Last May, I wrote that I would explore Scala for prototyping and potential outreach activities. Over the last half year, I have been reading its on-line documentation, relevant blogs and books, and example code on Github; and I have tried writing some code snippets. I encountered a bit of a learning curve —especially when it come to Scala’s type system acrobatics with its algebraic underpinnings— but, all in all, the language is rather pragmatic. Simple tasks are simple to implement, difficult ones a bit less simple. In summary, the moment has come to share a first few bits of code.
Before I present three little programs, I would like to thank Darren Wilkinson (https://darrenjw.github.io/). His work was instrumental to put the right ideas in my head and I appreciate his rationale that for developing stats, data science, and simulation code, we should take advantage of modern languages such as Scala (or Kotlin, Rust) over older ones like C/C++, Fortran, R, Matlab, and so on.
Now, for my first adventures in Scala I went back to the “Bioinformatic Processes” course of Paulien Hogeweg that I followed in the early 2000s. That course had some beautiful examples of spatial patterning and I was keen to recreate them. I dug up the simulation toolkit used in the course, called Cellular Automata in Simulated Hardware (CASH), and adapted a tiny part of its interface to my needs. I figured a tiny bit of cash is a few cents, hence the name of my Github repository: scala-cents.
In the Github repository and in the screenshots below, you can find:
- A simple test case: a voting rule cellular automaton (left panel);
- A predator-prey simulation of spiders that eat mosquitoes. Some settings give pretty, “turbulent” wave patterns (middle);
- And a classic origin-of-life scenario called the hypercycle. In the hypercycle, each bio-molecule catalyzes the copying of the next bio-molecule in the cycle. If you define a cycle > 3, you get spirals (right).
It should not surprise anyone that my next step will be to design and code support for gene regulatory networks.