Under lid

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 compar

Did everything we attempt to improve end in success? Alas, no. One thing I do n’t particularly like about the ORd’s calcium handling is the fact that the release from the sarcoplasmic reticulum (SR) depends directly on the L-type calcium current. The release should ideally depend, among other things, on the calcium concentration adjacent to the sarcoplasmic reticulum. Beyond the phenomenological nature of the dependence of release on L-type calcium current, this for example also prevents the possibility of the cells manifesting delayed afterdepolarisations in a physiological way [1]. Basically after finishing the ToR-ORd model as it is now, Dr. Alfonso Bueno-Orovio has suggested that calcium-dependent SR release would be a really nice thing to have, with which I heartily agreed, and we started working on it together. I really needed a break at the time, but then again, we thought that if it was achievable to change the SR release without breaking many other properties, it would b

A second topic of biggish advancement is the changes to L-type current (I CaL ) pertaining to the ionic activity coefficients and activation curve (all described in ToR-ORd paper), which this blog post is about. It’s much less of a step-by-step compared to the previous post, as it was much more about conditioning the brain and waiting for a heureka moment. The spark that triggered ToR-ORd development was when I was playing with some published data on post-infarction remodelling in ORd (including a major reduction in the fast sodium current I Na ) and got a major increase in calcium transient amplitude instead of the expected reduction. I tracked it down to the reduction in fast sodium current among all the remodelling factors, which alone was causing a major increase in the calcium transient amplitude. The next step was to go through the literature on sodium current and its pharmacological reduction, which suggested rather unequivocally that reduction in sodium current is suppose

Background In the last section, we’ve seen how MGAs may be used to develop a model. However, when using them, it is quite possible you may get a plot of fitness of creatures in the population as follows (apologies for its ugly printscreen nature; it comes from a quick snap into a digital logbook, as is the case with most figures in this section): On the x-axis is the fitness component corresponding to action potential morphology, on the y-axis is the compound value aggregating other criteria. You can see the tradeoff between the two dimensions easily. In case you’re wondering about what is the green rectangular object, it shows the zone that corresponds to what I thought would be a reasonably good solution at the time. Of course, nothing inside, nor even close :(  This means one of two things; either there is a model parameter that we are missing in the creature DNA (good luck finding it among the hundreds of possible numbers in the model), or there is a structural problem

  In this section, we’ll go over basic principles of genetic algorithm (GA), its multicriterial/multiobjective variant (MGA), and why I think it is a good tool for the task of optimizing computer cellular models. Of course feel free to search internet for other webpages about GAs, as it is a technique relevant to many fields and may take different flavors depending on the application. If you’re using Matlab to run your cell simulations, there are nice implementations of GA (ga.m) and MGA (gamultiobj.m) which may be of use. Let’s start with a simple, single-criterial genetic algorithm. This is an optimization technique which generates a population of creatures (encoding of candidate solutions to the problem) and then performs artificial evolution on them, gradually improving their quality according to set criteria. This is done by taking a starting population, and iterating the following three steps: Generate offspring using  crossover  of the parents. Perform  mutations  on