PhD thesis writing advice

When I sat down to write this post, I had an opening line in mind that was going to bemoan me being a bit remiss with my blogging of late. That was before I checked my last post and realised it had been ten months, and 'remiss' feels a bit inadequate: I basically stopped.

I had a pretty good reason, in that I needed to finish my PhD. Those ten months have basically been filled with write paper, submit paper, thesis thesis thesis, do a talk, thesis thesis thesis, get paper rejections, rewrite, resubmit, thesis thesis thesis … then finally viva (and a couple of more paper submissions). It is exhausting, and frankly having either a thesis or a paper to be writing at any one given time takes the shine off blogging somewhat.

Now that it's done and out of the way (no corrections, wahey!), I begin to turn my mind to getting the blog up and running again. What have I been up to lately that I could write about, I asked myself. Well, there's always this big blue beast.

Having been the major task of the last year of my life, I've spent more than a little time thinking about the process of thesis writing, so I thought I'd share some pointers and thoughts, maybe being the usual and the obvious.

1) Everything takes longer than expected.

This is the best advice I got going in, and is always the first thing I say to anyone who asks. In a perfect example of Hofstader's Law, no matter how much time you think a given task should take, in reality it will take longer.

2) Be consistent.

The longer a given document is, the more chance there is that inconsistencies will creep in. Theses are long documents that are invariably only read by accomplished academics (or those in training), many of whom have a keen eye for finding such consistencies. 'n=5' on one line and 'n = 4' on another. 'Fig1: Blah', 'Figure 2 – Blah blah' and 'Fig. 3 Even more blah'. These might not seem like big deals, but added up they can make the document feel less professional. Choice of spelling (e.g. UK or US English), where you put your spaces, how you refer to and describe your figures and references, all of these types of things: it doesn't matter so much how you choose to do them, as long as you do them the same each time.

On a related note, many technical notations – such as writing gene, protein or chemical names – are covered by exhaustive committee-approved nomenclatures. Use them, or justify why you're not using them, but again, be consistent.

3) Make it easy for yourself: get into good habits.

Writing long documents is difficult, so don't make it hard on yourself. Start as you mean to go on, and go on like that – I made a rather foolish decision to change how I formatted my figures one chapter in to my writing, and going back to re-format the old stuff was time I could have been spent writing*. Doing something right the first time is much more efficient than re-doing it two or three times.

Make sure you make full use of reference manager software. This will sound obvious to people that use them, but I am consistently surprised by the number of PhD students I meet who write their bibliographies and citations manually. I personally use Mendeley, which operates perfectly well and has a nice reading interface as well, although there are plenty of others. You are probably going to have a lot (hundreds) of references, and even more citations: doing them manually is a recipe for disaster.

Similarly, don't do any manual cross-referencing if you can avoid it – the document as you write it will likely be entirely fluid and subject to change for months, so any 'hard' references you put in could well end up needing to be changed, which not only takes time but increases the risk of you missing something and carrying an error along to your finished PhD.

If you have the time, I would recommend trying to get into LaTeX (with the 'X' pronounced as a 'K'), which is a free, open-source code-based type-setting program. It's a bit of a steep learning curve, but there are plenty of good templates and once you've got a grasp of the basic commands it's incredibly powerful. Crucially, as your file is just a text document (which effectively just 'links' to pictures when you compile your PDF) it remains small in size, and therefore easy to load, backup and play with. It also makes referencing, cross-referencing, and generally producing beautiful looking text a lot easier then most word processors.

Theses are often full of technical words and abbreviations, and it's entirely likely your examiners won't know them all beforehand – therefore they need to be defined the first time they're used. However, if you're moving chunks of text around (sometimes whole chapters), how do you know which one is the first time? My tactic was to not define anything while writing, but whenever I did use a new phrase I added it to a spreadsheet, along with its definition. Then once everything was set in place I worked through that spreadsheet and used 'find' to add the appropriate definition to the first instance of every term. What's more, that spreadsheet was then easily alphabetised and converted straight into a convenient glossary!

4) Be prepared to get sick of it ...

You will spend an unhealthy amount of time doing one thing: working on this one document. It will bore you, and it will make you boring, as it will take over your time, thoughts and life**. It is basically guaranteed that you will bone-weary of sitting down to your computer and working on it again. It's relentless, it just keeps going on and on and on, to the point where you forget that your life hasn't and will not always just be thesis-writing.

5) ... but remember it will end.

It might not seem like it at the time, but it will. You will finish writing, you will finish checking, you will hand it in. You'll then find some errors, but that's OK, your examiners are never going to read it as closely as you do when you check it. Remember that your supervisor(s)/thesis committees/whoever shouldn't let you submit unless they think you're ready, so the fact you're submitting means you're probably going to be fine!

* For what it's worth, the final way I did my figures was better, I should just have thought about it and done it first. Basically I outputted my plots from Python and R as SVG files and compiled them into whole figures in Inkscape (which is also great for making schematics) and saving these as PDFs. A word of warning thought – certain lines/boxes in occasional Python-saved SVGs failed to print (apparently something to do with the way fancy printers flatten the layers), so it is probably worth keeping backup EPS or non-vector versions of your Inkscape files on hand.

** Look at me, I've just closed my file for the last time (before uploading to university servers) and the first thing I do is go and write a thousand words about it!

Up-goer five PhD description

Someone recently pointed out the Up-Goer Five text editor to me, which only lets you type using the 1,000 top most common words (inspired by this xkcd). The challenge is then to describe your PhD project using just these words. Here's my try:

I study a type of blood cell that helps keep us safe and well, called 'T_cells'. These cells search the body for signs of problems or things that shouldn't be there, and fix them if they find any. They can look for many different kinds of problems; taken as a group, they can look for far more problem signs than almost any other cell type. They feel the face of other cells as they move around the body, each one looking for different signs that something isn't right. I'm working on checking to see what 'T_cells' people have, to see if we can learn why some people get sick more easily than others.

Max Perutz 2012 entry

Wow, I hadn't realised how long it's been since my last blog post - I blame the copious amount of food I ate over the holidays, and then the giant pile of work I've been doing since.

So, in order to keep the ball rolling, I thought I'd post a bit of science communication I wrote for my entry in last year's Max Perutz writing competition.

I was going to write an entry for the recent Euro-PMC competition, but I only got as far as deciding on the theme of the pun for my title (I was thinking something cheesy like 'Genius, or geneious?').

Anyway, here's the article as I wrote it last May (I didn't win).

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Unravelling the secrets of our immune system

Sometimes we only realise how important something is when it goes wrong. In the case of adaptive immunity, things going wrong can be fatal.

Adaptive immunity is one of the systems we have evolved to keep infectious germs at bay. It is our intelligent protection system, a biological firewall, where white blood cells patrol our bodies, keeping us safe from disease. Not only does it stop intruders in their tracks, it remembers the threats it's seen before, so it can defend against them faster the next time they attack.

There are two medical conditions in humans that reveal to us how important it is to have a working adaptive immune system. The first occurs when some children are born without working copies of genes that encode important immune molecules. This means they don't make some of the proteins that are required for adaptive immunity to develop.

Alternatively, people can lose their resistance to microbes later in life. This can happen when untreated HIV positive individuals develop AIDS, or in transplant patients who have taken suppressive drugs to prevent organ rejection.

People without a functioning adaptive immune system are compromised, exposed. They are at risk from any stray infection, vulnerable to all manner of viruses, bacteria and fungi. A simple bug that might not even give you a temperature could spell death to them.

This makes it important for us to know how adaptive immunity works. This is what I do in my research; I look at a particular aspect of this system, to try and understand what a healthy adaptive immune system 'looks' like, and how it goes wrong in disease.

In order to explain my work, you have to know a little bit about how our bodies generate this powerful immunity.

Cells don't have eyes or ears, so they have to use receptor molecules on their surface to detect what's going on around them in their environment. These receptors are proteins, the blueprints for which are encoded in the genes in our DNA.

This means white blood cells could have a receptor that recognises a certain bit of a bacteria say, or the fragment of the outside of a virus. If the receptor finds and binds its target, then that cell can tell that the body is infected with a particular parasite.

The problem lies in that there are far more bugs and germs out there that could potentially infect us then there are genes in our genome. How can we have evolved ways to detect and protect against such a barrage of disease with so few genes?

Maturing adaptive immune cells overcome our finite genomes by shuffling pieces of it around, making unique receptor genes out of genetic building blocks that all our cells contain. These cells, called T cells and B cells, physically loop the DNA over itself, and then cut out the chunks in between.

This means different sections can be moved next to each other, recombining to create new genes. Incidentally, this extraordinary feature makes them some of the only cells in the body that don't share the same genome as all the other cells.

Each developing cell shuffles their DNA around independently, stitching different gene segments together in order to produce its own distinctive receptor. As there are many segments to choose from, the number of different combinations is huge.

Moreover, the DNA sequence at the join sites can be randomly altered, meaning the eventual number of possible different receptor genes is truly phenomenal. The fact that we have millions of white blood cells inside us, each bearing one of the trillions of different potential receptors, has historically kept researchers from measuring this diversity.

My project is to use DNA sequencing technology – developed during the Human Genome Project – to read as many of these uniquely generated adaptive immune genes as we can. By doing this we can characterise a person's immune repertoire, seeing how prepared their body is to fight off infection, and perhaps even see what they’ve been infected by in the past. 

The hope is that we can use this technology to understand what it is that makes a healthy human immune system. Once we know this, we can compare this to the compromised or failing immune systems that we see in infection, cancer and autoimmunity, and maybe get an insight into how to treat or avoid these conditions.

Science has brought a lot of relief to those suffering from disease, and has prevented many more from joining them. However, the challenges faced in curing our ills can only be surmounted by learning as much as possible about the way that our bodies work, as well as the diseases that threaten us. Unravelling the secrets of our immune systems is another step towards that goal.