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Comment by dr_kiszonka | Hacker News Reader

Recommendations for good intro textbooks? : - )

Do folks mostly use MatLab or is Python more popular? (IIRC, MatLab used to have a nice GUI for things like system dynamics.)

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I’d say it’s a pretty even mix of matlab and python. My advisor would be upset if I didn’t plug his framework: https://pysb.org/

As far as textbooks go, I guess I’d recommend Uri Alon’s book: https://www.routledge.com/An-Introduction-to-Systems-Biology...

Full disclosure, I haven’t read it, but I like his research and he’s a good communicator.

It’s tough because it’s a newish field and not super well defined. The frustrating answer is that the best way to understand the field is maybe to read a book about biology, then a book about computational modeling, then a few papers where they put those together.

Alon's a good reference but makes for very dry reading.

Can you recommend an alternative?

Computational Cell Biology by Fall et al. and Physical Biology of the Cell by Phillips et al. are superb.

Be very very good at differential equations. Systems/synthetic biology primarily rely on dynamics to model behavior and side effects. AlphaFold is actually pretty different from the biomodel type of modeling.

This is a good intro to (non deep learning based) computational/synthetic biology. ML in biology is somewhat orthogonal but it is rapidly growing.

http://be150.caltech.edu/2020/content/index.html

Traditional bioinformatics is mostly text mining and string processing. Modern bioinformatics is heavily dominated by ML.

Any tips for breaking into the field? Been doing ml/signal processing type stuff towards the end of grad school, but did some diffeq/dynamical systems stuff at the beginning and was considering pivoting towards biological applications of ML. Interviewed with pumas.ai last summer but they eventually turned me down.

Go into industry not academia. Academia won’t pay well and they do not appreciated good quality software engineering. For most labs, ML is considered a tool, not a primary driver of innovation. Look towards places like Isomorphic labs and similar companies that are cash rich and has a strong engineering heritage.

Great course - thanks for sharing!

Regarding being very good at differential equations: if I remember correctly, one way to approach solving them is to convert them to difference equations and solve them numerically (and not analytically). Is this true? As you can probably tell, I am not good at differential equations at all, so I was thinking about using difference equations as a crutch.

The systems of differential equations produced by these types of models tend to be analytically intractable, so "numerical methods" are absolutely essential here. The phrase you want to look for is "initial value problem" (IVP): you know the initial values of the quantities described by the equations and you want to find out how those quantities evolve over time.

https://en.wikipedia.org/wiki/Numerical_methods_for_ordinary...

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