Least squares identification method for differential equations of gene regulatory networks

2014 ◽  
Author(s):  
Yanpu Gao ◽  
Dongqing Wang
Keyword(s):  
Differential Equations ◽  
Least Squares ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Identification Method ◽  
Gene Regulatory

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 Modelling Three Dimensional Gene Regulatory Networks

2021 ◽  
Vol 16 ◽  
pp. 755-763
Author(s):  
Inna Samuilik ◽  
Felix Sadyrbaev
Keyword(s):  
Differential Equations ◽  
Gene Regulatory Networks ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Linear Part ◽  
Three Dimensional ◽  
Special Kind ◽  
Sigmoidal Function ◽  
Solution Vector ◽  
Gene Regulatory

We consider the three-dimensional gene regulatory network (GRN in short). This model consists of ordinary differential equations of a special kind, where the nonlinearity is represented by a sigmoidal function and the linear part is present also. The evolution of GRN is described by the solution vector X(t), depending on time. We describe the changes that system undergoes if the entries of the regulatory matrix are perturbed in some way.

scholarly journals A Nullclines Approach to the Study of 2D Artificial Network

2019 ◽  
Vol 1 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Felikss Sadirbajevs
Keyword(s):  
Vector Field ◽  
Differential Equations ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Field Analysis ◽  
Geometrical Approach ◽  
Full Generality ◽  
First Order ◽  
Gene Regulatory ◽  
Artificial Network

The system of two the first order ordinary differential equations arising in the gene regulatory networks theory is studied. The structure of attractors for this system is described for three important behavioral cases: activation, inhibition, mixed activation-inhibition. The geometrical approach combined with the vector field analysis allows treating the problem in full generality. A number of propositions are stated and the proof is geometrical, avoiding complex analytic. Although not all the possible cases are considered, the instructions are given what to do in any particular situation.

scholarly journals A survey of gene regulatory networks modelling methods: from differential equations, to Boolean and qualitative bioinspired models

2020 ◽  
Vol 2 (3) ◽  
pp. 207-226 ◽  
Author(s):  
Roberto Barbuti ◽  
Roberta Gori ◽  
Paolo Milazzo ◽  
Lucia Nasti
Keyword(s):  
Differential Equations ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Quantitative Methods ◽  
Environmental Changes ◽  
Boolean Networks ◽  
Specific Cell ◽  
Qualitative Approaches ◽  
Gene Regulatory ◽  
Gillespie’S Algorithm

Abstract Gene Regulatory Networks (GRNs) represent the interactions among genes regulating the activation of specific cell functionalities, such as reception of (chemical) signals or reaction to environmental changes. Studying and understanding these processes is crucial: they are the fundamental mechanism at the basis of cell functioning, and many diseases are based on perturbations or malfunctioning of some gene regulation activities. In this paper, we provide an overview on computational approaches to GRN modelling and analysis. We start from the biological and quantitative modelling background notions, recalling differential equations and the Gillespie’s algorithm. Then, we describe more in depth qualitative approaches such as Boolean networks and some computer science formalisms, including Petri nets, P systems and reaction systems. Our aim is to introduce the reader to the problem of GRN modelling and to guide her/him along the path that goes from classical quantitative methods, through qualitative methods based on Boolean network, up to some of the most relevant qualitative computational methods to understand the advantages and limitations of the different approaches.

Sign in / Sign up

Export Citation Format

Share Document