Pairing of Competitive and Topologically Distinct Regulatory Modules Enhances Patterned Gene Expression

Biological networks are inherently modular, yet little is known about how modules are assembled to enable coordinated and complex functions. We used RNAi and time series, whole-genome microarray analyses to systematically perturb and characterize components of a Caenorhabditis elegans lineage-specific transcriptional regulatory network. These data are supported by selected reporter gene analyses and comprehensive yeast one-hybrid and promoter sequence analyses. Based on these results, we define and characterize two modules composed of muscle- and epidermal-specifying transcription factors that function together within a single cell lineage to robustly specify multiple cell types. The expression of these two modules, although positively regulated by a common factor, is reliably segregated among daughter cells. Our analyses indicate that these modules repress each other, and we propose that this cross-inhibition coupled with their relative time of induction function to enhance the initial asymmetry in their expression patterns, thus leading to the observed invariant gene expression patterns and cell lineage. The coupling of asynchronous and topologically distinct modules may be a general principle of module assembly that functions to potentiate genetic switches.Molecular and Cellular Biolog

Model Management in Electronic Markets for Decision Technologies: A Software Agent Approach

DecisionNet is a distributed, Web-based electronic market for decision technologies such as data, models, solution algorithms, and modeling environments. Consumer-provider interactions are facilitated by model management software agents provided by DecisionNet. To illustrate different approaches for designing this agent functionality, we present two agents that embody different designs for mediating consumer and provider interaction with the AMPL and GAMS modeling environments. The AMPL agent is lean, and places significant knowledge and reasoning requirements on both providers (when registering a technology) and consumers (when using technologies). In contrast,the GAMS agent encapsulates knowledge of the GAMS language and modeling environment to facilitate registration of models by providers and to create a run time interface to models for consumers. We discuss the relative advantages of both approaches and argue for the need incorporate them into environments such as DecisionNet

Caenorhabditis phylogeny predicts convergence of hermaphroditism and extensive intron loss

Despite the prominence of Caenorhabditis elegans as a major developmental and genetic model system, its phylogenetic relationship to its closest relatives has not been resolved. Resolution of these relationships is necessary for studying the steps that underlie life history, genomic, and morphological evolution of this important system. By using data from five different nuclear genes from 10 Caenorhabditis species currently in culture, we find a well resolved phylogeny that reveals three striking patterns in the evolution of this animal group: (i) Hermaphroditism has evolved independently in C. elegans and its close relative Caenorhabditis briggsae; (ii) there is a large degree of intron turnover within Caenorhabditis, and intron losses are much more frequent than intron gains; and (iii) despite the lack of marked morphological diversity, more genetic disparity is present within this one genus than has occurred within all vertebrates

"Connecting concepts helps put main ideas together": cognitive load and usability in learning biology with an AI-enriched textbook

Rapid developments in educational technology in higher education are intended to make learning more engaging and effective. At the same time, cognitive load theory stresses limitations of human cognitive architecture and urges educational developers to design learning tools that optimise learners’ mental capacities. In a 2-month study we investigated university students’ learning with an AI-enriched digital biology textbook that integrates a 5000-concept knowledge base and algorithms offering the possibility to ask questions and receive answers. The study aimed to shed more light on differences between three sub-types (intrinsic, germane and extraneous) of cognitive load and their relationship with learning gain, self-regulated learning and usability perception while students interacted with the AI-enriched book during an introductory biology course. We found that students displayed a beneficial learning pattern with germane cognitive load significantly higher than both intrinsic and extraneous loads showing that they were engaged in meaningful learning throughout the study. A significant correlation between germane load and accessing linked suggested questions available in the AI-book indicates that the book may support deep learning. Additionally, results showed that perceived non-optimal design, which deflects cognitive resources away from meaningful processing accompanied lower learning gains. Nevertheless, students reported substantially more favourable than unfavourable opinions of the AI-book. The findings provide new approaches for investigating cognitive load types in relation to learning with emerging digital tools in higher education. The findings also highlight the importance of optimally aligning educational technologies and human cognitive architecture.Funding: Marcus and Amalia Wallenberg Foundation [MAW 2014.0107]</p