We’ve
reached the end of this series – I hope it was at least somewhat interesting
and useful. Despite my not-fully-serious suggestion to avoid model development
if you can, I think the process is quite a unique experience that may change
quite a lot how one perceives computer models. In my case, it definitely made
me appreciate much more the limitations as well as strengths of computer models
and it transformed the way how I read and interpret modelling papers. Even though I've got low number of observations, my impression is that model developers are far more critical of models (theirs included) than model users. This is however probably
nothing new – people developing lab protocols also seem to me more aware of
caveats of the methodology than people who use it more or less as a black box.
Beyond being transformative with regards to computer modelling, making a model
may be really useful for one’s physiological intuition. Given one has to go
deeper to develop a model compared to using it, I think it’s much easier to understand
and internalize many links between ionic concentrations, currents, what is the
interplay of exchangers, etc. Some of it may be model-specific, but again, as
long as one is aware of this possibility, I think there is no problem. It’s
going back to the view of computer models a formalized literature review that
you can simulate to check how different studies are consistent and what is the
knowledge we’re missing. Developing a model really forces one to understand and
actively read a lot of literature in depth, which is good in itself.
By the way,
speaking of limitations – please let me know if you find issues with ToR-ORd.
Even though the process of making it was rather exhausting and I don’t want to
see it crumbling down, I think models will always have their problems and the
way to better models and research community as such is not pretending they are
perfect. There is no reason to feel offended [1] upon hearing criticism – I surely will be grateful. ToR-ORd is a snapshot of a
process of multiple streams flowing through the field of computational
cardiology where we felt that it was worth validating, packaging, and
publishing, but maybe there will be another version in the future, or it may
inspire other groups to make it better.
In this
place, I’d also like to say how important it was to work on ToR-ORd development
in an excellent group led by Prof. Blanca Rodriguez. Her supervision was really
great, very supportive, and with minimal pressure, which worked perfectly for this project. There were things that did take a while and if there was a supervisor of
the type “I need a solution in one week” it would make an already hard project
much harder. In such a case, one would have to take shortcuts, but taking shortcuts in model development usually means
you have to go the longer way in the end anyway. The supportive style of leadership was also key to maintain reasonable state of mental health, which can easily suffer in projects like this. Furthermore, given the group’s
status and connections, I could discuss some aspects of the work with top
experts in the field and with people in regulatory bodies – especially in the
latter phase, seeing interest from more senior researchers did help me find
energy to overcome the last few hurdles. Another critical factor was the
expertise already present in the group which I didn’t have and which saved us
loads of time and allowed us to make the paper all-around stronger. Dr. Alfonso
Bueno-Orovio and Dr. Xin Zhou created the CellML code and ran 1D simulation via
Chaste, Dr. Elisa Passini replicated her previous study on drug safety with ToR-ORd,
and Dr. Ana Minchole ran 3D torso simulation to extract pseudo-ECG [2].
Dr. Oliver Britton has provided me with annotated and pre-processed data from
the Szeged group of Prof. Varro, as he used it in his previous study. If I was
to do it myself in a group without such background/breadth of expertise, it
would have taken me ages to do all that. I suspect some of the validations we
did might become “this is not enough of a priority” and the final publication
would be probably quite a lot poorer for it. Obviously, the good spirit of the
group permeates everything, from group meetings to seminars and discussions –
everyone has contributed to this project in some form or another, and it was a
real pleasure to work there. So if you’re working on something similarly hard, make
sure you’re not alone in life, both personally and academically.
Thanks for
reading and let me know if you have any questions!
[1] It was
quite a shock when I was moving from the theoretical computer science community
(where people seemed to be predominantly extremely open to sound criticism and
they were grateful for it) to biomedical research, where one is basically dancing
in a minefield. Even as a junior researcher, I already lived through and heard
of various interesting stories that would be nearly unthinkable in computer science. Talking
to researchers like e.g., Dr. Michael Colman, whose words on the importance of
self-criticism and not taking offense came like a healing rain after hearing
some pretty bad stories of ego and revenge, really helped me to partially restore
faith in humanity (at least in academia).
[2] This was
ran on supercomputing resources that were available again only due to efforts
of the group – both in writing the grant applications, but also in writing publications that helped persuade the grant assessors that our group
should be funded.
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