May 26, 2016

Rectified and Ramifying Representations for the Purpose of Theoretical Expediency

One aim of the DevoWorm project is to take a tree structure (in this case a cell lineage tree from an embryo) and extract distributed structural information. This is done to find previously undiscovered patterns in early development (embryogenesis). One way in which this can be accomplished is by building undirected complex networks to represent the relationships between three-dimensional cellular position in a point model of the embryo. Indeed, rather than a branching tree, we are left with a much larger tree with a significant number of cycles. This allows us to examine previously undiscovered interactions between cells based on proximity (such as juxtacrine and paracrine signalling).

A tree with a cycle, indeed. Popular meme or research problem?

Now these ideas have been made concrete in the form of a poster and presentation that describe the methodology and results of representing approximations of cell nuclei in the embryo as a connected network. This work has been featured at the Network Frontiers Workshop (Northwestern University) and the Midwest Regenerative Medicine Meeting (Washington University, St. Louis). Here is the poster in slide form:

















Notice how this approach is both geometrically vivid and extensible to different modes of development. The graphs and statistics were rendered in Gephi, and other computation was done in MATLAB and R. Our next steps include developing customized modules in Gephi for drawing differentiation trees, developing hybrid directed acyclic graph (DAG)/undirected network graph structures, and refining the network construction methodology.

We are also working on a methodology called the scalable interactome, which simply involves using graphs to visualize and extract information at multiple spatial and temporal scales. One current example of this is OneZoom explorer, which renders the tree of life in a fractal manner. This can be extended to exploring the fractal and complex geometric nature of the embryo itself.




A slightly different view of human evolution and rejection of human exceptionalism. COURTESY: OneZoom Tree of Life.

"Miscellaneous Polyhedra" by Carol Branch (no pun intended).


With that nod to complexity, I would be remiss if I did not mention the old SimCity dictum? A gratuitous image of fractals and reference to a Wil Wright easter egg is the perfect way to end this post. 

May 6, 2016

It's a bird! It's a plane! It's a simulated cognitive epiphenomenon!


Is it a robot, or is it a human? Sometimes, the conclusions overlap. The first picture refers to the profanity habit picked up by IBM’s Watson early in the course of its training. It must be disconcerting to hear the word “bullshit” in a synthetic voice (even if it’s Q*bert). A more recent example of this comes from Microsoft's ill-fated attempt at politically-correct AI, giving us more of a robo-fascist instead [1].


Speaking of artificial systems that reveals the less-promoted side of intelligent behavior, I have run across a number of references to the computational exploration of artificial pareidolia [2]. Having first-hand experience with this phenomenon on Facebook, it's nice to see people exploring this oft-maligned feature of human cognition using artificial intelligence.

In these two articles [3, 4], studying digital pareidolia means generating certain types of false-positive using facial recognition software. Whether this highly-restricted definition [5] qualifies as the study of neurological pareidolia, there are many shapes and patterns that have many of the features found in faces [6].

Another way to study digital pareidolia is to evolve faces from a series of overlapping shapes [7]. In this way, we can see exactly how machine learning algorithms come to define a face in both a holistic and feature-based sense. The faces themselves can be evolved using genetic algorithms to breed faces that self-assemble, as has been implemented in an interactive algorithm called Pareidoloop.

Converging to Mona Lisa (using a fitness function).

NOTES:
[1] the goal was not actually to build a politically-correct AI, only a Twitterbot that did not pick up the worst habits of humanity. The project has since been terminated.

[2] Geere, D. Pareidolic robot looks for faces in clouds. Wired UK, October 14 (2012).

[3] Rosen, R.J.   Pareidolia: a bizarre bug of the human mind emerges in computers. The Atlantic, August 7 (2012).

[4] Borenstein, G. Machine Pareidolia: hello little fella meets facetracker. Ideas for Dozens blog, January 14 (2012).

[5] One problem with this definition involves the restriction of the pareidolia phenomenon to faces. The other (and potentially more significant) problem is that biologically speaking, faces (and perhaps other objects) may be processed holistically rather than by evaluating sets of landmarks.

[6] For more reading on the topic, please see:

Taubert, J., Apthorp, D., Aagten-Murphy, D., and Alais, D. The role of holistic processing in face perception: evidence from the face inversion effect. Vision Research, 51(11), 1273-1278 (2011).

Goffaux, V. The discriminability of local cues determines the strength of holistic face processing. Vision Research, 64, 17-22 (2012). Goffaux lab explainer.

Richler, J.J. and Gauthier, I. A meta-analysis and review of holistic face processing. Psychological Bulletin, 140(5), 1281-1302 (2014).

[7] Johansson, R. Evolution of Mona Lisa. December 7 (2008).

April 30, 2016

Claude Shannon, posthumously 1100100

How do you model a centennial birthday, Dr. Shannon? COURTESY: Hackaday blog.

Claude Shannon, the so-called father of information theory, was born 100 years ago today [1]. This is a Google Doodle-worthy event, even though he died in 2001. Hence, internet rule #34' [2]: "if there exists a milestone, there's a Google Doodle for it".

April 30, 2016 Google Doodle.

Claude was also a juggler and an inventor of mechanical toys, hence the zeros and ones being juggled in the Doodle. A few years ago I wrote a post detailing this "mechanical zoo". Not a real zoo, mind you, but a collection of mechanical wonders far removed from his information theory work [3].


NOTES:
Spectrum, April 27.

[2] I made up Rule #34' as a less-provocative variant of existing Rule #34.

[3] his Master's thesis and Bell Systems Technical Journal paper (pdf) were milestones in the then- emerging academic field.

April 6, 2016

Upcoming Update on DevoWorm Project to OpenWorm


Next Friday (4/15) at 9:00am Pacific Time, I will be presenting an update to the OpenWorm Journal Club on advances in the DevoWorm subproject (How a Worm Develops). It has been a year and a half since the previous update [1], and we have made significant progress on a number of fronts:

* as of right now, our group consists of myself, Richard Gordon, Tom Portegys, Steve McGrew, and Gabriel Pascualy.

* DevoWorm now consists of three interests groups, all of which are fairly informal: Digital Morphogenesis, Developmental Dynamics, and Reproduction and Developmental Plasticity. It is hoped that as the project matures and attracts more collaborators, the interest groups will keep the subproject focused on specific goals.



Tom, Steve, and Gabriel have been taking the lead on the Morphozoic platform, which is part of the Digital Morphogenesis interest group. Morphozoic is a hybrid model (Cellular Automata/ANN) that can approximate morphogenetic processes. The Cellular Automata component utilizes an approach called nested neighborhoods that captures the action of cell-cell communication and signaling gradients in a way conventional Moore neighborhoods do not. Tom has also produced a number of demos ranging from simulating biological pattern formation to image processing. This work will be featured in an soon to be published book chapter [2].

Richard Gordon and myself have been taking the lead on the Developmental Dynamics interest group. To this end, we have worked out differentiation trees [3] for Caenorhabditis elegans [4] and Ciona intestinalis [5]. Differentiation trees are essentially reorganizations of the lineage tree based on the size differential of daughter cells after a cell division event, and may point us to subtle spatial patterns such as the precursors of tissue formation. More generally, we have been attempting to work out cross-species comparisons of early embryonic development, as well as novel computational characterizations of both mosaic and regulative development in multiple species. Some of this work will be featured in an upcoming publication in a special issue of the journal Biology [6].

The Reproduction and Developmental Plasticity interest group is focused on the evolution and development of C. elegans life-history, and stems from work I did in Nathan Schroeder's Laboratory at UIUC [7, 8]. So far, this interest group has involved experimental evolution and the induction of developmental plasticity resulting from L1 larval arrest in mutant genotypes. This is the newest area of DevoWorm, but is a necessary component of understanding for working towards whole-organism simulation.

All three DevoWorm project interest groups in their "2-cell phenotype". 

If you are interested in joining the DevoWorm group or just attending one of our group meetings, please attend the OpenWorm presentation or contact one of the current group members. More generally, the OpenWorm project is currently recruiting volunteers, so fill out an application and state your skills and specific interests. We are looking for people with a diversity of backgrounds, from hard-core programming and data analysis skills to science communication specialists and biologists with an interest in theoretical synthesis.


NOTES:
[1] Alicea, B., McGrew, S., Gordon, R., Larson, S., Warrington, T., and Watts, M. (2014). DevoWorm: differentiation waves and computation in C. elegans embryogenesis. bioRxiv, doi:10.1101/009993

[2] Portegys, T., Pascualy, G., Gordon, R., and Alicea, B. (2016). Morphozoic: cellular automata with nested neighborhoods as a novel representation for morphogenesis. Forthcoming in Multi-Agent Based Simulations Applied to Biological and Environmental Systems.

[3] Gordon, R. (1999). The Hierarchical Genome and Differentiation Waves: novel unification of development, genetics and evolution. World Scientific and Imperial College Press, Singapore and London.

[4] Alicea, B. and Gordon, R. (2016). Caenorhabditis elegans Embryonic Differentiation Tree (10 division events). doi:10.6084/m9. figshare.2118049.

[5] Alicea, B. and Gordon, R. (2016). Ciona intestinalis Embryonic Differentiation Tree (1- to 112-cell stage). doi: 10.6084/m9.figshare.2117152.

[6] Alicea, B. and Gordon, R. (2016). Quantifying mosaic development: towards an Evo-Devo Postmodern Synthesis via differentiation trees of embryos. Biology (Special Issue: beyond the modern evolutionary synthesis). Submitted.

[7] Alicea, B. (2016). Evolution in Eggs and Phases: experimental evolution of fecundity and reproductive timing in Caenorhabditis elegans. bioRxiv, doi:10.1101/042143.

[8] Alicea, B. (2016). Genotype-specific developmental plasticity shapes the timing and robustness of reproductive capacity in Caenorhabditis elegans. bioRxiv, doi:10.1101/045609.


March 13, 2016

New Paper on Experimental Evolution (with Nematodes!)


Here is a new paper from the bioRxiv on experimental evolution in Nematodes titled "Evolution in Eggs and Phases: experimental evolution of fecundity and reproductive timing in Caenorhabditis elegans". This represents work done during 2015 in Nathan Schroeder's laboratory at UIUC [1], and is published as part of the new Reproduction and Developmental Plasticity theme in the DevoWorm group (currently consisting of just myself). Here is the abstract:
To examine the role of natural selection on fecundity in a variety of Caenorhabditis elegans genetic backgrounds, we used an experimental evolution protocol to evolve 14 distinct genetic strains over 15-20 generations. Beginning with three founder worms for each strain, we were able to generate 790 distinct genealogies, which provided information on both the effects of natural selection and the evolvability of each strain. Among these genotypes are a wildtype (N2) and a collection of mutants with targeted mutations in the daf-c, daf-d, and AMPK pathways. The overarching goal of our analysis is two-fold: to observe differences in reproductive fitness and observe related changes in reproductive timing. This yields two outcomes. The first is that the majority of selective effects on fecundity occur during the first few generations of evolution, while the negative selection for reproductive timing occurs on longer timescales. The second finding reveals that positive selection on fecundity results in positive and negative selection on reproductive timing, both of which are strain-dependent. Using a derivative of population size per generation called the reproductive carry-over (RCO) measure, it is found that the fluctuation and shape of the probability distribution may be informative in terms of developmental selection. While these consist of general patterns that transcend mutations in a specific gene, changes in the RCO measure may nevertheless be products of selection. In conclusion, we discuss the broader implications of these findings, particularly in the context of genotype-fitness maps and the role of uncharacterized mutations in individual variation and evolvability.

 C. elegans adults, juveniles, and eggs in an unsynchronized culture. COURTESY: Bowerman Lab, University of Oregon.

The entire dataset (genealogies for fecundity and reproductive carry-over measurements) is publically available. Below is a heat map (Figure 6 in the paper) featuring the distribution of that measurement for 14 wildtype and mutant genotypes.

NOTES
[1] For related work, please see "An Experimental Evolution Approach to Understanding C. elegans Adaptability", Poster 766C at the 20th International C. elegans Meeting (2015), Los Angeles, CA.

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