Delayed Stochastic Biochemical Reactions Reconstruction Based on Additive Reaction Model

2014 ◽  
Vol 894 ◽  
pp. 280-283
Author(s):  
Bin Yang ◽  
Chuan Zhu Liao ◽  
Ming Yan Jiang ◽  
Dong Feng Yuan
Keyword(s):  
Biological Networks ◽  
Metabolic Networks ◽  
Stochastic Dynamics ◽  
Reaction Model ◽  
Evolutionary Strategy ◽  
Biochemical Reactions ◽  
New Model ◽  
Gene Regulatory ◽  
Delayed Time ◽  
Additive Reaction

Stochastic dynamics and delayed time of biochemical reactions play an important role in the biological networks such as gene regulatory and metabolic networks. This paper presents a new model, called additive reaction model (ARM), to capture the stochastic dynamical and delayed behavior. The new evolutionary strategy is used to search the optimal biochemical model, in which genetic algorithm (GA) and particle swarm optimization (PSO) are employed to evolve the architecture and parameters of biochemical reactions, respectively. The results reveal that the delayed biochemical reaction modeling problems could be solved effectively and efficiently using our proposed new model and new evolutionary strategy.

Transcriptomics to Metabolomics

2018 ◽  
pp. 188-206 ◽  
Author(s):  
Ankush Bansal ◽  
Pulkit Anupam Srivastava
Keyword(s):  
Biological Networks ◽  
Metabolic Networks ◽  
Topological Analysis ◽  
In Silico Analysis ◽  
Small World ◽  
Recent Decade ◽  
Bipartite Networks ◽  
Metabolomics Data ◽  
Scale Free ◽  
Gene Regulatory

A lot of omics data is generated in a recent decade which flooded the internet with transcriptomic, genomics, proteomics and metabolomics data. A number of software, tools, and web-servers have developed to analyze the big data omics. This review integrates the various methods that have been employed over the years to interpret the gene regulatory and metabolic networks. It illustrates random networks, scale-free networks, small world network, bipartite networks and other topological analysis which fits in biological networks. Transcriptome to metabolome network is of interest because of key enzymes identification and regulatory hub genes prediction. It also provides an insight into the understanding of omics technologies, generation of data and impact of in-silico analysis on the scientific community.

scholarly journals Atlas: automatic modeling of regulation of bacterial gene expression and metabolism using rule-based languages

2020 ◽  
Author(s):  
Rodrigo Santibáñez ◽  
Daniel Garrido ◽  
Alberto J M Martin
Keyword(s):  
Gene Expression ◽  
Biological Networks ◽  
Regulatory Networks ◽  
Dynamic Models ◽  
Stochastic Dynamics ◽  
Divide And Conquer ◽  
Supplementary Information ◽  
Bacterial Gene ◽  
Rule Based ◽  
Gene Regulatory

Abstract Motivation Cells are complex systems composed of hundreds of genes whose products interact to produce elaborated behaviors. To control such behaviors, cells rely on transcription factors to regulate gene expression, and gene regulatory networks (GRNs) are employed to describe and understand such behavior. However, GRNs are static models, and dynamic models are difficult to obtain due to their size, complexity, stochastic dynamics and interactions with other cell processes. Results We developed Atlas, a Python software that converts genome graphs and gene regulatory, interaction and metabolic networks into dynamic models. The software employs these biological networks to write rule-based models for the PySB framework. The underlying method is a divide-and-conquer strategy to obtain sub-models and combine them later into an ensemble model. To exemplify the utility of Atlas, we used networks of varying size and complexity of Escherichia coli and evaluated in silico modifications, such as gene knockouts and the insertion of promoters and terminators. Moreover, the methodology could be applied to the dynamic modeling of natural and synthetic networks of any bacteria. Availability and implementation Code, models and tutorials are available online (https://github.com/networkbiolab/atlas). Supplementary information Supplementary data are available at Bioinformatics online.

Transcriptomics to Metabolomics

Biotechnology ◽  
2019 ◽  
pp. 361-379
Author(s):  
Ankush Bansal ◽  
Pulkit Anupam Srivastava
Keyword(s):  
Biological Networks ◽  
Metabolic Networks ◽  
Topological Analysis ◽  
In Silico Analysis ◽  
Small World ◽  
Recent Decade ◽  
Bipartite Networks ◽  
Metabolomics Data ◽  
Scale Free ◽  
Gene Regulatory

A lot of omics data is generated in a recent decade which flooded the internet with transcriptomic, genomics, proteomics and metabolomics data. A number of software, tools, and web-servers have developed to analyze the big data omics. This review integrates the various methods that have been employed over the years to interpret the gene regulatory and metabolic networks. It illustrates random networks, scale-free networks, small world network, bipartite networks and other topological analysis which fits in biological networks. Transcriptome to metabolome network is of interest because of key enzymes identification and regulatory hub genes prediction. It also provides an insight into the understanding of omics technologies, generation of data and impact of in-silico analysis on the scientific community.

scholarly journals BioNetLink - An Architecture for Working with Network Data

2014 ◽  
Vol 11 (2) ◽  
pp. 68-79
Author(s):  
Matthias Klapperstück ◽  
Falk Schreiber
Keyword(s):  
Experimental Data ◽  
Biological Networks ◽  
Regulatory Networks ◽  
Large Data ◽  
Biological Data ◽  
Network Data ◽  
Data Sets ◽  
Dynamic Views ◽  
Gene Regulatory ◽  
Multiple Network

Summary The visualization of biological data gained increasing importance in the last years. There is a large number of methods and software tools available that visualize biological data including the combination of measured experimental data and biological networks. With growing size of networks their handling and exploration becomes a challenging task for the user. In addition, scientists also have an interest in not just investigating a single kind of network, but on the combination of different types of networks, such as metabolic, gene regulatory and protein interaction networks. Therefore, fast access, abstract and dynamic views, and intuitive exploratory methods should be provided to search and extract information from the networks. This paper will introduce a conceptual framework for handling and combining multiple network sources that enables abstract viewing and exploration of large data sets including additional experimental data. It will introduce a three-tier structure that links network data to multiple network views, discuss a proof of concept implementation, and shows a specific visualization method for combining metabolic and gene regulatory networks in an example.

scholarly journals Stochastic resonances in a distributed genetic broadcasting system: the NF κ B/I κ B paradigm

2018 ◽  
Vol 15 (138) ◽  
pp. 20170809 ◽  
Author(s):  
Zhipeng Wang ◽  
Davit A. Potoyan ◽  
Peter G. Wolynes
Keyword(s):  
Gene Regulatory Networks ◽  
Binding Sites ◽  
Regulatory Networks ◽  
Stochastic Dynamics ◽  
Extracellular Signals ◽  
Broadcasting System ◽  
Gene Regulatory ◽  
And Performance ◽  
The Impact ◽  
Kinetic Regulation

Gene regulatory networks must relay information from extracellular signals to downstream genes in an efficient, timely and coherent manner. Many complex functional tasks such as the immune response require system-wide broadcasting of information not to one but to many genes carrying out distinct functions whose dynamical binding and unbinding characteristics are widely distributed. In such broadcasting networks, the intended target sites are also often dwarfed in number by the even more numerous non-functional binding sites. Taking the genetic regulatory network of NF κ B as an exemplary system we explore the impact of having numerous distributed sites on the stochastic dynamics of oscillatory broadcasting genetic networks pointing out how resonances in binding cycles control the network's specificity and performance. We also show that active kinetic regulation of binding and unbinding through molecular stripping of DNA bound transcription factors can lead to a higher coherence of gene-co-expression and synchronous clearance.

scholarly journals CytoMCS: A Multiple Maximum Common Subgraph Detection Tool for Cytoscape

2017 ◽  
Vol 14 (2) ◽  
Author(s):  
Simon J. Larsen ◽  
Jan Baumbach
Keyword(s):  
Biological Networks ◽  
Regulatory Networks ◽  
Conservation Score ◽  
Common Edge ◽  
Simple Graphs ◽  
Protein Protein Interaction ◽  
Local Search Heuristic ◽  
Gene Regulatory ◽  
Protein Protein Interaction Networks ◽  
Or Gene

AbstractComparative analysis of biological networks is a major problem in computational integrative systems biology. By computing the maximum common edge subgraph between a set of networks, one is able to detect conserved substructures between them and quantify their topological similarity. To aid such analyses we have developed CytoMCS, a Cytoscape app for computing inexact solutions to the maximum common edge subgraph problem for two or more graphs. Our algorithm uses an iterative local search heuristic for computing conserved subgraphs, optimizing a squared edge conservation score that is able to detect not only fully conserved edges but also partially conserved edges. It can be applied to any set of directed or undirected, simple graphs loaded as networks into Cytoscape, e.g. protein-protein interaction networks or gene regulatory networks. CytoMCS is available as a Cytoscape app at http://apps.cytoscape.org/apps/cytomcs.

scholarly journals Graph Algorithm to Find Core Periphery Structures using Mutual K-nearest Neighbors

2021 ◽  
Vol 12 (1) ◽  
pp. 1-18
Author(s):  
Divya Sardana ◽  
Raj Bhatnagar Bhatnagar
Keyword(s):  
Real World ◽  
Biological Networks ◽  
Metabolic Networks ◽  
Graph Algorithm ◽  
Nearest Neighbors ◽  
Weighted Graphs ◽  
K Nearest Neighbors ◽  
Definition Of ◽  
Affinity Measure ◽  
Meso Scale

Core periphery structures exist naturally in many complex networks in the real-world like social, economic, biological and metabolic networks. Most of the existing research efforts focus on the identification of a meso scale structure called community structure. Core periphery structures are another equally important meso scale property in a graph that can help to gain deeper insights about the relationships between different nodes. In this paper, we provide a definition of core periphery structures suitable for weighted graphs. We further score and categorize these relationships into different types based upon the density difference between the core and periphery nodes. Next, we propose an algorithm called CP-MKNN (Core Periphery-Mutual K Nearest Neighbors) to extract core periphery structures from weighted graphs using a heuristic node affinity measure called Mutual K-nearest neighbors (MKNN). Using synthetic and real-world social and biological networks, we illustrate the effectiveness of developed core periphery structures.

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