Is it game night yet?
Now, we are talking about Genspace, and we do have a bit of reputation to maintain. So as much as I would like to recommend everyone to get cracking on the battle.net with Starcraft 2 we’ll have to make do with something different; a computer game with science in it.
It’s called Phylo, and you can find it here. Phylo is an entirely browser based (flash based, to be specific. Sorry to disappoint all my iPad toting readers) and doesn’t require any serious computing muscle on the player’s end. I’ve been playing it for the last hour or so, and it’s an odd piece of work. On the surface the game follows some basic rules of pattern matching casual games you might be familiar with like Bejeweled. Yet the experience of playing the game feels far more complex than that, and I don’t necessarily mean that in a bad way. Also, there’s a real benefit to playing this game on your spare time, other than gaining the l33t skills to pwn the n00bs with.
You see, Phylo is ‘a human computing framework for comparative genomics.’ Basically it gives you real multiple sequence alignment problems represented by 4 color blocks scattered on a grid. And of course, budding bio-enthusiasts like us know what’s up when a science programs give us 4 of anything- they represent nucleotide sequences. As you match same colored blocks with each other, you contribute some of your brain power to finding aligned sequences between different genes. If you misalign the blocks you lose a point, and if you create gaps between the blocks (which represent mutation) you lose lots of points. You can gain points by aligning same color blocks on vertical row and you need to gain certain amount of points to pass a level or get another gene to align with your existing sequence. This is a very abstract process of optimization that is usually done with complex computer algorithms and lots of processing power, which would be prohibitively expensive when brute-forced. The authors of the program hope to use the human-computer interaction on a large scale to come up with optimized heuristic pattern.