Perti nets and dynamic trees for modeling systems biology

2005 ◽  
Vol 20 (4) ◽  
pp. 393
Author(s):  
R. Gandlin ◽  
S. Ta'asan
2018 ◽  
Vol 43 (3) ◽  
pp. 219-243 ◽  
Author(s):  
Szymon Wasik

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.


Author(s):  
Amit Chattopadhyay

This chapter reviews the principles of systems biology and their application through computational methods (bioinformatics, computational biomodeling, genomics, proteomics, oral human microbiome, molecular modeling, systems biology, protein structure prediction, structural genomics, computational biochemistry and computational biophysics methods and projects) that have been applied to oral diseases research. The emphasis of the chapter is on concepts from molecular biology, genetics, and traditional pathology to provide new insights into oral diseases, and the associated technologies to provide new diagnostic, therapeutic and prognostic information. Another goal of the manuscript will be to serve as a central reference to access of information about systems biology resources for research into oral diseases.


Entropy ◽  
2021 ◽  
Vol 24 (1) ◽  
pp. 66
Author(s):  
Spyridon A. Koutroufinis

Mathematical models applied in contemporary theoretical and systems biology are based on some implicit ontological assumptions about the nature of organisms. This article aims to show that real organisms reveal a logic of internal causality transcending the tacit logic of biological modeling. Systems biology has focused on models consisting of static systems of differential equations operating with fixed control parameters that are measured or fitted to experimental data. However, the structure of real organisms is a highly dynamic process, the internal causality of which can only be captured by continuously changing systems of equations. In addition, in real physiological settings kinetic parameters can vary by orders of magnitude, i.e., organisms vary the value of internal quantities that in models are represented by fixed control parameters. Both the plasticity of organisms and the state dependence of kinetic parameters adds indeterminacy to the picture and asks for a new statistical perspective. This requirement could be met by the arising Biological Statistical Mechanics project, which promises to do more justice to the nature of real organisms than contemporary modeling. This article concludes that Biological Statistical Mechanics allows for a wider range of organismic ontologies than does the tacitly followed ontology of contemporary theoretical and systems biology, which are implicitly and explicitly based on systems theory.


2019 ◽  
Vol 42 ◽  
Author(s):  
J. Alfredo Blakeley-Ruiz ◽  
Carlee S. McClintock ◽  
Ralph Lydic ◽  
Helen A. Baghdoyan ◽  
James J. Choo ◽  
...  

Abstract The Hooks et al. review of microbiota-gut-brain (MGB) literature provides a constructive criticism of the general approaches encompassing MGB research. This commentary extends their review by: (a) highlighting capabilities of advanced systems-biology “-omics” techniques for microbiome research and (b) recommending that combining these high-resolution techniques with intervention-based experimental design may be the path forward for future MGB research.


Author(s):  
Bernhard O. Palsson ◽  
Marc Abrams
Keyword(s):  

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