Smed Stroud (masstower5)

ssibly through inhibition of oocyte meiotic resumption.The development of multicellular organisms relies on correct patterns of cell fates to produce functional tissues in the mature organism. A commonly observed developmental pattern consists of alternating cell fates, where neighboring cells take on distinct cell fates characterized by contrasting gene and protein expression levels, and this cell fate pattern repeats over two or more cells. this website The patterns produced by these fate decisions are regulated by a small number of highly conserved signaling networks, some of which are mediated by long range diffusible signals and others mediated by local contact-dependent signals. However, it is not completely understood how local and long range signals associated with these networks interact to produce fate patterns that are both robust and flexible. Here we analyze mathematical models to investigate the patterning of cell fates in an array of cells, focusing on a two cell repeating pattern. Bifurcation analysis of a multicellular ODE model, where we consider the cells as discrete compartments, suggests that cells must balance sensitivity to external signals with robustness to perturbations. To focus on the patterning dynamics close to the bifurcation point, we derive a continuum PDE model that integrates local and long range signaling. For those cells with dynamics close to the bifurcation point, sensitivity to long range signals determines how far a pattern extends in space, while the number of local signaling connections determines the type of pattern produced. This investigation provides a general framework for understanding developmental patterning, and how both long range and local signals play a role in generating features observed across biology, such as species differences in nematode vulval development and insect bristle patterning, as well as medically relevant processes such as control of stem cell fate in the intestinal crypt.Evolution of metabolism is a longstanding yet unresolved question, and several hypotheses were proposed to address this complex process from a Darwinian point of view. Modern statistical bioinformatic approaches targeted to the comparative analysis of genomes are being used to detect signatures of natural selection at the gene and population level, as an attempt to understand the origin of primordial metabolism and its expansion. These studies, however, are still mainly centered on genes and the proteins they encode, somehow neglecting the small organic chemicals that support life processes. In this work, we selected steroids as an ancient family of metabolites widely distributed in all eukaryotes and applied unsupervised machine learning techniques to reveal the traits that natural selection has imprinted on molecular properties throughout the evolutionary process. Our results clearly show that sterols, the primal steroids that first appeared, have more conserved properties and that, from then on, more complex compounds with increasingly diverse properties have emerged, suggesting that chemical diversification parallels the expansion of biological complexity. In a wider context, these findings highlight the worth of chemoinformatic approaches to a better understanding the evolution of metabolism.Most traits expressed by organisms, such as gene expression profiles, developmental trajectories, behavioural sequences and reaction norms are function-valued traits (colloquially "phenotypically plastic traits"), since they vary across an individual's age and in response to various internal and/or external factors (state variables). Furthermore, most organisms live in populations subject to limited genetic mixing and are thus likely to interact with their relatives. We here formalise selection on genetically determined function-valued traits of individuals interacting in a group-structured population, by deriving the marginal version of Hamilton's rule for function-valued traits. This rule