Global modeling of transcriptional responses in interaction networks
Leo Lahti, Juha E. A. Knuuttila, Samuel Kaski
备注：
19 pages, 13 figures
发表信息：
Global modeling of transcriptional responses in interaction networks. Leo Lahti, Juha E.A. Knuuttila, and Samuel Kaski. Bioinformatics 26(21):2713-2720, 2010
Motivation: Cell-biological processes are regulated through a complex network of interactions between genes and their products. The processes, their activating conditions, and the associated transcriptional responses are often unknown. Organism-wide modeling of network activation can reveal unique and shared mechanisms between physiological conditions, and potentially as yet unknown processes. We introduce a novel approach for organism-wide discovery and analysis of transcriptional responses in interaction networks. The method searches for local, connected regions in a network that exhibit coordinated transcriptional response in a subset of conditions. Known interactions between genes are used to limit the search space and to guide the analysis. Validation on a human pathway network reveals physiologically coherent responses, functional relatedness between physiological conditions, and coordinated, context-specific regulation of the genes. Availability: Implementation is freely available in R and Matlab at http://netpro.r-forge.r-project.org
A scalable gene network model of regulatory dynamics in single cells
Mila, the Quebec AI Institute（米拉魁北克人工智能研究所）；Université de Montréal（蒙特利尔大学）；Institute of Computational Biology, Helmholtz Munich（亥姆霍兹慕尼黑计算生物学研究所）；Technical University of Munich（慕尼黑工业大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；TUM School of Life Sciences Weihenstephan, Technical University of Munich（慕尼黑工业大学魏恩施蒂芬生命科学学院）
Unsupervised learning of transcriptional regulatory networks via latent tree graphical models
Microsoft Research（微软研究院）；Massachusetts Institute of Technology（麻省理工学院）；University of California Irvine（加州大学尔湾分校）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；Morgridge Institute for Research（莫格里奇研究所）
Control-Anchored Residual Flow Matching Conditioned on Gene Geometry for Virtual Cell Perturbation Modeling
East China Normal University（华东师范大学）；Peking University（北京大学）；University of Science and Technology of China（中国科学技术大学）；Nanjing University（南京大学）；Shanghai Jiao Tong University（上海交通大学）
Competing endogenous RNA crosstalk at system level
microRNAs（miRNAs）通过抑制靶RNA分子在转录后水平调控基因表达。竞争结合miRNA往往反过来关联它们的靶标，建立有效的RNA-RNA相互作用，可以影响表达水平，缓冲波动并促进信号传播。这样的潜力已经在数学上表征为小图案在稳态和\red{away from stationarity}。另一方面，实验证据表明竞争性内源RNA（ceRNA）串扰相当弱。然而，扩展的miRNA-RNA网络可能有利于许多串扰相互作用的整合，导致显著的大规模效应，尽管单个链接的弱点。为了阐明串扰在多大程度上是由miRNA相互作用组维持的，我们在大规模的miRNA-RNA网络重建中研究了其涌现的系统特征。我们表明，虽然一般弱，系统水平的串扰模式（i）增强了转录异质性，（ii）可以实现高强度，甚至对于RNA，不共调节，（iii）是强大的转录速率的变化，和（iv）是显着的非本地，即弱相关的miRNA-RNA相互作用参数。此外，当串扰最强时，RNA水平一般更稳定。由于这些特征中的一些似乎被编码在网络的拓扑结构中，串扰可能在功能上受到自然选择的青睐。这些结果表明，除了它们的抑制作用外，miRNA还介导了一个弱但有弹性且与上下文无关的交叉调节相互作用网络，该网络将转录组互连，稳定表达水平并支持系统水平的反应。
MERLIN-SUITE: Probabilistic modular GRN inference from multi-omics data integrating regulatory priors and transcription factor activity
Wisconsin Institute for Discovery, University of Wisconsin-Madison（威斯康星发现研究所，威斯康星大学麦迪逊分校）；Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison（威斯康星大学麦迪逊分校生物统计与医学信息学系）
