Cargando…

Boolean Feedforward Neural Network Modeling of Molecular Regulatory Networks for Cellular State Conversion

The molecular regulatory network (MRN) within a cell determines cellular states and transitions between them. Thus, modeling of MRNs is crucial, but this usually requires extensive analysis of time-series measurements, which is extremely difficult to obtain from biological experiments. However, sing...

Descripción completa

Detalles Bibliográficos
Autores principales: Choo, Sang-Mok, Almomani, Laith M., Cho, Kwang-Hyun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7736109/
https://www.ncbi.nlm.nih.gov/pubmed/33335489
http://dx.doi.org/10.3389/fphys.2020.594151
_version_ 1783622756770775040
author Choo, Sang-Mok
Almomani, Laith M.
Cho, Kwang-Hyun
author_facet Choo, Sang-Mok
Almomani, Laith M.
Cho, Kwang-Hyun
author_sort Choo, Sang-Mok
collection PubMed
description The molecular regulatory network (MRN) within a cell determines cellular states and transitions between them. Thus, modeling of MRNs is crucial, but this usually requires extensive analysis of time-series measurements, which is extremely difficult to obtain from biological experiments. However, single-cell measurement data such as single-cell RNA-sequencing databases have recently provided a new insight into resolving this problem by ordering thousands of cells in pseudo-time according to their differential gene expressions. Neural network modeling can be employed by using temporal data as learning data. In contrast, Boolean network modeling of MRNs has a growing interest, as it is a parameter-free logical modeling and thereby robust to noisy data while still capturing essential dynamics of biological networks. In this study, we propose a Boolean feedforward neural network (FFN) modeling by combining neural network and Boolean network modeling approach to reconstruct a practical and useful MRN model from large temporal data. Furthermore, analyzing the reconstructed MRN model can enable us to identify control targets for potential cellular state conversion. Here, we show the usefulness of Boolean FFN modeling by demonstrating its applicability through a toy model and biological networks.
format Online
Article
Text
id pubmed-7736109
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-77361092020-12-16 Boolean Feedforward Neural Network Modeling of Molecular Regulatory Networks for Cellular State Conversion Choo, Sang-Mok Almomani, Laith M. Cho, Kwang-Hyun Front Physiol Physiology The molecular regulatory network (MRN) within a cell determines cellular states and transitions between them. Thus, modeling of MRNs is crucial, but this usually requires extensive analysis of time-series measurements, which is extremely difficult to obtain from biological experiments. However, single-cell measurement data such as single-cell RNA-sequencing databases have recently provided a new insight into resolving this problem by ordering thousands of cells in pseudo-time according to their differential gene expressions. Neural network modeling can be employed by using temporal data as learning data. In contrast, Boolean network modeling of MRNs has a growing interest, as it is a parameter-free logical modeling and thereby robust to noisy data while still capturing essential dynamics of biological networks. In this study, we propose a Boolean feedforward neural network (FFN) modeling by combining neural network and Boolean network modeling approach to reconstruct a practical and useful MRN model from large temporal data. Furthermore, analyzing the reconstructed MRN model can enable us to identify control targets for potential cellular state conversion. Here, we show the usefulness of Boolean FFN modeling by demonstrating its applicability through a toy model and biological networks. Frontiers Media S.A. 2020-12-01 /pmc/articles/PMC7736109/ /pubmed/33335489 http://dx.doi.org/10.3389/fphys.2020.594151 Text en Copyright © 2020 Choo, Almomani and Cho. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Physiology
Choo, Sang-Mok
Almomani, Laith M.
Cho, Kwang-Hyun
Boolean Feedforward Neural Network Modeling of Molecular Regulatory Networks for Cellular State Conversion
title Boolean Feedforward Neural Network Modeling of Molecular Regulatory Networks for Cellular State Conversion
title_full Boolean Feedforward Neural Network Modeling of Molecular Regulatory Networks for Cellular State Conversion
title_fullStr Boolean Feedforward Neural Network Modeling of Molecular Regulatory Networks for Cellular State Conversion
title_full_unstemmed Boolean Feedforward Neural Network Modeling of Molecular Regulatory Networks for Cellular State Conversion
title_short Boolean Feedforward Neural Network Modeling of Molecular Regulatory Networks for Cellular State Conversion
title_sort boolean feedforward neural network modeling of molecular regulatory networks for cellular state conversion
topic Physiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7736109/
https://www.ncbi.nlm.nih.gov/pubmed/33335489
http://dx.doi.org/10.3389/fphys.2020.594151
work_keys_str_mv AT choosangmok booleanfeedforwardneuralnetworkmodelingofmolecularregulatorynetworksforcellularstateconversion
AT almomanilaithm booleanfeedforwardneuralnetworkmodelingofmolecularregulatorynetworksforcellularstateconversion
AT chokwanghyun booleanfeedforwardneuralnetworkmodelingofmolecularregulatorynetworksforcellularstateconversion