Social networks to biological networks: systems biology of Mycobacterium tuberculosis

2013 ◽  
Vol 9 (7) ◽  
pp. 1584 ◽  
Author(s):  
Rohit Vashisht ◽  
Anshu Bhardwaj ◽  
OSDD Consortium ◽  
Samir K. Brahmachari
Keyword(s):  
Social Networks ◽  
Mycobacterium Tuberculosis ◽  
Systems Biology ◽  
Biological Networks

scholarly journals Systems Biology-Based Identification of Mycobacterium tuberculosis Persistence Genes in Mouse Lungs

mBio ◽  
2014 ◽  
Vol 5 (1) ◽  
Author(s):  
Noton K. Dutta ◽  
Nirmalya Bandyopadhyay ◽  
Balaji Veeramani ◽  
Gyanu Lamichhane ◽  
Petros C. Karakousis ◽  
...  
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Systems Biology ◽  
Biological Networks ◽  
Drug Targets ◽  
Experimental Testing ◽  
Data Types ◽  
Term Survival ◽  
Content Type ◽  
Mouse Lungs

ABSTRACTIdentifyingMycobacterium tuberculosispersistence genes is important for developing novel drugs to shorten the duration of tuberculosis (TB) treatment. We developed computational algorithms that predictM. tuberculosisgenes required for long-term survival in mouse lungs. As the input, we used high-throughputM. tuberculosismutant library screen data, mycobacterial global transcriptional profiles in mice and macrophages, and functional interaction networks. We selected 57 unique, genetically defined mutants (18 previously tested and 39 untested) to assess the predictive power of this approach in the murine model of TB infection. We observed a 6-fold enrichment in the predicted set ofM. tuberculosisgenes required for persistence in mouse lungs relative to randomly selected mutant pools. Our results also allowed us to reclassify several genes as required forM. tuberculosispersistencein vivo. Finally, the new results implicated additional high-priority candidate genes for testing. Experimental validation of computational predictions demonstrates the power of this systems biology approach for elucidatingM. tuberculosispersistence genes.IMPORTANCEMycobacterium tuberculosis, the causative agent of tuberculosis (TB), has a genetic repertoire that permits it to persist in the face of host immune responses. Identification of such persistence genes could reveal novel drug targets and elucidate mechanisms by which the organism eludes the immune system and resists drugs. Genetic screens have identified a total of 31 persistence genes, but to date only 15% of the ~4,000M. tuberculosisgenes have been tested experimentally. In this paper, as an alternative to brute force experimental screens, we describe computational methods that predict new persistence genes by combining known examples with growing databases of biological networks. Experimental testing demonstrated that these predictions are highly accurate, validating the computational approach and providing new information aboutM. tuberculosispersistence in host tissues. Using the new experimental results as additional input highlights additional genes for testing. Our approach can be extended to other data types and target organisms to characterize host-pathogen interactions relevant to this and other infectious diseases.

Modern Technologies and Approaches for Decoding Non-Coding Rna-Mediated Biological Networks in Systems Biology and Their Applications

2016 ◽  
pp. 106-132
Author(s):  
Devyani Samantarrai ◽  
Mousumi Sahu ◽  
Garima Singh ◽  
Jyoti Roy ◽  
Chandra Bhushan ◽  
...  
Keyword(s):  
Systems Biology ◽  
Biological Networks ◽  
Non Coding Rna ◽  
Modern Technologies

Graph Mining Techniques

2013 ◽  
pp. 446-464 ◽  
Author(s):  
Ana Paula Appel ◽  
Christos Faloutsos ◽  
Caetano Traina Junior
Keyword(s):  
Social Networks ◽  
Communication Networks ◽  
Real World ◽  
Biological Networks ◽  
Graph Mining ◽  
Recommendation Systems ◽  
Computer Communication Networks ◽  
Computer Communication ◽  
Good Pattern ◽  
As Graph

Graphs appear in several settings, like social networks, recommendation systems, computer communication networks, gene/protein biological networks, among others. A large amount of graph patterns, as well as graph generator models that mimic such patterns have been proposed over the last years. However, a deep and recurring question still remains: “What is a good pattern?” The answer is related to finding a pattern or a tool able to help distinguishing between actual real-world and fake graphs. Here we explore the ability of ShatterPlots, a simple and powerful algorithm to tease out patterns of real graphs, helping us to spot fake/masked graphs. The idea is to force a graph to reach a critical (“Shattering”) point, randomly deleting edges, and study its properties at that point.

Petri Net representations in systems biology

2003 ◽  
Vol 31 (6) ◽  
pp. 1513-1515 ◽  
Author(s):  
J.W. Pinney ◽  
D.R. Westhead ◽  
G.A. McConkey
Keyword(s):  
Quantitative Analysis ◽  
Systems Biology ◽  
Petri Nets ◽  
Petri Net ◽  
Biological Networks ◽  
Research Effort ◽  
Research Directions ◽  
Mathematical Structures ◽  
New Research

The mathematical structures known as Petri Nets have recently become the focus of much research effort in both the structural and quantitative analysis of all kinds of biological networks. This review provides a very brief summary of these interesting new research directions.

Analysis and modelling of signal transduction pathways in systems biology

2003 ◽  
Vol 31 (6) ◽  
pp. 1503-1509 ◽  
Author(s):  
K.-H. Cho ◽  
O. Wolkenhauer
Keyword(s):  
Signal Transduction ◽  
Systems Biology ◽  
Dynamic Systems ◽  
Biological Networks ◽  
Systems Approach ◽  
Life Sciences ◽  
Dynamic Modelling ◽  
Signal Transduction Pathways ◽  
Physical Sciences ◽  
Functional Relationships

There is general agreement that a systems approach is needed for a better understanding of causal and functional relationships that generate the dynamics of biological networks and pathways. These observations have been the basis for efforts to get the engineering and physical sciences involved in life sciences. The emergence of systems biology as a new area of research is evidence for these developments. Dynamic modelling and simulation of signal transduction pathways is an important theme in systems biology and is getting growing attention from researchers with an interest in the analysis of dynamic systems. This paper introduces systems biology in terms of the analysis and modelling of signal transduction pathways. Focusing on mathematical representations of cellular dynamics, a number of emerging challenges and perspectives are discussed.

scholarly journals VANESA - A Software Application for the Visualization and Analysis of Networks in Systems Biology Applications

2014 ◽  
Vol 11 (2) ◽  
pp. 43-57 ◽  
Author(s):  
Christoph Brinkrolf ◽  
Sebastian Jan Janowski ◽  
Benjamin Kormeier ◽  
Martin Lewinski ◽  
Klaus Hippe ◽  
...  
Keyword(s):  
Systems Biology ◽  
Petri Net ◽  
Biological Networks ◽  
Life Science ◽  
Experimental Results ◽  
Biological Processes ◽  
Molecular Systems ◽  
Software Application ◽  
Theoretical Approaches ◽  
Modeling Software

Summary VANESA is a modeling software for the automatic reconstruction and analysis of biological networks based on life-science database information. Using VANESA, scientists are able to model any kind of biological processes and systems as biological networks. It is now possible for scientists to automatically reconstruct important molecular systems with information from the databases KEGG, MINT, IntAct, HPRD, and BRENDA. Additionally, experimental results can be expanded with database information to better analyze the investigated elements and processes in an overall context. Users also have the possibility to use graph theoretical approaches in VANESA to identify regulatory structures and significant actors within the modeled systems. These structures can then be further investigated in the Petri net environment of VANESA. It is platform-independent, free-of-charge, and available at http://vanesa.sf.net.

COMMUNITY DETECTION IN SOCIAL NETWORKS EMPLOYING COMPONENT INDEPENDENCY

2009 ◽  
Vol 23 (17) ◽  
pp. 2089-2106 ◽  
Author(s):  
ZHONGMIN XIONG ◽  
WEI WANG
Keyword(s):  
Social Networks ◽  
Community Structure ◽  
Theoretical Analysis ◽  
Community Detection ◽  
Biological Networks ◽  
Real Data ◽  
Important Task ◽  
Underlying Structure ◽  
Data Sets ◽  
Detection Algorithms

Many networks, including social and biological networks, are naturally divided into communities. Community detection is an important task when discovering the underlying structure in networks. GN algorithm is one of the most influential detection algorithms based on betweenness scores of edges, but it is computationally costly, as all betweenness scores need to be repeatedly computed once an edge is removed. This paper presents an algorithm which is also based on betweenness scores but more than one edge can be removed when all betweenness scores have been computed. This method is motivated by the following considerations: many components, divided from networks, are independent of each other in their recalculation of betweenness scores and their split into smaller components. It is shown that this method is fast and effective through theoretical analysis and experiments with several real data sets, which have acted as test beds in many related works. Moreover, the version of this method with the minor adjustments allows for the discovery of the communities surrounding a given node without having to compute the full community structure of a graph.

HOW FAR AND HOW FAST CAN ONE MOVE ON NEUTRAL NETWORK?

2013 ◽  
Vol 12 (08) ◽  
pp. 1341010
Author(s):  
CHENGHANG DU ◽  
HAO CHEN ◽  
YUNJIE ZHAO ◽  
CHEN ZENG
Keyword(s):  
Cell Cycle ◽  
Systems Biology ◽  
Yeast Cell ◽  
Biological Networks ◽  
Neutral Network ◽  
Central Theme ◽  
Boolean Models ◽  
Structure And Dynamics ◽  
Conductive Component ◽  
Shed Light

A central theme in systems biology is to reveal the intricate relationship between structure and dynamics of many complex biological networks. Using Boolean models that describe yeast cell cycle process, we developed a unique logic-based theoretical framework to quantitatively determine the structure-dynamics mapping, also known as genotype–phenotype mapping. Moreover, under the dominant inhibition condition, we used a superposition property to show rigorously that the neutral network — the network of all possible structures that encode the same dynamics and are connected via single interaction mutations — forms one giant connected and conductive component. This may help shed light on the evolution landscape of biological networks based on the distance and speed a network can evolve on this neutral network.

Sign in / Sign up

Export Citation Format

Share Document