scholarly journals Rapid Reconstruction of Time-Varying Gene Regulatory Networks

Author(s):  
Saptarshi Pyne ◽  
Alok Ranjan Kumar ◽  
Ashish Anand
2019 ◽  
Author(s):  
Saptarshi Pyne ◽  
Ashish Anand

AbstractReconstruction of time-varying gene regulatory networks underlying a time-series gene expression data is a fundamental challenge in the computational systems biology. The challenge increases multi-fold if the target networks need to be constructed for hundreds to thousands of genes. There have been constant efforts to design an algorithm that can perform the reconstruction task correctly as well as can scale efficiently (with respect to both time and memory) to such a large number of genes. However, the existing algorithms either do not offer time-efficiency, or they offer it at other costs – memory-inefficiency or imposition of a constraint, known as the ‘smoothly time-varying assumption’. In this paper, two novel algorithms – ‘an algorithm for reconstructing Time-varying Gene regulatory networks with Shortlisted candidate regulators - which is Light on memory’ (TGS-Lite) and ‘TGS-Lite Plus’ (TGS-Lite+) – are proposed that are time-efficient, memory-efficient and do not impose the smoothly time-varying assumption. Additionally, they offer state-of-the-art reconstruction correctness as demonstrated with three benchmark datasets.Source Codehttps://github.com/sap01/TGS-Lite-supplem/tree/master/sourcecode


2018 ◽  
Author(s):  
Saptarshi Pyne ◽  
Alok Ranjan Kumar ◽  
Ashish Anand

Abstract—Rapid advancements in high-throughput technologies has resulted in genome-scale time series datasets. Uncovering the temporal sequence of gene regulatory events, in the form of time-varying gene regulatory networks (GRNs), demands computationally fast, accurate and scalable algorithms. The existing algorithms can be divided into two categories: ones that are time-intensive and hence unscalable; others that impose structural constraints to become scalable. In this paper, a novel algorithm, namely ‘an algorithm for reconstructing Time-varying Gene regulatory networks with Shortlisted candidate regulators’ (TGS), is proposed. TGS is time-efficient and does not impose any structural constraints. Moreover, it provides such flexibility and time-efficiency, without losing its accuracy. TGS consistently outperforms the state-of-the-art algorithms in true positive detection, on three benchmark synthetic datasets. However, TGS does not perform as well in false positive rejection. To mitigate this issue, TGS+ is proposed. TGS+ demonstrates competitive false positive rejection power, while maintaining the superior speed and true positive detection power of TGS. Nevertheless, main memory requirements of both TGS variants grow exponentially with the number of genes, which they tackle by restricting the maximum number of regulators for each gene. Relaxing this restriction remains a challenge as the actual number of regulators is not known a priori.ReproducibilityThe datasets and results can be found at: https://github.com/aaiitg-grp/TGS. This manuscript is currently under review. As soon as it is accepted, the source code will be made available at the same link. There are mentions of a ‘supplementary document’ throughout the text. The supplementary document will also be made available after acceptance of the manuscript. If you wish to be notified when the supplementary document and source code are available, kindly send an email to [email protected] with subject line ‘TGS Source Code: Request for Notification’. The email body can be kept blank.


2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Zhaohua Wu ◽  
Zhiming Wang ◽  
Tiejun Zhou

AbstractIn this paper, we investigate a class of fractional-order gene regulatory networks with time-varying delays and structured uncertainties (UDFGRNs). First, we deduce the existence and uniqueness of the equilibrium for the UDFGRNs by using the contraction mapping principle. Next, we derive a novel global uniform asymptotic stability criterion of the UDFGRNs by using a Lyapunov function and the Razumikhin technique, and the conditions relating to the criterion depend on the fractional order of the UDFGRNs. Finally, we provide two numerical simulation examples to demonstrate the correctness and usefulness of the novel stability conditions. One of the most interesting findings is that the structured uncertainties indeed have an impact on the stability of the system.


2021 ◽  
Vol 5 (4) ◽  
pp. 268
Author(s):  
Ivanka Stamova ◽  
Gani Stamov

This paper investigates a class of fractional-order delayed impulsive gene regulatory networks (GRNs). The proposed model is an extension of some existing integer-order GRNs using fractional derivatives of Caputo type. The existence and uniqueness of an almost periodic state of the model are investigated and new criteria are established by the Lyapunov functions approach. The effects of time-varying delays and impulsive perturbations at fixed times on the almost periodicity are considered. In addition, sufficient conditions for the global Mittag–Leffler stability of the almost periodic solutions are proposed. To justify our findings a numerical example is also presented.


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