Although many industry project planning sessions are an excuse to drink coffee and an opportunity for management to exert their authority over their minions it is strange that academia has never adopted any formal planning in research projects. If done properly, effective project planning can attempt to provide structure as the project begins and early warnings of things starting to slide. Gateways can be useful to decide when it’s time to stop, and milestones even if missed or not achieved present formal time points to re-assess progress or the project’s direction. While team members can become bogged down in the details of their own tasks and problems, the landmarks on the project plan are opportunities to step back and re-assess what everybody is doing. Perhaps things will change when funding dries up or the beneficiaries want to keep a closer eye on where their money is going if there’s a greater risk of loss in the global economic melt down.

Until then, I continue to wander on….

I recently stumbled over a slashdot story reporting on the Kahn Academy. I hadn’t found this before today and it would have been a great help when I was starting down the maths road. Salman Khan founded the Khan Academy with the goal of using technology to educate the world. Sal received his MBA from Harvard Business School. He also holds a Masters in electrical engineering and computer science, a BS in electrical engineering and computer science, and a BS in mathematics from the Massachusetts Institute of Technology. Not happy with the way he was taught at school Kahn bought himself a $200 camera, $80 tablet, some free software and youtube and began his mission to educate the world. All the material is published under the creative commons license meaning that it’s freely available for anybody to use. There are many hundreds of short video tutorials covering topics covering maths, chemistry, physics, biology, finance, statistics, and history. Kahn expertly takes the viewer through well structured material which is well presented, well delivered, and easy to follow. There are even follow-up tests available online with his “mental bootcamp”. I am currently working my way through the calculus section after brushing up on my algebra and I hope to get the time to explore the rest of the material.

The website is an amazing resource for anybody with an internet connection and the will to educate themselves, and in our corporate run capitalist society I really hope Salman Kahn achieves his mission to educate the world. If he can educate me, he can educate anybody!!

Lab morale was bad following the announcement and in the days following it turned into a full scale rout with PI’s prospecting our lab space and the entire group actively seeking alternative employment and the prospect of a period on benefits. I have meetings with the university to attempt to form some plan but things look bleak. It is hard to imagine how, after surviving a gruelling 1st year training period and a year and half of 12 hours days, 6 days a week, travelling 4 hours a day that it has come to this. I am re-activating my job search profiles but can’t face the prospect of returning to a science technician role where every day will be a reminder of how close I was to a career and a future.

Even if I was, by some miracle, to graduate I don’t know if I could face a lifetime career in a field as volatile as this. I have experienced redundancy rounds in industry, and numerous changes in senior management, but to be in a situation where an entire department can be liquidated by an individual with no warning has been horrendous. I just don’t think I can live day-to-day by the grace of a single individual who can end my research when the wind direction changes.

Over the past year and a half I have had the privilege to be allowed to work with loyal and hard working individuals who routinely work >12 hours a day, 7 days a week, 365 days a year with no holidays, breaks, or even sleep some of the time. Tireless machines that produce endless data at the highest quality they are capable of, who are suddenly cast to the four winds to fend for themselves with no prior warning or explanation.

As Newscientist publishes articles discussing the future of scientific research for the UK and Universities demanding the highest possible calibre of researcher and students to forge a new generation of cutting edge science, I cannot see how anybody of such calibre could tolerate, or survive a life of thankless servitude and uncertainty. Not while individuals with 1/10th the capability are paid more in a week than a scientist earns in a lifetime kicking a ball around a field. I think Universities need a reality check if they hope to build such a future, as the days of serfdom and medieval guilds have ended.

Nobody every claimed a Ph.D was easy!

nothing is certain but death and taxes.

Recently I have been going through the process of building a computer model of the gene circuitry I am building in the lab. I have found this activity to be quite beneficial, not only in the simulation tool I am creating but the process of creating it. I have found that simply thinking about the interactions of the various components and drawing out visual representations on paper, even without any sophisticated computer software helps to build on the strategy being undertaken in the lab. The approach highlights missing pieces of data and potentially important experiments to characterize the circuits behavior.

For the programming/modeling work (after a long time hammering away at Mathematica and Matlab) I decided to use the Copasi simulation and analysis software package as this gives me the fastest route to prototyping the gene circuit and provides the option to output as sbml which could later be implemented in Matlab if required. Copasi has enabled me to jump straight into the modeling from the point of view of the reactions and at the moment mass action kinetics seems to be sufficient to represent the genetic interactions. Copasi also enables optimization of the model. Signal to noise ratio is key to the circuitry I am building so I am able to optimize the model parameters with the input and output as objective functions. Copasi can then implement a number of deterministic and stochastic optimization algorithms to evolve the system towards the favored output.

The results of this approach have provided some hypothesis about the properties of the gene circuit components in order to achieve the desired output. This has provided a sort of first round iterative design/development cycle and once the lab data has been obtained should provide some interesting validation of the modeling/wet lab approach to the project.

More information on optimization algorithms can be found in Mendes & Kell (1998) Bioinformatics, Vol 14, 869-883.

I’m looking forward to meeting other synthetic biology researchers.  A few of the conferences I’ve attended so far as part of the systems biology program have been heavy on dry scientists and there has been little interest in wet lab experimentation, leaving me somewhat disjointed from the systems biology community.  I am hopeful that the workshops will enable me to gain advice and incite into the work I am doing, as well as the future aims for the field of synthetic biology.  I’ll try and write something while I’m there if I can get internet access.

A few weeks ago I attended a modelling and simulation Copasi workshop run at the MIB by Professor Pedro Mendes. I had attempted to blog about it previously but lab work got in the way.

The workshop was a 3 day event detailing all aspects of the Copasi software, much of which can be found in chapter 2 of the “methods in molecular biology” (2009) Volume 500. 1-43, available as a preview here. (There is also a publication associated with Copasi by Hoops et al. (2006) Bioinformatics 22, 3067-74).  I’m no mathematician, so my description of Copasi wont be the most accurate!  For me, Copasi is a graphical user interface into the world of mathematical modelling providing an immediate step up in to the capabilities of Matlab and Mathematica armed scientists without requiring particularly large amounts of experience of programming or modelling.  The software forms a fundamental toolkit of everything a biologist, or mathematician/computer scientist, needs to build models of systems of reactions and run simulations on them.  You can enter your reactions using symbolic algebra equations such as those found in many standard biochemistry textbooks, or directly as systems of ODE’s so it is familiar to both wet and dry scientists.  A large number of standard enzyme kinetics equations are available when creating your model such as Michaelies Menten types and hill equations as well as all kinds of inhibitor-substrate relationships,  and the ability to enter your own.

At its most basic you can input reactions between species of compounds using symbolic algebra and then create plots of the behaviour or those species as they react together in your system over time. You can quickly gain a grasp however of the underlying power of Copasi when you begin to make more sophisticated enquiries of your system.  Using the graphical interface you are able to perform a range of systems biology / modelling techniques from finding steady states, to metabolic control and sensitivity analysis.  The real power behind Copasi comes from the advanced features however, particularly the parameter scan which is currently not available in any of the equivalent simulation tools and would require substantial programming experience in Matlab or Mathematica. The parameter scan enables you to set a certain parameter at a range of values and repeatedly run the simulation, plotting the output from each iteration.  This, for me was a hugely powerful tool as you can test your model under a range of initial conditions, in a high throughput manner.

Copasi is also capable of performing parameter estimation, which allows you to input laboratory data into your model as parameter values and then fit your model parameter values to the data to “reduce the distance” between your model and your observations and reproduce in vivo representative behaviour.  In addition, there are a number of optimization algorithms built into Copasi that can optimize your model towards an objective function, or find conditions under which the model behaves in some particular way.  There are a wide range of algorithms pre-programmed into Copasi for these tasks including evolutionary programming, genetic algorithms, particle swarms, Praxis, Hooke and Jeeves and more.  For the even braver modellers, Copasi can be used in conjunction with Gepasi, an older relative of Copasi that can be used to run multiple simulations simultaneously.  For example, you can have multiple copies of a model representing a culture of interacting cells or systems and run multiple simulations on multiple interacting models!

Models can be imported and exported in xml and sbml format and ODE’s can be exported in LaTeX and MathML formats for transfer between different applications.  Models from biomodels.net can be imported directly into Copasi and there is a feature to update model details from the Miriam database.  There is also a command line version of Copasi that enables high throughput “automated” modelling processes to be run.  The Copasi group is also working on a web interface enabling scientists to access the software through a web browser interface.

The comprehensive tool set available in Copasi provides a hugely powerful tool for the budding systems biologists to immerse themselves in the field of mathematical modelling and perform some fairly rigorous and comprehensive modelling techniques without prior experience of complex mathematical programming.  It can also produce data for the biologist with a minimum of mathematical knowledge, providing some interesting incites for experimental hypothesis generation.

Copasi is available as open-source, and free for academic research from http://www.copasi.org,  and is under continuous development by a core team of programmers as well as a community of users interacting through an active forum.  The software is available for Windows, Mac, and Linux.  If you’re currently wresting with Matlab or modelling in general,  I would recommend Copasi as an excellent starting point to dive into the sometimes intractable world of mathematical modelling, particularly coming from a biological background.

During this years inter-DTC systems biology students conference there was a debate session on the subject “can scientists be multidisciplinary?”. The purpose of the session was to debate whether students are better equiped for scientific research by specialising in a particular field or diversifying across fields. I have heard this debate a number of times since joining the Manchester DTC, which I believe stems from an inherent insecurity in the decision to train in the emergent field of systems biology. Many scientists have the desire to label themselves with an identity through their specialism and join other scientists sharing their label in an almost medieval guild like behaviour. These guilds then form alliances between other like minded individuals within and across Universities. This approach has worked well in the existing reductionist scientific world where research is focused on specific areas of interest, enabling funding bodies to quickly identify and recruit suitably skilled scientists to complete their projects. With the advancements in high throughput automation and computer science an increasing number of projects are pushing towards integrative research projects, combining data and experimentation from diverse scientific fields. These advances have culminated recently in the emergent field of systems biology that is driving a new holistic scientific approach through the integration of many scientific disciplines into multidisciplinary research projects. The existing system of training and recruiting specialist scientists has been adapted to encompass a higher level of organisation, utilizing multidisciplinary teams with a central management body coordinating the multidisciplinary communal effort. Problems arising from this new multidisciplinary group working has been in the area of communication with resistance from each of the parties to work with the other, or a resistance to consider the others ways of working, thought processes, or general scientific rationale. The project coordinators have tackled this by continuing the segregation and coordinating from outside each of the groups, minimizing or eliminating the requirement for the component researchers to interact. More recently however, systems biology has promoted a new, more radical approach of training the individual component researchers with the fundamental key skills of their project colleagues, culminating in the development of pioneering University doctoral training centres such as the MIB where classically trained specialists are re-trained in their opposite fields, combining wet and dry lab skills in a single researcher, and the first wave of “systems biologists” are beginning to emerge.

To return to the point of my post, there were representatives on the conference debate panel from Astra Zenica and Pfizer, and the inevitable question “what are you looking for when recruiting a systems biologist?” came up. I found the answer unsurprising, but disappointing. The large pharma companies are searching for mathematicians who can build them models of biological systems that they can use to direct their research. This statement wasn’t followed up by any of the academic researchers. I was left considering the future and relevance of “multidisciplinary scientists” in the wider world. Philosophically, I believe all scientists should be inherently multidisciplinary, enabling them to investigate the world around them and draw conclusion from whichever realm of science that is relevant to the question. I believe that every individual is capable to a reasonable extent to learn anything, and not just “their field” with the only barrier often being attitude and effort. However, in the real world you have to pay the rent and put food on the table, so the starving scientist must match their skill set with the market demand. Current scientific infrastructure in academia and the private sector is set up to employ the person who has the specific skills to complete whatever task is required that their existing assets in the organisation cannot. A multidisciplinary scientist therefore could be considered a “jack of all trades and master of none” and be continuously out-competed by specialists. Existing academic and industry development frameworks are still configured to develop individuals in a specialist role and multidisciplinary individuals don’t fit in their human resources skills matrix, making employment and development impossible to place within their company hierarchy. In addition, the current “group approach” of systems biology means that multidisciplinary thinking is often only required from those coordinating the project, and not the component individuals facilitating the work, and could be interpreted as an unnecessary diversion, deflecting from the development of a key specialism that would be of value to a potential employer.  It gave me the feeling that the pharmaceutical industry is funding and promoting systems biology development with the aim of using it as a vector to cherry pick mathematicians that can be re-programmed with sufficient biological knowledge to adapt their mathematical skills to simulation of biological interactions, and the development of wet lab scientists an unfortunate by product. Mathematicians, after sufficient training in systems biology can manage the wet lab scientists and systems biology projects sufficiently to deliver the data required to populate the models and drive the biological hypothesis generation, with wet lab scientists a part of the process that is yet to be automated.

Just to qualify the above, I don’t intend for this post to sound of the “sucks to be you chained in the lab” general bench scientist rant. I believe that systems biology is a transitory scientific field born out of the lack of in situ data to build accurate real world simulations of biological systems. The wet scientists are employed purely to gather this data to populate the mathematicians models, which the mathematicians then use to generate hypothesis and direct the research. It is my belief that mathematicians are the multidisciplinary scientists in systems biology, and must be multidisciplinary for it to continue. The biologists on the other hand must continue to focus on specialisation to develop new measurement techniques, physically obtain the required information, and critically to retain their market value and employment. I believe that biological scientists in systems biology will eventually diversify into either their original fields, or new ones particularly synthetic biology, while systems biology retain the mathematicians and to a certain extent chemists who follow the computer science/simulation road.  I believe that adopting a multidisciplinary approach for systems biology is essential for the mathematicians but this approach is detrimental, if not career suicide for biologists, who would be better directed towards classical fields, or for those seeking something new and akin to systems biology – synthetic biology.

I would like to gain comment on this post. I intended for it to be a bit reactionary and maybe inflammatory so I can perhaps incense some response and be proved wrong. I joined the systems biology road trip with the hope of gaining new skills that would drive a new career in biological sciences, and I am hoping for it not to have been a mistake!

The talks were divided into sections covering many aspects of systems biology ranging across application to theory.  There were a number of interesting keynote speakers.  Dieter Weichart talked on “from ‘omics’ to systems biology, and David Broomhead gave an intriguing lecture on applying fractal maths to simulate the complex composition of the cell cytoplasm.  Professor Broomhead has an excellent way of communicating complex theorem to the most mathematically inept (me!).  Pedro Mendes presented a history of simulating biochemical reactions taking us back to the early days of punch cards and drive through size computers crunching away and amusingly producing the same graphs your quad core Intel 8gb DDR-3 powered nvidia SLi carbon footprint behemoth produces

The students presented a diverse range of projects representative of the style of the doctoral training centre in each University.  Oxford University gave an impressive visual presentation of their progress modelling blood vessels within the heart, building on the existing model of the heart by Noble.   There were a number of talks ranging across modelling cell signalling, tracking cell movements, and bioengineering of microorganisms for potential biotechnology applications.  In addition, there was also 2 interesting talks that diversified from the cell biology / microbiology themes, on modelling / “decoding” epilepsy EEG seizure data to provide new understanding of the underlying cause of the condition and provide potential technologies for predicting and managing seizures.  One talk focused on absent seizures while the other focused on grand mal seizures, bother using different mathematical and statistical methods to search for patterns in the inherantly complex and chaotic brain activity.

The event was a great way to encourage students to communicate between systems biology DTC’s as well as building links for current and future research.  I enjoyed the opportunity to meet with students working on areas similar to my own, as well as their supervisors who could give their own incite on the work that I was doing.  This helps to broaden your understanding, as well as gain fresh ideas from people outside of your usual research group.  After the conference we went for an excellent meal hosted by Professor Westerhoff and had a chance to interact in a more relaxed social environment.

Next year’s conference hopefully will be hosted by Oxford University, which would be a unique and priviledged opportunity to visit their prestigious campus, and experience their own blend of systems biology research.