CRISPR vs Genetic Algorithms

Explore this dynamic app to learn the basic principles of how this revolutionary tool works and the many ways in which scientists are using it in their research. Since it was first described in 2012, CRISPR-Cas9 (often shortened to “CRISPR”) has generated much interest both for its exciting potential for treating genetic disease and potential ethical and safety concerns, such as by creating “designer babies” or “superhumans.” So far, CRISPR-based technologies are having the biggest impact in research laboratories where they are applied to a broad range of biological studies. These technologies are relatively cheap, easy to use, and allow researchers to ask new questions and get results faster than previously possible. The CRISPR-Cas9: Mechanism & Applications interactive allows students to explore and learn about a biotechnology tool that is at the forefront of scientific research and hear directly from leading researchers about how they use CRISPR. It consists of a self-paced interactive animation and a series of short videos of different scientists who are using CRISPR-Cas9 technologies for basic research, medical, and agricultural applications.

Genetic algorithms are one of the search and optimisation methods. The aim of optimisation is to increase efficiency in reaching a certain optimal value. Genetic algorithms are based on the mechanisms of natural selection and heredity. The basic genetic algorithm is built from three operations: reproduction, crossing, and mutation. Genetic algorithms operate on populations of coding sequences and use random selection rules to search for the global optimal value. However, these random rules are defined to give the appropriate direction of the search (through various selection mechanisms or scaling of fitness functions). This basic procedure is enhanced by certain genetic manipulations, such as those seen in nature. They include the mechanisms of dominance, diploidy, inversion, and other reconfiguration mechanisms that occur at the chromosome level. Users can design textual or graphical scaling patterns for the fitness function and then use them in simulations. The application also provides a preliminary analysis of statistical data concerning the distribution of the fitness function in the population. The program allows the user to compare non-random procedures (e.g. scaling of fitness functions) by using the same pseudorandom sequence (for procedures requiring randomization, such as selection, etc.) in successive simulations. In order to better visualize the genetic processes taking place during the simulation, some of them are presented to the user using animations. Additionally, some of the data obtained from simulations can be saved and shared.