Khammampalli Srija, Vindal Vaibhav
Department of Biotechnology and Bioinformatics, School of Life Sciences, University of Hyderabad, Gachibowli, Hyderabad, India.
Methods Mol Biol. 2025;2952:243-257. doi: 10.1007/978-1-0716-4690-8_14.
Genetic engineering is a method used to alter an organism's DNA, which could entail altering a base pair, removing a section of DNA, or introducing a new DNA segment. Over time, genetic engineering has progressed from basic cloning for research purposes to advanced synthetic biology, leading to new biomedical applications. Targeted genomic editing is one method of cellular reprogramming that aims to change the state of a cell. The invention of CRISPR Cas systems has greatly simplified gene editing. These systems use a unique RNA-guided DNA endonuclease, a protein that can cut DNA and be trained to target new places by changing the sequence of its guide RNA. Integrating CRISPR-Cas systems with artificial intelligence opens new insights into the study of genetic engineering and its applications. Extensive research utilizing deep learning and machine learning has been conducted to predict the outcomes of CRISPR-Cas9 editing. Artificial intelligence also predicts RNA editing events and CRISPR off-target cleavage sites. Scientists often struggle to identify the ideal perturbation for their specific application because of the ample search space and expensive genetic trials. The algorithmic method using artificial intelligence utilizes the cause-and-effect link between variables in a complicated system like genome regulation to determine which perturbation is most effective in each successive round of testing, thereby making artificial intelligence an effective technique in gene editing.
基因工程是一种用于改变生物体DNA的方法，这可能涉及改变一个碱基对、去除一段DNA或引入一个新的DNA片段。随着时间的推移，基因工程已从用于研究目的的基础克隆发展到先进的合成生物学，带来了新的生物医学应用。靶向基因组编辑是细胞重编程的一种方法，旨在改变细胞状态。CRISPR Cas系统的发明极大地简化了基因编辑。这些系统使用一种独特的RNA引导的DNA内切酶，这种蛋白质可以切割DNA，并通过改变其引导RNA的序列来训练靶向新的位置。将CRISPR - Cas系统与人工智能相结合，为基因工程及其应用的研究开辟了新的视角。已经进行了大量利用深度学习和机器学习的研究来预测CRISPR - Cas9编辑的结果。人工智能还可以预测RNA编辑事件和CRISPR脱靶切割位点。由于搜索空间广阔且基因试验成本高昂，科学家们常常难以确定适合其特定应用的理想扰动。使用人工智能的算法方法利用基因组调控等复杂系统中变量之间的因果联系，来确定在每一轮连续测试中哪种扰动最有效，从而使人工智能成为基因编辑中的一种有效技术。
