Quantitative Modeling and Automated Analysis of Meiotic Recombination

Citation:

Martin A White, Shunxin Wang, Liangran Zhang, and Nancy Kleckner. 2017. “Quantitative Modeling and Automated Analysis of Meiotic Recombination.” Methods Mol Biol, 1471, Pp. 305-323.

Abstract:

Many morphological features, in both physical and biological systems, exhibit spatial patterns that are specifically characterized by a tendency to occur with even spacing (in one, two, or three dimensions). The positions of crossover (CO) recombination events along meiotic chromosomes provide an interesting biological example of such an effect. In general, mechanisms that explain such patterns may (a) be mechanically based, (b) occur by a reaction-diffusion mechanism in which macroscopic mechanical effects are irrelevant, or (c) involve a combination of both types of effects. We have proposed that meiotic CO patterns arise by a mechanical mechanism, have developed mathematical expressions for such a process based on a particular physical system with analogous properties (the so-called beam-film model), and have shown that the beam-film model can very accurately explain experimental CO patterns as a function of the values of specific defined parameters. Importantly, the mathematical expressions of the beam-film model can apply quite generally to any mechanism, whether it involves mechanical components or not, as long as its logic and component features correspond to those of the beam-film system. Furthermore, via its various parameters, the beam-film model discretizes the patterning process into specific components. Thus, the model can be used to explore the theoretically predicted effects of various types of changes in the patterning process. Such predictions can expand detailed understanding of the bases for various biological effects. We present here a new MATLAB program that implements the mathematical expressions of the beam-film model with increased robustness and accessibility as compared to programs presented previously. As in previous versions, the presented program permits both (1) simulation of predicted CO positions along chromosomes of a test population and (2) easy analysis of CO positions, both for experimental data sets and for data sets resulting from simulations. The goal of the current presentation is to make these approaches more readily accessible to a wider audience of researchers. Also, the program is easily modified, and we encourage interested users to make changes to suit their specific needs. A link to the program is available on the Kleckner laboratory website: http://projects.iq.harvard.edu/kleckner_lab .