April 30, 2015

Talk to UIUC Fly/Worm Club

Here is a link to the slides (short, 20 minute talk) I presented last week to the Fly/Worm Club [1] at the University of Illinois Urbana-Champaign. The talk (an evo-devo perspective on Nematode life-history) is entitled "Natural Variation, Development, and Adaptive Phenotypes in C. elegans".

[1] the Fly/Worm club is hosted by the Smith-Bolton lab in the Department of Cell and Developmental Biology, and includes people interested in flies (Drosophila) and worms (Nematodes, Planaria) from across campus. It meets occasionally.

April 25, 2015

Reading Carnival, April Edition

A new feature here on Synthetic Daisies, which features a variety of readings from blogs, the popular press, and journals of both immediate and long-term interest. This edition features six pieces ranging topically from intellectual property and data analysis to evolutionary biology and complexity theory.

Haydari, S. and Smead, R.   Does Longer Copyright Protection Help of Hurt Scientific Knowledge Creation? JASSS, 18(2), 23 (2015).

An agent-based modeling approach (featuring a type of spatial lattice called an epistemic plane) is used to better understand how copyright protections can both enable and hinder knowledge creation. The model represents knowledge creation in two ways: knowledge can either either "discovered" by agents or remain "undiscovered". Discovered knowledge can be disseminated in either a high-access (proprietary) or open-access (freely-distributable) fashion. This distributed model of scholar behavior has revealed that extended periods of intellectual property protection can act to hinder innovation. While open-access can serve the public good, there is also a role for individual incentives which are served by limited periods of proprietary protection. Whether these returns are served through monetary compensation or social capital accumulation go unexplored.

Lind, P.A., Farr, A.D., and Rainey, P.B.   Experimental evolution reveals hidden diversity in evolutionary pathways. eLife, 10.7554/eLife.07074 (2015).

By examining 28 morphs of the wrinkly spreader phenotype in Pseudomonas fluorescens (a gram-negative bacterium), the authors were able to discover a number of new pathways through which diversity is generated. These unique pathways involved unique, uncharacterized mutations that provided variation to the existing taxonomic group. As instances of parallel evolution, they provided a means to suggest a set of principles that involve changing the regulation of genes followed by a change of function for those genes.

Kiers, E.T. and West, S.A.   Evolving new organisms via symbiosis. Science, 348(6233), 392-394 (2015).

A mini-review on the evolution of symbiont species and how it may account for major transitions in the tree of life.

Dennett, D. and Roy, D.   Our Transparent Future: No secret is safe in the digital age. Scientific American, 312(3), 32-27 (2015).

This essay compares the rise of information transparency, enabled through internet technologies, to the explosion of life's complexity as it occurred during the Cambrian explosion. As a result, the practice of information-handling by individuals and organizations will change due to key innovations. These innovations are analogous to the camera-like retinas, claws, jaws, and shells that emerged amongst animals during the Cambrian. A very Rodney Brooks-esque style argument for internet-enabled (or -forced, depending on your point of view) cultural evolution.

Ellenburg, J.   The Amazing, Autotuning Sandpile. Nautil.us, 23(1) (2015).

A popular science take on the Abelian Sandpile model and its role in pattern formation. The beginning of the article presents a neccessary contrast with the domino model of causality. Unlike a linear model of system dynamics (one event leads to another with a predictable timing), the sanpile model produces nonlinear dynamics with unpredictable timing. While both models involve a simplistic physical structure, but only one produces a highly complex output. Latter portions of the article focus on geometric abstractions (cellular automata) which produce self-organizing and "life-like" behavior.

Brown, C.T.   Cultural confusions about data - the intertidal zone between two styles of biology. Living in an Ivory Basement blog, April 2 (2015).

An interesting blog post (with links and comments) on the cultural meaning of data and what constitutes useful datasets when comparing both academic fields (e.g. computational biology vs. molecular biology) and research outputs (e.g. genome sequences vs. experimental outcomes).

April 22, 2015

Earth Day 2015 Links

Happy Earth Day 2015, by way of Google's Doodle series.

Here are some Earth Day Doodles of years past. And here is an op-ed piece on how the transition from fossil fuels is closer than the pundits believe. Then, enjoy the pale blue dot.

Earth from a slightly different perspective. You are somewhere in "there". COURTESY: Planetary society.

April 7, 2015

Frontiers of (Doing) Science

An update from one of my former colleagues (Steve Suhr), by way of the Lansing (MI) State Journal. Steve and Marie-Claude Senut (his wife, who is also a molecular biologist/Neuroscientist) decided to open their own scientific business (Biomilab LLC) after their soft-money (grant-dependent) positions ended at Michigan State University. As research grants have become both more scarce and more competitive, their options were to either uproot their lives yet again or strike out on their own. Having moved around the country extensively in the name of doing science, this was more of a rational lifestyle choice than a risk-taking venture.

Mom-and-pop entrepreneurship in the name of science. In this case, "shop" could be replaced by "collaborate". I don't suppose that they will limit their collaborations to the "local", however.

Described by Steve as a "Mom-and-Pop biotech company", Biomilab's business revolves around contracting with academic labs to do protocols and analyses involving specialized equipment or expeertise that the contracting lab does not have immediate access to. Steve specializes in genetic engineering (the creation of transgenic constructs), while Marie-Claude specializes in neurogenesis and neural differentiation. Good luck Steve and Marie-Claude!

Steven T. Suhr, at the edge of a new frontier. COURTESY: Lansing State Journal.

March 30, 2015

Causality, part II (was it caused by Part I?)

This post serves as a follow-up to a Synthetic Daisies post written in 2012 on new methods to detect causality in data.

Here are a few interesting readings at the intersection of data analysis and the philosophy of science. The first [1] is a new arXiv paper [2] that evaluates two approaches to evaluating causality using two machine learning techniques. A plethora of discriminative machine learning techniques have emerged in recent years to address relatively simple relationships. In terms of cause and effect itself, the distinguishing signal is often subtle and unclear even for seemingly obvious sets of relationships. In [2], techniques called Additive Noise Methods [3] and Information Geometric Causal Influence [4]. A dataset called CauseEffectPairs [5] was used to benchmark each method, and show that causal relationships can be uncovered from a wide variety of data.

The second paper (or rather series of papers) is on the topic of strong inference [6]. Strong inference is an alternative to hyper-reductionism and the use of over-simplified models. Strong inference involves the use of a conditional inductive tree to examine the possible causes for a given phenomenon [7]. Potential causes (or hypotheses) represent nodes of the tree, and these hypotheses are falsified as one moves through the tree using either inductive or empirical criteria. Unlike the machine learning models we discussed, the goal is to lead a researcher to key experiments that help to uncover the sources of variation. In general, this process of elimination lead us to the best answers, Yet according to Platt in [2], this approach can ultimarely provide us with axiomatic statements.

Conceptual steps involved in strong inference. COURTESY: Figure 1 in [8].

While this seems to be a fruitful methodology, it has turned out to be more inspirational than as a source of analytical rigor [9]. Strong inference hs inflenced a variety of scientific fields concentrated in the biological and social sciences. Platt predicted [2] that sciences that concurred with strong inference would be fields that experienced a greater number of breakthrough advances. However, in testing Platt's predictions regarding the efficacy of Strong Inference, is have been found that advances are not directly related to the adoption of the method [10]. This could be due to our incomplete understanding of the factors that drive scientific discovery and the rate of advancement. 

[2] Mooij, J.M., Peters, J., Janzing, D., Zscheischler, J., and Scholkopf, B.   Distinguishing cause from effect using observational data: methods and benchmarks. arXiv, 1412.3773 (2014).

[3] Hoyer, P.O., Janzing, D., Mooij, J.M., Peters, J., and Scholkopf, B.   Nonlinear causal discovery with additive noise models. In Advances in Neural Information Processing Systems (NIPS), 21, 689-696 (2009).

[4] Daniusis, P., Janzing, D., Mooij, J.M., Zscheischler, J., Steudel, B., Zhang, K., and Scholkopf, B. Inferring deterministic causal relations. In Proceedings of the 26th Annual Conference on Uncertainty in Artificial Intelligence (UAI), 143-150 (2010).

[5] This work was part of the CauseEffect Pairs Challenge and was presented at NIPS 2013.

[6] Platt, J.R.   Strong Inference: certain systematic methods of scientific thinking may produce much more rapid progress than others. Science, 146(3642), 347-352 (1964).

[7] Neuroskeptic   Is Science Broken? Let's Ask Carl Popper. Neuroskeptic blog, March 15 (2015).

[8] Fudge, D.S.   Fifty years of J.R. Platt's Strong Inference. Journal of Experimental Biology, 217, 1202-1204 (2014).

[9] Davis, R.H.   Strong Inference: rationale or inspiration? Perspectives in Biology and Medicine, 49(2), 238-250 (2006).

[10] O'Donohue, W. and Buchanan, J.A.   The Weaknesses of Strong Inference. Behavior and Philosophy, 29, 1-20 (2001).