paper-with-me

Papers

SMART: Spatial Modeling Algorithms for Reaction and Transport

2023-06-12 · Justin G. Laughlin, Jørgen S. Dokken, Henrik N. T. Finsberg, Emmet A. Francis, Christopher T. Lee, Marie E. Rognes, Padmini Rangamani

Recent advances in microscopy and 3D reconstruction methods have allowed for characterization of cellular morphology in unprecedented detail, including the irregular geometries of intracellular subcompartments such as membrane-bound organelles. These geometries are now compatible with predictive modeling of cellular function. Biological cells respond to stimuli through sequences of chemical reactions generally referred to as cell signaling pathways. The propagation and reaction of chemical substances in cell signaling pathways can be represented by coupled nonlinear systems of reaction-transport equations. These reaction pathways include numerous chemical species that react across boundaries or interfaces (e.g., the cell membrane and membranes of organelles within the cell) and domains (e.g., the bulk cell volume and the interior of organelles). Such systems of multi-dimensional partial differential equations (PDEs) are notoriously difficult to solve because of their high dimensionality, non-linearities, strong coupling, stiffness, and potential instabilities. In this work, we describe Spatial Modeling Algorithms for Reactions and Transport (SMART), a high-performance finite-element-based simulation package for model specification and numerical simulation of spatially-varying reaction-transport processes. SMART is based on the FEniCS finite element library, provides a symbolic representation framework for specifying reaction pathways, and supports geometries in 2D and 3D including large and irregular cell geometries obtained from modern ultrastructural characterization methods.

📄 PDF Abstract BibTeX arXiv:2306.07368

Code (1)

rangamanilabucsd/smart 공식 구현

Tasks

3D Reconstruction

Similar Papers 제목 키워드 기반

Spatial modeling algorithms for reactions and transport (SMART) in biological cells

2024-05-24 · Emmet A. Francis, Justin G. Laughlin, Jørgen S. Dokken, Henrik N. T. Finsberg 외

Biological cells rely on precise spatiotemporal coordination of biochemical reactions to control their many functions. Such cell signaling networks have been a common focus for mathematical models, but they remain challe…

Ultra-Fast Reactive Transport Simulations When Chemical Reactions Meet Machine Learning: Chemical Equilibrium

2017-08-16 · Allan M. M. Leal, Dmitrii A. Kulik, Martin O. Saar

During reactive transport modeling, the computational cost associated with chemical reaction calculations is often 10-100 times higher than that of transport calculations. Most of these costs results from chemical equili…

BIG-bench Machine Learning

A deep learning modeling framework to capture mixing patterns in reactive-transport systems

2021-01-11 · N. V. Jagtap, M. K. Mudunuru, K. B. Nakshatrala

Prediction and control of chemical mixing are vital for many scientific areas such as subsurface reactive transport, climate modeling, combustion, epidemiology, and pharmacology. Due to the complex nature of mixing in he…

EpidemiologyFuture prediction

A Supervised Machine Learning Model For Imputing Missing Boarding Stops In Smart Card Data

2020-03-10 · Nadav Shalit, Michael Fire, Eran Ben-Elia

Public transport has become an essential part of urban existence with increased population densities and environmental awareness. Large quantities of data are currently generated, allowing for more robust methods to unde…

BIG-bench Machine LearningImputationOrdinal ClassificationTransfer Learning

Feature Transportation Improves Graph Neural Networks

2023-07-29 · Moshe Eliasof, Eldad Haber, Eran Treister

Graph neural networks (GNNs) have shown remarkable success in learning representations for graph-structured data. However, GNNs still face challenges in modeling complex phenomena that involve feature transportation. In …

Node Classification