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What research have you seen on modeling gene expression? I'm genuinely curious, as I haven't really seen many convincingab initio studies towards this. I could

by chaxor 5y ago

What research have you seen on modeling gene expression? I'm genuinely curious, as I haven't really seen many convincingab initio studies towards this. I could see finding certain features like this spacing as predictive of perhaps some other feature, but I haven't seen any research that really tackles generation of gene expression data from first principles and input of DNA sequence. It's my understanding that modeling the kinetics is difficult, as we really haven't tried making the full network of differential equations. Does anyone have a project that points to the 'final solution' to this? I know recently there was a paper in cell that modeled the cell with the smallest viable genome to predict cell division, but that's a bit further away from complete modeling of our 30k genes' (much less isoforms) dynamics.

jashephe 5y ago
Global models of gene expression for an entire cell are fairly distant at this point, but there is quite a bit of work into modeling transcriptional activity from sequence. If you're interested in reading more, a relevant technology to search for would be the "Massively Parallel Reporter Assay", or MPRA, which couples pools of 10⁴–10⁵+ synthetic DNA sequences with RNA sequencing to measure transcriptional output. Data from MPRA experiments is being used to train models, although these models are not anywhere near a point where you could model the gene expression of all regulatory elements in a cell; they are usually focused on a specific factor or regulatory sequence.
- sooheon 5y ago
  Notably DeepMind had a recent paper on using transformers to predict long range interactions in gene expression: https://www.nature.com/articles/s41592-021-01252-x https://www.nature.com/articles/s41592-021-01252-x
- chaxor 5y ago
  The "train models" or ML portion is what I'm disappointed with unfortunately. I make ML models to predict things from genetic information somewhat regularly, but we all are aware of the enormous issues with that. I am more interested in the ab initio methods, as I have seen them be spectacularly useful in other fields - like Bethe salpeter equations in condensed matter physics.

by chaxor 5y ago

jashephe 5y ago

Global models of gene expression for an entire cell are fairly distant at this point, but there is quite a bit of work into modeling transcriptional activity from sequence. If you're interested in reading more, a relevant technology to search for would be the "Massively Parallel Reporter Assay", or MPRA, which couples pools of 10⁴–10⁵+ synthetic DNA sequences with RNA sequencing to measure transcriptional output. Data from MPRA experiments is being used to train models, although these models are not anywhere near a point where you could model the gene expression of all regulatory elements in a cell; they are usually focused on a specific factor or regulatory sequence.

sooheon 5y ago
Notably DeepMind had a recent paper on using transformers to predict long range interactions in gene expression: https://www.nature.com/articles/s41592-021-01252-x https://www.nature.com/articles/s41592-021-01252-x
chaxor 5y ago
The "train models" or ML portion is what I'm disappointed with unfortunately. I make ML models to predict things from genetic information somewhat regularly, but we all are aware of the enormous issues with that. I am more interested in the ab initio methods, as I have seen them be spectacularly useful in other fields - like Bethe salpeter equations in condensed matter physics.