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Computational Systems Biology Perspective on Tuberculosis in Big Data Era: Challenges and Future Goals
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Author(s): Amandeep Kaur Kahlon (CSIR-Central Institute of Medicinal and Aromatic Plants (CIMAP), India)and Ashok Sharma (CSIR-Central Institute of Medicinal and Aromatic Plants (CIMAP), India)
Copyright: 2015
Pages: 25
Source title:
Big Data Analytics in Bioinformatics and Healthcare
Source Author(s)/Editor(s): Baoying Wang (Waynesburg University, USA), Ruowang Li (Pennsylvania State University, USA)and William Perrizo (North Dakota State University, USA)
DOI: 10.4018/978-1-4666-6611-5.ch011
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Abstract
The major concern in this chapter is to understand the need of system biology in prediction models in studying tuberculosis infection in the big data era. The overall complexity of biological phenomenon, such as biochemical, biophysical, and other molecular processes, within pathogen as well as their interaction with host is studied through system biology approaches. First, consideration is given to the necessity of prediction models integrating system biology approaches and later on for their replacement and refinement using high throughput data. Various ongoing projects, consortium, databases, and research groups involved in tuberculosis eradication are also discussed. This chapter provides a brief account of TB predictive models and their importance in system biology to study tuberculosis and host-pathogen interactions. This chapter also addresses big data resources and applications, data management, limitations, challenges, solutions, and future directions.
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