Tag Archives: mathematics

Evolutionary Genetics and Cultural Traits

The chapter explains why evolutionary genetics – a mathematical body of theory developed since the 1910s – eventually got to deal with culture: the frequency dynamics of genes like “the lactase gene” in populations cannot be correctly modeled without including social transmission. While the body of theory requires specific justifications, for example meticulous legitimations of describing culture in terms of traits, the body of theory is an immensely valuable scientific instrument, not only for its modeling power but also for the amount of work that has been necessary to build, maintain, and expand it. A brief history of evolutionary genetics is told to demonstrate such patrimony, and to emphasize the importance and accumulation of statistical knowledge therein. The probabilistic nature of genotypes, phenogenotypes and population phenomena is also touched upon. Although evolutionary genetics is actually composed by distinct and partially independent traditions, the most important mathematical object of evolutionary genetics is the Mendelian space, and evolutionary genetics is mostly the daring study of trajectories of alleles in a population that explores that space. The ‘body’ is scientific wealth that can be invested in studying every situation that happens to turn out suitable to be modeled as a Mendelian population, or as a modified Mendelian population, or as a population of continuously varying individuals with an underlying Mendelian basis. Mathematical tinkering and justification are two halves of the mutual adjustment between the body of theory and the new domain of culture. Some works in current literature overstate justification, misrepresenting the relationship between body of theory and domain, and hindering interdisciplinary dialogue.


Look for it in the Publications page (with additional links):

Serrelli E (forthcoming). Evolutionary genetics and cultural traits in a ‘body of theory’ perspective. In Panebianco F, Serrelli E, eds. Understanding cultural traits. A multidisciplinary perspective on cultural diversity. Springer, Chapter 11. [http://hdl.handle.net/10281/49987]

The landscape metaphor in development

“It seems that thtowards-theory-developmente landscape metaphor will continue to stay with us, at least for a while”.

We start defining a landscape as a function of multiple variables and show how this can be interpreted as a dynamical system. From the perspective of dynamical systems modelling, we move to analyze Waddington’s ‘epigenetic landscape’ and landscape representations in current developmental biology literature. Then we delve into the problem of models and metaphorical representations in science, which stands out as a crux for assessing the use of landscapes in development, and analyze the somehow parallel stories of Wright’s and Waddington’s landscapes. We conclude with some ideas on developmental landscapes in the context of visualization in science, with a focus on theoretical work in developmental biology.


Look for it in the Publications page (with additional links):

Fusco G, Carrer R, Serrelli E (2014). The landscape metaphor in development. In Minelli A, Pradeu T, eds., Towards a theory of development, Oxford University Press, Oxford, pp. 114-128. ISBN 978-0-19-967142-7 [http://hdl.handle.net/10281/48518]

Active Training Apprenticeships for Mathematics and Physics School Teachers

tirocinio-formativo-attivo-secondo-cicloIn a.y. 2012/2013 I have taught a laboratory in the new Italian teachers training course for high school teachers, subject areas 47 (mathematics) e 49 (mathematics and physics).

To know more about this course and its institutional problems see, for example, this article on the University of Torino’s journal Rivista di Lingue e Letterature Straniere e Culture Moderne, or this document of the European Parliament.

Some of the teaching materials I used are on Academia.edu.

University of Sydney HPS Research Seminar Series

For 80 years now, a famous and influent picture have been around in evolutionary biology: it is the adaptive landscape, a hilly or rugged surface with peaks and valleys onto which combinations of traits are mapped, the elevation representing the fitness value of these combinations. As a communication and heuristic tool, the adaptive landscape well conveyed several ideas, e.g., adaptation seen as peak climbing. It also set research questions, e.g., how can a population cross a low-fitness valley.
In the mid 1990s, with a certain non-chalance, Princeton mathematician and population geneticist Sergey Gavrilets began to propose an idea which soon several evolutionists regarded as potentially explosive. Gavrilet’s “holey landscapes” were about fitness distribution in the genotype space of a population with realistic number of loci and alleles: backed by newly introduced mathematical methods and empirical evidence, they depicted fitness distribution by means of flat or nearly-flat surfaces drilled with large holes.
The explicit reference and, at the same time, the striking differences between holey landscapes and the adaptive landscape fueled a reflection on crucial themes like the role of adaptation, the extent of neutralism, the meaning of speciation, and even the possibility of non-gradual evolution. Reconsiderations and revisions of the history of adaptive landscapes, since its first introduction by Sewall Wright in 1932, flourished. More deeply, holey landscapes are offering an occasion of rethinking the nature of evolutionary biology as a scientific enterprise.


Look for it in the Talks page (with additional links):

2012, Sep 17 (h.6-8 PM) – Unit for the History and Philosophy of Science, HPS Research Seminar Series Semester Two, University of Sydney, Faculty of Science: Holey landscapes and rethinking evolutionary biology. Seminar.

http://www.mail-archive.com/sydphil@arts.usyd.edu.au/index.html

EGENIS – The ESRC Centre for Genomics in Society, University of Exeter

Ankeny & Leonelli (2011) recently spelled out a number of epistemological characteristics of model organisms which, they think, make them special in the more general category of experimental organisms. In this seminar I show how some similar epistemological characteristics apply to a theoretical model, i.e. the Mendelian population, making it special in respect to other theoretical constructs (equations). Both cases seem to suggest restrictions in the usage of the term “model” to the advantage of a defined model notion. Here I aim to refine and broaden such notion of a model, and explore the epistemological issues it raises.

Ankeny & Leonelli define model organisms as “non-human species that are extensively studied in order to understand a range of biological phenomena, with the hope that data and theories generated through use of the model will be applicable to other organisms, particularly those that are in some way more complex than the original model” (p. 313).

Mathematical population genetics – a major pillar of neo-Darwinian evolutionary theory – is often referred to as a great set or family of models, where “models” mean, arguably, equations of gene frequencies or phenotypic change.

The glaring discrepancy between organisms and equations seems to characterize experimental biology and population genetics by two irreducibly different “modeling strategies”: the material and the empirical (cf. Leonelli 2006). By diverting the attention away from equations, in this seminar I challenge such classical distinction.

I present population genetics in a uncommon way: I dismiss the term “model” for equations, and save it for the Mendelian population, i.e. the fundamental formal combination space population genetics equations are about.

One interesting result of my approach is to liken a formal system to an organic system – at least for some “key epistemological characteristics” (cf. Ankeny & Leonelli, cit.). I explore the notion of a model as a stable target of explanation (cf. Keller 2002) that I think captures both objects, and the related epistemological problems about representation, explanation, and prediction. Models as stable targets of explanation are systems selected for intensive research, yielding their stability and a cost-effective apparatus of experimental resources; they feature some degree of artificiality, and are never exhaustively known, even in case of complete artificiality.

Ankeny, R. a & Leonelli, S., 2011. What’s so special about model organisms? Studies In History and Philosophy of Science Part A, 42(2):313-323.

Keller, E. F. (2002). Making sense of life: Explaining biological development with models, metaphors and machines. Cambridge, MA: Harvard University Press.


Look for it in the Talks page (with additional links):

2012, Jan 23 (h.3:00-4:30 PM) – The ESRC Centre for Genomics in Society (Egenis), University of Exeter: Model as a “stable target of explanation”: Mendelian population like model organisms?. Seminar.

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