Fireside Science: Incredible, Evo-Developmental, and Aestastical Readings!

Here is yet another set of features from my micro-blog Tumbld Thoughts, although this time they will be cross-posted to Fireside Science. Also at Fireside Science is a short feature on my Orthogonal Research initiative. Among these three features are publications, articles, and videos from my reading queue, serving up some Summertime (Summer is Aestas in Latin) inspiration. 

I. Incredible Technologies!

Real phenomena, incredible videos. Here is a reading list on resources on how film and animation are used to advance science and science fiction alike. Here they are in no particular order: 

Gibney, E.   Model Universe Recreates Evolution of the Cosmos. Nature News, May 7 (2014).

A Virtual Universe. Nature Video, May 7 (2014).

Creating Gollum. Nature Video, December 11 (2013).

Letteri, J.   Computer Animation: Digital heroes and computer-generated worlds. Nature, 504, 214-216 (2013).

Laser pulse shooting through a bottle and visualized at a trillion frames per second. Camera Culture Group YouTube Channel, December 11 (2011).

Hardesty, L.   Trillion Frame-per-Second Video. Phys.org, December 13 (2011).

Ramesh Raskar: imaging at a trillion frames per second. Femto-photography TED Talk, July 26 (2012).

Preston, E.   How Animals See the World. Nautil.us, Issue 11, March 20 (2014).

How Animals See the World. BuzzFeed Video YouTube Channel, July 5 (2012).

In June, a Synthetic Daisies post from 2013 was re-published on the science and futurism site Machines Like Us. The post, entitled "Perceptual time and the evolution of informational investment", is a cross-disciplinary foray into comparative animal cognition, the evolution of the brain, and the evolution of technology. 

Evo-Developmental Findings (new)!

Phylogenetic representation of sex-determination mechanism. From Reading [3]

Here are some evolution-related links from my reading queue. Topics: morphological transformations [1], colinearity in gene expression [2], and sex determination [3].

The first two readings [1,2] place pattern formation in development in an evolutionary context, while the third [3] is a brand new paper on the phylogeny, genetic mechanisms, and dispelling of common myths involved with sex determination.

III. Aestastical Readings (on Open Science)!

 

Welcome to the long tail of science. This tour will consist of three readings: two on the sharing of "dark data", and one on measuring "inequality" of citation rates. In [4, 5], the authors introduce us to the concept of dark data. When a paper is published, the finished product typically includes only a small proportion of data generated to create the publication (Supplemental Figures notwithstanding). Thus, dark data is the data that are not used, ranging from superfluous analyses to unreported experiments and even negative results. With the advent of open science, however, all of these data are potentially available to both secondary analysis and presentation as something other than a formal journal paper. The authors of [5] contemplate the potential usefulness of sharing these data.

Dark data and data integration meet yet again. This time, however, the outcome might be maximally informative. From reading [5].

In the third paper [6], John Ioannidis and colleagues contemplate patterns in citation data that reveal a Pareto/Power Law structure. That is, about 1% of all authors in the Scopus database produce a large share of all published scientific papers. This might be related to the social hierarchies of scientific laboratories, as well as publishing consistency and career longetivity. But not to worry -- if you occupy the long-tail, there could be many reasons for this, not all of which are harmful to one's career.

NOTES:

[1] Arthur, W.   D'Arcy Thompson and the Theory of Transformations. Nature Reviews Genetics, 7, 401-406 (2006).

[2] Rodrigues, A.R. and Tabin, C.J.   Deserts and Waves in Gene Expression. Science, 340, 1181-1182 (2013).

[3] Bachtrog et.al and the Tree of Sex Consortium   Sex Determination: Why So Many Ways of Doing It? PLoS Biology, 12(7), e1001899 (2014).

[4] Wallis, J.C., Rolando, E., and Borgman, C.L.   If We Share Data, Will Anyone Use Them? Data Sharing and Reuse in the Long Tail of Science and Technology. PLoS One, 8(7), e67332 (2013).

[5] Heidorn, P.B.   Shedding Light on the Dark Data in the Long Tail of Science. Library Trends, 57(2), 280-299 (2008).

[6] Ioannidis, J.P.A., Boyack, K.W., and Klavans, R.   Estimates of the Continuously Publishing Core in the Scientific Workforce. PLoS One, 9(7), e101698 (2014).

One Evolutionary Trajectory, Many Processes

Two months ago, I wrote a post on how we might think more deeply about human biological variation. This post also involved a discussion about the recent Nicholas Wade book (A Troublesome Inheritance), which has engendered its own rancor on the internet [1]. In the second part of the post, I discussed some potential ways we can more effectively model human variation. This was decidedly exploratory, the intent of which was to follow-up on the discussion. In this post, I will discuss the need for taking cultural evolution and other factors into account when interpreting genetic variation.

To understand where I am coming from here, consider the difference between population genetics and behavioral ecology approaches. In population genetics, the concern is over observing the patterns of standing variation in a population. We discuss topics such as allele frequencies, admixture, and the mechanisms of genetic differentiation. But populations also behave, and in behavioral ecology topics such as sexual selection, foraging patterns, and strategic behaviors are also taken into account.

While there is indeed implicit overlap between these two fields in the literature, there is little direct theoretical synthesis in this direction. For example, if one takes species concepts into account [2], we can see the issue rear its head: one can apply a host of species concepts which explain both the behavioral and genealogical dynamics of a population, but a unified conceptual framework (e.g. one that is not contradictory) is elusive.

Yet even when only taking behavioral dynamics into account, there are a multitude of factors that make direct comparisons between populations difficult. Species differ in both their sociality and acquisition of culture. This differentiation is even more profound in terms of how culture has shaped a species' ability to adaptively radiate and persist over multiple generations. Humans are not only an intensely eusocial species, but also fall into the latter category of being shaped by culture as much as by environmental selection.

One might simply refer to this as "cultural selection", but a better approach is to model the process of genealogical and cultural (or social) evolution as nominally separate but interrelated processes. In "Playing the Long Game of Human Biological Variation", I advocated for the use of dual process models. Such models treat the same population as being subject to two or more distinct processes simultaneously. In a Synthetic Daisies post re-published at Humanity+ [3], I introduced a dual process Artificial Life-based model that integrates genealogical dynamics and biogeographic processes (specifically changes in geomorphology).

There are a good number of examples of dual process models in the literature which integrate cultural and biological evolution. A good starting point is the work of Richerson, Boyd, McElreath, and Henrich [4, 5], who use a dual inheritance model (DIT) with similar genetical and cultural inheritance mechanisms. While this does not distinguish between the mode transmission for genetical units (genealogy) and cultural units (social learning), it does allow for their dynamics to differ within a population.

This provides us with a conceptual expression of "nature" not being equivalent to "nurture", even though we end up in a place similar to the species concept example. But this does not necessarily solve a key issue; namely, that culture and genetics do not simply have the potential to follow divergent trajectories. Culture might also provide a coherent and context-dependent evolutionary constraint [6] which can influence "fast" human evolution [7]. Specifically, culture might influence genetic evolution indirectly through evolutionary constraints (EC) on admixture, migration, local environmental genetic polymorphisms, and demographic fluctuations [8].

One example of a dual process model (in this case, an example from niche construction). COURTESY: Niche Construction page, Semiotics Encyclopedia Online.

Notice that this is quite a bit different than claims of genetics influencing cultural evolution, or culture acting as a multiplier of genetic differences. In fact, the effect is not a feedback or other type of causal mechanism at all, but rather an incongruence [9]. Evolutionary incongruence (EI) occurs when the evolutionary trajectory of the genome and the cultural environment do not lead in the same direction [8].

For example, even though you might possess a genotype that makes you very unfit for a certain environment, possessing a cultural adaptation on top of this genotype might make you fit enough (or even very fit). EI and EC can also determine more general outcomes in a dual process model. In the case of humans, where culture enables humans to survive in environments beyond what is enabled by genes alone, EI is much more dominant than EC. You can still find genetic variants that result from adaptation to a specific local environment, but they are not the determining factor in survival. In a very different context, for example in the case of a solitary species, EC might dominate over EI.

In summary, accounting for variation within and between human groups might best be done using a sophisticated theoretical framework. This framework includes 1) the use of a dual process model that represents cultural and genetic evolutionary processes, and 2) the identification of how culture contributes to genetic variation, namely either through constraint (which enables feedback between genes and culture) or incongruence (where the variation contributed by genetic and cultural evolutionary processes point in different directions). In the case of eusocial species that possess culture (example: Homo sapiens), incoherence will be predominant, although constraint can drive forward local genetic adaptation when needed.

 Examples of eusocial (left) and solitary (right) species.

In a future post, I will explore another theme in the original "Long Game" blog post, namely the idea that panmixia might not be the best way to assess the absence of population subdivision. Instead of using a traditional population genetics model, using scale-free networks to represent the null hypothesis might give us a more profound theory and more realistic results. Look forward to it.

NOTES:
[1] I'm not particularly interested in ideological debates. But be aware that this post will be largely theoretical and perhaps a bit too speculative. That's the way theoretical advances are made!

[2] Wheeler, Q. and Meier, R.   Species Concepts and Phylogenetic Theory: a debate. Columbia University Press (2000).

[3] Alicea, B.   Artificial Life meets Geodynamics (EvoGeo). Humanity+ Magazine, December 7 (2012).

[4] Richerson, P.J. and Boyd, R.   Not By Genes Alone: How Culture Transformed Human Evolution. University of Chicago Press (2005).

[5] McElreath, R. and Henrich, J.   Dual inheritance theory: the evolution of human cultural capacities and cultural evolution. In "Oxford Handbook of Evolutionary Psychology", R. Dunbar and L. Barrett eds., Oxford University Press (2007).

[6] Boyd, R. and Richerson, P.   The cultural transmission of acquired variation: effects on genetic fitness. Journal of Theoretical Biology, 100, 567-596 (1983).

[7]  Hawks, J., Wang, E.T., Cochran, G.M., Harpending, H.C., and Moyzis, R.K.   Recent acceleration of human adaptive evolution. PNAS, 104(52), 20753–20758 (2007).

[8] NOTE: The terms and abbreviations for evolutionary constraint (EC) and evolutionary incongruence (EI) are of my own coinage.

[9] Laland K. and Brown, G.   Sense and Nonsense: Evolutionary Perspectives on Human Behavior. Oxford: Oxford University Press (2002).

Four Readings and an Open Science Argument

Here are some papers from my reading queue. Four readings on human culture, behavior, and evolution, and one feature (set of readings) on Open Science. 

Four Readings......

Here are four readings from the reading queue on human culture, behavior, and evolution. The picture below (only tangentially related to the first paper) is from [1].

The first paper [2] is on the genetic architecture of economic and political preferences. Using a SNP analysis, the authors demonstrate that such traits have a polygenicarchitecture (e.g. many genes, small effect size for each). Studies that are underpowered (and no one really knows what the appropriate sample sizes should be) can potentially generate many false positive associations between genes and behavior. Nevertheless, understanding the presence of key variants for social preferences might help us understand why some people seem to be inherently "liberal" or "conservative".

The second paper [3] presents us with a premise that equates (or perhaps confounds) the psychophysiology of political ideologies with the roots of more general ideological bias. Are we really looking at "natural" differences between liberals and conservatives? Or does this simply demonstrate that high-profile social issues with already polar liberal and conservative positions [4] are undergirded by strong emotional responses? The standard evolutionary psychology explanation is a bit contrived as well. But it goes well with the previous article.

Crossmodal and cross-cultural comparisons, unite! In this study [5], people from several different cultures were asked to make both "congruent" and "incongruent" associations between smells and colors. The authors come to the conclusion that cultural context through experience has both statistical (covariance) and semantic (linguistic) components.

The fourth article [6] is a gateway article to several recent studies in the area of neuroplasticity. The gateway leads to the work being done in the laboratory of Michael Stryker [7]. Learn about the "neural volume control knob" and much, much more.

.....And An Open Science Argument

Here are some additional readings on networking and open science from my reading queue. The first is a paper on the life-cycle of a preprint on the arXiv [8] The top image is Figure 2 in the paper. The other two readings advocate for the use of open access protocols and social media to disseminate research [9] and counter cultural biases towards keeping research behind laboratory doors [10].

NOTES:

[1] Top picture courtesy: http://www.ucsf.edu/news/2009/01/8225/study-powerful-genes-and-cancer-points-vitamin-d-inflammation.

[2] Benjamin, D.J. et.al    The genetic architecture of economic and political preferences. PNAS, 10:1073/ pnas.1120666109 (2014).

[3] Mooney, C.   Scientists Are Beginning To Figure Out Why Conservatives Are ...... Conservative. Mother Jones, July 15 (2014).

[4] Related to this is the concept of the news filter bubble. One recent paper on this phenomenon: Koutra, D., Bennett, P., and Horvitz, E.   Events and Controversies: influences of a shocking news event on information seeking. arXiv, 1405.1486 (2014).

[5] Ren et.al   Cross-Cultural Color-Odor Associations. PLoS One, 9(7), e101651 (2014).

[6] Stix, G.   Neuroplasticity: new clues to just how much the adult brain can change. Scientific American blog, July 14 (2014).

[7] Two notable publications:

a) Fu, Y., Tucciarone, J.M., Espinosa, S., Sheng, N., Darcy, D.P., Nicoll, R.A., Huang, J., and Stryker, M.P.   A Cortical Circuit for Gain Control by Behavioral State. Cell, 156, 1139–1152 (2014).

b) Niell, C.M. and Stryker, M.P.   Modulation of Visual Responses by Behavioral State in Mouse Visual Cortex. Neuron, 65, 472-479 (2010).

[8] Shuai, X., Pepe, A., and Bollen, J.   How the Scientific Community Reacts to Newly Submitted Preprints: Article Downloads, Twitter Mentions, and Citations. PLoS One, 7(11), e47523 (2012).

[9] Allen , E.   “All research should be OA”. We agree! ScienceOpen blog, July 14 (2014).

[10] Konkiel, S.   How to become an academic networking pro on LinkedIn. ImpactStory blog, April 24 (2014).

Free Artificial Life (XIV edition) and Other Evolution Readings

Here is some Artificial Life for your summer reading. The Proceedings of Artificial Life XIV (complement to the conference at the end of this month in New York City) is a peer-reviewed venue for papers covering the following topics: evolutionary dynamics, soft robots, agent behavior, collective behaviors, social dynamics and evolution, and cellular automata/ self-organizing systems.

And continuing in the spirit of evolutionary scholarship, here are a few more readings from the queue in the last few months. I would call it the "summer of evolution", but every summer is the season of evolution.

I. Reticulating Clades (not Splines) and the Three Domains

Is it possible that the three domains of life are not monophyletic? A new study in Nature Reviews Microbiology [1] using cutting-edge phylogenetic methods suggests that they are not [2]. As an alternative, they suggest the ring of life hypothesis summarized above, with reticulations between each domain's clade Perhaps we are all not just Eukaryotic, and in a manner that does not directly involve symbiosis.

II. Saturday Morning Skepticism

Saturday Morning Breakfast Cereal meets potential evolutionary mechanisms. This comic from Zach Weinersmith is a joke about Horizonal Gene Transfer (HGT). Which got me to thinking: how over-hyped is HGT? The following article [3] and blog posts [4, 5] should give you some idea, as well as to the true scope of the phenomenon. Short answer: selective in Prokaryotes, nearly non-existent in Eukaryotes, and the enabler of salterns, sushi, worms and aphids [3, 6].

NOTES:

[1] McInerney, J.O., O’Connell, M.J., and Pisani, D.   The hybrid nature of the Eukaryota and a consilient view of life on Earth. Nature Reviews Microbiology, doi:10.1038/nrmicro3271 (2014).

[2] McInerney, J.   The three domains of life hypothesis has been falsified. Bioinformatics and Molecular Evolution Unit blog (2014).

[3] Zhaxybayeva, O. and Doolittle, W.F.   Lateral Gene Transfer. Current Biology, 21(7), R242-R246 (2011).

[4] Coyne, J.   How important is lateral Gene Transfer? Why Evolution is True blog, April 13 (2011).

[5] Kuchner, T.   Horizontal Gene Transfer Takes a Turn: Expansins from Plants to their Bacterial and Eukaryotic Parasites. Molecular Evolution Forum blog, March 14 (2014).

[6] Boto, L.   Horizontal gene transfer in the acquisition of novel traits by metazoans. Proceedings of the Royal Society B, 281, 20132450 (2014).

The diverse places where HGT occurs. COURTESY: Figure 1 in [3].

Contributions to the bioRxiv, Summer 2014

I have been busy finishing up some work done in the Cellular Reprogramming Lab between 2010 and 2013. These two papers were submitted to and rejected as a single paper from PLoS One (after two rounds of revision). They were subsequently split them into their wet-lab molecular biology (written as an extended protocol) and computational components (written as a more conventional manuscript) for publication on bioRxiv.

The first paper (wet-lab molecular biology) is called "Using Polysome Isolation with Mechanism Alteration to Uncover Transcriptional and Translational Dynamics in Key Genes", in which we explore the world of mRNA regulation during adaptive cellular processes. The first part of the title (polysome isolation) involves harvesting mRNA from the polysome (translation-related mRNA). Harvesting this in tandem with mRNA associated with transcription provide us with a direct comparison between the transcriptome (TST) and translatome (TLT).

The second part of the title involves administering drug treatments to fibroblast populations which have systematic effects on transcription and protein production. These treatments are called "mechanism alteration" because they mimic changes that occur in a dying or transforming cell.

 

The third part of the title involves looking at transcriptional and translational dynamics for key genes. One criticism of the combined paper involved the use of candidate genes instead of high-throughput data. High-throughput data is great if one can afford it. On the other hand, large datasets can leave you with more questions than answers, which might be particularly true of this work. 

These figures demonstrate the analysis of experiments which validate the polysome recovery technique and the effects of drug treatments (mechanism disruption) on both transcriptome- and translatome- related mRNA (TST and TLT, respectively).

The second paper (computational) is called "Modeling Cellular Information Processing Using a Dynamical Approximation of Cellular mRNA". There is also a Github repository that contains associated Matlab code and simulations. Work from the first and second papers have been presented previous to their bioRxiv release, notably at the Stem Cell and Regenerative Medicine Conference held on the Oakland University (MI) campus in 2012.

We will go through this title in backwards order this time. The last part (cellular mRNA) refers to its connection to the first paper. Gene expression measured at both the transcriptome (TST) and translatome (TLT) will be used to model the cell's general response to mechanism alteration. In this case, an assumption is made: fluctuations of both mRNA fractions and at multiple points in time represents a regulatory process. 

Thus, a first-order feedback model can be constructed, with a simplified set of feedforward, feedback, and decay components. While there are a multitude of mRNA decay pathways and processing functions, this model focuses on a much simpler abstraction: the path from DNA to protein with single sources of decay and feedback. Each fraction of mRNA can be represented as a point process controlled by inputs and outputs.

The middle part of the title (dynamical approximation) then refers to the simulation of mRNA dynamics using the model, its components, and biological data. The idea is to approximate meaningful trends at certain points in a biological process, which is expected to differ by gene and by model component. This is where the proof-of-concept nature of this paper is most evident. 

While a somewhat contrived means to approximate a complex biological process is used in both papers, the original application was to be for understanding the early stages of cellular reprogramming. However, it proved to be exceedingly difficult to go from iPS cultures to meaningful computational inference.

An example of the first-order feedback model. A: a graphical example of the model components. B: an example of activity among the components over time.

Finally, the first part of the title (modeling cellular information processing) is based on the interpretation of the model output. the notion of cellular information processing treats the regulation of mRNA as an information processing problem. That is, you have an input, a process, and an output. systematic noise can also be added to the model, depending on the application. The process itself (mRNA processing from DNA transcription to RNA translation) is a transformation of information. 

When a cell is challenged by an environmental stimulus or the need to change phenotype, information provided by mRNA can be operated upon in a number of ways (linear responses, accumulation, delayed responses). Three information processing principles are used to interpret phenomena such as linear decay, the sequestration of mRNA at either the TST or TLT, and the differential response among individual genes.

Finally, I must point out how cool the altimetric support is on the bioRxiv. Here is a screen shot showing the number of tweets, abstract views, and .pdf downloads for the wet-lab paper:

Almost as functional as the analytics data on Blogger (which is not saying much, but for a formal publication venue, it's pretty impressive). The people at Cold Spring Harbor Lab have done a good job on this, and are ahead of the people at arXiv on this. As it turns out, however, the arXiv has a principled policy on this. Whether viewership stats are a sign of vanity and worthy of scare quotes is another matter.

Carnivals and Toy Models

Two items of business (blog carnival and new paper announcements) in this post, neither of which involve carnival toys or any variant thereof. But keep reading anyway.

Nothing to see here. Move along.

Carnival of Evolution #73:

 

Time for another Carnival of Evolution. In this edition (#73) hosted by Pleiotropy blog, the theme is a tournament-style presentation based on the ongoing World Cup. This month’s Synthetic Daisies submission lost a close one early, and you will have to read CoE 73 to know who won the whole thing. Thanks go to BjornOstman for all of his work and the clever theme. Have an evolution-related blog post that needs publicizing? Please submit it to the CoE Facebook page.

Alicea, B. and Gordon, R.   Toy models for Macroevolutionary Patterns and Trends, Biosystems, 122, 25-37.

I invite you to take a look at a new paper by myself and Richard Gordon called "Toy Models for Macroevolutionary Patterns and Trends", out now in the journal Biosystems [1]. This will eventually be part of a special issue called "Patterns of Evolution". There is also a Github repository, which will house examples of toy models and other supplemental information. The paper reviews and/or describes 13 toy models, some pre-existing and others brand new examples. Toy models are representations that are intentionally oversimplified, used to approximate overarching trends while at the same time being sensitive to evolutionary context.

 The coupled avalanche model, an example of a macroevolutionary toy model.

We introduce 13 different toy models that cover a range of macroevolutionary phenomena such as the generation of diversity, the representation of lineages, and nonlinear evolutionary changes. There is also an undercurrent of meta-theory and why that is important to evolutionary theory-building.

The paper also provides examples of application domains, such as Artificial Life simulations and the analysis of high-throughput data. Toy models can also be used in tandem to approximate difficult evolutionary problems. While I do not want to give away too much of the details, I will say that the paper should prove useful to hard-core biologists, evolutionary modelers, bioinformaticians, and philosophers of science alike.

[1] the link provided might lead you beyond the Great Wall of Rentier. If you need a copy, e-mail me.