The opening ceremony of the Virtual Physiological Human (VPH) conference was something special, it featured a lecture-performance with our physiologically based dancing digital twins. The VPH Institute has now published a news letter were you can read more about the development of the digital twins for the past 20 years, and more about the future visions for using them within biomedical teaching and patient-centric care.
“Take your digital twin with you throughout your health journey: from dance performances to patient-centric, preventive healthcare” For link click HERE
The 21st International Conference on Systems Biology (ICSB 2022) was held in Berlin, Germany, these past few days. We had a great time there and had the opportunity to meet great people, take part in interesting discussions, and see some of Berlin. During the conference, we presented several posters and gave some talks (summarized below). We would like to extend our thanks to the ICSB committee for a well-organized conference. Until next time, from the ISB group.
The full program can be found here: https://www.icsb2022.berlin/, and down below is a summary of our contributions.
M4-health: digital twins that follow you throughout your health journey, Gunnar Cedersund
Authors: Gunnar Cedersund
An interconnected multi-level mechanistic model of the human brain, Nicolas Sundqvist
Authors: Nicolas Sundqvist, Henrik Podéus, Malin Ejneby Silverå, Sebastian Sten, Salvador Dura-Bernal, Soroush Safaei, Maria Engström and Gunnar Cedersund
Digital twins and hybrid modelling for simulation of physiological variables and stroke risk, Tilda Herrgårdh
Authors: Tilda Herrgårdh, Elizabeth Hunter, Kajsa Tunedal, John D. Kelleher and Gunnar Cedersund
Insights on hemodynamic changes in hypertension and T2D through non-invasive cardiovascular modeling, Kajsa Tunedal
Authors: Kajsa Tunedal, Carl-Johan Carlhäll, Federica Viola, Tino Ebbers and Gunnar Cedersund
Mathematical modeling of cytokine interplay in human monocytes during LPS stimulation, Niloofar Nikaein
Authors: Niloofar Nikaein, Kedeye Tuerxun, Daniel Eklund, Alexander Persson, Robert Kruse, Eva Särndahl, Eewa Nånberg, Antje Thonig, Gunnar Cedersund, Elin Nyman, Dirk Repsilber and On Behalf Of The X-Hide Consortium
Connecting the Neurovascular coupling and Electrophysiological signaling – a modeling approach, Henrik Podéus
Authors: Henrik Podéus, Gunnar Cedersund and Salvador Dura-Bernal
An in silico resection to estimate global and regional hepatobiliary function in patients undergoing hepatectomy, Christian Simonsson
Authors: Christian Simonsson, Wolf Claus Bartholomä, Anna Lindhoff Larsson, Markus Karlsson, Bengt Norén, Gunnar Cedersund, Nils Dahlström, Per Sandström and Peter Lundberg
A comprehensive mechanistic model of adipocyte signaling with layers of confidence, William Lövfors
Authors: William Lövfors, Cecilia Jönsson, Charlotta S. Olofsson, Gunnar Cedersund and Elin Nyman
Sundqvist N, Grankvist N, Watrous J, Mohit J, Nilsson R, Cedersund G. Validation-based model selection for 13C metabolic flux analysis with uncertain measurement errors. PLoS Comput Biol. 2022 Apr 11;18(4):e1009999. doi: 10.1371/journal.pcbi.1009999
Author summary: Measuring metabolic reaction fluxes in living cells is difficult, yet important. The gold standard is to label extracellular metabolites with 13C, to use mass spectrometry to find out where the 13C-atoms ends up, and finally use mathematical modelling to calculate how quickly each reaction must have flowed, for the 13C-atoms to end up like that. This measurement thus relies on usage of the right mathematical model, which must be selected among various candidate models. In this manuscript, we present a new way to do this model selection step, utilizing validation data. Using an adopted approach to calculate the uncertainty of model predictions, we identify new validation experiments, which are neither too similar, nor too dissimilar, compared to the previous training data. The model candidate that is best at predicting this new validation data is the one chosen. Tests on simulated data where the true model is known, shows that the validation-based method is robust when the magnitude of the error in the measurement uncertainty is unknown, something that conventional methods are not. This improvement is important since true uncertainties can be difficult to estimate for these data. Finally, we demonstrate how the new method can be used on real data, to identify fluxes and important reactions.
