Month: September 2022

DATA RICHNESS. “The transformation of biology by genomics from a relatively data-poor into a data-intensive field has motivated the development of novel computational, machine-learning and other quantitative methods for genomic analysis that attracted a large number of engineers, physicists, and mathematicians into biology.” https://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0509-8-S2-S1 View in LinkedIn

ALL ABOUT EXPRESSING THE BLUEPRINT. “Whereas the elements that make up the genetic definition of living organisms are encoded into the genome, it is the ensemble of expressed genes that are the actual manifestation of the biological system.” https://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0509-8-S2-S1 View in LinkedIn

AN INFERRED STRUCTURE. “While the measurement of expression genome-wide is only the first step in deriving system-level knowledge, it presents analytical challenges to this day. The main reason is that genomic experimental techniques measure individual parts of the system in parallel but cannot directly measure the system structure, which needs to be inferred.” https://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0509-8-S2-S1 View in LinkedIn

THE UNDERDETERMINATION PROBLEM. “This inference is complicated by the high variable to observation ratio of genomics, which causes the intrinsic and heavily underdetermined nature of the genomics/systems biology marriage. Experimental designs involving time courses and or perturbation can provide significantly more, but rarely enough, information on the underlying system structure. Computational biologists address this high underdetermination problem using strategies such as variable selection.” 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

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

“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

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

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

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