Algorithmic Darwinism

The workshop on computational theories of evolution started off on Monday, March 17th with Leslie Valiant — one of the organizers — introducing his model of evolvability (Valiant, 2009). This original name was meant to capture what type of complexity can be achieved through evolution. Unfortunately — especially at this workshop — evolvability already had a different, more popular meaning in biology: mechanisms that make an organism or species ‘better’ at evolving, in the sense of higher mutations rates, de novo genes, recombination through sex, etc. As such, we need a better name and I am happy to take on the renaming task.
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Why academics should blog and an update on readership

It’s that time again, TheEGG has passed a milestone — 150 posts under our belt!– and so I feel obliged to reflect on blogging plus update the curious on the readerships statistics.

About a month ago, Nicholas Kristof bemoaned the lack of public intellectuals in the New York Times. Some people responded with defenses of the ‘busy academic’, and others agreement but with a shift of conversation medium to blogs from the more traditional media Kristof was focused on. As a fellow blogger, I can’t help but support this shift, but I also can’t help but notice the conflation of two very different notions: the public intellectual and the public educator.
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Computational theories of evolution

If you look at your typical computer science department’s faculty list, you will notice the theorists are a minority. Sometimes they are further subdivided by being culled off into mathematics departments. As such, any institute that unites and strengthens theorists is a good development. That was my first reason for excitement two years ago when I learned that a $60 million grant would establish the Simons Institute for the Theory of Computing at UC, Berkeley. The institute’s mission is close to my heart: bringing the study of theoretical computer science to bear on the natural sciences; an institute for the algorithmic lens. My second reason for excitement was that one of the inaugural programs is evolutionary biology and the theory of computing. Throughout this term, a series workshops are being held to gather and share the relevant experience.

Right now, I have my conference straw hat on, as I wait for a flight transfer in Dallas on my way to one of the events in this program, the workshop on computational theories of evolution. For the next week I will be in Berkeley absorbing all there is to know on the topic. Given how much I enjoyed Princeton’s workshop on natural algorithms in the sciences, I can barely contain my excitement.
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From heuristics to abductions in mathematical oncology

As Philip Gerlee pointed out, mathematical oncologists has contributed two main focuses to cancer research. In following Nowell (1976), they’ve stressed the importance of viewing cancer progression as an evolutionary process, and — of less clear-cut origin — recognizing the heterogeneity of tumours. Hence, it would seem appropriate that mathematical oncologists might enjoy Feyerabend’s philosophy:

[S]cience is a complex and heterogeneous historical process which contains vague and incoherent anticipations of future ideologies side by side with highly sophisticated theoretical systems and ancient and petrified forms of thought. Some of its elements are available in the form of neatly written statements while others are submerged and become known only by contrast, by comparison with new and unusual views.

If you are a total troll or pronounced pessimist you might view this as even leading credence to some anti-scientism views of science as a cancer of society. This is not my reading.

For me, the important takeaway from Feyerabend is that there is no single scientific method or overarching theory underlying science. Science is a collection of various tribes and cultures, with their own methods, theories, and ontologies. Many of these theories are incommensurable.
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Misleading models in mathematical oncology

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I have an awkward relationship with mathematical oncology, mostly because oncology has an awkward relationship with math. Although I was vaguely familiar that evolutionary game theory (EGT) could be used in cancer research, mostly through Axelrod et al. (2006), I never planned to work on cancer. I wasn’t eager to enter the field because I couldn’t see how heuristic models could be of use in medicine; I thought only insilications could be useful, but EGT was not at a level of sophistication where it could build predictive models. I worried that selling non-predictive models as advice for treatment would only cause harm. However, the internet being the place it is, I ended up running into David Basanta — one of the major advocates of EGT in oncology — and Jacob Scott on twitter. After looking through some of the literature, I realized that most of experimental cancer research was more piecemeal than I expected and theory was based mostly on ad-hoc mental models. This convinced me that there is room for clear mathematical (and maybe computational) reasoning to help formalize and explore these mental models. Now we have a paper applying the Ohtsuki-Nowak transform to studying edge effects in the go-grow game prepped (Kaznatcheev, Scott, & Basanta, 2013), and David and I have a project on chronic myeloid leukemia in the works. The first is a heuristic model building on top of previously developed tools (from my experience, it is rather uncommon to build directly on others’ work in evolutionary game theory and mathematical oncology) and the other an abductive model using a combination of analytic and machine learning techniques to produce a predictive tool useful in the clinic.
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