Discovery of principles of nature from mathematical modeling of DNA microarray data

Alter, Orly

doi:10.1073/pnas.0607650103

Cited by 21 publications

(25 citation statements)

References 27 publications

(26 reference statements)

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“…The gene selection method and the thresholding approach were previously used on a different case [19]. The genes with highest absolute scores on the discriminant component are the ones whose relative concentration is mostly influenced by the "regulation pattern" correspondent to the component itself, consequently the study of the mutual correlations among these genes carried out separately in control and patient data set is expected to give important hints on the pathological mechanisms of the studied disease [20].…”

Section: Discussionmentioning

confidence: 99%

Gene Regulation Networks in Early Phase of Duchenne Muscular Dystrophy

Bernardini

Censi

Lattanzi

et al. 2013

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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The aim of this study was to analyze previously published gene expression data of skeletal muscle biopsies of Duchenne muscular dystrophy (DMD) patients and controls (gene expression omnibus database, accession #GSE6011) using systems biology approaches. We applied an unsupervised method to discriminate patient and control populations, based on principal component analysis, using the gene expressions as units and patients as variables. The genes having the highest absolute scores in the discrimination between the groups, were then analyzed in terms of gene expression networks, on the basis of their mutual correlation in the two groups. The correlation network structures suggest two different modes of gene regulation in the two groups, reminiscent of important aspects of DMD pathogenesis.

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Section: Discussionmentioning

confidence: 99%

Gene Regulation Networks in Early Phase of Duchenne Muscular Dystrophy

Bernardini

Censi

Lattanzi

et al. 2013

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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“…Contributions from Physics can be expected to play a decisive role along the way. 2 This example goes back to a review by Orly Alter [149]; see also [150].…”

Section: Discussionmentioning

confidence: 99%

The Physics behind Systems Biology

Radde

Hütt

2016

EPJ Nonlinear Biomed Phys

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Systems Biology is a young and rapidly evolving research field, which combines experimental techniques and mathematical modeling in order to achieve a mechanistic understanding of processes underlying the regulation and evolution of living systems. Systems Biology is often associated with an Engineering approach: The purpose is to formulate a data-rich, detailed simulation model that allows to perform numerical ('in silico') experiments and then draw conclusions about the biological system. While methods from Engineering may be an appropriate approach to extending the scope of biological investigations to experimentally inaccessible realms and to supporting data-rich experimental work, it may not be the best strategy in a search for design principles of biological systems and the fundamental laws underlying Biology. Physics has a long tradition of characterizing and understanding emergent collective behaviors in systems of interacting units and searching for universal laws. Therefore, it is natural that many concepts used in Systems Biology have their roots in Physics. With an emphasis on Theoretical Physics, we will here review the 'Physics core' of Systems Biology, show how some success stories in Systems Biology can be traced back to concepts developed in Physics, and discuss how Systems Biology can further benefit from its Theoretical Physics foundation.

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“…Only a relatively minor portion of the efforts have been devoted to the second drawback which, in our opinion, could be the main way to put biology into an ''emergentist'' track (Alter et al 2000;Alter 2006;Holter et al 2000). In the case of Fig.…”

Section: Genetics the Systems Biology Challengementioning

confidence: 98%

Biological uncertainty

Zbilut

Giuliani

2008

Theory Biosci.

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Heisenberg's uncertainty relation has inspired speculations in a variety of scientific fields. Most of these speculations have wandered significantly far from the original formulation; yet, they may have been useful for a critical examination of methodological issues. As molecular genetics and its complexities evolve amid a backdrop of technological innovation, new "uncertainties" may have emerged. We present some of these uncertainties not as impediments, but as challenges to be recognized and managed.

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Discovery of principles of nature from mathematical modeling of DNA microarray data

Cited by 21 publications

References 27 publications

Gene Regulation Networks in Early Phase of Duchenne Muscular Dystrophy

Gene Regulation Networks in Early Phase of Duchenne Muscular Dystrophy

The Physics behind Systems Biology

Biological uncertainty

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