scholarly journals A Novel Algorithm for the Maximal Fit Problem in Boolean Networks

2016 ◽  
Author(s):  
Guy Karlebach
Keyword(s):  
Time Series ◽  
Regulatory Networks ◽  
Time Series Data ◽  
Boolean Networks ◽  
Large Datasets ◽  
Series Data ◽  
Np Complete ◽  
Regulation Functions ◽  
Regulatory Controls ◽  
Novel Algorithm

AbstractGene regulatory networks (GRNs) are increasingly used for explaining biological processes with complex transcriptional regulation. A GRN links the expression levels of a set of genes via regulatory controls that gene products exert on one another. Boolean networks are a common modeling choice since they balance between detail and ease of analysis. However, even for Boolean networks the problem of fitting a given network model to an expression dataset is NP-Complete. Previous methods have addressed this issue heuristically or by focusing on acyclic networks and specific classes of regulation functions. In this paper we introduce a novel algorithm for this problem that makes use of sampling in order to handle large datasets. Our algorithm can handle time series data for any network type and steady state data for acyclic networks. Using in-silico time series data we demonstrate good performance on large datasets with a significant level of noise.

Improved Fuzzy Cognitive Maps for Gene Regulatory Networks Inference Based on Time Series Data

2021 ◽  
Author(s):  
Marzieh Emadi ◽  
Farsad Zamani Boroujeni ◽  
jamshid Pirgazi
Keyword(s):  
Time Series ◽  
Compressed Sensing ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Time Series Data ◽  
Cognitive Maps ◽  
Boolean Networks ◽  
Fuzzy Cognitive Maps ◽  
Series Data ◽  
Gene Regulatory

Abstract Recently with the advancement of high-throughput sequencing, gene regulatory network inference has turned into an interesting subject in bioinformatics and system biology. But there are many challenges in the field such as noisy data, uncertainty, time-series data with numerous gene numbers and low data, time complexity and so on. In recent years, many research works have been conducted to tackle these challenges, resulting in different methods in gene regulatory networks inference. A number of models have been used in modeling of the gene regulatory networks including Boolean networks, Bayesian networks, Markov model, relational networks, state space model, differential equations model, artificial neural networks and so on. In this paper, the fuzzy cognitive maps are used to model gene regulatory networks because of their dynamic nature and learning capabilities for handling non-linearity and inherent uncertainty. Fuzzy cognitive maps belong to the family of recurrent networks and are well-suited for gene regulatory networks. In this research study, the Kalman filtered compressed sensing is used to infer the fuzzy cognitive map for the gene regulatory networks. This approach, using the advantages of compressed sensing and Kalman filters, allows robustness to noise and learning of sparse gene regulatory networks from data with high gene number and low samples. In the proposed method, stream data and previous knowledge can be used in the inference process. Furthermore, compressed sensing finds likely edges and Kalman filter estimates their weights. The proposed approach uses a novel method to decrease the noise of data. The proposed method is compared to CSFCM, LASSOFCM, KFRegular, ABC, RCGA, ICLA, and CMI2NI. The results show that the proposed approach is superior to the other approaches in fuzzy cognitive maps learning. This behavior is related to the stability against noise and offers a proper balance between data error and network structure.

scholarly journals ATEN: And/Or tree ensemble for inferring accurate Boolean network topology and dynamics

2019 ◽  
Author(s):  
Ning Shi ◽  
Zexuan Zhu ◽  
Ke Tang ◽  
David Parker ◽  
Shan He
Keyword(s):  
Time Series ◽  
Network Topology ◽  
Regulatory Networks ◽  
Boolean Network ◽  
Target Gene ◽  
Time Series Data ◽  
Boolean Networks ◽  
Series Data ◽  
Prime Implicants ◽  
Cell Life

Abstract Motivation Inferring gene regulatory networks from gene expression time series data is important for gaining insights into the complex processes of cell life. A popular approach is to infer Boolean networks. However, it is still a pressing open problem to infer accurate Boolean networks from experimental data that are typically short and noisy. Results To address the problem, we propose a Boolean network inference algorithm which is able to infer accurate Boolean network topology and dynamics from short and noisy time series data. The main idea is that, for each target gene, we use an And/Or tree ensemble algorithm to select prime implicants of which each is a conjunction of a set of input genes. The selected prime implicants are important features for predicting the states of the target gene. Using these important features we then infer the Boolean function of the target gene. Finally, the Boolean functions of all target genes are combined as a Boolean network. Using the data generated from artificial and real-world gene regulatory networks, we show that our algorithm can infer more accurate Boolean network topology and dynamics from short and noisy time series data than other algorithms. Our algorithm enables us to gain better insights into complex regulatory mechanisms of cell life. Availability and implementation Package ATEN is freely available at https://github.com/ningshi/ATEN. Supplementary information Supplementary data are available at Bioinformatics online.

Inference of gene regulatory networks based on nonlinear ordinary differential equations

2020 ◽  
Vol 36 (19) ◽  
pp. 4885-4893 ◽  
Author(s):  
Baoshan Ma ◽  
Mingkun Fang ◽  
Xiangtian Jiao
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Steady State ◽  
Differential Equations ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Time Series Data ◽  
Series Data ◽  
State Data ◽  
Gene Regulatory

Abstract Motivation Gene regulatory networks (GRNs) capture the regulatory interactions between genes, resulting from the fundamental biological process of transcription and translation. In some cases, the topology of GRNs is not known, and has to be inferred from gene expression data. Most of the existing GRNs reconstruction algorithms are either applied to time-series data or steady-state data. Although time-series data include more information about the system dynamics, steady-state data imply stability of the underlying regulatory networks. Results In this article, we propose a method for inferring GRNs from time-series and steady-state data jointly. We make use of a non-linear ordinary differential equations framework to model dynamic gene regulation and an importance measurement strategy to infer all putative regulatory links efficiently. The proposed method is evaluated extensively on the artificial DREAM4 dataset and two real gene expression datasets of yeast and Escherichia coli. Based on public benchmark datasets, the proposed method outperforms other popular inference algorithms in terms of overall score. By comparing the performance on the datasets with different scales, the results show that our method still keeps good robustness and accuracy at a low computational complexity. Availability and implementation The proposed method is written in the Python language, and is available at: https://github.com/lab319/GRNs_nonlinear_ODEs Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Boolean dynamics of genetic regulatory networks inferred from microarray time series data

2007 ◽  
Vol 23 (7) ◽  
pp. 866-874 ◽  
Author(s):  
Shawn Martin ◽  
Zhaoduo Zhang ◽  
Anthony Martino ◽  
Jean-Loup Faulon
Keyword(s):  
Time Series ◽  
Regulatory Networks ◽  
Time Series Data ◽  
Genetic Regulatory Networks ◽  
Series Data ◽  
Boolean Dynamics

scholarly journals Windowed Granger causal inference strategy improves discovery of gene regulatory networks

2018 ◽  
Vol 115 (9) ◽  
pp. 2252-2257 ◽  
Author(s):  
Justin D. Finkle ◽  
Jia J. Wu ◽  
Neda Bagheri
Keyword(s):  
Time Series ◽  
Network Structure ◽  
Time Delays ◽  
Regulatory Networks ◽  
Network Inference ◽  
Time Series Data ◽  
Series Data ◽  
Directed Network ◽  
Rapid Characterization

Accurate inference of regulatory networks from experimental data facilitates the rapid characterization and understanding of biological systems. High-throughput technologies can provide a wealth of time-series data to better interrogate the complex regulatory dynamics inherent to organisms, but many network inference strategies do not effectively use temporal information. We address this limitation by introducing Sliding Window Inference for Network Generation (SWING), a generalized framework that incorporates multivariate Granger causality to infer network structure from time-series data. SWING moves beyond existing Granger methods by generating windowed models that simultaneously evaluate multiple upstream regulators at several potential time delays. We demonstrate that SWING elucidates network structure with greater accuracy in both in silico and experimentally validated in vitro systems. We estimate the apparent time delays present in each system and demonstrate that SWING infers time-delayed, gene–gene interactions that are distinct from baseline methods. By providing a temporal framework to infer the underlying directed network topology, SWING generates testable hypotheses for gene–gene influences.

scholarly journals Linear time-varying models can reveal non-linear interactions of biomolecular regulatory networks using multiple time-series data

2008 ◽  
Vol 24 (10) ◽  
pp. 1286-1292 ◽  
Author(s):  
Jongrae Kim ◽  
Declan G. Bates ◽  
Ian Postlethwaite ◽  
Pat Heslop-Harrison ◽  
Kwang-Hyun Cho
Keyword(s):  
Time Series ◽  
Regulatory Networks ◽  
Time Series Data ◽  
Linear Time ◽  
Series Data ◽  
Multiple Time ◽  
Time Varying ◽  
Multiple Time Series ◽  
Linear Interactions ◽  
Time Varying Models
Sign in / Sign up

Export Citation Format

Share Document