High-throughput fluorescence microscopy for systems biology

2006 ◽  
Vol 7 (9) ◽  
pp. 690-696 ◽  
Author(s):  
Rainer Pepperkok ◽  
Jan Ellenberg
Keyword(s):  
Systems Biology ◽  
Fluorescence Microscopy ◽  
High Throughput

A novel tool for detecting lysosomal membrane permeabilization by high-throughput fluorescence microscopy

2020 ◽  
Author(s):  
Karla Alvarez-Valadez ◽  
Allan Sauvat ◽  
Hélène Fohrer-Ting ◽  
Christophe Klein ◽  
Oliver Kepp ◽  
...  
Keyword(s):  
Fluorescence Microscopy ◽  
High Throughput ◽  
Membrane Permeabilization ◽  
Lysosomal Membrane ◽  
Lysosomal Membrane Permeabilization

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.

scholarly journals Micropilot: automation of fluorescence microscopy–based imaging for systems biology

2011 ◽  
Vol 8 (3) ◽  
pp. 246-249 ◽  
Author(s):  
Christian Conrad ◽  
Annelie Wünsche ◽  
Tze Heng Tan ◽  
Jutta Bulkescher ◽  
Frank Sieckmann ◽  
...  
Keyword(s):  
Systems Biology ◽  
Fluorescence Microscopy

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.

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