October 23, 2012

Scale and Evolution: a phenomonological perspective

What are the effects of scale on the evolutionary process and its outcomes? I have done a fair amount of thinking about this topic, and have come up with a few diagrams to present this idea more clearly (Figures 1 and 2). This model I am presenting  is a phenomonological model for understanding physiological complexity in animals. As such, it does not focus on the complex features of populations, particularly natural selection or drift, in any explicit way. However, these features can be incorporated when appropriate, and so may be useful for understanding evolutionary dynamics [1].

While relevant to the idea of multilevel selection [2], multiscale evolution is not intended to resolve this controversy. When I say the word “scale”, I actually mean the confluence of two factors: time and organization. Temporal scale can be measured in years, and only by looking at multiple timescales can we understand how processes unfold. However, organizational scale is much less straightforward. 

Figure 1. The hypothesized multiscalar evolution space: temporal vs. organizational scale. The red branching structures are lineages consisting of different biological entities (e.g. molecular networks) that evolve over time. The black arrows (or influence arcs) show the flow of influence between different organizational scales.

In Figure 1, I have selected key transitions in complexity, from molecules to phyletic groups. These are not size scales per se, nor are they nested hierarchies. For example, although organs are bigger than molecules, this is not always the basis of organizational levels (see ecosystems vs. phyletic groups). The foundation for my organizational scheme involves transitions [3] that enable biocomplexity. Moreover, while molecules make up molecular complexes and tissues make up organs, these relationships are not nested (e.g. not all tissues are part of a “parent” organ). In many cases, organizational scales are linked via trophic relationships [4]. In other cases (such as organisms -- communities -- societies), the relationships can be interpreted as nested hierarchies. 

Figure 2. Examples of biological systems at various organizational scales.

Another notable feature of Figure 1: influence arcs, denoted by black arrows. These arrows symbolize the flow of information and/or constraints across levels. For example, there are processes (e.g. reproductive preferences) at the community and societal levels that influence the composition of species, ecosystems, and even phyletic groups. Likewise, physiological processes at the level of organism can constrain (in deterministic fashion) the behavior of cells, connectivity of molecular networks, and the activity of molecules. While this might suggest that transfer functions are necessary to characterize interactions between each level, such tools are rather elusive, especially across biological contexts.

Figure 2 shows the types of systems at each level. This graph is less theoretical, showing the types of systems typical of their level of organization (e.g. coral communities, coastal marsh ecosystems) at the timescale of 10-100 years. In a theoretical sense, systems representing a number of these scales (notably phyletic groups) can exist both at relatively short timescales (the emergence of cichlid diversity in Lake Victoria [5]) and much longer timescales (the adaptive radiation of neural architectures in mammals [6]). It is my hope that studying the highly complex nature of evolution using this framework may lead to new insights. Otherwise, it is a work in progress. In a future post, I will approach the problem of scale and evolution from a more quantitative perspective. In the mean time, the authors of [7] have provided us with a guide (Figure 3) to what mathematical modeling and experimental approaches are typically used with each scale of physiological complexity, from single molecules to the organism.

Figure 3. Diagram of physiological scales of complexity (center) in relation to commonly-used mathematical modeling strategies (left) and experimental strategies (right). COURTESY: Figure 1 in [7].

[1] flexibility with specificity is a key positive attribute of a phenomenological model.

[2] A number of relevant papers exist on this topic. Two that are particularly relevant to this post are:

a) Hogeweg, P.   Multilevel processes in evolution and development: Computational models. Lecture Notes in Physics (Biological Evolution and Statistical Physics), 585, 217-239 (2002).

b) Wade, M.J., Wilson, D.S., Goodnight, C., Taylor, D., Bar-Yam, Y., de Aguiar, M., Stacey, B., Werfel, J., Hoelzer, G., Brodie, E., Fields, P., Breden, F., Linksvayer, T., Fletcher, J., Richerson, P.J., Bever, J.D., Van Dyken, J.D., Zee, P.   Multilevel and kin selection in a connected world. Nature, 463, E8-E9 (2010).

[3] Although the term “evolutionary transition” was originally intended to characterize the origins of biocomplexity, the same concept can be used to characterize extant biodiversity. For original reference, please see: Szathmary, E. and Maynard-Smith, J.M.   The Major Transitions in Evolution. Oxford University Press, Oxford, UK (1995).

[4] For more information on trophic models of organizational scale, please see: Alicea, B. Hierarchies of Biocomplexity: modeling life’s energetic complexity. arXiv Repository, arXiv:0810.4547 [q-bio.PE, q-bio.OT] (2008).

For more information on other multiscale relationships (such as causality between genotype and phenotype), please see: Noble, D.     Genes and causation. Philosophical Transactions of the Royal Society A, 366, 3001-3015 (2008).

[5] For an example, please see: Terai, Y., Takahashi, K., Nishida, M., Sato, T., Okada, N.   Using SINEs to probe ancient explosive speciation: "hidden" radiation of African cichlids? Molecular Biology and Evolution, 20(6), 924-930 (2003).

[6] For an example, please see: de Winter, W.   Evolutionary radiations and convergences in the structural organizations of Mammalian brains. Nature, 409, 710-714 (2001).

[7] Meier-Schellersheim, M., Fraser, I.D.C. and Klauschen, F. (2009) Multi-scale modeling in cell biology. Wiley Interdisciplinary Review of Systems Biology in Medicine, 1(1), 4–14.

October 19, 2012

Moving the Still, courtesy of the .gifted

Animated .gifs are now (apparently) a rediscovered artform [1]. Since acquiring a tumblr account, I have run across the work of many gifted and amazing animated .gif artists. While being displaced by Flash animation for more commercial applications, animated .gifs have a quality that makes then highly amenable to artistic expression. Animated .gif are created using software such as Advanced GIF Animator or Gimp. The software is used to encode a series of sequential still pictures (in order to generate the illusion of movement) utilizing the Graphics Interchange Format (gif) [2]. One advantage of animated .gifs involves being able to dilate the playback rate by over-representing some images more than others. I have even made animated .gifs out of faux 3D images [3], which looks quite reasonable using a standard pair of 3D glasses.

Figure 1: Example #1 of slowing down animation speed in the middle of a sequence, in this case the trajectory of a ball (the AMIGA logo). If you cannot see the full animation, please reload the page.
Figure 2: Example #2 of slowing down animation speed in the middle of a sequence, in this case a semaphore embedded in two circles. If you cannot see the full animation, please reload the page.

And now, Art Basel Miami Beach is hosting an animated .gif exhibit/competition called "Moving the Still" [4]:

Exhibits will be posted to the Tumblr site as they are received. Michael Stipe (of REM fame) is one of the organizers. Should be lots of fun.


[1] the entryway to my website is (and has been for about 12 years) fronted with various animated .gifs.

[2] the .gif format uses LZW lossless compression, which keeps the file size small and preserves most color variation in source images (some features, however, may not be preserved).

[3] technically, this would be called an animated anaglyphs. I use the software Anaglyph Maker to properly align regular .bmp images. The depth-illusory .bmps then served as input for the animated .gif. An example can be seen here:

[4] Submit entries for "Moving the Still" at their Tumblr site.

BONUS: here is an excellent video on the history of Animated .gifs from the PBS show "Off Book".

October 9, 2012

The Spawn of Gurdon’s Frogs

A few days after I returned from giving a presentation [1] to the Midwestern Stem Cell Conference (hosted by Oakland University), it was announced that John Gurdon [2] and Shinya Yamanaka [3] just won the 2012 Nobel Prize in Physiology and Medicine (Figure 1). The award was given for work in cellular reprogramming, which over the past 30 years has moved from the fringe of biological science to a well-established set of biotechnological protocols with much potential for enabling future scientific breakthroughs (Figure 2). 

Figure 1. Headshots of John Gurdon (left) and Shinya Yamanaka (right). COURTESY: Nobel Prize committee website.

Gurdon’s seminal work with frog embryos (1962) helped to establish an approach called nuclear (or indirect) reprogramming [4]. This method ultimately enabled technologies such as animal cloning using somatic cell nuclear transfer (SCNT). The advent of indirect cell reprogramming allowed for the reversion of a mature somatic cell to a stem-like state, which at the time went against prevailing ideas about cellular differentiation. This lead to the creation of Dolly the Sheep, the first mammal reproduced using cloning techniques (Figure 3, top). While a powerful technology in its own right, indirect reprogramming is a low-throughput (e.g. serial) technique that has a rather low overall efficiency. While this is sufficient for reproductive applications, a more flexible approach was needed for other applications.

Figure 2. Intellectual trajectory of Gurdon and Yamanaka’s work. COURTESY: Figure 1 in [5].

These drawbacks provided an impetus for the development of direct reprogramming methods. Direct reprogramming methods involve delivery of a reprogramming agent directly into the cell (for example, see Figure 4), changing gene expression, epigenetics, and ultimately cell pheontype rather than replacing the cell nucleus wholesale [6]. Early experiments with the transcription factor MyoD was found to be sufficient for converting fibroblast cells into skeletal muscle fibers [7]. While this process is also one with relatively low efficiency, it can be administered to large populations of cells simultaneously.

It took another 20 years for Yamanaka (2007) to achieve his winning result (fibroblasts to induced pluripotent, or iPS, cells [8] – see Figure 3, bottom) with four factors (Oct4, Sox2, Klf4, and c-Myc) [9]. However, the field is taking off in a number of promising directions. One is the use of iPS cells for therapeutic applications: fibroblasts taken from a patient donor (e.g. a sufferer of Alzheimer Disease) can be used to create an iPS model of the disease [10]. Another is the role transcription factor cross-antagonisms [11] and other systems-level phenomena play in efficient conversion of somatic cells to a stable iPS state [12]. In addition to induced pluripotent cells, the same basic techniques have been used to generate induced neural cells (iNCs) and induced cardiomyocytes (iCMs) that are fully functional and phenotypically stable [13].

Figure 3. Clonal animals at play (top), and clonal (e.g. iPS) cell lines at work forming a colony (bottom). COURTESY: Stem Cell School.

Figure 4. Examples of cell reprogramming using a variety of transcription factors and input cells. Notice that these methods result in a variety of cell phenotypes. COURTESY: Figure 1 from [6].

More observations on and explanations about this wonderful field of science can be found in the Notes section below. Have fun!


[1] This was a very well-run small regional conference with many excellent talks. My talk was only tangentially related to stem cell biology. It was entitled: “Simulating the Dynamic Regulation of a Cell: Relevance to Cell Reprogramming”. See my talk here

[2] Gurdon, J.B. and Byrne, J.A.   The first half-century of nuclear transplantation. PNAS, 100, 8048-8052 (2003).

[3] Takahashi, K. et.al   Induction of Pluripotent Stem Cells from Adult Human Fibroblasts by Defined Factors. (2007).Cell, 131(5), 861-872.

[4] Gurdon, J.B. and Melton, D.A.   Nuclear reprogramming in cells. Science, 322, 1811-1815 (2008).

[5] Yamanaka, S.   Induced Pluripotent Stem Cells: past, present, and future. STEM, 10(6), 678-684 (2012).

[6] Similar to an approach called lineage reprogramming or transdifferentiation. For more information, please see: Thomas, G. and Enver, T.   Forcing cells to change lineages. Nature, 462, 587-594 (2009).

[7] While this transcription factor (MyoD) was delivered using cDNA constructs, more recent approaches have used transgenes encoded on a viral vector

Paper describing conversion of fibroblast cells to skeletal muscle cells using MyoD: Davis, R.L., Weintraub, H., and Lassar, A.B.   Expression of a single transfected cDNA converts fibroblasts to myoblasts. Cell, 51, 987-1000 (1987).

[8] fibroblasts are the main cell type in skin. Here is a generic example of fibroblast cells in culture:

iPS cells exhibit extensive phenotypic diversity (e.g. partial iPS, or piPS, phenotypes), the extent of which has not been fully characterized. Here is an example of this diversity (COURTESY: myself (primary data), Cellular Reprogramming Laboratory, Michigan State University):

[9] For generation of iPS cells, the four factor approach (Oct4, Sox2, Klf4, and c-Myc) is generally used. Oct4 and Sox2 are the most important factors (the main “hub” in the pluripotency gene network), while c-Myc is viewed by some people as dispensable. The following visuals might help:

Abbey Road to pluripotency: relative importance of the four factors?

The four transcription factors used for making iPS cells. COURTESY: Stem Cell School.

Yamanaka arrived at the four factor cocktail by screening a much larger number of factors, and then reducing the list to the smallest number of factors sufficient for creating a reprogrammed phenotype in a significant number of cells. For the skeptics out there, I can only say that this is not yet an exact or predictive science. See my previous post on the Stem Cell School web resource.

[10] iPS disease models can be used either for basic science or for cell therapy. For more information, please see the following citations: 

a) Kiskinis, E. and Eggan, K.  Progress toward the clinical application of patient-specific pluripotent stem cells. Journal of Clinical Investigations, 120(1), 51–59 (2010) 

b) Patel, M. and Yang, S.  Advances in Reprogramming Somatic Cells to Induced Pluripotent Stem Cells. Stem Cell Reviews, 6(3), 367–380 (2010) 

c) Park, I.H., Arora, N., Huo, H., Maherali, N., Ahfeldt, T., Shimamura, A., Lensch, M.W., Cowan, C., Hochedlinger, K., and Daley, G.Q.   Disease-specific induced pluripotent stem cells. Cell, 134, 877-886 (2008).

[11] Visvader, J.E., Elefanty, A.G., Strasser, A., and Adams, J. M.   GATA-1 but not SCL induces megakaryocytic differentiation in an early myeloid line. EMBO Journal, 11, 4557–4564 (1992).

[12] While the role of each of the four factors is fairly well-established, it is not clear what the cumulative effects of their downstream interactions are. 

For more information, please see: Loh, K.M and Lim, B.   A Precarious Balance: pluripotency factors as lineage specifiers. Stem Cell, 8(4), 363-369 (2011) AND Iwasaki, H. et al. The order of expression of transcription factors directs hierarchical specification of hematopoietic lineages. Genes and Development, 20, 3010–3021 (2006).

Other genes may play a critical role in establishing pluripotency, especially with respect to the time course of reprogramming. For a recent paper on the topic, please see:

Buganim, Y., Faddah, D.A., Cheng, A.W., Itskovich, E., Markoulaki, S., Ganz, K., Klemm, S.L. van Oudenaarden, A., and Jaenisch, R.   Single-Cell Expression Analyses during Cellular Reprogramming Reveal an Early Stochastic and a Late Hierarchic Phase. Cell, 150, 1209–1222 (2012).

[13] For more information on iNCs, please see: Pang, Z.P., Yang, N., Vierbuchen, T., Ostermeier, A., Fuentes, D.R., Yang, T.Q., Citri, A., Sebastiano, V., Marro, S., Sudhof, T.C., Wernig, M.   Induction of human neuronal cells by defined transcription factors. Nature, 476, 220-223 (2011).

For more information on iCMs, please see: Srivastava, D. and Ieda, M.   Critical Factors for Cardiac Reprogramming. Circulation Research, 111, 5-8 (2012).

For more information on in vivo generation of iCMs in mouse, please see: Song, K., Nam, Y-J., Luo, X., Qi, X., Tan, W., Huang, G.N., Acharya, A., Smith, C.L., Tallquist, M.D., Neilson, E.G., Hill, J.A., Bassel-Duby, R., and Olson, E.N.   Heart repair by reprogramming non-myocytes with cardiac transcription factors. Nature, 485, 599-606 (2012).

October 7, 2012

October 3, 2012

Technojunk and the Synthi-tones present....

Here's a cool video I found on YouTube. It is an alternate mix of the Gotye song "Somebody That I Used to Know" [1], played using a cast of modified consumer electronic devices [2].

[1] Gotye, "Somebody that I Used to Know": Official Video.

[2] Gotye, "Somebody that I Used to Know": computer remix (courtesy, bd594).

bd594 (YouTube alias) has produced a few more hits for our enjoyment:

Little Drummer Boy (starring robotic snare drum and HP Scanjet):

House of the Rising Sun (starring old HD on drums and HP Scanjet -- original song by The Animals):

This was all inspired by James Houston doing a "tech" cover of Radiohead's "Nude" (his version is titled "Big Ideas: don't get any").

October 1, 2012

Carnival of Evolution #52 is live!

It's the first of the month again, and Carnival of Evolution, #52 (the network edition) is now live at The Genealogical World of Phylogenetic Networks. The baton has been passed (from CoE #46) in nonlinear fashion [1]. That is to say that the hosts of Carnival of Evolution, #52 have expanded upon the phylogenetic-like results presented in CoE #46, The (tree) Structures of Life by presenting several examples of reticulating phylogenies [2]. They did a pretty good job.

Whereas phylogenetic trees are directed, acyclic graphs (DAGs), reticulating networks allow for possible recombination along the path from common ancestor to the taxonomic units under analysis. As a result, shared and derived traits among taxa in the graph (or network) cannot easily be traced back to a common origin. Here are two of my favorite features from CoE #52:

1) reticulating evolution as a cube (median graphs):

Median graphs are an instance of minimum spanning trees [3]. Using a distance metric, we are able to infer a graph topology based on distance between different taxa. Median networks are undirected because the center of a network (in this case embedded in a metric space) is based on the median value between all taxa under analysis. Thus, there is no clearly defined root nor independent evolutionary trajectories. This is not to be confused with the use of hypercubes in the study of evolvability and robustness [4].

2) Example of horizontal gene transfer (HGT) using blog posts about ENCODE:

Earlier this month, Nature featured a special edition full of papers released from the ENCODE project [5]. The goal of ENCODE is to use high-throughput techniques and bioinformatics to create a genome-wide library of functional elements [6]. This has stimulated much debate in the blogosphere regarding the true meaning behind results and interpretation offered by the study's authors [7] as well as the role of media hype in the scientific process. While I will not jump into the fray here, this debate/critique provides an excellent opportunity for the folks at CoE #52 to demonstrate how this evolutionary process (HGT) is represented in a phylogenetic context.

[1] Of course. I wouldn't have it any other way.

[2] These results are also based on non-replicating blog posts, and as such also have a neo-last common ancestor (neo-LCA). Also featured in CoE #52 is a post on single-organism oriented (e.g. genetic regulatory and physiological) networks from this blog entitled: Cascades in Common: biological network function in evolution.

[3] for an example from human genetics, please see: Bandelt, H-J., Forster, P., and Rohl, A.   Median-Joining Networks for Inferring Intraspeciļ¬c Phylogenies. Molecular Biology and Evolution, 16(1):, 37–48 (1999).

YouTube tutorial on minimum spanning trees, presented by Steven Skiena, SUNY-Stonybrook.

[4] In this case, the networks are directed based on the numerical values (e.g. n-tuples) of individual nodes. For more information/examples, please see:

Wagner, A.   Robustness and evolvability: a paradox resolved. Proceedings of the Royal Society B, 275 (1630), 91-100 (2008) AND Iordache, O.   Self-evolvability for Biosystems. In Understanding Complex Systems, J.A.S. Kelso ed. pp. 101-134. Springer, Berlin (2012).

[5] They have organized all associated papers into a series of topical threads. This is a really innovative way to organize the primary literature.

[6] With all of the tea-leaf reading and going out on a limb that this entails. Keep in mind that this is the first such attempt since the draft sequence of the human genome was released.

[7] Is most of the genome functional, or is there a difference between explicit (causal) and implicit (merely associative) function? Is the criterion for "function" set forth by the ENCODE project a reasonable one? And just how relevant is this result to applications such as curing human disease or changing our understanding of evolution?