Understanding Large Perturbation Models

Written By:

Spencer Hey

3 minute read
April 15, 2024
Probability of success for a new medicine

A few weeks ago, I attended Lab of the Future in Boston. One of the standout presentations for me was Patrick Schwab’s, highlighting the potential of Large Perturbation Models for in-silico experimentation to help accelerate biological discoveries. It’s somewhat mind-blowing stuff, but definitely worth trying to understand. Here’s my attempt to explain the value of LPMs for the masses.

Imagine you're a scientist in a lab, trying to understand how different factors affect a living organism or a biological system. Each factor you consider is a potential "perturbation". This could be anything from adding a chemical, to modifying a gene, to changing the environment. Scientists study these perturbations to understand cause and effect within biological systems, like figuring out which gene mutations might cause a disease.

Enter Large Perturbation Models (LPMs): These are advanced computational frameworks designed to simulate and understand the effects of various perturbations on biological systems. These models leverage vast datasets and sophisticated machine learning techniques to predict how changes at a molecular or genetic level can influence the behavior or state of cells, tissues, or organisms. By simulating experiments in silico (i.e., conducted on computer or via computer simulation), LPMs allow researchers to explore the potential outcomes of experiments without the need for physical execution, which can be time-consuming, expensive, and ethically challenging.

In other words: We can think of LPMs as highly sophisticated computer programs that can simulate vast numbers of experiments digitally, without needing to physically test each perturbation in a real lab.

What makes LPMs revolutionary?

Speed and Efficiency: Traditional experiments can take a long time and require lots of resources. LPMs can run thousands of simulated experiments in the time it takes to do just one in the real world, speeding up scientific discovery significantly.

Context-Awareness: Real-life biological systems are complex and their responses to perturbations can depend heavily on the context (like the presence of other chemicals, specific genetic backgrounds, etc.). LPMs are designed to take this context into account, making their predictions more accurate.

Exploring the Unseen: There are countless possible experiments scientists could run, but time, money, and ethical considerations limit what can be done in reality. LPMs allow scientists to explore a much broader range of possibilities, including those that would be difficult or impossible to test in a real lab.

Precision Medicine: In healthcare, understanding how different genetic factors influence disease can lead to personalized treatments. LPMs can help predict how specific genetic changes in an individual might affect their disease risk or treatment response, paving the way for more tailored therapies.

The Takeaway

Having LPMs is like having a super-powered computer game that simulates real-life biology. By playing this "game," scientists can learn how to cure diseases, develop new drugs, and understand life's mysteries much faster.

To Learn More

Reach out to Patrick himself. His personal website is here.

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Understanding Large Perturbation Models

A brief, layperson's introduction to Large Perturbation Models (LPMs), a new tool in the drug development toolkit to simulate vast numbers of experiments digitally

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