- High-Throughput Omics: The Application of Automated High-Throughput Methods and Systems for Solutions in Systems Biology

OMICS ◽  
2016 ◽  
pp. 194-223
Keyword(s):  
Systems Biology ◽  
High Throughput

Co-expressional conservation in virulence and stress related genes of three Gammaproteobacterial species: Escherichia coli, Salmonella enterica and Pseudomonas aeruginosa

2015 ◽  
Vol 11 (11) ◽  
pp. 3137-3148
Author(s):  
Nazanin Hosseinkhan ◽  
Peyman Zarrineh ◽  
Hassan Rokni-Zadeh ◽  
Mohammad Reza Ashouri ◽  
Ali Masoudi-Nejad
Keyword(s):  
Gene Expression ◽  
Escherichia Coli ◽  
Pseudomonas Aeruginosa ◽  
Systems Biology ◽  
Gene Expression Data ◽  
High Throughput ◽  
Expression Data ◽  
P Gene ◽  
Stress Related Genes ◽  
High Throughput Gene Expression

Gene co-expression analysis is one of the main aspects of systems biology that uses high-throughput gene expression data.

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.

Approaching Type 2 Diabetes Mellitus by Systems Biology

2009 ◽  
pp. 361-376
Author(s):  
Axel Rasche
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Systems Biology ◽  
Animal Models ◽  
High Throughput ◽  
Complex Disease ◽  
Current Knowledge ◽  
High Throughput Experimentation

We acquired new computational and experimental prospects to seek insight and cure for millions of afflicted persons with an ancient malady. Type 2 diabetes mellitus (T2DM) is a complex disease with a network of interactions among several tissues and a multifactorial pathogenesis. Research conducted in human and multiple animal models has strongly focused on genetics so far. High-throughput experimentation technics like microarrays provide new tools at hand to amend current knowledge. By integrating those results the aim is to develop a systems biology model assisting the diagnosis and treatment. Beside experimentation techniques and platforms or rather general concepts for a new term in biology and medicine this chapter joins the conceptions with a rather actual medical challenge. It outlines current results and envisions a possible alley to the comprehension of T2DM.

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.

MECHANICAL SYSTEMS BIOLOGY IN BONE: TOWARDS HIGH-THROUGHPUT QUANTIFICATION OF GENE EXPRESSION IN INDIVIDUAL OSTEOCYTES

2012 ◽  
Vol 45 ◽  
pp. S433
Author(s):  
Andreas J. Trüssel ◽  
Duncan J. Webster ◽  
Nathalie Cuerq ◽  
Felix Kurth ◽  
Petra Dittrich ◽  
...  
Keyword(s):  
Gene Expression ◽  
Systems Biology ◽  
High Throughput ◽  
Mechanical Systems

scholarly journals Causal Concepts Guiding Model Specification in Systems Biology

Disputatio ◽  
2017 ◽  
Vol 9 (47) ◽  
pp. 499-527
Author(s):  
Dana Matthiessen
Keyword(s):  
Systems Biology ◽  
High Throughput ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Causal Structure ◽  
Model Specification ◽  
Biology I ◽  
Adequate Representation ◽  
Gene Regulatory ◽  
High Throughput Experiments

Abstract In this paper I analyze the process by which modelers in systems biology arrive at an adequate representation of the biological structures thought to underlie data gathered from high-throughput experiments. Contrary to views that causal claims and explanations are rare in systems biology, I argue that in many studies of gene regulatory networks modelers aim at a representation of causal structure. In addressing modeling challenges, they draw on assumptions informed by theory and pragmatic considerations in a manner that is guided by an interventionist conception of causal structure. While doubts have been raised about the applicability of this notion of causality to complex biological systems, it is here seen to be an adequate guide to inquiry.

Uncovering complex molecular networks in host–pathogen interactions using systems biology

2019 ◽  
Vol 3 (4) ◽  
pp. 371-378
Author(s):  
Joshua M. Peters ◽  
Sydney L. Solomon ◽  
Christopher Y. Itoh ◽  
Bryan D. Bryson
Keyword(s):  
Systems Biology ◽  
High Throughput ◽  
Cell Biology ◽  
Molecular Networks ◽  
Host Pathogen Interactions ◽  
Exciting Field ◽  
Specific Host ◽  
Biomolecular Network ◽  
Host Pathogen ◽  
Analytical Frameworks

Abstract Interactions between pathogens and their hosts can induce complex changes in both host and pathogen states to privilege pathogen survival or host clearance of the pathogen. To determine the consequences of specific host–pathogen interactions, a variety of techniques in microbiology, cell biology, and immunology are available to researchers. Systems biology that enables unbiased measurements of transcriptomes, proteomes, and other biomolecules has become increasingly common in the study of host–pathogen interactions. These approaches can be used to generate novel hypotheses or to characterize the effects of particular perturbations across an entire biomolecular network. With proper experimental design and complementary data analysis tools, high-throughput omics techniques can provide novel insights into the mechanisms that underlie processes from phagocytosis to pathogen immune evasion. Here, we provide an overview of the suite of biochemical approaches for high-throughput analyses of host–pathogen interactions, analytical frameworks for understanding the resulting datasets, and a vision for the future of this exciting field.

scholarly journals Bridging the Microalga Mesoscale: High-throughput Systems Biology and Bioimaging for Guided Biodesign of Microalgae

2020 ◽  
Vol 26 (S2) ◽  
pp. 2534-2537
Author(s):  
Chuck Smallwood ◽  
Jian-Hua Chen ◽  
James Evans ◽  
Gerry McDermott
Keyword(s):  
Systems Biology ◽  
High Throughput
Sign in / Sign up

Export Citation Format

Share Document