Designing Activities to Teach Higher-Order Skills: How Feedback and Constraint Affect Learning of Experimental Design.

Active learning approaches to biology teaching, including simulation-based activities, are known to enhance student learning, especially of higher-order skills; nonetheless, there are still many open questions about what features of an activity promote optimal learning. Here we designed three versions of a simulation-based tutorial called Understanding Experimental Design that asks students to design experiments and collect data to test their hypotheses. The three versions vary the experimental design task along the axes of feedback and constraint, where constraint measures how much choice students have in performing a task. Using a variety of assessments, we ask whether each of those features affects student learning of experimental design. We find that feedback has a direct positive effect on learning. We further find that small changes in constraint have only subtle and mostly indirect effects on learning. This work suggests that designers of tools for teaching higher-order skills should strive to include feedback to increase impact and may feel freer to vary the degree of constraint within a range to optimize for other features such as the ability to provide immediate feedback and time-on-task.

A new assessment to monitor student performance in introductory neurophysiology: Electrochemical Gradients Assessment Device.

The basis for understanding neurophysiology is understanding ion movement across cell membranes. Students in introductory courses recognize ion concentration gradients as a driving force for ion movement but struggle to simultaneously account for electrical charge gradients. We developed a 17-multiple-choice item assessment of students' understanding of electrochemical gradients and resistance in neurophysiology, the Electrochemical Gradients Assessment Device (EGAD). We investigated the internal evidence validity of the assessment by analyzing item characteristic curves of score probability and student ability for each question, and a Wright map of student scores and ability. We used linear mixed-effect regression to test student performance and ability. Our assessment discriminated students with average ability (weighted likelihood estimate: -2 to 1.5 Θ); however, it was not as effective at discriminating students at the highest ability (weighted likelihood estimate: >2 Θ). We determined the assessment could capture changes in both assessment scores (model r2 = 0.51, P < 0.001, n = 444) and ability estimates (model r2 = 0.47, P < 0.001, n = 444) after a simulation-based laboratory and course instruction for 222 students. Differential item function analysis determined that each item on the assessment performed equitably for all students, regardless of gender, race/ethnicity, or economic status. Overall, we found that men scored higher (r2 = 0.51, P = 0.014, n = 444) and had higher ability scores (P = 0.003) on the EGAD assessment. Caucasian students of both genders were positively correlated with score (r2 = 0.51, P < 0.001, n = 444) and ability (r2 = 0.47, P < 0.001, n = 444). Based on the evidence gathered through our analyses, the scores obtained from the EGAD can distinguish between levels of content knowledge on neurophysiology principles for students in introductory physiology courses.

Control of Drosophila endocycles by E2F and CRL4(CDT2).

Endocycles are variant cell cycles comprised of DNA synthesis (S)- and gap (G)-phases but lacking mitosis. Such cycles facilitate post-mitotic growth in many invertebrate and plant cells, and are so ubiquitous that they may account for up to half the world's biomass. DNA replication in endocycling Drosophila cells is triggered by cyclin E/cyclin dependent kinase 2 (CYCE/CDK2), but this kinase must be inactivated during each G-phase to allow the assembly of pre-Replication Complexes (preRCs) for the next S-phase. How CYCE/CDK2 is periodically silenced to allow re-replication has not been established. Here, using genetic tests in parallel with computational modelling, we show that the endocycles of Drosophila are driven by a molecular oscillator in which the E2F1 transcription factor promotes CycE expression and S-phase initiation, S-phase then activates the CRL4(CDT2) ubiquitin ligase, and this in turn mediates the destruction of E2F1 (ref. 7). We propose that it is the transient loss of E2F1 during S phases that creates the window of low Cdk activity required for preRC formation. In support of this model overexpressed E2F1 accelerated endocycling, whereas a stabilized variant of E2F1 blocked endocycling by deregulating target genes, including CycE, as well as Cdk1 and mitotic cyclins. Moreover, we find that altering cell growth by changing nutrition or target of rapamycin (TOR) signalling impacts E2F1 translation, thereby making endocycle progression growth-dependent. Many of the regulatory interactions essential to this novel cell cycle oscillator are conserved in animals and plants, indicating that elements of this mechanism act in most growth-dependent cell cycles.

Ingeneue: a software tool to simulate and explore genetic regulatory networks.

Here I describe how to use Ingeneue, a software tool for constructing, simulating, and exploring models of gene regulatory networks. Ingeneue is an open source, extensible Java application that allows users to rapidly build ordinary differential equation models of a gene regulatory network without requiring extensive programming or mathematical skills. Models can be in a single cell or 2D sheet of cells, and Ingeneue is well suited for simulating both oscillatory and pattern forming networks. Ingeneue provides features to allow rapid model construction and debugging, sophisticated visualization and statistical tools for model exploration, and a powerful framework for searching parameter space for desired behavior. This chapter provides an overview of the mathematical theory and operation of Ingeneue, and detailed walkthroughs demonstrating how to use the main features and how to construct networks in Ingeneue.

Effects of ploidy and recombination on evolution of robustness in a model of the segment polarity network.

Many genetic networks are astonishingly robust to quantitative variation, allowing these networks to continue functioning in the face of mutation and environmental perturbation. However, the evolution of such robustness remains poorly understood for real genetic networks. Here we explore whether and how ploidy and recombination affect the evolution of robustness in a detailed computational model of the segment polarity network. We introduce a novel computational method that predicts the quantitative values of biochemical parameters from bit sequences representing genotype, allowing our model to bridge genotype to phenotype. Using this, we simulate 2,000 generations of evolution in a population of individuals under stabilizing and truncation selection, selecting for individuals that could sharpen the initial pattern of engrailed and wingless expression. Robustness was measured by simulating a mutation in the network and measuring the effect on the engrailed and wingless patterns; higher robustness corresponded to insensitivity of this pattern to perturbation. We compared robustness in diploid and haploid populations, with either asexual or sexual reproduction. In all cases, robustness increased, and the greatest increase was in diploid sexual populations; diploidy and sex synergized to evolve greater robustness than either acting alone. Diploidy conferred increased robustness by allowing most deleterious mutations to be rescued by a working allele. Sex (recombination) conferred a robustness advantage through "survival of the compatible": those alleles that can work with a wide variety of genetically diverse partners persist, and this selects for robust alleles.

Slow Na+ inactivation and variance adaptation in salamander retinal ganglion cells.

The retina adapts to the temporal contrast of the light inputs. One component of contrast adaptation is intrinsic to retinal ganglion cells: temporal contrast affects the variance of the synaptic inputs to ganglion cells, which alters the gain of spike generation. Here we show that slow Na+ inactivation is sufficient to produce the observed variance adaptation. Slow inactivation caused the Na+ current available for spike generation to depend on the past history of activity, both action potentials and subthreshold voltage variations. Recovery from slow inactivation required several hundred milliseconds. Increased current variance caused the threshold for spike generation to increase, presumably because of the decrease in available Na+ current. Simulations indicated that slow Na+ inactivation could account for the observed decrease in excitability. This suggests a simple picture of how ganglion cells contribute to contrast adaptation: (1) increasing contrast causes an increase in input current variance that raises the spike rate, and (2) the increased spike rate reduces the available Na+ current through slow inactivation, which feeds back to reduce excitability. Cells throughout the nervous system face similar problems of accommodating a large range of input signals; furthermore, the Na+ currents of many cells exhibit slow inactivation. Thus, adaptation mediated by feedback modulation of the Na+ current through slow inactivation could serve as a general mechanism to control excitability in spiking neurons.