As someone who works primarily with models in which individuals have spatial coordinates, I have struggled a lot with visualising these systems. If you are using similar models, or biological data sets that have a similar structure of datapoints with x- and y-coordinates, here’s a few things I’ve picked up to make them prettier.
But first, let’s start at step 0, and read some data…
After years of development, Virtual Microbes are finally starting to give us new biological insights. The first piece of work is now peer-reviewed and published, but if you have little time, see a video abstract of what we did right here:
One of the most well-known carbon sources for bacterial growth is glucose, which converted into pyruvate in the process of glycolysis, which is then further processed in the TCA cycle (tricarboxylic acid cycle). Together, these pathways are among the most important chemical pathways for aerobic organisms, resulting both in the release of energy as well as building blocksused for the synthesis of amino acids and other essential metabolites required for growth. For microbes, growth is of course very directly related to reproduction, as every cell division results in a new (smaller) individual that again needs to grow. It is therefor no accident that both energy and building blocks are at the bedrock of the Virtual Microbe modelling platform. In the form of a user-defined artificial chemistry, Virtual Microbes have to synthesise both energy as well as building blocks, which are not directly available as a resource (see the tutorial if you are interested).
On the 22nd of August (2018), I had the pleasure to present work on Virtual Microbes at II Joint Congress on Evolutionary Biology. It was very inspiring to finally meet some of the people who designed/conducted the awesome experimental evolution studies I came to know and love over the past 5-10 years. It was also somewhat a relief to see how, vice versa, Virtual Microbes were well received by said experimentalists. Before this becomes any more self-serving, I’d just like to share this tweet by Vaughn Cooper: “Evolved populations learn to trust us, but die if not fed”. I could not have put it any better 🙂
If you have ever heard about emergence, you have likely come across the phrase “the whole is more than the sum of its parts”. The idea is simple. If a collection of small parts give rise to something with more complex traits, the system does more than one could have expected from any of the smaller parts. Here I discuss some simple computer models to illustrate how one could also argue for the opposite view: the whole is less than the sum of its parts.
I am a computational biologist studying microbial ecology and evolution with a key focus on horizontal gene transfer (HGT). This website is primarily dedicated to the science, the programming, and the visualisation of cool biological systems. However, it may also contain whatever else I feel like sharing.