scholarly journals Large-scale dynamic gene regulatory network inference combining differential equation models with local dynamic Bayesian network analysis

2011 ◽  
Vol 27 (19) ◽  
pp. 2686-2691 ◽  
Author(s):  
Zheng Li ◽  
Ping Li ◽  
Arun Krishnan ◽  
Jingdong Liu
Keyword(s):  
Differential Equation ◽  
Network Analysis ◽  
Regulatory Network ◽  
Large Scale ◽  
Network Inference ◽  
Dynamic Bayesian Network ◽  
Local Dynamic ◽  
Gene Regulatory Network Inference ◽  
Differential Equation Models ◽  
Gene Regulatory

Overview of Gene Regulatory Network Inference Based on Differential Equation Models

2020 ◽  
Vol 21 (11) ◽  
pp. 1054-1059
Author(s):  
Bin Yang ◽  
Yuehui Chen
Keyword(s):  
Differential Equation ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Network Inference ◽  
New Drugs ◽  
Gene Regulatory Network Inference ◽  
Ode Models ◽  
Differential Equation Models ◽  
Linear Ode ◽  
Gene Regulatory

: Reconstruction of gene regulatory networks (GRN) plays an important role in understanding the complexity, functionality and pathways of biological systems, which could support the design of new drugs for diseases. Because differential equation models are flexible androbust, these models have been utilized to identify biochemical reactions and gene regulatory networks. This paper investigates the differential equation models for reverse engineering gene regulatory networks. We introduce three kinds of differential equation models, including ordinary differential equation (ODE), time-delayed differential equation (TDDE) and stochastic differential equation (SDE). ODE models include linear ODE, nonlinear ODE and S-system model. We also discuss the evolutionary algorithms, which are utilized to search the optimal structures and parameters of differential equation models. This investigation could provide a comprehensive understanding of differential equation models, and lead to the discovery of novel differential equation models.

Large scale gene regulatory network inference with a multi-level strategy

2016 ◽  
Vol 12 (2) ◽  
pp. 588-597 ◽  
Author(s):  
Jun Wu ◽  
Xiaodong Zhao ◽  
Zongli Lin ◽  
Zhifeng Shao
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Large Scale ◽  
Network Inference ◽  
Biological Processes ◽  
Molecular Processes ◽  
Gene Regulatory Network Inference ◽  
Cell Functions ◽  
Multi Level ◽  
Gene Regulatory

Transcriptional regulation is a basis of many crucial molecular processes and an accurate inference of the gene regulatory network is a helpful and essential task to understand cell functions and gain insights into biological processes of interest in systems biology.

scholarly journals gpuZoo: Cost-effective estimation of gene regulatory networks using the Graphics Processing Unit

2021 ◽  
Author(s):  
Marouen Ben Guebila ◽  
Daniel C Morgan ◽  
Kimberly Glass ◽  
Marieke Lydia Kuijjer ◽  
Dawn L DeMeo ◽  
...  
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Large Scale ◽  
Network Inference ◽  
Cost Effective ◽  
Gene Regulatory Network Inference ◽  
Genomic Studies ◽  
On Line ◽  
Gene Regulatory

Gene regulatory network inference allows for the study of transcriptional control to identify the alteration of cellular processes in human diseases. Our group has developed several tools to model a variety of regulatory processes, including transcriptional (PANDA, SPIDER) and post-transcriptional (PUMA) gene regulation, and gene regulation in individual samples (LIONESS). These methods work by performing repeated operations on data matrices in order to integrate information across multiple lines of biological evidence. This limits their use for large-scale genomic studies due to the associated high computational burden. To address this limitation, we developed gpuZoo, which includes GPU-accelerated implementations of these algorithms. The runtime of the gpuZoo implementation in MATLAB and Python is up to 61 times faster and 28 times less expensive than the multi-core CPU implementation of the same methods. gpuZoo takes advantage of the modern multi-GPU device architecture to build a population of sample-specific gene regulatory networks with similar runtime and cost improvements by combining GPU acceleration with an efficient on-line derivation. Taken together, gpuZoo allows parallel and on-line gene regulatory network inference in large-scale genomic studies with cost-effective performance. gpuZoo is available in MATLAB through the netZooM package https://github.com/netZoo/netZooM and in Python through the netZooPy package https://github.com/netZoo/netZooPy.

scholarly journals The Reasonable Effectiveness of Randomness in Scalable and Integrative Gene Regulatory Network Inference and Beyond

Computation ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 146
Author(s):  
Michael Banf ◽  
Thomas Hartwig
Keyword(s):  
Computational Biology ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Large Scale ◽  
Network Inference ◽  
Gene Regulatory Network Inference ◽  
Vast Number ◽  
Gene Regulatory ◽  
Randomized Methods

Gene regulation is orchestrated by a vast number of molecules, including transcription factors and co-factors, chromatin regulators, as well as epigenetic mechanisms, and it has been shown that transcriptional misregulation, e.g., caused by mutations in regulatory sequences, is responsible for a plethora of diseases, including cancer, developmental or neurological disorders. As a consequence, decoding the architecture of gene regulatory networks has become one of the most important tasks in modern (computational) biology. However, to advance our understanding of the mechanisms involved in the transcriptional apparatus, we need scalable approaches that can deal with the increasing number of large-scale, high-resolution, biological datasets. In particular, such approaches need to be capable of efficiently integrating and exploiting the biological and technological heterogeneity of such datasets in order to best infer the underlying, highly dynamic regulatory networks, often in the absence of sufficient ground truth data for model training or testing. With respect to scalability, randomized approaches have proven to be a promising alternative to deterministic methods in computational biology. As an example, one of the top performing algorithms in a community challenge on gene regulatory network inference from transcriptomic data is based on a random forest regression model. In this concise survey, we aim to highlight how randomized methods may serve as a highly valuable tool, in particular, with increasing amounts of large-scale, biological experiments and datasets being collected. Given the complexity and interdisciplinary nature of the gene regulatory network inference problem, we hope our survey maybe helpful to both computational and biological scientists. It is our aim to provide a starting point for a dialogue about the concepts, benefits, and caveats of the toolbox of randomized methods, since unravelling the intricate web of highly dynamic, regulatory events will be one fundamental step in understanding the mechanisms of life and eventually developing efficient therapies to treat and cure diseases.

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