Introduction to Metabonomics in Systems Biology Research

2014 ◽  
pp. 1-24
Author(s):  
François-Pierre Martin ◽  
Sunil Kochhar
Keyword(s):  
Systems Biology ◽  
Biology Research

scholarly journals Partition Quantitative Assessment (PQA): A Quantitative Methodology to Assess the Embedded Noise in Clustered Omics and Systems Biology Data

2021 ◽  
Vol 11 (13) ◽  
pp. 5999
Author(s):  
Diego A. Camacho-Hernández ◽  
Victor E. Nieto-Caballero ◽  
José E. León-Burguete ◽  
Julio A. Freyre-González
Keyword(s):  
Systems Biology ◽  
Clustering Analysis ◽  
Quantitative Assessment ◽  
Clustering Algorithm ◽  
Statistical Evaluation ◽  
Quantitative Methodology ◽  
Typical Problem ◽  
Statistical Validation ◽  
Common Features ◽  
Biology Research

Identifying groups that share common features among datasets through clustering analysis is a typical problem in many fields of science, particularly in post-omics and systems biology research. In respect of this, quantifying how a measure can cluster or organize intrinsic groups is important since currently there is no statistical evaluation of how ordered is, or how much noise is embedded in the resulting clustered vector. Much of the literature focuses on how well the clustering algorithm orders the data, with several measures regarding external and internal statistical validation; but no score has been developed to quantify statistically the noise in an arranged vector posterior to a clustering algorithm, i.e., how much of the clustering is due to randomness. Here, we present a quantitative methodology, based on autocorrelation, in order to assess this problem.

scholarly journals PathCase-SB: integrating data sources and providing tools for systems biology research

2012 ◽  
Vol 6 (1) ◽  
pp. 67 ◽  
Author(s):  
Sarp A Coskun ◽  
Xinjian Qi ◽  
Ali Cakmak ◽  
En Cheng ◽  
A Cicek ◽  
...  
Keyword(s):  
Systems Biology ◽  
Data Sources ◽  
Biology Research

The Biodegradation Network, a New Scenario for Computational Systems Biology Research

2005 ◽  
pp. 252-256
Author(s):  
Florencio Pazos ◽  
David Guijas ◽  
Manuel J. Gomez ◽  
Almudena Trigo ◽  
Victor de Lorenzo ◽  
...  
Keyword(s):  
Systems Biology ◽  
Computational Systems Biology ◽  
Computational Systems ◽  
Biology Research

scholarly journals Bioelectrical understanding and engineering of cell biology

2020 ◽  
Vol 17 (166) ◽  
pp. 20200013 ◽  
Author(s):  
Zoe Schofield ◽  
Gabriel N. Meloni ◽  
Peter Tran ◽  
Christian Zerfass ◽  
Giovanni Sena ◽  
...  
Keyword(s):  
Systems Biology ◽  
Cell Biology ◽  
Genetic Basis ◽  
Control Cell ◽  
Status Quo ◽  
Cell Behaviour ◽  
Cellular Processes ◽  
Mechanical Responses ◽  
Predictive Understanding ◽  
Biology Research

The last five decades of molecular and systems biology research have provided unprecedented insights into the molecular and genetic basis of many cellular processes. Despite these insights, however, it is arguable that there is still only limited predictive understanding of cell behaviours. In particular, the basis of heterogeneity in single-cell behaviour and the initiation of many different metabolic, transcriptional or mechanical responses to environmental stimuli remain largely unexplained. To go beyond the status quo , the understanding of cell behaviours emerging from molecular genetics must be complemented with physical and physiological ones, focusing on the intracellular and extracellular conditions within and around cells. Here, we argue that such a combination of genetics, physics and physiology can be grounded on a bioelectrical conceptualization of cells. We motivate the reasoning behind such a proposal and describe examples where a bioelectrical view has been shown to, or can, provide predictive biological understanding. In addition, we discuss how this view opens up novel ways to control cell behaviours by electrical and electrochemical means, setting the stage for the emergence of bioelectrical engineering.

scholarly journals Systems biology research at BGRS-2018

2019 ◽  
Vol 13 (S1) ◽  
Author(s):  
Yuriy L. Orlov ◽  
Ralf Hofestädt ◽  
Ancha V. Baranova
Keyword(s):  
Systems Biology ◽  
Biology Research

scholarly journals PhosphoPep—a phosphoproteome resource for systems biology research in Drosophila Kc167 cells

2007 ◽  
Vol 3 (1) ◽  
pp. 139 ◽  
Author(s):  
Bernd Bodenmiller ◽  
Johan Malmstrom ◽  
Bertran Gerrits ◽  
David Campbell ◽  
Henry Lam ◽  
...  
Keyword(s):  
Systems Biology ◽  
Biology Research
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