Computational Systems Biology Perspective on Tuberculosis in Big Data Era

2015 ◽  
pp. 240-264 ◽  
Author(s):  
Amandeep Kaur Kahlon ◽  
Ashok Sharma
Keyword(s):  
Big Data ◽  
Systems Biology ◽  
Data Management ◽  
High Throughput ◽  
Predictive Models ◽  
System Biology ◽  
Prediction Models ◽  
Computational Systems Biology ◽  
Future Directions ◽  
Computational Systems

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.

Computational Systems Biology Perspective on Tuberculosis in Big Data Era

Web Services ◽  
2019 ◽  
pp. 2230-2254
Author(s):  
Amandeep Kaur Kahlon ◽  
Ashok Sharma
Keyword(s):  
Big Data ◽  
Systems Biology ◽  
Data Management ◽  
High Throughput ◽  
Predictive Models ◽  
System Biology ◽  
Prediction Models ◽  
Computational Systems Biology ◽  
Future Directions ◽  
Computational Systems

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.

scholarly journals Towards the integration of computational systems biology and high-throughput data: supporting differential analysis of microarray gene expression data

2008 ◽  
Vol 5 (1) ◽  
pp. 57-71 ◽  
Author(s):  
Nicola Segata ◽  
Enrico Blanzieri ◽  
Corrado Priami
Keyword(s):  
Gene Expression ◽  
Systems Biology ◽  
High Throughput ◽  
System Level ◽  
Expression Data ◽  
Differential Analysis ◽  
Computational Systems Biology ◽  
Microarray Gene Expression ◽  
High Throughput Data ◽  
Computational Systems

Summary The paradigmatic shift occurred in biology that led first to high-throughput experimental techniques and later to computational systems biology must be applied also to the analysis paradigm of the relation between local models and data to obtain an effective prediction tool. In this work we introduce a unifying notational framework for systems biology models and high-throughput data in order to allow new integrations on the systemic scale like the use of in silico predictions to support the mining of gene expression datasets. Using the framework, we propose two applications concerning the use of system level models to support the differential analysis of microarray expression data. We tested the potentialities of the approach with a specific microarray experiment on the phosphate system in Saccharomyces cerevisiae and a computational model of the PHO pathway that supports the systems biology concepts.

scholarly journals Computational systems biology in the big data era

2013 ◽  
Vol 7 (Suppl 2) ◽  
pp. S1 ◽  
Author(s):  
Yong Wang ◽  
Xiang-Sun Zhang ◽  
Luonan Chen
Keyword(s):  
Big Data ◽  
Systems Biology ◽  
Computational Systems Biology ◽  
Computational Systems

scholarly journals Metabolic Reprogramming of Fibroblasts as Therapeutic Target in Rheumatoid Arthritis and Cancer: Deciphering Key Mechanisms Using Computational Systems Biology Approaches

Cancers ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 35
Author(s):  
Sahar Aghakhani ◽  
Naouel Zerrouk ◽  
Anna Niarakis
Keyword(s):  
Rheumatoid Arthritis ◽  
Extracellular Matrix ◽  
Systems Biology ◽  
Healing Process ◽  
Metabolic Reprogramming ◽  
Critical Event ◽  
Wound Healing Process ◽  
Computational Systems Biology ◽  
Structural Framework ◽  
Computational Systems

Fibroblasts, the most abundant cells in the connective tissue, are key modulators of the extracellular matrix (ECM) composition. These spindle-shaped cells are capable of synthesizing various extracellular matrix proteins and collagen. They also provide the structural framework (stroma) for tissues and play a pivotal role in the wound healing process. While they are maintainers of the ECM turnover and regulate several physiological processes, they can also undergo transformations responding to certain stimuli and display aggressive phenotypes that contribute to disease pathophysiology. In this review, we focus on the metabolic pathways of glucose and highlight metabolic reprogramming as a critical event that contributes to the transition of fibroblasts from quiescent to activated and aggressive cells. We also cover the emerging evidence that allows us to draw parallels between fibroblasts in autoimmune disorders and more specifically in rheumatoid arthritis and cancer. We link the metabolic changes of fibroblasts to the toxic environment created by the disease condition and discuss how targeting of metabolic reprogramming could be employed in the treatment of such diseases. Lastly, we discuss Systems Biology approaches, and more specifically, computational modeling, as a means to elucidate pathogenetic mechanisms and accelerate the identification of novel therapeutic targets.

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