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There are interesting attempts at modeling cells using Boolean networks (for example inferred using SAT/SMT), differential equations (preferably stochastic) and also with process algebra. Some are pretty successful.

> [...] we don’t have the tools to physically observe what’s happening in real-time

We do have single-cell technology, which is pretty close as you can get thousands of snapshots of different cells from the same lineage. One can then reorder them into some sort of pseudotime. There are also interesting real-time reporting systems for bacterial cells, but they are not high throughput.

I think the field is still very young, and the current generation of biology professors is mostly allergic to formalism. Some departments are not, a notable exception are Caltech and Cold Spring Harbor (where the author of the essay is based).

A related read is Luca Cardelli's followup: Can a systems biologist fix a Tamagotchi? http://lucacardelli.name/Papers/Can%20a%20Systems%20Biologis...

And much deeper: Abstract Machines of Systems Biology http://lucacardelli.name/Papers/Abstract%20Machines%20of%20S...



Single cell sequencing technology is analogous to taking many radios, and listing all the parts the radios contains. Then clustering radios by similarity in their part abundances.

Useful, but a parts list is still far from figuring out how a radio works.


Not just that. For example one can perturb cells and try to understand their dynamics: https://cordis.europa.eu/project/id/810296

Also, from purely observational studies it is possible to infer mechanistic models as I outlined above, e.g. using SAT.




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