Clustering of dispersal corridors in metapopulations leads to higher rates of recovery following subpopulation extinction

2020 ◽  
Author(s):  
Helen M. Kurkjian
Keyword(s):  
Graph Theory ◽  
Pseudomonas Syringae ◽  
Biological Networks ◽  
Empirical Data ◽  
Physical Characteristics ◽  
Absolute Time ◽  
Dispersal Corridors ◽  
Extinction Events

AbstractUnderstanding how spatially divided populations are affected by the physical characteristics of the landscapes they occupy is critical to their conservation. While some metapopulations have dispersal corridors spread relatively evenly through space in a homogeneous arrangement such that most subpopulations are connected to a few neighbors, others may have corridors clustered in a heterogeneous arrangement, creating a few highly connected subpopulations and leaving most subpopulations with only one or two neighbors. Graph theory and empirical data from other biological and non-biological networks suggest that heterogeneous metapopulations should be the most robust to subpopulation extinction. Here, I used Pseudomonas syringae pv. syringae B728a in metapopulation microcosms to compare the recovery of metapopulations with homogeneous and heterogeneous corridor arrangements following small, medium, and large subpopulation extinction events. I found that while metapopulations with heterogeneous corridor arrangements had the fastest rates of recovery following extinction events of all sizes and had the shortest absolute time to recovery following medium-sized extinction events, metapopulations with homogeneous corridor arrangements had the shortest time to recovery following the smallest extinction events.

Introduction

2021 ◽  
pp. 1-36
Author(s):  
Vadim Zverovich
Keyword(s):  
Graph Theory ◽  
Biological Networks ◽  
Real Life ◽  
Scale Free ◽  
Graph Theoretic ◽  
Life Problems ◽  
Scale Free Networks ◽  
Web Graph ◽  
The Web

This chapter gives a brief overview of selected applications of graph theory, many of which gave rise to the development of graph theory itself. A range of such applications extends from puzzles and games to serious scientific and real-life problems, thus illustrating the diversity of applications. The first section is devoted to the six earliest applications of graph theory. The next section introduces so-called scale-free networks, which include the web graph, social and biological networks. The last section describes a number of graph-theoretic algorithms, which can be used to tackle a number of interesting applications and problems of graph theory.

Protein Interactions for Functional Genomics

2012 ◽  
Vol 3 (4) ◽  
pp. 15-30
Author(s):  
Pablo Minguez ◽  
Joaquin Dopazo
Keyword(s):  
Graph Theory ◽  
Protein Interactions ◽  
Biological Networks ◽  
State Of The Art ◽  
Protein Protein Interactions ◽  
Pairwise Interactions ◽  
Novel Approach ◽  
Functional Profiling ◽  
Available Resources

Here the authors review the state of the art in the use of protein-protein interactions (ppis) within the context of the interpretation of genomic experiments. They report the available resources and methodologies used to create a curated compilation of ppis introducing a novel approach to filter interactions. Special attention is paid in the complexity of the topology of the networks formed by proteins (nodes) and pairwise interactions (edges). These networks can be studied using graph theory and a brief introduction to the characterization of biological networks and definitions of the more used network parameters is also given. Also a report on the available resources to perform different modes of functional profiling using ppi data is provided along with a discussion on the approaches that have typically been applied into this context. They also introduce a novel methodology for the evaluation of networks and some examples of its application.

scholarly journals Study of biological networks using graph theory

2018 ◽  
Vol 25 (6) ◽  
pp. 1212-1219 ◽  
Author(s):  
Wei Gao ◽  
Hualong Wu ◽  
Muhammad Kamran Siddiqui ◽  
Abdul Qudair Baig
Keyword(s):  
Graph Theory ◽  
Biological Networks

Classification of Temporal Patterns in Dynamic Biological Networks

1998 ◽  
Vol 10 (7) ◽  
pp. 1831-1846 ◽  
Author(s):  
Patrick D. Roberts
Keyword(s):  
Graph Theory ◽  
Metric Space ◽  
Biological Networks ◽  
Temporal Patterns ◽  
State Transitions ◽  
Synaptic Connections ◽  
Functional Differences ◽  
Algebraic Properties ◽  
General Method

A general method is presented to classify temporal patterns generated by rhythmic biological networks when synaptic connections and cellular properties are known. The method is discrete in nature and relies on algebraic properties of state transitions and graph theory. Elements of the set of rhythms generated by a network are compared using a metric that quantifies the functional differences among them. The rhythms are then classified according to their location in a metric space. Examples are given, and biological implications are discussed.

scholarly journals The Matthew effect in empirical data

2014 ◽  
Vol 11 (98) ◽  
pp. 20140378 ◽  
Author(s):  
Matjaž Perc
Keyword(s):  
Biological Networks ◽  
Empirical Data ◽  
Preferential Attachment ◽  
Social Systems ◽  
Scientific Progress ◽  
Power Laws ◽  
Matthew Effect ◽  
Future Research ◽  
The Matthew Effect ◽  
The Rich

The Matthew effect describes the phenomenon that in societies, the rich tend to get richer and the potent even more powerful. It is closely related to the concept of preferential attachment in network science, where the more connected nodes are destined to acquire many more links in the future than the auxiliary nodes. Cumulative advantage and success-breads-success also both describe the fact that advantage tends to beget further advantage. The concept is behind the many power laws and scaling behaviour in empirical data, and it is at the heart of self-organization across social and natural sciences. Here, we review the methodology for measuring preferential attachment in empirical data, as well as the observations of the Matthew effect in patterns of scientific collaboration, socio-technical and biological networks, the propagation of citations, the emergence of scientific progress and impact, career longevity, the evolution of common English words and phrases, as well as in education and brain development. We also discuss whether the Matthew effect is due to chance or optimization, for example related to homophily in social systems or efficacy in technological systems, and we outline possible directions for future research.

scholarly journals Computational analysis of molecular networks using spectral graph theory, complexity measures and information theory

2019 ◽  
Author(s):  
Chien-Hung Huang ◽  
Jeffrey J. P. Tsai ◽  
Nilubon Kurubanjerdjit ◽  
Ka-Lok Ng
Keyword(s):  
Graph Theory ◽  
Frequency Distribution ◽  
Shannon Entropy ◽  
Biological Networks ◽  
Spectral Graph Theory ◽  
Molecular Networks ◽  
Network Motifs ◽  
Complexity Measures ◽  
Spectral Graph ◽  
Graph Energy

AbstractMolecular networks are described in terms of directed multigraphs, so-called network motifs. Spectral graph theory, reciprocal link and complexity measures were utilized to quantify network motifs. It was found that graph energy, reciprocal link and cyclomatic complexity can optimally specify network motifs with some degree of degeneracy. Biological networks are built up from a finite number of motif patterns; hence, a graph energy cutoff exists and the Shannon entropy of the motif frequency distribution is not maximal. Also, frequently found motifs are irreducible graphs. Network similarity was quantified by gauging their motif frequency distribution functions using Jensen-Shannon entropy. This method allows us to determine the distance between two networks regardless of their nodes’ identities and network sizes.This study provides a systematic approach to dissect the complex nature of biological networks. Our novel method different from any other approach. The findings support the view that there are organizational principles underlying molecular networks.

Protein Interactions for Functional Genomics

2009 ◽  
pp. 223-238 ◽  
Author(s):  
Pablo Minguez ◽  
Joaquin Dopazo
Keyword(s):  
Graph Theory ◽  
Functional Genomics ◽  
Protein Interactions ◽  
Biological Networks ◽  
Protein Protein Interactions ◽  
Pairwise Interactions ◽  
Novel Approach ◽  
Functional Profiling ◽  
Available Resources

Here the authors review the state of the art in the use of protein-protein interactions (ppis) within the context of the interpretation of genomic experiments. They report the available resources and methodologies used to create a curated compilation of ppis introducing a novel approach to filter interactions. Special attention is paid in the complexity of the topology of the networks formed by proteins (nodes) and pairwise interactions (edges). These networks can be studied using graph theory and a brief introduction to the characterization of biological networks and definitions of the more used network parameters is also given. Also a report on the available resources to perform different modes of functional profiling using ppi data is provided along with a discussion on the approaches that have typically been applied into this context. They also introduce a novel methodology for the evaluation of networks and some examples of its application.

scholarly journals Extended study of biological networks using graph theory

2021 ◽  
Author(s):  
Tomáš Vetrík
Keyword(s):  
Graph Theory ◽  
Biological Networks ◽  
Topological Index ◽  
Topological Indices ◽  
Extended Study

We represent biological networks by a function that maps the structure of a network to a number called topological index. Topological indices have been studied for biological networks in which a person transmits a virus to two other people, and a person having the virus is in contact with exactly one other person who got the virus from someone else. We extend research in this area by studying biological networks in which a person transmits a virus to n other people, where n ≥ 2, and a person having the virus is in contact with p other people (0 ≤ p ≤ n-2) who got the virus from some other person.

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