Choo Sang-Mok, Almomani Laith M, Cho Kwang-Hyun
Department of Mathematics, University of Ulsan, Ulsan, South Korea.
Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea.
Front Physiol. 2020 Dec 1;11:594151. doi: 10.3389/fphys.2020.594151. eCollection 2020.
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.
细胞内的分子调控网络（MRN）决定细胞状态及其之间的转变。因此，对MRN进行建模至关重要，但这通常需要对时间序列测量进行广泛分析，而从生物学实验中极难获得此类测量数据。然而，诸如单细胞RNA测序数据库之类的单细胞测量数据最近通过根据差异基因表达按伪时间对数千个细胞进行排序，为解决这个问题提供了新的见解。可以将时间数据用作学习数据来进行神经网络建模。相比之下，MRN的布尔网络建模越来越受到关注，因为它是一种无参数的逻辑建模，因此对噪声数据具有鲁棒性，同时仍能捕捉生物网络的基本动态。在本研究中，我们通过结合神经网络和布尔网络建模方法，提出一种布尔前馈神经网络（FFN）建模，以便从大型时间数据重建实用且有用的MRN模型。此外，对重建的MRN模型进行分析可以使我们识别潜在细胞状态转换的控制目标。在此，我们通过一个玩具模型和生物网络展示布尔FFN建模的适用性，从而证明其有用性。
