Shaping development by stochasticity and dynamics in gene regulation

Abstract

Animal development is orchestrated by spatio-temporal gene expression programmes that drive precise lineage commitment, proliferation and migration events at the single-cell level, collectively leading to large-scale morphological change and functional specification in the whole organism. Efforts over decades have uncovered two ‘seemingly contradictory’ mechanisms in gene regulation governing these intricate processes: (i) stochasticity at individual gene regulatory steps in single cells and (ii) highly coordinated gene expression dynamics in the embryo. Here we discuss how these two layers of regulation arise from the molecular and the systems level, and how they might interplay to determine cell fate and to control the complex body plan. We also review recent technological advancements that enable quantitative analysis of gene regulation dynamics at single-cell, single-molecule resolution. These approaches outline next-generation experiments to decipher general principles bridging gaps between molecular dynamics in single cells and robust gene regulations in the embryo.

Assessing phenotype order in molecular data

“…In contrast to the developmental processes, of which the order of stages is tightly regulated by a genetic program 5,29 , ageing is influenced by multiple factors 30 . Nevertheless we resulted in comparable results for the dataset that measures age related gene expression changes.…”

The indispensable contribution of s38 protein to ovarian-eggshell morphogenesis in Drosophila melanogaster

“…High levels of gene expression and/or efficient operation of negative feedback loops (e.g. a protein encoded by a gene negatively controls its own transcription) can reduce the effects derived from stochastic gene expression 47 , 48 . Hence, the presumable ability of s38 protein to provide developmentally regulated (stage 14) signals for its own gene repression (negative feedback loop) and the strong s38 -gene expression being mechanistically induced during early choriogenesis (e.g.…”

Developmental structuring of phenotypic variation: A case study with a cellular automata model of ontogeny

“…Many computational models have been explored to try to tease out the regularities which may be eventually guide us to a general theory (Dong & Liu, 2017;Scholes, DePace, & Sánchez, 2017;Tsuchiya, Giuliani, Hashimoto, Erenpreisa, & Yoshikawa, 2016). Many computational models have been explored to try to tease out the regularities which may be eventually guide us to a general theory (Dong & Liu, 2017;Scholes, DePace, & Sánchez, 2017;Tsuchiya, Giuliani, Hashimoto, Erenpreisa, & Yoshikawa, 2016).…”

“…There is yet no general theory as to how evolution may shape the variational structure of phenotypic variation. Many computational models have been explored to try to tease out the regularities which may be eventually guide us to a general theory (Dong & Liu, ; Scholes, DePace, & Sánchez, ; Tsuchiya, Giuliani, Hashimoto, Erenpreisa, & Yoshikawa, ). Here, we revisit one such computational model which has produced intriguing results: the cellular automaton model of development, EvCA.…”