Enhanced blueprint for manipulating cellular makeup to achieve a novel condition in engineering
In the realm of cellular reprogramming, researchers are pushing the boundaries with targeted genetic interventions, potentially revolutionizing immunotherapy, identifying life-saving treatments for cancer, and even regenerating disease-riddled organs. However, the complexity of the human genome coupled with the vast number of potential genetic perturbations makes finding the ideal intervention challenging.
Enter MIT and Harvard University researchers who have devised a novel computational approach to tackle this issue. Their algorithmic strategy harnesses the cause-and-effect relationship within complex systems, such as genome regulation, to prioritize the most effective intervention in consecutive experiments.
Clearly stating that too many large-scale experiments are typically designed haphazardly, lead author Jiaqi Zhang, a graduate student and Eric and Wendy Schmidt Center Fellow, stipulates that a careful causal framework for sequential experimentation could potentially identify optimal interventions with fewer trials, drastically reducing experimental costs.
This groundbreaking work, published in Nature Machine Intelligence, flourishes through the use of active learning. Active learning is a machine-learning approach that involves collecting data to learn a system model and subsequently designing an acquisition function to evaluate potential interventions and pick the best one for testing.
However, traditional acquisition functions primarily consider correlations between factors, which often leads to slow convergence and ignores regulatory relationships or causal structure within the system.
To combat this, the MIT and Harvard researchers leveraged the underlying causal structure within their technique. They devised an algorithm that can only learn models accounting for causal relationships, and an acquisition function that concentrates on the most informative interventions, or those most likely to lead to the optimal intervention in subsequent experiments.
By focusing on causal models instead of correlation-based models, the researchers claim, they can already rule out certain interventions. Whenever new data is gathered, they can learn a more accurate causal model and subsequently shrink the pool of potential interventions.
Their approach was successfully tested using real biological data in a simulated cellular reprogramming experiment. The acquisition functions consistently outperformed baseline methods, identifying better interventions throughout the multi-stage experiment.
The researchers are now collaborating with lab-based scientists to apply their technique to cellular reprogramming experiments. Furthermore, their approach could also be utilized in problems outside of genomics, such as determining optimal consumer product prices or optimizing feedback control in fluid mechanics applications.
In the future, they plan to enhance their technique beyond mean-seeking optimizations and explore the use of AI to learn causal relationships within systems.
This work was supported, in part, by various entities like the Office of Naval Research, the MIT-IBM Watson AI Lab, and the Air Force Office of Scientific Research.
- The MIT and Harvard researchers have developed a computational approach, leveraging the cause-and-effect relationship within complex systems, for identifying optimal interventions in cellular reprogramming research.
- Jiaqi Zhang, a graduate student and Eric and Wendy Schmidt Center Fellow, emphasizes that this strategy could potentially reduce experimental costs by prioritizing effective interventions in consecutive experiments.
- The researchers' algorithmic approach, published in Nature Machine Intelligence, flourishes through the use of active learning, which involves collecting data to learn a system model and evaluating potential interventions.
- Traditional acquisition functions, which primarily consider correlations between factors, often lead to slow convergence and ignore regulatory relationships or causal structure within the system.
- To combat this, the researchers devised an algorithm that learns models accounting for causal relationships and an acquisition function focusing on the most informative interventions.
- This focus on causal models prevents them from considering certain interventions, as whenever new data is gathered, they can learn a more accurate causal model and subsequently shrink the pool of potential interventions.
- This work has potential applications not only in genomics but also in other fields, such as optimizing consumer product prices or optimizing feedback control in fluid mechanics applications.
