What exactly are digital twins, and how can we use them in the healthcare of the future? Gunnar Cedersund explains how it works. How can we make a digital model of ourselves and test different forms of treatment on our personal artificial twin before the treatment is actually performed.
Earlier this autumn Gunnar, Malin, and Henrik traveled to New York to meet several collaborators at SUNY Downstate Health Science University, including Henriks co-supervisor, Salvador Dura-Bernal, and the group of Willam Lytton. We discussed modelling, neuronal networks, synaptic plasticity, future brain modelling projects, and explored their work-environment. This was very interesting for Henrik since he will spend one year in New York during his PhD. In the end of the visit Gunnar also gave a lecture presenting our research at Linköping University. Malin and Gunnar also visited other collaborators in New York, and Malin helped out during a clinical trial.
It was an educational trip and great stay! We would like to thank the Dura-Bernal lab, Neurosim lab, and many other for their great hospitality and nice discussions.
LiU Scene for AI arrange lectures and events with a focus on AI. This time the event will focus on AI in Medical research and healthcare. For a whole afternoon you will be able to listen to presentations that will show LiU:s world leading strengths in this exciting field, including Gunnars presentation: M4-health and digital twins: a foundation for general AI in healthcare.
The event is open for academia, industry and the general public, and can also be watched online afterwards. The number of seats is limited so don’t forget to register in the link below.
Where? Wrannesalen, CMIV, Campus US, Linköping (North entrence, entrence 7) When? Tuesday 8/11, 14.00 – 16.30 CET
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.
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
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.
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.
“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.”
A while back we made our expanded brain model, capable of describing and predicting various multi-species data, available on bioRxiv (https://doi.org/10.1101/2021.03.25.437053). After some further work we are aiming to submit our work, In the meanwhile feel free to take part of the currently available version.
Abstract: The neurovascular coupling (NVC) forms the foundation for functional imaging techniques of the brain, since NVC connects neural activity with observable hemodynamic changes. Many aspects of the NVC have been studied both experimentally and with mathematical models: various combinations of blood volume and flow, electrical activity, oxygen saturation measures, blood oxygenation level-dependent (BOLD) response, and optogenetics have been measured and modeled in rodents, primates, or humans. We now present a first inter-connected mathematical model that describes all such data types simultaneously. The model can predict independent validation data not used for training. Using simulations, we show for example how complex bimodal behaviors appear upon stimulation. These simulations thus demonstrate how our new quantitative model, incorporating most of the core aspects of the NVC, can be used to mechanistically explain each of its constituent datasets.