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].

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.

[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.

[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.

February 29, 2016

Stardates and Interdigitated Rabbits

Today's Google Doodle animation is in honor of leap year in the Gregorian Calendar. As you can see from the images below, legend has it that rabbit #29 jumps in between rabbits #28 and #1 without disturbing their sleep. Whether any of these cartoon rabbits are related to Inspector #5 is not clear. 

A bit more seriously (but still in the realm of fiction) is the art and science of timekeeping. The leap year, occurring once every four years, is actually a transannual correction on the 365 day year. As it actually takes 365.25 days for the Earth to make a single orbit around the Sun, the Gregorian calendar falls short. In fact, there has yet to be a calendar created that perfectly captures the length of a solar year. This brings us to a potential candidate, the well-known Stardate.

However, despite stardates being the primary mode of timekeeping in a fictional interstellar civilization, they are surprisingly fluid from one part of the galaxy to the next, and from one series to the next. But you can download a more stable version for your own computer, as the concept of a stardate is based on a standard mathematical model.

Regardless of the inconsistencies in  the Stardate system, time travel occurred a number of times in the Star Trek franchise. As this is the 50th anniversary of the first season of Star Trek: TOS, it's a good time to look at instances of time travel in the Trek franchise:

Ex Astris Scientia, Time Travel in the Abramsverse

Memory Alpha Wiki, Temporal mechanics

Time travel tech, Trek style. COURTESY: ArsTechnica and Paramount Pictures.

February 26, 2016

Kluged Curiosities and Network Connectivity, February 2016

While this blog has matured past the stamp-collecting phase of inquiry, we will nevertheless review a series of curiosities from the last few months. This includes a few readings from the network science literature that have been percolating (pardon the pun) through my reading queue.

The first of these is a game that relies on your pattern recognition skills as well as a keen eye for outliers. The "Guess the Correlation" game trains you to see the signal through the noise, provided that signal is a linear correlation amongst less than 100 datapoints.

Visual approximation of an embedded signal. COURTESY: guessthecorrelation.com

This is also a nice example of domain expertise versus the precision of statistical techniques [1], and perhaps a lesson in naive feature creation.

Crowdfunding, or raising modest amounts of money from large numbers of individuals, is emerging as an alternative way of raising money for side projects and attaining short-term research goals. A new paper on crowdfunding in PLoS Biology [2] called "A Guide to Scientific Crowdfunding" gives excellent tips for starting your own crowdfunding campaign and a bibliography for further reading on scientific crowdfunding.

Last year was the 55th anniversary marking the discovery of the giant component (a key foundation in the area we now call network science) by Erdos and Renyi [3]. Disrupting this giant component by reducing the connectivity of a complex network has many practical applications [4]. Kovacs and Barabasi [5] introduce us to the concept of connective destruction, which refers to the selective removal of nodes that partitions a network into smaller, disconnected components (effectively isolating subnetworks)?

Morone and Makse [6] attempt to solve this NP-hard problem by developing the collective influence algorithm. Collective influence takes into account a node's extended degree, or the degree of a central nodes as well as its connections up to several links away. By taking into account both the strong and weak links of a given node of high-degree, the computational complexity of optimal network partitioning can be reduced to O(n log n).

Explosive percolation, now officially famous. COURTESY: Allen Beattie and Nature Physics.

A related (and perhaps in some ways inverse) problem is that of explosive percolation. Explosive percolation is the sudden emergence of large-scale connectivity in networks [7]. As connective destruction makes it easier to control, say, a disease outbreak, explosive percolation makes it harder. Fortunately, we are discovering ways to control this transition [8]. For example, approaches based on Achlioptas processes (a form of competitive graph evolution) can be successful at delaying or otherwise controlling the onset of explosive percolation [9]. 

Recently, I got into a series of conversations about the use of Lena Soderberg's image as a standard computer vision benchmark. Apparently, one reason it is used is because it is the visual equivalent of a pangram [10]. Regardless, here is the historical background on one of the most benchmarked photos in Computer Science history [11].

The oft-benchmarked photo (circa 1972).

[1] Driscoll, M.E. The Data Science Debate: domain expertise or machine learning? Data Utopian blog. Accessed on 2/26/2016.

[2] Vachelard, J., Thaise Gambarra-Soares, T., Augustini, G., Riul, P., Maracaja-Coutinho, V. 2016. A Guide to Scientific Crowdfunding. PLoS Biology, 14(2), e1002373.

[3] Spencer, J. 2010. The Giant Component: the golden anniversary. Notices of the AMS, June/July, 720-724.

* discusses interesting historical links between discovery of the giant component and Galton-Watson processes (the mathematics of branching processes in biology).

[4] Kovacs, I.A. and Barabasi, A-L. 2015. Destruction perfected. Nature News and Views, 524, 38-39.

[5] Keeling, M.J. and Eames, K.T.D. 2005. Networks and epidemic models. Journal of the Royal Society Interface, 2(4), 295–307.

[6] Morone, F. and Makse, H.A. 2015. Influence maximization in complex networks through optimal percolation. Nature, 524, 65-68.

[7] Ouellette, J. 2015. The New Laws of Explosive Networks. Quanta Magazine, July 14.

[8] Achlioptas, D., D'Souza, R.M., and Spencer, J. 2009. Explosive Percolation in Random Networks. Science, 323(5920), 1453-1455.

[9] D'Souza, R.M. and Nagler, J. 2015. D'Souza, R.M. and Nagler, J. 2015. Anomalous critical and supercritical phenomena in explosive percolation. Nature Physics, 11, 531-538.

[10] A phrase that uses all of the letters in the available alphabet. One example: "the quick brown fox jumped over the lazy dog".

[11] Hutchinson, J. 2001. Culture, Communication, and an Information-Age Madonna. IEEE Professional Communication Society Newsletter, 45(3).