I’ve spent much of this week at the University of Nottingham, meeting other scientists and learning about Mathematical Modelling. I feel like I asked, “So what do you do and where do you come from?” a hundred times.
My biological research is concerned with intracellular signal transduction. Also termed cell signalling, this process describes the way that a single cell receives a signal from the extracellular space, perhaps in the form of a hormone or growth factor, and communicates it to the cell nucleus to effect a change in gene transcription, or other appropriate response. Traditionally cell signalling has been investigated using molecular biology to investigate protein amino acid sequence and structure (gene over-expression, silencing and mutagenesis), and using biochemistry to look at protein-protein interactions and enzyme or protein function. Cell biology then contextualises this information, using microscopy to determine the localisation of the protein of interest within a particular cell type. Over many many man hours and large sums of money, this knowledge on individual protein signalling modules can be built up into larger signalling networks. Eventually clues as to the molecular basis of diseases are unravelled, potential drug targets may be identified and the pharmaceutical industry begins to get excited.
In a time in which science funding is scarce (to put it mildly), this activity of painstakingly characterising a single protein and its immediate contacts can seem incredibly futile. For the last three years I’ve been working on a previously uncharacterised lipid transfer protein. After all the late nights and weekends in the lab, I can tell you which phospholipids it likes to bind and two proteins it interacts with. I have little clue as to what the protein actually does or what the interactions mean. Despite this, my progress in three years is considered to be good.
Alongside researchers like myself, toiling away on the mysteries of a single gene, others have used high throughput approaches to produce large datasets on particular aspects of a cell or tissue. A good example is a database of phosphorylations, post-translational protein modifications used to regulate protein function. Such databases can tell you exactly which residue of a protein is modified under particular conditions. It won’t tell you who’s doing the modifying or what activity it’s regulating, but it can tell you which residue to mutate to look at regulation of your single protein.
Enter the mathematician: mathematicians and computer scientists are increasingly finding a place for themselves in the world of biological research as bioinformaticians, mathematical biologists and systems biologists. I place these three terms in order of willingness to dirty their hands with actual biological experiments, with bioinformaticians being the least likely, and systems biologists much more likely to be an experimental biologist learning or collaborating closely with researchers doing mathematics. Earlier this year I attended a Systems Biology conference and encountered a group of Bioinformaticians I have worked with in the past, now re-branding themselves as Systems Biologists and moaning about how long experiments to confirm protein-protein interaction predictions were taking.
But this is good. Finally the field is realising the need to present a united front and use mathematics to begin to combine all the seemingly disparate pieces of information. Which brings me back to my week in Nottingham. I have attended many Systems Biology talks over the last few years and I have to be honest in saying that only one or maybe two have left me feeling excited about what the field can offer. Most usually have been talks by mathematicians who don’t appear to quite understand the signalling pathway they are working on, and are all too quick to show how pretty their differential equations are, quickly excluding the maths-shy biologists in the audience. At the Biochemical Society Signalling conference in Edinburgh in June this year, I finally heard a scientist say (paraphrased), “Using our experimental data as a starting point, we used mathematical modelling to bring together spatial and temporal data, which led us to discover two different pools of signalling molecules.” Finally someone has used maths to tell them something they didn’t already know.
So this is why I went to Nottingham, to a course entitled, ‘Mathematical Modelling for Biologists’. It’s going to take a while for my brain cells to recover from the assault, and even more time to process and fully understand what I have learnt. So I’ll have to let you know how I get on.