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Another brilliant must-see by artist Rasmus Hansen of crustacean. https://lnkd.in/dQhXfvt View in LinkedIn

EXTREME NODE. “The combinatorial nature of gene regulation points to another extreme, where we are interested in identifying relatively small cohorts of genomic regions that show similar coordinated changes of chromatin marks and transcription factor binding across many data sets and multiple cell types.” https://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0509-8-S2-S1 View in LinkedIn

TWO SYSTEMS. “The systems biology of gene expression is frequently understood as a problem of gene regulatory network inference, where gene networks capture how the expression profile of individual genes interacts with each other.” https://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0509-8-S2-S1 View in LinkedIn

MAJOR BENCHMARKS. “When considering the interplay between systems biology and genomics, two major paradigms stand out: one is the use of gene expression measurements to obtain the structure of the system and infer Gene Regulatory Networks, while the other is the leveraging of system properties to interpret observed gene expression patterns using pathway enrichment methods.” https://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0509-8-S2-S1 View in LinkedIn

BIOLOGY GETS COMPLEX. “Approaches that come closer to a systems driven analysis of differential expression have used gene network data to guide the multivariate analysis under the assumption that genes for which an interaction exist are correlated in their differential expression states or have taken an Empirical Bayes approach by modeling networks as a Markov random field (MRF) to identify genes and sub-networks that are related to diseases.” https://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0509-8-S2-S1 View in LinkedIn

“A FIRST STEP in this direction is the application of multivariate methodologies to transcriptome analysis that exploits the covariance structure of the expression data matrix to infer patterns of gene expression and select genes for their relevance in those patterns.” https://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0509-8-S2-S1 View in LinkedIn

OBSCURED GENE SYSTEMS. “While these methods incorporate parameterizations to account for the high dimensional nature of genomics data, such as pooled variance estimates or multiple testing correction, they completely ignore the interactions of genes as parts of large-scale biological pathways and system.” https://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0509-8-S2-S1 View in LinkedIn

OCCLUDED MOLECULAR SYSTEMS. “There are still methodological and conceptual limitations that must be overcome to bridge the gap between simple gene expression analysis and the inference of molecular systems. For example, most of the popular differential gene expression methods that are used for variable selection provide single gene-based assessments of differential expression.” https://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0509-8-S2-S1 View in LinkedIn

SPACE AND TIME, are the two variables that dominate physics. So with biology: dimensional scales and time scales are of considerable importance. The quantum scale is ruled by different forces than the classical scale. Time for a bacteria is on a different scale than for a human. View in LinkedIn

BIOLOGICAL MEANING. “Functional enrichment analysis methods (also referred to as pathway or gene set enrichment) are methodologies that allow us to analyze gene expression data for the biological meaning of particular expression patterns in order to gain additional insight into the actual biology of the system.” https://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0509-8-S2-S1 View in LinkedIn