So, how is that writing going?

As mentioned in my previous post, I’m trying to improve my writing by dedicating one hour per work day to writing projects. Ultimately this means breaking bad habits, introducing good habits, and also improving my grammar. I plan to do this the only way I know how: learning some rules, then some trial and error to implement the rules. In other words, it won’t happen overnight. To help me along the way I have invested in some books.

The book I’m working my way through at the moment is called “How to Write a Lot: A Practical Guide to Productive Academic Writing” by Paul J. Silva [1]. The first few chapters deal with creating time to write. The author suggests that a fixed time per day or week is much more productive that binge writing or waiting to be “inspired”.  I have to agree with this advice given my level of “inspiration” typically shows an inverse correlation with “time left before needing to submit”. I suspect I am no the only person guilty of this. I have been trying to record my writing progress and you can also keep an eye on it here should you be interested.

Since starting this in April I have had varying success. I found the first week involved a lot of binge writing, then forgetting to do some days. I decided I needed to keep a good track on my progress in order to prevent these fluctuations. The results of this tracking can be seen here. I also decided to fix a time each day for writing. I always find my brain functions least first thing in the morning, so for me the first hour of work is always a little slow and I struggle to get motivated. I thought this would be a perfect time to fill with a dedicated task. The (tentative) results so far have been positive. This hour allows me time to organise my thoughts from the previous day and get my head in the frame of mind for a good days work. The rest of the day follows with a more focussed attitude to work and more ideas than I can complete.

You can also see from the spreadsheet that the writing tasks are slowly getting completed. This is as much of a reward as the fuzzy feeling of success I get inside.

 

References

1. Silva, P. J. (2007). How to Write a Lot: A Practical Guide to Productive Academic Writing. American Psychological Association. Retrieved from http://www.amazon.co.uk/How-Write-Lot-Practical-Productive/dp/1591477433

Back to business, Doctor style.

A small recap for those interested: I passed my viva in November with only minor corrections. After 2 hours of grilling I was released into the big-wide-world (i.e. the local pub) as Dr Stanford. I even managed to don a dress for the occasion.

Hooray!

Since then I have taken up residence in Berlin and turned my scientific wits to a new organism: cyanobacteria. The goal is to make it into a super-duper-biofuel-producing-machine, one algorithm at a time. It has been a big learning curve getting my head around some new biology, and also many  new computational techniques. Luckily, I seem to be making some  progress now, and hopefully will cure the ills of the world in the next few months.

Delusions of grandeur aside, I have recently been writing (and re-writing, and re-writing, interspersed with a little more re-writing) many papers. Most of these are related to my thesis, but there are a few other projects in the pipeline for the coming months. The process has taught me three things:

1. Writing is hard.
2. Re-writing is even harder.
3. Practice makes perfect.

 

On the move

After 7 years in the glorious city that is Manchester, and a life time in North West England, I am finally ready to pack up my little suitcase and move on to pastures new. The destination: Berlin. More precisely, the Theoretical Biology group at Humboldt University.

My student days met their end on September 30th, with a ceremonial hand in of ‘The Thesis’ (it feels like there should be some dramatic music here). The conclusion of the PhD, however, is far from over, with the viva still to be arranged.Until then, the move has given me plenty to occupy my time. One of the (many) things I need to do is discard all of my belongings.

As a firm believer of ‘saving the earth’ and second-hand goods I have decided not to ship my previous life, but instead confine myself to a suitcase and sell everything else. I have made a provisional list below of things I need to sell which I will keep updated as much as possible. Anything left over will be going to the British Heart Foundation before I leave, so don’t feel obliged to buy, it will all be going to good place one way or the other.

32inch Samsung TV……£150
Two seater leather sofa, 4 foot 6 inches wide …..£70
3 tier chest of drawers (in pine colour)…£10
Car steering wheel lock …..£10
Table lamp ….£5
19inch Phillips computer monitor …£40
bean bag …..£8
Long mirror, 4 foot tall, 1 foot wide. …£10
Logitex computer speaker system with subwoofer ….£30
Beko washing machine …..£0
Toaster ….£10
Kettle ……£5
4 mugs ….£1 each
DVD player ….£3
Babyliss Hair dryer ……£15
Surge protection plug extenders …..£5
Towel hanger for back of door ….£5
Candle holders ….£1 each
Tea towels x4 ….50p each
Purple George hand towels  x 2……£3each
Purple George bath towels x2 ….£5 each
Blue fairy lights…£3
Cranium…£5
Simpsons Monopoly…£5
Laundry Basket…£3
Clothes Airer…£5
Alarm clock …. Free to a good home.

A conundrum of sorts.

For some time I have been a supporter of the 10:23 campaign. This campaign is an opposition to Homeopathy, and seeks to create awareness that these ‘drugs’ are neither ‘herbal’ or ‘natural’ (as some believe), but are actually expensive sugar pills.

Homeopathy is built upon the scientifically unfounded assertion that like cures like. So, for instance, it is believed hayfever symptoms should be treated using something like Euphrasia, which is a flower. In addition to this Homeopaths practice ‘potentization’, which essentially involves a serial dilution, using succussion (banging each dilution on the desk). An example can be seen here.

10.23 is a reference to the number of molecules in 1 mole of a substance, known as Avagadros constant. The high dilution of homeopathic remedies means that it is unlikely that even 1 molecule of active substance is present in the 602214179000000000000000 molecules per mole of the solution. When this solution is infused into a pill format, it is essentially just water.  10.23 supporters have held mass overdoses in many major cities to highlight this fact. Each participant takes many bottles of a remedy of choice and shows that no ill effects are brought about, because the pills contain no active ingredients.

Given that followers of homeopathy believe that diluting something makes it more potent, I wonder if the overdoses actually demonstrate anything when homeopathic logic is applied to it?  A lot of pills technically means a higher concentration, which should mean a lower potency, should it not? Perhaps to homeopathically overdose we just need to lick the pill (and, presumably, drop dead)? Or maybe just not take it all? Oh the mind boggles.

A fluxing good Christmas!

After another year of hard PhD slog, and not so much posting, it seems like the perfect time to roll out my Christmas gift to you. For this gift I sat down during an evening of flu pain (delusion always helps these decisions) and asked myself “What have I learned this year that I wish someone would have explained to me a long, long time ago?”. The answer FLUX BALANCE ANALYSIS, or “FBA” for all those cool kids in the know!

The key to FBA is simple. All you need is:

• A stoichiometric reaction network
• An objective function (eg. Maximise growth)
• A known and defined system input (eg uptake rate)
• A maximum and minimum flux boundary for each reaction.

Using this information and a linear programming tool, you can calculate an optimized flux distribution through the system. For more detailed coverage of the theory I recommend checking out the FBA fortress of the wiki-god. The page has recently been given an overhaul by a fellow DTC PhD researcher based in Leeds, Mr Thomas Forth. The page is now more comprehensive, and will direct you to many good articles and tutorials on FBA.

As mentioned in an earlier post by my office buddy and supervisor Kieran Smallbone, the COBRA (COnstraint-Based Reconstruction and Analysis) toolbox seems to be the ‘community choice’ for running FBA, so I thought I’d write a brief “idiots” guide to using it…

What you need:

• MATLAB
• GLPK (or similar linear programming kit)
• COBRA

What to do:

1. Install all programs.
2. Load in the example model using:  model = readCbModel(‘Ec_iJR904_GlcMM.xml’)
3. Tell MATLAB to solve your problem:  solution = optimizeCbModel(model,’max’,false,false)
4. Check the flux result: solution.x

Congratulations you have just completed your first FBA run.

You can also set up your own network:

1. Generate and SBML network without kinetics.
2. For each reaction add the following parameters into the kinetic law:
   • LOWER_BOUND (-inf or 0)
   • UPPER_BOUND (inf)
   • OBJECTIVE_COEFFICIENT (1 for the reaction you want to maximise)
3. Load the SBML as before and away you go.

There you go, the first steps to FBA.

Happy Christmas!

A jovial offering for POETS day!

Having just renewed my hold on ‘PhDFodder’ for another year, I felt it was overdue a post.Sadly, blogging time has been overtaken by impending deadlines, and planning for some exciting lab-based travelling opportunities.Needless to say the next few months will be interesting. I therefore leave you, in the post interim, with some thing that has helped me through many a difficult afternoon: a terribly misplaced advert, showing us why ‘related adverts’ are perhaps not always a good thing.

References

1. Duck Advert, courtesy of Bad Journalism, Twitter post.

The results are in…

…from this months “I’m a Scientist”.  Unfortunately due to my lax posting over the past few months it’s not the one that I took part in.  This time around it was a much larger affair, with 100 scientists and 5000 students.  But what was it like…well, I’m sure it was a little more hectic than last time, but reports from my friend Duncan, who took part,  suggest it was just as enjoyable.

During my stint I found myself voted off second, but I still couldn’t keep away from the site.  It was a truly hectic but engaging experience.  The two weeks involved online MSN style chats and answering lots of posted questions from the students.  The chats were very chaotic and lasted around an hour to an hour and half.  We were bombarded by a torrent of questions from 20 or more students with usually just 3 scientists to respond.  Keeping track was a challenge, but thankfully any questions that were missed usually got posted later and we could get back to the students then.  All praise to the moderators in our section, too.  They helped us skirt around many firewall issues and managed to pacify the students who’s questions couldn’t be answered.  Without them I fear we would have all met our match.

The best things about the experience were how engaged the students were, how profound their questions were (why do we die?  Anyone?) and most importantly how much of a difference it made to their understanding of science and scientists.  There was one occasion where a group of students stayed 15 minutes after the end of school bell because they were enjoying themselves so much.  This made me think that as scientists we don’t do enough to engage school students, or educate them about the reality of science and science related jobs.  Why else would they care so much about being able to talk for an hour with us?

The ‘Science Busker’ of our group, Martin, won the competition.  It was a well deserved win based purely on how the money will be used.  His science busking takes real science to the general public.  Not all the fancy explosions, coloured liquids and CO2 gassing out of a conical flask stuff, but real science.  You can find more information on what he does here.

So lets raise a glass to Martin, and perhaps it’s worthwhile remembering that popping your head into a school classroom for an hour could really make a difference.  Maybe we should do it a little more often.

I’m a Scientist, Get me out of here!!

I’ve been lucky enough to be chosen as one of the scientists to take part in “I’m a Scientist, Get me out of here”.  I’ve been sectioned into the Helium zone along with four other scientific folk.  For two weeks we will be pitted against each other in a battle to entertain school kids with science.  The ultimate prize is a £500 Wellcome trust gift to use to communicate our work.
Over the past week we have all set up a profile that the kids will get acquainted with before we go live on Monday 15th March (That’s tomorrow – eep).  Over the course of the two weeks we will answer lots of questions from the students and also take part in a number of live chats.  As the two weeks progress the students get to vote for their favourite scientist – periodically the scientist with the least votes will have to exit the competition until one winner from each zone emerges.

As far as I’m aware it’s only the school kids that get to post questions, however everyone can keep up to date with what’s happening via the site.  So get your popcorn ready and settle down for lots of scientific fun.  I can’t wait, It’s going to be awesome however long it lasts

I don’t want to be a politician, I just want to research!

These were my thoughts as I entered the media training workshop set up by The S factor, in partnership with WiSET.  I’d agreed to attend the workshop, held at The Manchester Conference Centre, after hearing the ominous and slightly scary phrasing of ‘public engagement’ being batted about the office.

It’s no secret that scientists have a certain stereotypical portrayal within the media.  The ‘average’ scientist is either an absentminded professor or a bespectacled, nasally congested geek with the charisma of a soggy crisp.  With this reputation it’s not difficult to see why public engagement for science is important.  Unfortunately it’s also not difficult to see why the actual ‘average’ scientist is wary of cavorting with the media.

The negative stereotyping isn’t the only barrier between Scientist and Journalist, either. There is also the inherent belief among the research community that the media over-hypes and over promises on scientific deliverables, opportunities and threats.  This behaviour leads to public misunderstanding and can cause major damage to growing research fields (take the GM debate for instance).

The Workshop

The content of the media workshop was driven by Sheila McClennon.  The aim of the training was to make us, a body of heterogeneous research scientists, understand how important it was to be able to talk about our research with anyone, regardless of their knowledge of the field.  Unfortunately the start of this meant accepting things we perhaps hadn’t noticed before.

After hearing some playback from our entrance interviews we were shown how scientists can come across as boring: mainly when we don’t explain things at a level for others to understand.   It was also clear that the nature of our work leads us to be extremely focused. Whilst this is good for research it can leave us  seeing only our own projects and worst off all we assume everyone should know as much about it as we do – but this is not the case.  As a group we were also very over cautious. We were all worried about promising too much from our work, even when that ‘thing’ was a likely downstream product from it.

They were all interesting points to note, and were also quite true.  Even as a collection of scientists it was difficult to fully understand each others work.  Not to mention the proverbial ‘blood from a stone’ endeavour Sheila had getting us to admit basic research could possibly have a use in helping us tackle some major health issues occurring currently (this is still a little difficult to admit because basic research pursues understanding more than output, but admittedly some of our biggest innovations have been discovered using this type of research, but it’s hard to know exactly where it will lead to).

After we’d had time to discuss and digest this we were then sent away to think about our projects, particularly which aspects which we could learn to explain more clearly.  This coupled to a mock interview with Sheila proved the most important part of the day for the majority of us.  Both led to the realisation that the more adept we are at helping people understand our work the less likely it is that the true nature of it will get lost in translation.

Overall the thesis put forward by the Sheila was, when engaging the media, it is our responsibility to be clear, passionate and honest about what we do.  The less we leave to interpretation and abstraction the more likely the true picture of our research will be delivered effectively.

This workshop was a far cry from turning us all into politicians.  Perhaps, though, it may improve our skills as a researchers.  After all, this is advice that can be put into practice in any situation.

references

1. “The Absent Minded Professor” image by Brandon Hambright

edited – Paragraph 5 has been edited.  Upon re-reading it became apparent that it’s interpretation would lead to a misrepresentation of The WiSET training day and Sheila’s work.  Some aspects read as quotes from Sheila but were my interpretations.  This has now been rectified.

“You know Science… Is this as awesome as it sounds?”

Asked a good friend of mine at the very sociable hour of 12.45am. He was referring to the latest Cornell Computational synthesis Laboratory software, Eureqa.  A program designed for reverse engineering dynamical systems. After a brief moment of wistfully staring into space and wishing I did indeed know science (there’s a lot of it!!) I gave my knee jerk response: “No”.  I’ll be honest, at that point I had only read a press release and not the original paper, nor had I test driven the software.  Terrible, I know, but the marketing of these things is usually so over-hyped it’s hard to believe it could really be that awesome.  Fear not though, I have rectified this situation and spent the past few evenings reading all about it whilst having a good old tinker with the software.  I have to say it is as awesome as it sounds.

The Interface

For freeware, the interface is elegant and well thought out.  The tabulated environment provides an intuitive walk through the stages of data analysis  (data input, data smoothing, options for equation development, starting the search and analyzing the solutions).  As a result the detailed instructions are not strictly necessary for a first run, but do come in handy for fine tuning a run.

Entering Data

The software requires the input of raw data.  If the data is noisy you can smooth it in Eureqa (with or without relation to a confidence rating).  For complex data sets it is recommended that you pre-process the data in another application before copying into Eureqa.

The second stage is selecting the type of equation the data represents.  This is done by selecting the parameter (x) you are interested in and which variables can be used within the formula such that x = f (selected parameters).  You also need to select how you want the fitness of the system to be measured.  There are 12 fitness objectives to choose from and weightings can be applied to all.

Perhaps the most the complex part of the selection criteria is choosing the ‘building blocks’ of the equation. Here you use your knowledge of the data set to limit the formula search to a certain group of mathematical terms (add, subtract, multiply etc).

Finding the Solution

Once all options are selected the fun part starts.  Just click ‘Play’ and watch the error reduce as the software attempts to compute a viable solution to the data set.  The program uses an algorithm that takes the derivative/s of the data set/s.  It then combines the previously selected building blocks into multiple equations.  The performance of each equation is compared to the data set and the equation with the smallest calculated error is kept (the ‘fittest’ solution).  The next set of equations is based on the fittest solution from the previous set of calculations.  Again, these equations are tested against the data set and the fittest solution is kept.  This cycle is repeated iteratively until the fitness of the system is optimized (the computed error becomes negligible when compared to the data).

Testing

Now, the best part about science is getting your hands dirty.  I decided to start with a simple data set just to test the stability of the program and the computational speed.  To this end I fed it with a column of  x = 1:39 and a second column of x^2-x.  I asked it to find the relationship between these two columns.  It quickly computed the correct solution.  Great.  Although, perhaps not all that impressive seeing as this is something that I could have solved by inspection.  Next stop something harder.

Using the true logic of a scientist I went from ‘little test case’ to ‘here is a shed load of data I’ve collected and don’t yet understand, do something impressive with it’.  This understandably was not so successful.  There are many reasons for this.  First of all the data could have benefited from pre-processing, whilst the data set should be curved the limited data points led to Eureqa fitting a straight line.  I also left the ‘building blocks’ option as the defaults (I haven’t yet decided what should and shouldn’t be available for computing the data set).   Still, Eureqa happily cranked the handle to produce an equation that fitted the data reasonably well.  Unfortunately, the sequence of errors led to the computation of an equation that had several constants, and only one variable (time, if you’re interested…).  Simple analytical inspection of the data suggests there is a minimum of 3 variables.  It did however notice that there was a non-linear dependence in the data.  A minor win.

Overall

On the whole it’s failure at my second test is no surprise.  The software is limited by the knowledge the user has of the data set, the amount of pre-processing that has been conducted on the data set and the mathematical terms that could apply to the system. I certainly would not say this is a drawback of the software at all.  It is a tool for aiding scientists discover otherwise indiscernible inter-dependencies within complex data sets.  And, like with most tools in science, as long as the user understands its limitations it remains a valid tool for discovery.  I’d certainly use it again, and hope to continue to tinker with it until eventually it gives me something as good as Newtons second law of motion – but one that has not been discovered yet

References

1. Brandon Kiem (2009) “Download your own robot scientist”, Wired Science, Dec 14 -18th.
2. Schmidt M., Lipson H. (2009) “Distilling Free-Form Natural Laws from Experimental Data,” Science, Vol. 324, no. 5923, pp. 81 – 85.
3. Handinflow Image taken from CS4FN (Computer Science for Fun)
4. Paul Jones (2009) The fun and joys of early hours tweeting, Twitter.