Gene regulatory network reconstruction is a fundamental problem in computational biology. We recently developed an algorithm, called PANDA (Passing Attributes Between Networks for Data Assimilation), that integrates multiple sources of 'omics data and estimates regulatory network models. This approach was initially implemented in the C++ programming language and has since been applied to a number of biological systems. In our current research we are beginning to expand the algorithm to incorporate larger and most diverse data-sets, to reconstruct networks that contain increasing numbers of elements, and to build not only single network models, but sets of networks. In order to accomplish these "Big Data" applications, it has become critical that we increase the computational efficiency of the PANDA implementation. In this paper we show how to recast PANDA's similarity equations as matrix operations. This allows us to implement a highly readable version of the algorithm using the MATLAB/Octave programming language. We find that the resulting M-code much shorter (103 compared to 1128 lines) and more easily modifiable for potential future applications. The new implementation also runs significantly faster, with increasing efficiency as the network models increase in size. Tests comparing the C-code and M-code versions of PANDA demonstrate that this speed-up is on the order of 20-80 times faster for networks of similar dimensions to those we find in current biological applications.
PyPanda: a Python Package for Gene Regulatory Network Reconstruction
PANDA（Passing Attributes between Networks for Data Assimilation）是一种使用消息传递来集成多个组学数据源的基因调控网络推理方法。PANDA最初用C++编写。在本应用笔记中，我们描述了PyPanda，PANDA的Python版本。PyPanda的运行速度比C++版本快得多，并包括用于网络分析的附加功能。可用性：开源的PyPanda Python包可以在https://github.com/davidvi/pypanda上免费获得。联系人：d.g.p.van ijzendoorn@lumc.nl
Generalization of the construction method for multistability-equivalent gene regulatory networks to systems with multi-input multi-output loopbreaking
Schittler et al.（2013）最近解决了不同维度的基因调控网络模型之间多稳定性方面的等价性问题。作者在这项工作中提出了一个高维动力系统的建设规则，当给定一个低维动力系统和高维网络结构。然而，其中的证明仅限于所有内部反馈回路都可以通过产生单输入单输出（SISO）系统的回路破坏方法来破坏的系统类。在这份报告中，我们提出了推广的证明，任何数量的内部反馈回路，这将被打破了一个广义的破环的方法，从而在一个多输入多输出（MIMO）系统的系统。这种方法的推广使得构建方法适用于广泛的一类基因调控网络模型，从而促进了从核心基序模型到更现实的高维基因调控模型的结果转移。我们通过将其应用于间充质干细胞分化中的基因调控网络的一个例子来证明我们的方法的潜力和价值。
Spatially-extended Flow Phixer (SpeF-Phixer): A Spatially Extended $φ$-Mixing Framework for Gene Regulatory Causal Inference in Spatial Gene Field
Association of Medical Artificial Intelligence Curation（医疗人工智能策展协会）；Kobe University Graduate School of Medicine（神户大学大学院医学研究科）；Kindai University Faculty of Medicine（近畿大学医学院）；Kobe University Graduate School of Health Sciences（神户大学大学院健康科学研究科）
NSF-Simons National Institute for Theory and Mathematics in Biology（NSF-Simons生物理论与数学国家研究所）；James Franck Institute, The University of Chicago（芝加哥大学詹姆斯·弗兰克研究所）；Division of Biology and Biological Engineering, California Institute of Technology（加州理工学院生物学与生物工程系）；Department of Bioengineering, Stanford University（斯坦福大学生物工程系）；Department of Applied Physics, Stanford University（斯坦福大学应用物理系）；Departments of Molecular & Cell Biology and of Physics, Biophysics Graduate Group, Institute for Quantitative Biosciences-QB3, University of California, Berkeley（加州大学伯克利分校分子与细胞生物学系、物理学系、生物物理学研究生组、定量生物科学研究所-QB3）；Chan Zuckerberg Biohub–San Francisco（陈·扎克伯格生物枢纽旧金山中心）；Department of Physics, California Institute of Technology（加州理工学院物理学系）；Leinweber Institute for Theoretical Physics, The University of Chicago（芝加哥大学莱因韦伯理论物理研究所）；Chan Zuckerberg Biohub–Chicago（陈·扎克伯格生物枢纽芝加哥中心）
