The ISBGroup Blog

We recently attended the International Study Group for Systems Biology (ISGSB) 2022 conference where we gave two talks. Our first talk was given by Gunnar Cedersund and was on how we can use M4-models and digital twin tools to help patients on their health journey throughout various aspects of their lives. The second talk was given by Nicolas Sundqvist and was on our efforts to develop an interconnected multi-level mechanistic model of the human brain. The conference was a success as we partook in interesting discussions and offered our contributions to the ISGSB community. On another fun note, our poster designed and presented by Nicolas Sundqvist was voted joint best poster of the conference. We are looking forward to the next iteration of the ISGSB conference.

Read our new article published in PLoS Computational Biology!

You find the full link here:

https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1010469

Silfvergren O, Simonsson C, Ekstedt M, Lundberg P, Gennemark P, Cedersund G. Digital twin predicting diet response before and after long-term fasting. PLoS Comput Biol. 2022 Sep 12;18(9):e1010469. doi: 10.1371/journal.pcbi.1010469.

ABSTRACT

“Fasting and diet are central components of prevention against cardiovascular disease. Unfortunately, there is little consensus regarding which diet schemes are optimal. This is partially because different clinical studies contribute with different non-connected pieces of knowledge, which have not been fully integrated into a useful and interconnected big picture. In principle, mathematical models describing meal responses could be used for such an integration. However, today’s models still lack critical mechanisms, such as protein metabolism and a dynamic glycogen regulation. Herein, we present a) a new expanded model structure including these mechanisms; b) a set of parameters which can simultaneously describe a wide array of complementary estimation data, in both healthy and diabetic populations; c) a personalisation-script, which allows these generic parameters to be tuned to an individual/sub-population, using demographics (age, weight, height, diabetes status) and historic metabolic data. We exemplify how this personalisation can be used to predict new independent data, including a new clinical study, where a qualitatively new prediction is validated: that an oral protein tolerance test gives a clear response in plasma glucose, after, but not before, a 48h fasting period. Our combined model, parameters, and fitting script lay the foundation for an offline digital twin.”