Applications and Challenges of Machine Learning to Enable Realistic Cellular Simulations

In this perspective, we examine three key aspects of an end-to-end pipeline for realistic cellular simulations: reconstruction and segmentation of cellular structures; generation of cellular structures; and mesh generation, simulation, and data analysis. We highlight some of the relevant prior work...

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Main Authors:	Ritvik Vasan, Meagan P. Rowan, Christopher T. Lee, Gregory R. Johnson, Padmini Rangamani, Michael Holst
Format:	Article
Language:	English
Published:	Frontiers Media S.A. 2020-01-01
Series:	Frontiers in Physics
Subjects:	machine learning cellular structures segmentation reconstruction meshing simulation
Online Access:	https://www.frontiersin.org/article/10.3389/fphy.2019.00247/full

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spelling	doaj-d1064c7cb8614994af8e7032d5d6c53c2020-11-25T02:19:36ZengFrontiers Media S.A.Frontiers in Physics2296-424X2020-01-01710.3389/fphy.2019.00247509500Applications and Challenges of Machine Learning to Enable Realistic Cellular SimulationsRitvik Vasan0Meagan P. Rowan1Christopher T. Lee2Gregory R. Johnson3Padmini Rangamani4Michael Holst5Michael Holst6Department of Mechanical and Aerospace Engineering, University of California San Diego, La Jolla, CA, United StatesDepartment of Bioengineering, University of California San Diego, La Jolla, CA, United StatesDepartment of Mechanical and Aerospace Engineering, University of California San Diego, La Jolla, CA, United StatesAllen Institute of Cell Science, Seattle, WA, United StatesDepartment of Mechanical and Aerospace Engineering, University of California San Diego, La Jolla, CA, United StatesDepartment of Mathematics, University of California San Diego, La Jolla, CA, United StatesDepartment of Physics, University of California San Diego, La Jolla, CA, United StatesIn this perspective, we examine three key aspects of an end-to-end pipeline for realistic cellular simulations: reconstruction and segmentation of cellular structures; generation of cellular structures; and mesh generation, simulation, and data analysis. We highlight some of the relevant prior work in these distinct but overlapping areas, with a particular emphasis on current use of machine learning technologies, as well as on future opportunities.https://www.frontiersin.org/article/10.3389/fphy.2019.00247/fullmachine learningcellular structuressegmentationreconstructionmeshingsimulation
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Ritvik Vasan Meagan P. Rowan Christopher T. Lee Gregory R. Johnson Padmini Rangamani Michael Holst Michael Holst
spellingShingle	Ritvik Vasan Meagan P. Rowan Christopher T. Lee Gregory R. Johnson Padmini Rangamani Michael Holst Michael Holst Applications and Challenges of Machine Learning to Enable Realistic Cellular Simulations Frontiers in Physics machine learning cellular structures segmentation reconstruction meshing simulation
author_facet	Ritvik Vasan Meagan P. Rowan Christopher T. Lee Gregory R. Johnson Padmini Rangamani Michael Holst Michael Holst
author_sort	Ritvik Vasan
title	Applications and Challenges of Machine Learning to Enable Realistic Cellular Simulations
title_short	Applications and Challenges of Machine Learning to Enable Realistic Cellular Simulations
title_full	Applications and Challenges of Machine Learning to Enable Realistic Cellular Simulations
title_fullStr	Applications and Challenges of Machine Learning to Enable Realistic Cellular Simulations
title_full_unstemmed	Applications and Challenges of Machine Learning to Enable Realistic Cellular Simulations
title_sort	applications and challenges of machine learning to enable realistic cellular simulations
publisher	Frontiers Media S.A.
series	Frontiers in Physics
issn	2296-424X
publishDate	2020-01-01
description	In this perspective, we examine three key aspects of an end-to-end pipeline for realistic cellular simulations: reconstruction and segmentation of cellular structures; generation of cellular structures; and mesh generation, simulation, and data analysis. We highlight some of the relevant prior work in these distinct but overlapping areas, with a particular emphasis on current use of machine learning technologies, as well as on future opportunities.
topic	machine learning cellular structures segmentation reconstruction meshing simulation
url	https://www.frontiersin.org/article/10.3389/fphy.2019.00247/full
work_keys_str_mv	AT ritvikvasan applicationsandchallengesofmachinelearningtoenablerealisticcellularsimulations AT meaganprowan applicationsandchallengesofmachinelearningtoenablerealisticcellularsimulations AT christophertlee applicationsandchallengesofmachinelearningtoenablerealisticcellularsimulations AT gregoryrjohnson applicationsandchallengesofmachinelearningtoenablerealisticcellularsimulations AT padminirangamani applicationsandchallengesofmachinelearningtoenablerealisticcellularsimulations AT michaelholst applicationsandchallengesofmachinelearningtoenablerealisticcellularsimulations AT michaelholst applicationsandchallengesofmachinelearningtoenablerealisticcellularsimulations
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Applications and Challenges of Machine Learning to Enable Realistic Cellular Simulations

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