Last week, I defended my dissertation. While I knew how to prepare the written dissertation, and the public talk, the third component – the closed-door session in which my committee examined me – was mysterious. I’d asked about 8 people over the years what their closed-door sessions were like, and all of them told me that they couldn’t remember. So, while my experience might not be typical, it is at least an experience, and thus potentially a useful data point. Your mileage may vary.
Bacteria flourish in nearly every place on Earth imaginable including in and on humans. They also reproduce and therefore evolve much more quickly than we do, so understanding their evolution is vital to every aspect of our lives. A surprisingly common strategy for bacteria - controlling everything from virulence to production of useful resources such as cellulase - is a form of communication called quorum sensing. Bacterial cells using quorum sensing consistently produce a signal molecule and then detect the concentration of that molecule in the surrounding area. The bacteria generally have a signal-molecule density threshold, called a quorum, that triggers them to start performing a new behavior. The behaviors controlled by quorum sensing are usually only useful if there are enough organisms doing them at the same time. Producing public goods such as light or enzymes […]
This weekend, Josh, Cliff, and I ran a Software Carpentry workshop targeted at people interested in learning to use Avida for their research. After taking the instructor training course in May, we realized that the core skills covered in Software Carpentry (shell scripting, git, and a programming language) align very well with the skills needed to use Avida. The only thing left to do was drop a lesson on Avida in as the fourth session! Since there has been an ever-growing group of biologists interested in learning to use Avida, this seemed like a good idea. So, this weekend, we gave it a try.
I often argue that a big advantage of working with computational artificial life systems is that you can perform evolution experiments that would be difficult (or impossible!) with natural organisms. However, any time you want to use a digital system to understand how or why a particular trait or behavior evolves, your first challenge is to get that trait evolve at all! Warning: this can be a lot harder than it sounds. Of course, in this quest you will also learn a lot more about your study system, gain more insight into the dynamics you are investigating, and probably come up with at least half-a-dozen follow-up studies. Below are the steps that I suggest for new projects.
Recently, I was nominated for the Board of Directors for the International Society for Artificial Life (ISAL). After serious consideration about whether I could devote the necessary time and whether I felt I would be able to make a difference, I’ve decided to run. Normally, I try to say “no” to all such requests, but in this case I care quite a bit about the society and the field as a whole, and I want to play my part. Below is the 250-word statement I submitted with my acceptance of the nomination, followed by some more details that I couldn’t fit in the limited space.