Enhancing the Precision of CRISPR Gene Editing

Researchers at Lehigh University are embarking on an innovative project  to enhance the safety and efficacy  of CRISPR gene editing technologies. Funded by the National Science  Foundation, this initiative is led by  bioengineering researcher Tomas  Gonzalez-Fernandez and focuses on  developing predictive models using  artificial intelligence and deep learning. CRISPR, a revolutionary tool  for genome editing, enables precise  modifications to DNA. However, as  Gonzalez-Fernandez noted in a recent  press release, “CRISPR is very powerful, but it comes with side effects.” Altering one gene can inadvertently affect  multiple others, leading to unintended  outcomes, he said. To address this,  Gonzalez-Fernandez has assembled an  interdisciplinary team, including faculty from bioengineering and computer science, as well as Joshua Graham, a  third-year bioengineering PhD student  who is integrating machine learning  techniques into the project. “Machine  learning has been used for enhancing CRISPR's precision before, but this is  the first time it’s being used to create a  surrogate genome model,” GonzalezFernandez said. This model will allow  researchers to simulate the effects of  gene modifications on the entire genome, facilitating the identification of suitable genetic targets while avoiding adverse consequences. “If we have a  specific therapeutic application, but we  don’t know what gene to modify, the model will help us identify different  candidates,” he explained. Their work  has significant implications for various medical fields, including cancer  treatment and regenerative medicine.  For instance, they aim to enhance the  differentiation of induced pluripotent  stem cells into cancer-fighting cells  and improve the development of  cartilage cells for treating osteoarthritis. Additionally, the project addresses  the delivery mechanism for CRISPR  components through nanoparticle  vehicles, which can negatively impact  cell viability. The team will utilize  computer modeling to predict and  mitigate these effects. Gonzalez-Fernandez emphasized the collaborative  nature of the research, which merges  computer science, genetic engineering, and molecular biology to tackle  the complex challenges posed by  CRISPR technology. With this work,  the team aspires to unlock new therapeutic applications, making CRISPR  a safer and more reliable tool for  treating a variety of diseases. Larkin C. Advancing CRISPR: Lehigh researchers to develop predictive models for  gene editing. Lehigh University. Published  October 7, 2024. https://engineering. lehigh.edu/news/article/advancing-crisprlehigh-researchers-develop-predictivemodels-gene-editing

22 February 2025

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Introduction to Golden Genes
Golden Genes is a specialized organization operating in the fields of genetics, biotechnology, bioinformatics, and laboratory artificial intelligence, with the mission of improving the accuracy of genetic diagnostics and advancing the digitalization of laboratory processes. By drawing on a highly qualified team of genetics specialists, artificial intelligence engineers, and information technology experts, Golden Genes is committed to innovation in chromosomal analysis, the design of specialized intelligent software, and the development of modern laboratory infrastructures
Core Areas of Activity of Golden Genes
Professional chromosomal analysis and interpretation, including karyotyping and ideogram analysis
Design and development of intelligent chromosome detection and counting systems based on artificial intelligence
Establishment of specialized cytogenetic and molecular genetics centers
Online chromosome analysis training and education
Remote and online chromosomal analysis services for genetic laboratories
Education and transfer of technical knowledge in modern genetic and bioinformatics techniques
Specialized consultancy for the establishment of genetics departments and the implementation of laboratory standards
Development of dedicated software solutions for the automation of genetic reporting and interpretation
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