Learning Delays in Biological Regulatory Networks from Time Series Data

Models of Biological Regulatory Networks are generally based on prior knowledge, either derived from literature and/or the manual analysis of biological observations. With the development of high-throughput data, there is a growing need for methods that automatically generate admissible models. To have a better understanding of the dynamical phenomena at stake in the influences between biological components, it would be necessary to include delayed influences in the model. The main purpose of this work is to have a resulting network as consistent as possible with the observed datasets regarding the conflicts and the simultaneity between transitions. The originality of our work is threefold: (i) the identification the sign of the interactions, (ii) the direct integration of quantitative time delays in the learning approach and (iii) the identification of the qualitative discrete levels that lead to the systems dynamics.In this work the precision of our automatic approach is discussed by applying it on dynamical biological models coming from the DREAM4 Challenge datasets.

Download Full-text

Investigating noise tolerance in an efficient engine for inferring biological regulatory networks

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720015410061 ◽

2015 ◽

Vol 13 (03) ◽

pp. 1541006 ◽

Cited By ~ 1

Author(s):

Asako Komori ◽

Yukihiro Maki ◽

Isao Ono ◽

Masahiro Okamoto

Keyword(s):

Regulatory Networks ◽

Time Series Data ◽

Biological Systems ◽

Optimization Methods ◽

Series Data ◽

Noise Tolerance ◽

Biological Regulatory Networks ◽

Experimental Time Series ◽

Noise Data ◽

Signaling Components

Biological systems are composed of biomolecules such as genes, proteins, metabolites, and signaling components, which interact in complex networks. To understand complex biological systems, it is important to be capable of inferring regulatory networks from experimental time series data. In previous studies, we developed efficient numerical optimization methods for inferring these networks, but we have yet to test the performance of our methods when considering the error (noise) that is inherent in experimental data. In this study, we investigated the noise tolerance of our proposed inferring engine. We prepared the noise data using the Langevin equation, and compared the performance of our method with that of alternative optimization methods.

Download Full-text

Windowed Granger causal inference strategy improves discovery of gene regulatory networks

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1710936115 ◽

2018 ◽

Vol 115 (9) ◽

pp. 2252-2257 ◽

Cited By ~ 15

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.

Download Full-text

Modeling Delayed Dynamics in Biological Regulatory Networks from Time Series Data

Algorithms ◽

10.3390/a10010008 ◽

2017 ◽

Vol 10 (1) ◽

pp. 8 ◽

Cited By ~ 2

Author(s):

Emna Ben Abdallah ◽

Tony Ribeiro ◽

Morgan Magnin ◽

Olivier Roux ◽

Katsumi Inoue

Keyword(s):

Time Series ◽

Regulatory Networks ◽

Time Series Data ◽

Series Data ◽

Biological Regulatory Networks

Download Full-text

Inference of Delayed Biological Regulatory Networks from Time Series Data

Computational Methods in Systems Biology - Lecture Notes in Computer Science ◽

10.1007/978-3-319-45177-0_3 ◽

2016 ◽

pp. 30-48

Author(s):

Emna Ben Abdallah ◽

Tony Ribeiro ◽

Morgan Magnin ◽

Olivier Roux ◽

Katsumi Inoue

Keyword(s):

Time Series ◽

Regulatory Networks ◽

Time Series Data ◽

Series Data ◽

Biological Regulatory Networks

Download Full-text

Infer Gene Regulatory Networks from Time Series Data with Probabilistic Model Checking

2015 IEEE/ACM 3rd FME Workshop on Formal Methods in Software Engineering ◽

10.1109/formalise.2015.12 ◽

2015 ◽

Cited By ~ 9

Author(s):

Michele Ceccarelli ◽

Luigi Cerulo ◽

Giuseppe De Ruvo ◽

Vittoria Nardone ◽

Antonella Santone

Keyword(s):

Time Series ◽

Model Checking ◽

Gene Regulatory Networks ◽

Probabilistic Model ◽

Regulatory Networks ◽

Time Series Data ◽

Series Data ◽

Probabilistic Model Checking ◽

Gene Regulatory

Download Full-text

Inference of gene regulatory networks based on nonlinear ordinary differential equations

Bioinformatics ◽

10.1093/bioinformatics/btaa032 ◽

2020 ◽

Vol 36 (19) ◽

pp. 4885-4893 ◽

Cited By ~ 2

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.

Download Full-text

Model selection in the reconstruction of regulatory networks from time-series data

BMC Research Notes ◽

10.1186/1756-0500-2-68 ◽

2009 ◽

Vol 2 (1) ◽

pp. 68

Author(s):

Eugene Novikov ◽

Emmanuel Barillot

Keyword(s):

Time Series ◽

Model Selection ◽

Regulatory Networks ◽

Time Series Data ◽

Series Data

Download Full-text

Estimating the state of a geophysical system with sparse observations: time delay methods to achieve accurate initial states for prediction

Nonlinear Processes in Geophysics ◽

10.5194/npg-24-9-2017 ◽

2017 ◽

Vol 24 (1) ◽

pp. 9-22 ◽

Cited By ~ 5

Author(s):

Zhe An ◽

Daniel Rey ◽

Jingxin Ye ◽

Henry D. I. Abarbanel

Keyword(s):

Time Delays ◽

Time Series Data ◽

Chaotic Behavior ◽

Weather Prediction ◽

Series Data ◽

State Variables ◽

Nonlinear Shallow Water Equations ◽

Delayed Measurements ◽

Lagrangian Drifter ◽

Geophysical System

Abstract. The problem of forecasting the behavior of a complex dynamical system through analysis of observational time-series data becomes difficult when the system expresses chaotic behavior and the measurements are sparse, in both space and/or time. Despite the fact that this situation is quite typical across many fields, including numerical weather prediction, the issue of whether the available observations are "sufficient" for generating successful forecasts is still not well understood. An analysis by Whartenby et al. (2013) found that in the context of the nonlinear shallow water equations on a β plane, standard nudging techniques require observing approximately 70 % of the full set of state variables. Here we examine the same system using a method introduced by Rey et al. (2014a), which generalizes standard nudging methods to utilize time delayed measurements. We show that in certain circumstances, it provides a sizable reduction in the number of observations required to construct accurate estimates and high-quality predictions. In particular, we find that this estimate of 70 % can be reduced to about 33 % using time delays, and even further if Lagrangian drifter locations are also used as measurements.

Download Full-text