Having come to the UK from Nigeria when I was just 15, it was quite difficult as I had to adapt to a new system of teaching and learning. I had done several biology experiments both in my Nigerian high school, and in the UK during my A levels and as a medic, but none of these truly prepared me for what I was going to face in iGEM.

I was told about iGEM during the second year of my A levels by my friend Fatemeh who had participated the year before me. Although I applied to St Andrews to study medicine, I looked forward earnestly to iGEM, with a vague idea of the work ahead of me. So far, I have learnt a lot, not just about biology, but also about working in a team. As a team member, I learnt the importance of division of labour and learning to work with others, as well as trusting them to do their very best knowing that we all have the same interest in mind and we’re all working together to achieve it. I have also become versed in the use of various lab equipment and basic lab protocols such as ligation, transformations, amongst others, most of which I had only a very fundamental knowledge of before iGEM. Neither my Nigerian high school, nor my A level biology knowledge, had prepared me for the skill needed in the lab.

In Nigeria, the biology lab work consisted mainly of drawing, identifying and classifying organisms such as earthworms. In the UK however, it was a bit more hands on as we grew microorganisms on agar plates, but we never had to make one ourselves and most of the lab work involved sampling insects or other organisms.

We were taken through a basic tutorial of how to use the lab instruments before the start of iGEM, including making agar gels, pouring plates, amongst an abundance of other things and this helped greatly to bridge the gap in my knowledge. Nevertheless, my knowledge of Biology from A levels as well as my first year of medicine did make a major difference in understanding the theory behind the different procedures we had to carry out.

In retrospect, I am delighted that I did this project, as I intend to do a research project as part of my dissertation in the third year of my preclinical training, and this has most definitely boosted my interest in research as a whole. Also, being a part of a new and exponentially growing branch of science is definitely a privilege I am very grateful to have.

I’m a mathematician. Am I a modeller?

What does it take to be a synthetic biology modeller?

I don’t believe that there’s any particular answer to this dilemma. There are engineers, mathematical biologists and computer scientists amongst the field that delve into the realm of modeling, but what, I wonder, does it take to be a modeller? And what are the obstacles that an aspiring modeller can expect to face?

In the preliminary stages of modelling, there are a few articles and journals which to consider the basics of the modelling premise. The ideas that are discussed in (name a few papers), provide the foundations for the basic equations required. The subsequent step involved is to decide which method to investigate your particular system with; deterministic, stochastic and then, perhaps, consequentially parameter sensitivity analysis.

From my novice modeller’s viewpoint this has been a minefield of theory and a swamp of biological knowledge to have ploughed through. I have found that, although I have a background in applied mathematics, the synthetic biology discipline is like embarking into a completely unfounded territory. Alongside this, I have a limited knowledge of biology which was an obvious obstacle at the beginning of the iGEM project. Consequently within our own working system, I have been on a steep learning curve in the attempt to produce the workings of a model.

Throughout this educatory experience, there have been several frustrating moments in conjunction with various ‘eureka’ moments.

One of the exasperating factors is that, from a mathematician’s vantage point, I require precision, accuracy and, quite often, numerical values. However for the duration of the iGEM competition, there has been a continuous struggle to acquire the relevant data (including parameters) that can be applied to our system. This annoying state of affairs could easily be rectified with an readily available database of facts and figures (such as Bionumbers). What can also be infuriating is that even whilst researching these numbers, I explored and analysed various past iGEM competitors’ modelling and they, like ourselves, have had to estimate constraints in order to proceed. Not only has there been a limited set of resources but, due to this constraint, there has been a quite a large proportion of estimation involved in the parameters and even the composition of equations.

Even with what little biological knowledge I had at the very outset of our iGEM project, I was able to grasp the science behind this emerging ‘subfield’ within synthetic biology, and began to fill in the gaps in numerical data. These constants and parameters are among mystical legends (rather like Nessie); you believe they exist but their presence is hidden in mystery.

These approximations and assumptions loosely compose the already precarious fundamentals of the modelling. Personally, I have little confidence in our outcomes and predictions which have been placed upon our model and this is purely due to the absence of experimental documentation available. This opinion is based upon several years of vigorous proofs and thorough examinations of methods all branching from my mathematical skillbase, and so without the robust strength of these, my faith is slightly fractured in our modelling.

Overall the modelling can be a rewarding venture, even with the rocky predictions, that can, quite remarkably and fairly accurately, follow the biology to present the system rather effectively. Successful modelling can complement the biology and, in some instances, can provide assistance in the experimental side of the project.

Throughout iGEM, the aspect of modelling has developed my abilities and I have appreciated the incomparable opportunity to learn the variety of skills that have I have utilised. I believe that with the right mindset and basic intuitive knowledge of the particular working biological system that the team is working in, that any person could learn about modelling.

So what’s necessary to be a modeller? I believe three things are key: some coding and biology knowledge, an eagerness to learn and, but most importantly, motivation. Lastly, do I believe that I’m a modeller? I hope that in the duration of iGEM, I have progressed into becoming a better modeller.