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metamodeling-and-control-of-medical-digital-twins_2024

Repository containing the files used in the manuscript entitled: Metamodeling and Control of Medical Digital Twins.

https://github.com/laboratoryforsystemsmedicine/metamodeling-and-control-of-medical-digital-twins_2024

Science Score: 65.0%

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Repository containing the files used in the manuscript entitled: Metamodeling and Control of Medical Digital Twins.

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README.md

Metamodeling and Control of Medical Digital Twins

This repository contains the files used in the manuscript entitled: Metamodeling and Control of Medical Digital Twins.
Under review in Science Advances. Preprint at arXiv.

The manuscript uses two agent-based models (ABMs) to illustrate the different methodologies for creating ODE approximations of ABMs for control purposes. The two ABMs are the sheep-wolves-grass version of the Wolf Sheep Predation model and a purposely built model of a toy metabolic pathway.

Files used for approximating the Wolf Sheep Predation model

  • Wolf Sheep Predation original.nlogo Original code for the Wolf Sheep Predation model.
  • Wolf Sheep Predation bigworld.nlogo Netlogo file used to generate dataset I.
  • Wolf Sheep Predation bigworld 2nd DataSet.nlogo Netlogo file used to generate dataset II.
  • Wolf Sheep Predation bigworld_ConGrass2.nlogo Netlogo file used to generate dataset III obtained by removing 2% of grass per time-step.
  • Wolf Sheep Predation bigworld_ConSheep2.nlogo Netlogo file used to generate dataset IV obtained by removing 2% of sheep per time-step.
  • Wolf Sheep Predation bigworld_ConWolves1.5.nlogo Netlogo file used to generate dataset V obtained by removing 1.5% of wolves per time-step.
  • 'Dados01.mat' MAT-file containing dataset I time series. This dataset resulted from averaging 100 simulations of the Wolf Sheep Predation bigworld.nlogo model.
  • Dados02.mat MAT-file containing dataset II time series. This dataset resulted from averaging 100 simulations of the Wolf Sheep Predation bigworld 2nd DataSet.nlogo model.
  • DadosConGrass2.mat MAT-file containing dataset III time series. This dataset resulted from averaging 100 simulations of the Wolf Sheep Predation bigworld_ConGrass2.nlogo model.
  • DadosConGrass2s.mat MAT-file containing standard deviation associated with dataset III time series. Used only for plotting.
  • DadosConSheep2.mat MAT-file containing dataset IV time series. This dataset resulted from averaging 100 simulations of the Wolf Sheep Predation bigworld_ConSheep2.nlogo model.
  • DadosConSheep2s.mat MAT-file containing standard deviation associated with dataset IV time series. Used only for plotting.
  • DadosConWolves1.5.mat MAT-file containing dataset V time series. This dataset resulted from averaging 100 simulations of the Wolf Sheep Predation bigworld_ConWolves1.5.nlogo model.
  • DadosConWolves1.5s.mat MAT-file containing standard deviation associated with dataset V time series. Used only for plotting.
  • SWG_Case1_Mech_I_II.m MATLAB script containing the model and best-fit parameter-set for the mechanistic approximation (Case 1) fitted against datasets I and II.
  • SWG_Case1_Mech_I_V.m MATLAB script containing the model and best-fit parameter-set for the mechanistic approximation (Case 1) fitted against datasets I, II, III, IV, and V.
  • SWG_Case2_GMA_I_II.m MATLAB script containing the model and best-fit parameter-set for the GMA approximation (Case 2) fitted against datasets I and II.
  • SWG_Case2_GMA_I_V.m MATLAB script containing the model and best-fit parameter-set for the GMA approximation (Case 2) fitted against datasets I, II, III, IV, and V.
  • SWG_Case3_Linear_I.m MATLAB script containing the model and best-fit parameter-set for the first-order polynomial approximation (Case 3) fitted against dataset I.
  • SWG_Case3_Quad_I_II.m MATLAB script containing the model and best-fit parameter-set for a second-order polynomial approximation (Case 3) fitted against datasets I and II.
  • SWG_Case3_Quad_I_V.m MATLAB script containing the model and best-fit parameter-set for a second-order polynomial approximation (Case 3) fitted against datasets I, II, III, IV, and V.
  • SWG_Case4_Ssystem_I_II.m MATLAB script containing the model and best-fit parameter-set for the S-system approximation (Case 4) fitted against datasets I and II.
  • SWG_Case4_Ssystem_I_V.m MATLAB script containing the model and best-fit parameter-set for the S-system approximation (Case 4) fitted against datasets I, II, III, IV, and V.

## Files used for approximating the Metabolic Pathway model - Met_Pathway_dataset_80k_20k_20k_10_10_NoDil.m MATLAB script for the Metabolic Pathway model operating under Batch mode. - Met_Pathway_dataset_80k_20k_20k_10_10_wDil_wFeed.m MATLAB script for the Metabolic Pathway model operating under Continuous mode with an inflow of 1.0. - Met_Pathwayv2_S80k_P20k_Q20k_noDil.mat MAT-file containing dataset I time series. This dataset resulted from a single simulation of the Metabolic Pathway model in Batch mode, obtained using Met_Pathway_dataset_80k_20k_20k_10_10_NoDil.m. - Met_Pathwayv2_S80k_P20k_Q20k_Dil0005In1.mat MAT-file containing dataset II time series. This dataset resulted from a single simulation of the Metabolic Pathway model in Continuous mode with an inflow of 1.0, obtained using Met_Pathway_dataset_80k_20k_20k_10_10_wDil_wFeed.m. - MetPw_TrainingDatasets.mat MAT-file containing datasets III, IV, and V time series. These datasets were obtained by averaging 100 simulations. Dataset III - same conditions as dataset I (Batch mode). Dataset IV - same conditions as dataset II (Continuous mode with an inflow of 1.0). Dataset V - similar conditions as dataset II, in Continuous mode, but with an inflow of 0.2. - MetPw_Case1_Mech_I.m MATLAB script containing the model and best-fit parameter-set for the mechanistic approximation (Case 1) fitted against datasets I and II. - MetPw_Case1_Mech_C.m MATLAB script containing the model and best-fit parameter-set for the mechanistic approximation (Case 1) fitted against datasets III, IV, and V. - MetPw_Case2_GMA_I.m MATLAB script containing the model and best-fit parameter-set for the GMA approximation (Case 2) fitted against datasets I and II. - MetPw_Case2_GMA_C.m MATLAB script containing the model and best-fit parameter-set for the GMA approximation (Case 2) fitted against datasets III, IV, and V. - MetPw_Case3_Linear_I.m MATLAB script containing the model and best-fit parameter-set for the first-order polynomial approximation (Case 3) fitted against datasets I and II. - MetPw_Case3_Quad_I.m MATLAB script containing the model and best-fit parameter-set for a second-order polynomial approximation (Case 3) fitted against datasets I and II. - MetPw_Case4_Ssystem_I.m MATLAB script containing the model and best-fit parameter-set for the S-system approximation (Case 4) fitted against datasets I and II. - MetPw_Case4_Ssystem_C.m MATLAB script containing the model and best-fit parameter-set for the S-system approximation (Case 4) fitted against datasets III, IV, and V.

Owner

  • Name: Laboratory for Systems Medicine
  • Login: LaboratoryForSystemsMedicine
  • Kind: organization
  • Location: Gainesville, FL, USA

The Laboratory for Systems Medicine strives to improve human health via the development of mathematical algorithms and innovative data science solutions.

Citation (CITATION.cff) 

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: Metamodeling and Control of Medical Digital Twins
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Luis
    name-particle: L.
    family-names: Fonseca
    email: llfonseca@medicine.ufl.edu
    affiliation: >-
      Laboratory for Systems Medicine, Department of
      Medicine, University of Florida, Gainesville, FL, USA
    orcid: 'https://orcid.org/0000-0002-7902-742X'
  - given-names: Lucas
    family-names: Böttcher
    email: l.boettcher@fs.de
    affiliation: >-
      Department of Computational Science and Philosophy,
      Frankfurt School of Finance and Management, 60322
      Frankfurt am Main, Germany
    orcid: 'https://orcid.org/0000-0003-1700-1897'
  - given-names: Borna
    family-names: Mehrad
    affiliation: >-
      Laboratory for Systems Medicine, Department of
      Medicine, University of Florida, Gainesville, FL, USA
    email: Borna.Mehrad@medicine.ufl.edu
    orcid: 'https://orcid.org/0000-0001-5198-065X'
  - affiliation: >-
      Laboratory for Systems Medicine, Department of
      Medicine, University of Florida, Gainesville, FL, USA
    given-names: Reinhard
    family-names: Laubenbacher
    name-particle: C.
    email: Reinhard.Laubenbacher@medicine.ufl.edu
    orcid: 'https://orcid.org/0000-0002-9143-9451'
identifiers:
  - type: doi
    value: 10.48550/arXiv.2402.05750
    description: arXiv
repository-code: >-
  https://github.com/LaboratoryForSystemsMedicine/Metamodeling-and-Control-of-Medical-Digital-Twins_2024
abstract: >-
  The vision of personalized medicine is to identify
  interventions that maintain or restore a person’s health
  based on their individual biology. Medical digital twins,
  computational models that integrate a wide range of
  health-related data about a person and can be dynamically
  updated, are a key technology that can help guide medical
  decisions. Such medical digital twin models can be
  high-dimensional, multi-scale, and stochastic. To be
  practical for healthcare applications, they need to be
  simplified into low-dimensional metamodels that can be
  used for forecasting and optimal design of interventions.
  This paper introduces metamodeling algorithms for the
  purpose of optimal control applications. It uses
  agent-based models as a use case, a common model type in
  biomedicine for which there are no readily available
  optimal control algorithms. With systems of ordinary
  differential equations as metamodels, optimal control
  methods can be applied to the metamodels, and results can
  be lifted to the agent-based model. 
license: MIT
date-released: '2024-02-08'

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