The Power of Genetic Perturbations: An Efficient Approach to Cellular Reprogramming
Introduction
Cellular reprogramming is a strategy that involves genetically engineering cells to change their state. This technique shows promise in immunotherapy and regenerative medicine, but finding the right genetic perturbation can be challenging and costly due to the immense complexity of the human genome. However, researchers from MIT and Harvard University have developed a new computational approach that can identify optimal genetic perturbations more efficiently than traditional methods.
A New Approach to Genetic Perturbations
In their study published in Nature Machine Intelligence, the researchers describe their algorithmic technique that leverages the cause-and-effect relationships within a complex system, such as genome regulation. By prioritizing the most informative interventions, their approach significantly reduces the search space for optimal interventions.
Active Learning
Scientists often perform experiments sequentially to design effective interventions for complex systems. To aid this process, the researchers utilized active learning, a machine-learning approach. They developed an acquisition function that evaluates potential interventions based on causal relationships rather than just correlations between factors. This allows for more efficient intervention design.
Enhanced Efficiency in Cellular Reprogramming
The researchers tested their algorithms using real biological data in a simulated cellular reprogramming experiment and consistently outperformed baseline methods in identifying optimal interventions at every step. Their approach not only saves time and resources but also achieves the same or better results with fewer experiments.
Furthermore, this computational approach can be applied to a wide range of problems beyond genomics, including pricing optimization and feedback control in fluid mechanics applications.
Future Developments
The researchers plan to enhance their technique to tackle optimizations beyond mean matching and explore the use of AI to learn causal relationships in complex systems. This work was made possible through funding from various organizations, including the Office of Naval Research, the MIT-IBM Watson AI Lab, and the Air Force Office of Scientific Research.