Boolean feedforward neural network modeling of molecular regulatory networks for cellular state conversion

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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.
Publisher
FRONTIERS MEDIA SA
Issue Date
2020-12
Language
English
Article Type
Article
Citation

FRONTIERS IN PHYSIOLOGY, v.11

ISSN
1664-042X
DOI
10.3389/fphys.2020.594151
URI
http://hdl.handle.net/10203/279427
Appears in Collection
BiS-Journal Papers(저널논문)
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