Investigating the common pathogenic mechanism for drug design between different strains of Candida albicans infection in OKF6/TERT-2 cells by comparing their genetic and epigenetic host/pathogen networks: Big data mining and computational systems biology approaches

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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Computational systems biology in the big data era

BMC Systems Biology ◽

10.1186/1752-0509-7-s2-s1 ◽

2013 ◽

Vol 7 (Suppl 2) ◽

pp. S1 ◽

Cited By ~ 12

Author(s):

Yong Wang ◽

Xiang-Sun Zhang ◽

Luonan Chen

Keyword(s):

Big Data ◽

Systems Biology ◽

Computational Systems Biology ◽

Computational Systems

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Computational Systems Biology Perspective on Tuberculosis in Big Data Era

Big Data Analytics in Bioinformatics and Healthcare - Advances in Bioinformatics and Biomedical Engineering ◽

10.4018/978-1-4666-6611-5.ch011 ◽

2015 ◽

pp. 240-264 ◽

Cited By ~ 1

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.

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Faculty Opinions recommendation of Computational systems biology and in silico modeling of the human microbiome.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.717697961.793153071 ◽

2012 ◽

Author(s):

Nicola Mulder

Keyword(s):

Systems Biology ◽

In Silico ◽

Human Microbiome ◽

Computational Systems Biology ◽

In Silico Modeling ◽

Computational Systems

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

Cancers ◽

10.3390/cancers13010035 ◽

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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