My current research involves four related threads that integrate issues from biology and philosophy. All three draw upon my background in software engineering, both as a resource for ideas about change in complex systems, and as training for modelling some of these processes using computer simulations.

Engineering and Evolution

The use of engineering analogies in evolution is often controversial. François Jacobs's "Tinkering and Evolution" (Jacob 1977), is perhaps the most well known paper that suggests why engineering is a poor guide to thinking about evolution (see Pauwels (2013) for a recent example).

Arguments like Jacob's derive much of their credence from an over-simplistic view of engineering, which focuses on optimality as the primary goal in engineering. When we look to the demands of constructing large, complex systems that undergo frequent change (such as modern software), a different set of biologically relevant issues comes into focus, more closely related to issues about evolvability than optimality. At least some engineering looks more like tinkering---Jacob's alternative to engineering. In recent papers and ongoing work, I'm exploring whether ideas from engineering can provide any insights to thinking about evolvability.

Genes, Information, and Programs

Though terms such as information, encoding, and programming are commonplace in biology, it has proved surprisingly difficult to ground these ideas theoretically, and many philosophers have dismissed as simple metaphors. Along with Paul Griffiths, Karola Stotz, and Arnaud Pocheville from Sydney University, I'm exploring some new ways to connect biological information to recent work on causal explanation in philosophy, and to work on evolution of signals and conventions.

Modularity and Evolvability in Gene Networks

Modularity is important for both engineered and evolved systems---it permits localised changes to be made without impacting the entire system, and it allows already functioning parts in a system to be re-used in novel ways. In evolution, however, we need to understand what conditions can produce modularity in the first place. A number of recent network models have successfully evolved modularity (Kashtan and Alon 2005; Clune, Mouret, and Lipson 2013; Espinosa-Soto and Wagner 2010).

I'm working on a novel method to evolve modularity that focuses primarily on the functional interfaces between modules, rather than measures of network structure based on Mark Newman's community structure (M. E. J. Newman 2006). This approach has links to recent work on the evolution of signaling (Skyrms 2009), and also to use of interfaces in software engineering.

Novelties: Integrating Genetic and Physical Factors

Novelties in multicellular form are often explained by referring to "... mechanistic changes in the genetic regulatory program" (Davidson and Erwin 2006). Recently, a number of papers by Stuart Newman and co-authors Gerd Müller, Ramray Bhat, and Gabor Forgacs have proposed that many evolution novelties are explained---at least, in part---by a set of self-organising physical interactions that pattern soft matter (S. A. Newman and Müller 2000; S. A. Newman, Forgacs, and Muller 2006; S. A. Newman and Bhat 2008; S. A. Newman and Bhat 2009). They refer to these processes as Dynamic Patterning Modules (DPMs). These physical processes do involve genes, but Newman et al. explicitly distinguish two broad categories of "toolkit" genes: those involved in the DPMs, and those involved in developmental transcription factors (DTFs). According to them, an explanation purely in terms of change within genetic networks involving DTFs is insufficient to explain the origins of multicellular form.

I aim to integrate the basic model of gene regulation (worked on above) with a simple implementation of multicellular development, enabling the model to evolve a variety of basic physical forms. Such a model will allow manipulation of physical, genetic, and environmental parameters. My aim then is to apply recent philosophical work that identifies distinctive kinds of causal explanatory relationships (see Woodward 2010) to quantify the contribution that regulatory genes versus patterning modules make to explaining evolutionary change within the model.

References

Clune, Jeff, Jean-Baptiste Mouret, and Hod Lipson. 2013. “The Evolutionary Origins of Modularity.” Proceedings of the Royal Society B: Biological Sciences 280 (1755): 20122863. doi:10.1098/rspb.2012.2863.

Davidson, Eric H, and Douglas H Erwin. 2006. “Gene Regulatory Networks and the Evolution of Animal Body Plans.” Science 311 (5762): 796–800. doi:10.1126/science.1113832.

Espinosa-Soto, Carlos, and Andreas Wagner. 2010. “Specialization Can Drive the Evolution of Modularity.” PLoS Computational Biology 6 (3): e1000719. doi:10.1371/journal.pcbi.1000719.

Jacob, F. 1977. “Evolution and Tinkering.” Science 196 (4295): 1161–66. doi:10.1126/science.860134.

Kashtan, Nadav, and Uri Alon. 2005. “Spontaneous Evolution of Modularity and Network Motifs.” Proceedings of the National Academy of Sciences of the United States of America 102 (39): 13773–8. doi:10.1073/pnas.0503610102.

Newman, M E J. 2006. “Modularity and Community Structure in Networks.” Proceedings of the National Academy of Sciences of the United States of America 103 (23): 8577–82. doi:10.1073/pnas.0601602103.

Newman, Stuart A, and Ramray Bhat. 2008. “Dynamical Patterning Modules: physico-Genetic Determinants of Morphological Development and Evolution.” Physical Biology 5 (1): 015008. doi:10.1088/1478-3975/5/1/015008.

———. 2009. “Dynamical Patterning Modules: a ‘Pattern Language’ for Development and Evolution of Multicellular Form.” The International Journal of Developmental Biology 53 (5-6): 693–705. doi:10.1387/ijdb.072481sn.

Newman, Stuart A, and Gerd B Müller. 2000. “Epigenetic Mechanisms of Character Origination,” no. April: 304–17.

Newman, Stuart A, Gabor Forgacs, and Gerd B Muller. 2006. “Before Programs: the Physical Origination of Multicellular Forms.” The International Journal of Developmental Biology 50 (2-3): 289–99. doi:10.1387/ijdb.052049sn.

Pauwels, Eleonore. 2013. “Mind the Metaphor.” Nature, 5–6. doi:10.1038/500523a.

Skyrms, Brian. 2009. “Evolution of Signalling Systems with Multiple Senders and Receivers.” Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 364 (1518): 771–9. doi:10.1098/rstb.2008.0258.

Woodward, James. 2010. “Causation in Biology: Stability, Specificity, and the Choice of Levels of Explanation.” Biology and Philosophy 25: 287–318. doi:10.1007/s10539-010-9200-z.