More on the theme of “The Digital Cell“: using quantitative, computational approaches in cell biology. So you want to get started? Well, the short version of this post is: Programming I make no claim to be a computer wizard. My first taste of programming was the same as anyone who went to school in the UK in the 1980s: BBC Basic.
The future of cell biology, even for small labs, is quantitative and computational. What does this mean and what should it look like? My group is not there yet, but in this post I’ll describe where we are heading. The graphic below shows my current view of the ideal workflow for my lab. The graphic is pretty self-explanatory, but to walk you through: A lab member sets up a microscopy experiment.
If you are a cell biologist, you will have noticed the change in emphasis in our field. At one time, cell biology papers were – in the main – qualitative . Micrographs of “representative cells”, western blots of a “typical experiment”… This descriptive style gave way to more quantitative approaches, converting observations into numbers that could be objectively assessed.
I needed to generate a uniform random distribution of points inside a circle and, later, a sphere. This is part of a bigger project, but the code to do this is kind of interesting. There were no solutions available for IgorPro, but stackexchange had plenty of examples in python and mathematica. There are many ways to do this.
Today I saw a tweet from Manuel Théry (an Associate Ed at Mol Biol Cell ). Which said that he heard that the Editor-in-Chief of MBoC, David Drubin shops for interesting preprints on bioRxiv to encourage the authors to submit to MBoC.
I have written previously about Journal Impact Factors (here and here). The response to these articles has been great and earlier this year I was asked to write something about JIFs and citation distributions for one of my favourite journals. I agreed and set to work. Things started off so well. A title came straight to mind.
An occasional series in esoteric programming issues. As part of a larger analysis project I needed to implement a short program to determine the closest distance of two line segments in 3D space. This will be used to sort out which segments to compare… like I say, part of a bigger project. The best method to do this is to find the closest distance one segment is to the other when the other one is represented as an infinite line.
Top Trumps is a card game for children. The mind can wander when playing such games with kids… typically, I start thinking: what is the best strategy for this game? But also, as the game drags on: what is the quickest way to lose? Since Top Trumps is based on numerical values with simple outcomes, it seemed straightforward to analyse the cards and to simulate different scenarios to look at these questions.
2016 was the 400 year anniversary of William Shakespeare’s death. Stratford-upon-Avon Rotary Club held the Shakespeare Marathon on the same weekend. Runners had an option of half or full marathon. There were apparently 3.5 K runners. Only 700 of whom were doing the full marathon. The chip results were uploaded last night and can be found here. Similar to my post on the Coventry Half Marathon, I thought I’d quickly analyse the data.
Well, the 2015/2016 season was one to forget for Crewe Alexandra. Relegation to League Two (English football’s 4th tier) was confirmed on 9th April with a 3-0 defeat to local rivals Port Vale. Painful. Maybe Repeat Failure is a bit strong. Under Dario Gradi, the Railwaymen eventually broke into League One/Championship (the 2nd Tier) where they punched above their weight for 8 seasons.
Raw Data: A novel on Life in Science by Pernille Rørth (Springer, 2016) I was keen to read this “lab lit” novel written by renowned cell biologist Pernille Rørth. I’d seen lots of enthusiastic comments about the book, and it didn’t disappoint.