Force-dependent recruitment from myosin OFF-state increases end-systolic pressure-volume relationship in left ventricle

Submitted by jfw859 on

Finite element (FE) modeling is becoming increasingly prevalent in the world of cardiac mechanics; however, many existing FE models are phenomenological and thus do not capture cellular-level mechanics. This work implements a cellular-level contraction scheme into an existing nonlinear FE code to model ventricular contraction. Specifically, this contraction model incorporates three myosin states: OFF-, ON-, and an attached force-generating state.

VPD Dissemination and Outreach Possible Liaisons

Viral Pandemic Working Group, Dissemination and Outreach Subgroup: Possible Liaisons

This is a rough  and preliminary list meant to collect possible organizations, including governmental, educational, industrial, medical and professional, that the Viral Pandemics groups might try to setup a formal liaison with. Suggestions are welcome and can be sent to jsluka@iu.edu.

Last Updated: May 13, 2021

Viral Pandemics Group Publications Page

Publications From the Viral Pandemics Group and Group Members

  1. Böttcher L, Fonseca LL, Laubenbacher RC. Control of medical digital twins with artificial neural networks. Philos Trans A Math Phys Eng Sci. 2025 Mar 13;383(2292):20240228. doi: 10.1098/rsta.2024.0228. Epub 2025 Mar 13. PMID: 40078154; PMCID: PMC11904622.
  2. Fonseca LL, Böttcher L, Mehrad B, Laubenbacher RC. Optimal control of agent-based models via surrogate modeling. PLoS Comput Biol. 2025 Jan 14;21(1):e1012138.

Viral Pandemics Woking Group Random Things of Interest

Random Things of Interest

Here you will find links to various things of broad interest to the group. Items are not necessarily scientific and may include opinion pieces, press items, and newspaper articles.

DHS SCIENCE AND TECHNOLOGYMaster Question List forCOVID-19 (caused by SARS-CoV-2)

https://drive.google.com/file/d/1ftMMnGqRwxTvLzJ9goBJO5yThGz1viZq/view?usp=sharing

Version of 29 June 2021

A modular computational framework for medical digital twins

Submitted by shapirob on

This paper presents a modular software design for the construction of computational modeling technology that will help implement precision medicine. In analogy to a common industrial strategy used for preventive maintenance of engineered products, medical digital
twins are computational models of disease processes calibrated to individual patients using multiple heterogeneous data streams.
They have the potential to help improve diagnosis, prognosis, and personalized treatment for a wide range of medical conditions.

A modular computational framework for medical digital twins

What is being modeled?
A modular computational framework for medical digital twins
Description & purpose of resource

This paper presents a modular software design for the construction of computational modeling technology that will help implement precision medicine. In analogy to a common industrial strategy used for preventive maintenance of engineered products, medical digital
twins are computational models of disease processes calibrated to individual patients using multiple heterogeneous data streams.
They have the potential to help improve diagnosis, prognosis, and personalized treatment for a wide range of medical conditions.
Their large-scale development relies on both mechanistic and data-driven techniques and requires the integration and ongoing update
of multiple component models developed across many different laboratories. Distributed model building and integration requires an open-source modular software platform for the integration and simulation of models that is scalable and supports a decentralized,
community-based model building process. This paper presents such a platform, including a case study in an animal model of a respiratory fungal infection.

Spatial scales
molecular
cellular
tissue
organ
whole organism
Temporal scales
<10-6 s (chemical reactions)
10-6 - 10-3 s
10-3 - 1 s
1 - 103 s
This resource is currently
under early-stage development
mature and useful in ongoing research
Has this resource been validated?
No
Can this resource be associated with other resources? (e.g.: modular models, linked tools and platforms)
No
Key publications (e.g. describing or using resource)
  • Masison, J., J. Beezley, Y. Mei, H. a. L. Ribeiro, A. C. Knapp, L. Sordo Vieira, B. Adhikari, et al. “A Modular Computational Framework for Medical Digital Twins.” Proceedings of the National Academy of Sciences 118, no. 20 (May 18, 2021). https://doi.org/10.1073/pnas.2024287118.
DOI link to publication describing this resource

10.1073/pnas.2024287118

Link to resource
https://doi.org/10.1073/pnas.2024287118
https://www.imagwiki.nibib.nih.gov/resources/publications/modular-computational…
Collaborators
J. Masison
J. Beezley
Y. Mei
H. al Ribiero
A. C. Knapp
L. Sordo Vieira
B. Adhikari
Y. Scindia
M. Grauer
B. Helba
W. Schroeder (PI)
B. Mehrad (PI)
R. Laubenbacher (PI)
PI contact information
R. Laubenbacher (Reinhard.Laubenbacher@medicine.ufl.edu)
Keywords
Steady-state binding
Modular design
Slow binding
Table sorting checkbox
Off
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