-index of 32 (which is quite impressive). He also has an Erdos number of 2i (long story).

Animation of the Oregonator (activator/inhibitor system). COURTESY: Scholarpedia.

[2] Here is a link to the version of this talk (.pdf slides) presented in the Embryo Physics course on March 20, 2012.

[3] One of these was a Wurfel, which is a bunch of wooden blocks joined together with an elastic string. I own one of these, and before this lecture I had no idea as to its name!

The Wurfel was used to demonstrate the configurational constraints and opportunities afforded to the genome due to a cell's biophysical and epigenetic context. For more fun (and combinatorics) with puzzles, please see the following blog post: Puzzle Cube. Paleotechnologist blog, August 31 (2011).

[4] According to his talk, these may either be calcium waves or something functionally similar. For an introduction to embryonic calcium waves (and how to image them), please see:

Gillot, I. and Whittaker, M.   Imaging Calcium Waves in Eggs and Embryos. Journal of Experimental Biology, 184, 213–219 (1993).

[5] Here is a video from Jeff Clune (University of Wyoming) demonstrating how modularity might have evolved using the software platform HyperNEAT (evolutionary neural networks). Based on the following paper:

Clune, J., Mouret, J-B., and Lipson, H.   The evolutionary origins of modularity. Proceedings of the Royal Society B, 280, 2012-2863 (2013).

[5] Here is a YouTube video that explains Spemann's organizer experiments in more detail.

[6] This fits very much within the scope of the Hard-to-define-Events (HTDE) approach. For more information, please see the HTDE 2012 workshop website.

[7] Here are some examples of morphogenesis (sensu Turing) the morphogen concept modeled using the Gro programming language (from the Klavins Lab, University of Washington).

The morphogen concept was some of Turing's later work. Even though Turing was a computing pioneer, his coupled reaction-diffusion model of chemical morphogenesis have become a prevailing view of how developmental morphogenesis proceeds. However, these ideas are also useful in the computational modeling of textures. See Turing's classic paper for more information:

Turing, A.M.   The Chemical Basis of Morphogenesis. Philosophical Transactions of the Royal Society of London, 237 (641), 37–72 (1952).

[8] While not formally a MOOC, the Embryo Physics course is an example of distributed learning. For more information, watch for the forthcoming paper:

Gordon, R.   The Second Life Embryo Physics Course. Systems Biology in Reproductive Medicine, x(x), xxx-xxx (2013).