Skip to main content

About

I am Jakub, an interdisciplinary cardiac researcher currently working at UC Davis and University of Oxford. In my work, I combine computational simulations, experiments, and data analyses to (mainly) study the mechanisms of arrhythmia in the heart.

I understand why scientific publications are so polished and streamlined. They do (or should) give the background of the research question studied, describe methodology, results, and discuss what it all means. One thing that is missing is all the little paths of the research work that didn't quite work, how the project unravelled over time, or how were the discoveries made. At the same time, I think such information can enhance one's research process and bring insights into how to tackle issues that are invariably encountered.

This blog has started with a series of posts "Making a model", which aims to give some extra insights into how the computational human ventricular myocyte ToR-ORd model was developed. It gives several peeks "under the lid" of the development process, also explaining the motivations and thought processes in greater depth than the paper itself. Second, I describe and discuss the process of applying for a junior fellowship in the UK.

Only the future will tell if there are any further series of posts...

Comments

Post a Comment

Popular posts from this blog

Several tips on junior fellowship applications

It turns out I was fortunate enough to receive the Sir Henry Fellowship from Wellcome Trust. This is a four-year international fellowship that will allow me to spend some time at UC Davis, California as well as University of Oxford, focusing on interdisciplinary investigation of diabetic arrhythmogenesis. It was certainly an interesting and person-developing experience (obviously viewed more favourably given the outcome). I had the advantage of working under/with highly successful people who gave me valuable advice about the process and requirements. I am quite sure that  I would not have gotten the fellowship without the support of Profs. Manuela Zaccolo, Blanca Rodriguez, and Don Bers, to whom I'm deeply grateful. However, not everyone has such nice and investing-in-you supervisors and beyond very generic advice, there is very little information on the internet on what the process of applying for junior fellowship entails [1]. The aim of this text is to share some findings I ma...
Post a Comment
Read more

Making a Model: Part 0 - Introduction

Welcome, dear reader. This is the start of a short series of blog posts aimed at providing some insight into the process of development of a computational model of a cell. The type of the model we’ll focus at is one which simulates the development of ionic concentrations and behavior of ionic currents and fluxes over time (probably most relevant for excitable cells such as cardiomyocytes or neurons). I'm hoping that tips and observations in this series will be of use to graduate students and researchers who are interested in computer simulations. While the posts are about the development of human ventricular cardiomyocyte model ToR-ORd ( https://elifesciences.org/articles/48890 ), I mostly try to focus on general observations (usually those I wish I knew about when I started). I decided to write up the topics in the form of blog, given that scientific publications tend to have a somewhat rigid format, and tend to focus at what is done, how, and what it means, rather than at ...
Post a Comment
Read more

Making a model: Part 1 - Development strategy

This is just a short post about the criteria that one sets for the model to fulfill when making a model. In our paper, we decided to strictly separate criteria for model calibration (based on data that are used to develop the model) and validation (based on data not used in model creation that are used to assess it after the development has finished). Drawing a parallel to the world of machine learning, one could say that calibration criteria correspond to training data, while validation criteria correspond to testing [1] data. In the world of machine learning, the use of testing set to assess model quality is firmly established to the degree that it is hard to imagine that performance on training set would be reported. Returning from machine learning to the literature on cardiac computer modelling, it becomes rather apparent that the waters are much murkier here. Most modelling papers do not seem to specify whether the model was directly made to do the reported behaviours, or wh...
Post a Comment
Read more