Stochastic changes in gene expression promote chaotic dysregulation of homeostasis in clonal breast tumors

Stochastic gene expression in auxin signaling in the floral meristem of Arabidopsis thaliana

Cells display striking stochasticity in gene expression, which plays an important role in development, disease, and regeneration. Previous studies have found stochastic gene expression in bacteria, yeast, and from constitutive promoters in Arabidopsis. However, most promoters are non-constitutive. Stochastic gene expression from non-constitutive promoters in a multicellular organism, especially those with key developmental roles, remains largely uncharacterized. Here, we report stochastic expression of auxin responsive genes in the Arabidopsis floral meristem, using promoter reporters of DR5, ARABIDOPSIS HISTIDINE PHOSPHOTRANSFER PROTEIN6 (AHP6), and DNA BINDING WITH ONE FINGER5.8 (DOF5.8). We find highly variable DR5 expression patterns among younger meristems. Such variability is largely attributed to stochastic expression of DR5, mainly influenced by cell-intrinsic molecular noise. Expression of AHP6 and DOF5.8 is also noisy, although their noise is lower and has distinct spatiotemporal patterns unlike DR5. Finally, we propose spatial averaging as a mechanism that buffers cellular gene expression noise, allowing the formation of robust global expression patterns. Our study reveals stochastic gene expression downstream of auxin signaling, a key developmental player. Thus, stochastic gene expression from non-constitutive promoters, including those involved in hormone signaling, is an ordinary part of multicellular life.

Biological Computation and Compatibility Search in the Possibility Space as the Mechanism of Complexity Increase During Progressive Evolution

The idea of computational processes, which take place in nature, for example, DNA computation, is discussed in the literature. DNA computation that is going on in the immunoglobulin locus of vertebrates shows how the computations in the biological possibility space could operate during evolution. We suggest that the origin of evolutionarily novel genes and genome evolution constitute the original intrinsic computation of the information about new structures in the space of unrealized biological possibilities. Due to DNA computation, the information about future structures is generated and stored in DNA as genetic information. In evolving ontogenies, search algorithms are necessary, which can search for information about evolutionary innovations and morphological novelties. We believe that such algorithms include stochastic gene expression, gene competition, and compatibility search at different levels of structural organization. We formulate the increase in complexity principle in terms of biological computation and hypothesize the possibility of in silico computing of future functions of evolutionarily novel genes.

Exploiting the DepMap cancer dependency data using the depmap R package

The `depmap` package facilitates access in the R environment to the data from the DepMap project, a multi-year collaborative effort by the Broad Institute and Wellcome Sanger Institute, mapping genetic and chemical dependencies and other molecular biological measurements of over 1700 cancer cell lines. The 'depmap' package formats this data to simply the use of popular R data analysis and visualizing tools such as 'dplyr' and 'ggplot2'. In addition, the 'depmap' package utilizes 'ExperimentHub', storing versions of the DepMap data accessible from the Cloud, which may be selectively downloaded, providing a reproducible research framework to support exploiting this data. This paper describes a workflow demonstrating how to access and visualize the DepMap data in R using this package.