scholarly journals Insights from Systems Biology in Physiological Studies: Learning from Context

2017 ◽  
Vol 42 (3) ◽  
pp. 939-951
Author(s):  
Pedro Henrique Imenez Silva ◽  
Diogo Melo ◽  
Pedro Omori Ribeiro  de Mendonça
Keyword(s):  
Systems Biology ◽  
High Throughput ◽  
Biological System ◽  
Biological Systems ◽  
Experimental Studies ◽  
Holistic View ◽  
Physiological Studies ◽  
High Throughput Experiments

Systems biology presents an integrated view of biological systems, focusing on the relations between elements, whether functional or evolutionary, and providing a rich framework for the comprehension of life. At the same time, many low-throughput experimental studies are performed without influence from this integrated view, whilst high-throughput experiments use low-throughput results in their validation and interpretation. We propose an inversion in this logic, and ask which benefits could be obtained from a holistic view coming from high-throughput studies―and systems biology in particular―in interpreting and designing low-throughput experiments. By exploring some key examples from the renal and adrenal physiology, we try to show that network and modularity theory, along with observed patterns of association between elements in a biological system, can have profound effects on our ability to draw meaningful conclusions from experiments.

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.

scholarly journals Mathematical and Computational Modeling in Complex Biological Systems

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Zhiwei Ji ◽  
Ke Yan ◽  
Wenyang Li ◽  
Haigen Hu ◽  
Xiaoliang Zhu
Keyword(s):  
Systems Biology ◽  
Computational Modeling ◽  
Cancer Progression ◽  
High Throughput ◽  
Computational Models ◽  
Molecular Mechanisms ◽  
Biological Process ◽  
Biological Systems ◽  
Complex Diseases ◽  
Modeling Approaches

The biological process and molecular functions involved in the cancer progression remain difficult to understand for biologists and clinical doctors. Recent developments in high-throughput technologies urge the systems biology to achieve more precise models for complex diseases. Computational and mathematical models are gradually being used to help us understand the omics data produced by high-throughput experimental techniques. The use of computational models in systems biology allows us to explore the pathogenesis of complex diseases, improve our understanding of the latent molecular mechanisms, and promote treatment strategy optimization and new drug discovery. Currently, it is urgent to bridge the gap between the developments of high-throughput technologies and systemic modeling of the biological process in cancer research. In this review, we firstly studied several typical mathematical modeling approaches of biological systems in different scales and deeply analyzed their characteristics, advantages, applications, and limitations. Next, three potential research directions in systems modeling were summarized. To conclude, this review provides an update of important solutions using computational modeling approaches in systems biology.

scholarly journals Modelling the Virtual Physiological Human

2011 ◽  
Vol 33 (1) ◽  
pp. 50-51
Author(s):  
Clare Sansom
Keyword(s):  
Systems Biology ◽  
Biological System ◽  
Experimental Analysis ◽  
Biological Systems ◽  
Computational Modelling ◽  
Nobel Laureate ◽  
Philosophy Of Biology ◽  
Bottom Up ◽  
Genome Projects

Systems biology – the theme of this issue of The Biochemist – can be thought of as more a philosophy of biology than a distinct set of techniques. It arose out of, but is distinct from, the genome projects and associated initiatives. The ‘catalogues’ of genes and proteins produced in recent years have generated enormous advances, but they do not tell the whole story. Nobel Laureate Sydney Brenner said in 2001 that “I know one approach that will fail, which is to start with genes, make proteins from them and try to build things bottom-up”1. In contrast with the reductionism of genomics, systems biology is ‘integrative’: as another Nobel Laureate, David Baltimore, writes, “it seeks to understand the integration of the pieces to form biological systems”2. Thus a typical systems biology study will involve both experimental analysis and computational modelling of a biological system at a number of levels: theoretically, at least, including the molecule, the pathway, the organelle, the cell, the tissue or organ, and the organism.

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 Discovering collectively informative descriptors from high-throughput experiments

2009 ◽  
Vol 10 (1) ◽  
Author(s):  
Clark D Jeffries ◽  
William O Ward ◽  
Diana O Perkins ◽  
Fred A Wright
Keyword(s):  
High Throughput ◽  
High Throughput Experiments

scholarly journals Modeling Biological Systems Using Crowdsourcing

2018 ◽  
Vol 43 (3) ◽  
pp. 219-243 ◽  
Author(s):  
Szymon Wasik
Keyword(s):  
Systems Biology ◽  
Serious Games ◽  
Domain Knowledge ◽  
Biological Systems ◽  
Effective Technique ◽  
Design Models ◽  
Modeling Systems ◽  
Vast Network

Abstract Crowdsourcing is a very effective technique for outsourcing work to a vast network usually comprising anonymous people. In this study, we review the application of crowdsourcing to modeling systems originating from systems biology. We consider a variety of verified approaches, including well-known projects such as EyeWire, FoldIt, and DREAM Challenges, as well as novel projects conducted at the European Center for Bioinformatics and Genomics. The latter projects utilized crowdsourced serious games to design models of dynamic biological systems, and it was demonstrated that these models could be used successfully to involve players without domain knowledge. We conclude the review of these systems by providing 10 guidelines to facilitate the efficient use of crowdsourcing.

INFERRING PROTEIN-PROTEIN INTERACTIONS FROM MESSENGER RNA EXPRESSION PROFILES WITH SVM

2005 ◽  
Vol 13 (03) ◽  
pp. 287-298 ◽  
Author(s):  
JUN CAI ◽  
YING HUANG ◽  
LIANG JI ◽  
YANDA LI
Keyword(s):  
High Throughput ◽  
Protein Interactions ◽  
Messenger Rna ◽  
Expression Profiles ◽  
Support Vector ◽  
Svm Classifier ◽  
Good Prediction ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
High Throughput Experiments

In post-genomic biology, researchers in the field of proteome focus their attention on the networks of protein interactions that control the lives of cells and organisms. Protein-protein interactions play a useful role in dynamic cellular machinery. In this paper, we developed a method to infer protein-protein interactions based on the theory of support vector machine (SVM). For a given pair of proteins, a new strategy of calculating cross-correlation function of mRNA expression profiles was used to encode SVM vectors. We compared the performance with other methods of inferring protein-protein interaction. Results suggested that, through five-fold cross validation, our SVM model achieved a good prediction. It enables us to show that expression profiles in transcription level can be used to distinguish physical or functional interactions of proteins as well as sequence contents. Lastly, we applied our SVM classifier to evaluate data quality of interaction data sets from four high-throughput experiments. The results show that high-throughput experiments sacrifice some accuracy in determination of interactions because of limitation of experiment technologies.

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