Morphogen gradients pattern tissues and organs during development. When morphogen production is spatially restricted, diffusion and degradation are sufficient to generate sharp concentration gradients. It is less clear how sharp gradients can arise within the source of a broadly expressed morphogen. A recent solution relies on localized production of an inhibitor outside the domain of morphogen production, which effectively redistributes (shuttles) and concentrates the morphogen within its expression domain. Here, we study how a sharp gradient is established without a localized inhibitor, focusing on early dorsoventral patterning of the Drosophila embryo, where an active ligand and its inhibitor are concomitantly generated in a broad ventral domain. Using theory and experiments, we show that a sharp Toll activation gradient is produced through "self-organized shuttling," which dynamically relocalizes inhibitor production to lateral regions, followed by inhibitor-dependent ventral shuttling of the activating ligand Spätzle. Shuttling may represent a general paradigm for patterning early embryos.

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Patterning by morphogen gradients relies on the capacity to generate reproducible distribution profiles. Morphogen spread depends on kinetic parameters, including diffusion and degradation rates, which vary between embryos, raising the question of how variability is controlled. We examined this in the context of Toll-dependent dorsoventral (DV) patterning of the Drosophila embryo. We find that low embryo-to-embryo variability in DV patterning relies on wntD, a Toll-target gene expressed initially at the posterior pole. WntD protein is secreted and disperses in the extracellular milieu, associates with its receptor Frizzled4, and inhibits the Toll pathway by blocking the Toll extracellular domain. Mathematical modeling predicts that WntD accumulates until the Toll gradient narrows to its desired spread, and we support this feedback experimentally. This circuit exemplifies a broadly applicable induction-contraction mechanism, which reduces patterning variability through a restricted morphogen-dependent expression of a secreted diffusible inhibitor.

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Understanding genetic mechanisms affords the ability to provide causal explanations for genetic phenomena. These mechanisms are difficult to teach and learn. It has been shown that students sometimes conceive of genes as traits or as trait-bearing particles. We termed these “nonmechanistic” conceptions of genetic phenomena because they do not allow the space required for a mechanism to exist in the learner’s mind. In this study, we investigated how ninth- and 12th-grade students’ conceptions of genetic phenomena affect their ability to learn the underlying mechanisms. We found that ninth- and 12th-grade students with nonmechanistic conceptions are less successful at learning the mechanisms leading from gene to trait than students with mechanistic conceptions. Our results suggest that nonmechanistic conceptions of a phenomenon may create a barrier to learning the underlying mechanism. These findings suggest that an initial description of a phenomenon should hint at a mechanism even if the mechanism would be learned only later.

Mechanisms are central in scientific explanations. However, developing mechanistic explanations is difficult for students especially in domains in which mechanisms involve abstract components and functions, such as genetics. One of the core components of genetic mechanisms are proteins and their functions. Students struggle to reason about the role of proteins while learning genetics and show limited ability to provide mechanistic explanations of genetic phenomena. In genetics education there are currently two competing theoretical frameworks regarding what domain‐specific knowledge about proteins is important for reasoning about genetic mechanisms. One framework assumes knowledge about specific protein functions in the body, a tool kit of functions; the other framework assumes more abstracted knowledge about protein interactions that are common to all protein functions. These frameworks implicate different instructional frameworks: One offers to provide concrete examples of protein functions while the other offers a more general description of protein activity. Our aim in this study was to ascertain the ways in which students' reasoning about proteins' role in genetic phenomena (both familiar and novel) relates to the two theoretical frameworks. Toward this end we engaged 7th grade students in learning about proteins functions in the mechanisms underlying genetic traits using an online simulation environment that embodied key aspects of both frameworks. We analyzed students' responses to the final test questions in which they were asked to generatively reason about the underlying mechanisms of two novel genetic traits. Our findings suggest that students use proteins in their explanations mainly when they can explain the protein function and that knowledge about a few specific functions is insufficient to support conceptualization of new functions. Moreover, knowledge of general protein activities common to most functions is also insufficient. We suggest a new combined approach to supporting students' understanding of proteins' role in genetic mechanisms.

This research investigates how students reason about the phenomenon of phenotypic plasticity. An analysis of student interviews reviled two types of mechanistic explanations, one of which seems to be less intuitive but is critical for reasoning about core biological ideas such as homeostasis and development.

Many studies have characterized students' difficulties in understanding and reasoning about scientific mechanisms. Some of those studies have drawn implications on teaching mechanisms and how to guide students while reasoning mechanistically. In this theoretical article, I claim that one component that has not garnered much attention in the science education literature, unlike other components of mechanistic explanations, is the black box construct, that is, missing mechanistic parts within mechanistic explanations (explanatory black box). By reviewing the literature on mechanisms and mechanistic explanations in the philosophy of science and cognitive psychology, I argue that explanatory black boxes are an inherent part of mechanistic explanations and that their recognition is essential for learning mechanisms, scientific literacy, and understanding the nature of science. Examples from biology education are provided as a case of a complex multileveled scientific field. In the absence of a pedagogical approach for teaching explanatory black boxes, I turn to studies and frameworks from computer science education that may guide educators on how to begin discussing this construct in the science classroom.