Automa.jl VS Modia.jl

You don't need to have any particular skills except familiarity with Julia, but it's obviously an advantage to have a bio background - depending on what you're going to do.

Usually, the best packages come about when people are motivated to creating something specific, for example if they think the status quo in some domain is not good enough.

I'm sure we can dig up a handful of old, badly maintained projects that could use some love. Off the top of my head, it would be nice to have

* Micro-optimized our smith-waterman algorithm. That's probably fairly easy to get started with if you're not a bio person

* A number of our parsers have not been properly maintained. We use finite state automata https://github.com/BioJulia/Automa.jl to create parsers. That's for more advanced users

Feel free to get in touch on the Julia Slack, or send me an email :)

I'm not in bioinformatics and don't do string processing, so I can't really help too much here.

I'd really urge you to come ask this on the julia Discourse Forum or Zulip and I promise you'll get high quality useful responses from people who understand your needs better than I.

> How do I find substrings & replace them efficiently? How do you split a string with delimiters? Regular Expressions?

This is something that the BioJulia people have put a lot of work into. Yes, you can use regular expressions, but they've managed to squeeze a lot of performance out of more specialized approaches, e.g.

https://github.com/BioJulia/Automa.jl

https://github.com/jakobnissen/ScanByte.jl

But for more straightforward usage, julia has the `findfirst` function which can search for occurrences of a substring, `replace` which can do replacements either with a literal pattern or a regex, and `split` which can split a string with delimiters.

For circuits a lot of them are represented by differential-algebraic equations (DAEs) and require modeling tools in order to handle the high differential index of the systems. This is the reason why they are typically handled via acausal modeling systems which can do index reduction. For Julia, this is the ModelingToolkit portion of the SciML ecosystem (https://docs.sciml.ai/ModelingToolkit/stable/), and some modeling tools like https://github.com/ModiaSim/Modia.jl and OpenModelica front-ends https://github.com/OpenModelica/OMJulia.jl.

The thing to watch in the space of Simulink/Modelica is https://github.com/SciML/ModelingToolkit.jl . It's an acausal modeling system similar to Modelica (though extended to things like SDEs, PDEs, and nonlinear optimization), and has a standard library (https://github.com/SciML/ModelingToolkitStandardLibrary.jl) similar to the MSL. There's still a lot to do, but it's pretty functional at this point. The two other projects to watch are FunctionalModels.jl (https://github.com/tshort/FunctionalModels.jl, which is the renamed Sims.jl), which is built using ModelingToolkit.jl and puts a more functional interface on it. Then there's Modia.jl (https://github.com/ModiaSim/Modia.jl) which had a complete rewrite not too long ago, and in its new form it's fairly similar to ModelingToolkit.jl and the differences are more in the details. For causal modeling similar to Simulink, there's Causal.jl (https://github.com/zekeriyasari/Causal.jl) which is fairly feature-complete, though I think a lot of people these days are going towards acausal modeling instead so flipping Simulink -> acausal, and in that transition picking up Julia, is what I think is the most likely direction (and given MTK has gotten 40,000 downloads in the last year, I think there's good data backing it up).

And quick mention to bring it back to the main thread here, the DataDrivenDiffEq symbolic regression API gives back Symbolics.jl/ModelingToolkit.jl objects, meaning that the learned equations can be put directly into the simulation tools or composed with other physical models. We're really trying to marry this process modeling and engineering world with these "newer" AI tools.

Maybe of interest in that context:

https://github.com/ModiaSim/Modia.jl

The authors of that tool have a strong background in modeling and simulation of differential algebraic equations. Not so much in designing DSLs, though, so there maybe some technical oddities. But I expect the simulation aspect to be quite decent.