This is such a cool example of how to incorporate domain-specific, interdisciplinary knowledge in a tractable way! I also love the questions at the end - incorporating physics constraints is something physicists too are working on with their own algorithms and also helps with the interpretability of the algorithm - really cool to see these principles applied in a cell biology context
Unless you're at Elliot's level of expertise, I earnestly advise everyone read this essay at least twice through s..l..o..w..l..y! In my own case I'm going to have to read it over many times! Thanks Elliot, for providing us with this!
This is extremely cool! To further boost utility of physics engines do you have any sense of what kinds of colloidal interactions need to be better accounted for? For example, could overlooking enhanced diffusion processes require tweaking molecular transport parameters to improve prediction.
This is such a cool example of how to incorporate domain-specific, interdisciplinary knowledge in a tractable way! I also love the questions at the end - incorporating physics constraints is something physicists too are working on with their own algorithms and also helps with the interpretability of the algorithm - really cool to see these principles applied in a cell biology context
Wow, an amazing future ahead!
Layman's summary from Claude:
The Problem with Biology Engineering
- Biology is complicated and designs usually require lots of trial and error before working properly
- The standard engineering workflow is "Design, Build, Test, Learn" - iterating through many cycles of testing and learning from failures
A New Approach - Physics-Based Modeling
- Pioneer Drew Endy believes synthetic biology should mature into an engineering discipline where designs work the first time
- This requires:
- Better standardized biological parts
- New abstractions for describing biology
- Separating design from fabrication
- A new study from Endy's lab took a physics-based approach for tuning protein synthesis rates
Using a Physics "Engine" for Biology
- They used a computer simulation called a "colloidal physics engine"
- Models molecular interactions at a very detailed level
- This physics-based CAD allowed them to reliably calculate the right abundances of tRNA molecules needed to achieve a desired protein synthesis rate
- Their designs worked the first time without needing trial and error
The Potential of Physics-Based Modeling
- Shows the potential for physics-based modeling and simulation to make synthetic biology more of an engineering discipline
- Offers an alternative to data-driven machine learning approaches that treat biology as a black box
Future Possibilities
- Combining physics-based and data-driven approaches could enable powerful new ways to design, model and build synthetic biological systems
- The end goal is reliably engineering biology the first time around, without excessive trial and error
Unless you're at Elliot's level of expertise, I earnestly advise everyone read this essay at least twice through s..l..o..w..l..y! In my own case I'm going to have to read it over many times! Thanks Elliot, for providing us with this!
Could we grow houses very fast? To live in an oak?
This is extremely cool! To further boost utility of physics engines do you have any sense of what kinds of colloidal interactions need to be better accounted for? For example, could overlooking enhanced diffusion processes require tweaking molecular transport parameters to improve prediction.