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Title
Abstract
Introduction: From Pathways to Networks
Network Analysis
Network Analysis Tools
Network Topology Analysis
Network Analysis for Systems Biomedicine
Network Analysis of Disease
Conclusions
References
All Topics
Biology
Systems Biology

On Different Aspects of Network Analysis in Systems Biology

Profile image of Hassan AhmedHassan Ahmed

2013, Systems Biology

https://doi.org/10.1007/978-94-007-6803-1_6
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Abstract

Network analysis is an essential component of systems biology approaches toward understanding the molecular and cellular interactions underlying biological systems functionalities and their perturbations in disease. Regulatory and signalling pathways involve DNA, RNA, proteins and metabolites as key elements to coordinate most aspects of cellular functioning. Cellular processes depend on the structure and dynamics of gene regulatory networks and can be studied by employing a network representation of molecular interactions. This chapter describes several types of biological networks, how combination of different analytic approaches can be used to study diseases, and provides a list of selected tools for network visualization and analysis. It also introduces proteinprotein interaction networks, gene regulatory networks, signalling networks and metabolic networks to illustrate concepts underlying network representation of cellular processes and molecular interactions. It finally discusses how the level of An erratum to this chapter is available at 10.

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The search for disease-specific biomarkers for diagnosis, illness monitoring, therapy evaluation, and, prognosis prediction is one of the major challenges in biomedical research. It has long been that diseases are rarely caused by abnormality in a single protein, gene or cell. But by disorder of different processes manifested by intracellular network of interactions between the molecular components in such biological

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Computational Network Approaches and Their Applications for Complex Diseases
Tiratha Singh

2017

Network biology has been widely used for the interaction studies and analysis in modern era. Studies associated with biological networks, their modeling, analysis, and visualization are imperative to the biological world. The advancements in network biology have helped us better understand the biomolecular complexities which in earlier times were difficult to study in vivo. High-throughput technologies have revolutionized the genomic-sequencing procedures for the generation of tremendous data that need to be analyzed and interpreted rigorously. However it has still been difficult to construe biological networks completely due to the complexity of the interactions that exist between its components. Great efforts have been made to disclose the maximum possible interactions that are significant to maintain the potential mechanisms with the help of network biology. With improvement in the analysis process, network biology has thought to be playing a key role in understanding the complex...

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Biological network analyses: computational genomics and systems approaches
Christina Chan

Molecular Simulation, 2006

The complex responses of cells to stimuli are the aggregate of alterations at the genetic, protein, metabolic and cellular levels. The immense quantities of data now available from high-throughput genomic, proteomic and metabolomic sources require specialized analytical approaches. The integration of such data for the computational elucidation and analysis of cellular pathways and networks is an area of considerable current interest. As the quantity of available data continues to increase, strategies to extract the useful information from the data will continue to provide answers to important biological questions. Chemical engineers are actively involved in this field that has taken on the global title of "Systems Biology". These research groups have made considerable impact from both the computational and experimental standpoints. This commentary describes some of the recent contributions of chemical engineering researchers to the computational analysis of highthroughput, multi-source data as described in recent publications and presentations from the 2005 AIChE Annual Meeting.

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