Working Paper Article Version 1 This version is not peer-reviewed

Neural Differentiation Dynamics Controlled by Multiple Feedback Loop Motifs in a Comprehensive Molecular Interaction Network

Version 1 : Received: 23 December 2019 / Approved: 24 December 2019 / Online: 24 December 2019 (11:20:25 CET)

A peer-reviewed article of this Preprint also exists.

Iwasaki, T.; Takiguchi, R.; Hiraiwa, T.; Yamada, T.G.; Yamazaki, K.; Hiroi, N.F.; Funahashi, A. Neural Differentiation Dynamics Controlled by Multiple Feedback Loops in a Comprehensive Molecular Interaction Network. Processes 2020, 8, 166. Iwasaki, T.; Takiguchi, R.; Hiraiwa, T.; Yamada, T.G.; Yamazaki, K.; Hiroi, N.F.; Funahashi, A. Neural Differentiation Dynamics Controlled by Multiple Feedback Loops in a Comprehensive Molecular Interaction Network. Processes 2020, 8, 166.

Journal reference: Processes 2020, 8, 166
DOI: 10.3390/pr8020166

Abstract

Computational simulation using mathematical models is a useful method for understanding the complex behavior of a living system. The majority of studies using mathematical models to reveal biological mechanisms uses one of the two main approaches: the bottom-up or the top-down approach. When we aim to analyze a large-scale network, such as a comprehensive knowledge-integrated model of a target phenomenon, for example a whole-cell model, the variation of analyses is limited to particular kind of analysis because of the size and complexity of the model. To analyze a large-scale regulatory network of neural differentiation, we developed a hybrid method that combines both approaches. To construct a mathematical model, we extracted network motifs, subgraph structures that recur more often in a metabolic network or gene regulatory network than in a random network, from a large-scale regulatory network, detected regulatory motifs among them, and combined these motifs. We confirmed that the model reproduced the known dynamics of HES1 and ASCL1 before and after differentiation, including oscillation and equilibrium of their concentrations. The model also reproduced the effects of overexpression and knockdown of the Id2 gene. Our model suggests that the characteristic change in HES1 and ASCL1 expression in the large-scale regulatory network is controlled by a combination of four feedback loops, including a large loop which has not been focused on. The model extracted by our hybrid method has the potential to reveal the critical mechanisms of neural differentiation. The hybrid method is applicable to other biological events.

Subject Areas

neural differentiation; regulatory motif; feedback regulation; signaling pathway; mathematical models

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