BIOINTERACTION · Biological interaction and the intermittent character of Evolution

Biological interaction and the intermittent character of Evolution

Project results The main result of this two-year project has been to train the researcher Dr Ignacio Gallo to be able to write independent research in mathematical biology, a field to which he had had no exposure before this research experience. Thus, he can now add as an area of expertise to his doctoral training in statistical physics and the quantitative social sciences. During the Marie Curie Action the researcher has also set up connections with experienced researchers in the area of quantitative biology at a global level in institutions such as the University of New York at Stony Brook, and Shanghai Jiao Tong University, where he now also holds a research partnership with a research assistant working in his same area of expertise. The preliminary results of his research have been presented at Shanghai Jiao Tong, at Jacobs University Bremen and at the Royal College of Physicians, as well as at Imperial College, both in the United Kingdom. From a scientific point of view, the main result of Dr Gallo's work in the present project has been to characterise a simple mathematical model that uses population observables to differentiate genetic functional features, which are involved in reproductive mechanisms, from features which are involved in survival mechanisms. The researcher has chosen to study this problem in view of the dramatic increase in the availability of DNA sequence data over the last few decades, which has made it imperative that first-principles approaches be pursued to complement correlation studies on the functional significance of genetic data. In particular, at present our understanding of the relation of an organism's genotype and its expression within an environment is founded mainly on statistical correlations, such those found between patients suffering from medical conditions and relevant genetic markers characterising their genetic background. The model which has been studied by the researcher during his fellowship takes the opposite viewpoint, and derives probabilistic regularities which arrive from basic assumptions regarding an organism's life cycle, such its expected number of offspring and its expected lifespan. In particular, one of the project's salient insights is the observation that the analytical tools available from population genetics are sufficient to link a gene's effect on an organism's lifespan to a population's degree of inbreeding. This result, which constitutes a slight variation from the point of view held by standard population genetics, could be a first step towards a more causative connection between features observable at the level of individuals and those observable at the level of populations, and as such it has the potential to contribute in a positive way to the understanding of biological processes.

Data: CORDIS, © European Union