The Advent of Mass Spectrometry-Based Proteomics in Systems Biology Research

Author(s):  
P. Blattmann ◽  
R. Aebersold
2021 ◽  
Vol 11 (13) ◽  
pp. 5999
Author(s):  
Diego A. Camacho-Hernández ◽  
Victor E. Nieto-Caballero ◽  
José E. León-Burguete ◽  
Julio A. Freyre-González

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.


2021 ◽  
Vol 133 (23) ◽  
Author(s):  
Camille Lombard‐Banek ◽  
Jie Li ◽  
Erika P. Portero ◽  
Rosemary M. Onjiko ◽  
Chase D. Singer ◽  
...  

2012 ◽  
Vol 6 (1) ◽  
pp. 67 ◽  
Author(s):  
Sarp A Coskun ◽  
Xinjian Qi ◽  
Ali Cakmak ◽  
En Cheng ◽  
A Cicek ◽  
...  

Author(s):  
Florencio Pazos ◽  
David Guijas ◽  
Manuel J. Gomez ◽  
Almudena Trigo ◽  
Victor de Lorenzo ◽  
...  

2013 ◽  
pp. 1494-1521
Author(s):  
Jose M. Garcia-Manteiga

Metabolomics represents the new ‘omics’ approach of the functional genomics era. It consists in the identification and quantification of all small molecules, namely metabolites, in a given biological system. While metabolomics refers to the analysis of any possible biological system, metabonomics is specifically applied to disease and physiopathological situations. The data collected within these approaches is highly integrative of the other higher levels and is hence amenable to be explored with a top-down systems biology point of view. The aim of this chapter is to give a global view of the state of the art in metabolomics describing the two analytical techniques usually used to give rise to this kind of data, nuclear magnetic resonance, NMR, and mass spectrometry. In addition, the author will focus on the different data analysis tools that can be applied to such studies to extract information with special interest at the attempts to integrate metabolomics with other ‘omics’ approaches and its relevance in systems biology modeling.


Author(s):  
Jose M. Garcia-Manteiga

Metabolomics represents the new ‘omics’ approach of the functional genomics era. It consists in the identification and quantification of all small molecules, namely metabolites, in a given biological system. While metabolomics refers to the analysis of any possible biological system, metabonomics is specifically applied to disease and physiopathological situations. The data collected within these approaches is highly integrative of the other higher levels and is hence amenable to be explored with a top-down systems biology point of view. The aim of this chapter is to give a global view of the state of the art in metabolomics describing the two analytical techniques usually used to give rise to this kind of data, nuclear magnetic resonance, NMR, and mass spectrometry. In addition, the author will focus on the different data analysis tools that can be applied to such studies to extract information with special interest at the attempts to integrate metabolomics with other ‘omics’ approaches and its relevance in systems biology modeling.


2020 ◽  
Vol 17 (166) ◽  
pp. 20200013 ◽  
Author(s):  
Zoe Schofield ◽  
Gabriel N. Meloni ◽  
Peter Tran ◽  
Christian Zerfass ◽  
Giovanni Sena ◽  
...  

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.


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