Abstract
I shall discuss the application of algorithmic and combinatorial tools in the area of population genomics, which has not been the traditional stomping ground for algorithmicists.
The modeling of the evolutionary dynamics of evolving populations as random graphs offers a new methodology for analysis. This exploration begins as a quest for understanding the reconstructability of common evolutionary history of populations. It provides new insights including a purely topological (or graph theoretic definition) of traditional population genomic entity like the GMRCA (Grand Most Common Ancestor) of individuals under mutations as well as recombinations. Apart from giving interesting characterizations of another important structure called the ARG (Ancestral Recombinations Graph), it provides the basis for identifying a mathematical minimal nonredundant structure in the ARG and for adapting very naturally the coalescence theory (a wellstudied notion in population genetics) in designing ARG sampling algorithms. This connection also opens the door for many interesting questions ranging from human migration paths, to genetic diversity study in plant (cacao) cultivars.
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Parida, L. (2013). Using Random Graphs in Population Genomics. In: Bonizzoni, P., Brattka, V., Löwe, B. (eds) The Nature of Computation. Logic, Algorithms, Applications. CiE 2013. Lecture Notes in Computer Science, vol 7921. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39053-1_40
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