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Beyond the Central Dogma: Model-Based Learning of How Genes Determine Phenotypes.

ABSTRACT: In an introductory biology course, we implemented a learner-centered, model-based pedagogy that frequently engaged students in building conceptual models to explain how genes determine phenotypes. Model-building tasks were incorporated within case studies and aimed at eliciting students' understanding of 1) the origin of variation in a population and 2) how genes/alleles determine phenotypes. Guided by theory on hierarchical development of systems-thinking skills, we scaffolded instruction and assessment so that students would first focus on articulating isolated relationships between pairs of molecular genetics structures and then integrate these relationships into an explanatory network. We analyzed models students generated on two exams to assess whether students' learning of molecular genetics progressed along the theoretical hierarchical sequence of systems-thinking skills acquisition. With repeated practice, peer discussion, and instructor feedback over the course of the semester, students' models became more accurate, better contextualized, and more meaningful. At the end of the semester, however, more than 25% of students still struggled to describe phenotype as an output of protein function. We therefore recommend that 1) practices like modeling, which require connecting genes to phenotypes; and 2) well-developed case studies highlighting proteins and their functions, take center stage in molecular genetics instruction.

SUBMITTER: Reinagel A

PROVIDER: S-EPMC4803093 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Considerable evidence suggests that multiple learning systems can drive behavior. Choice can proceed reflexively from previous actions and their associated outcomes, as captured by "model-free" learning algorithms, or flexibly from prospective consideration of outcomes that might occur, as captured by "model-based" learning algorithms. However, differential contributions of dopamine to these systems are poorly understood. Dopamine is widely thought to support model-free learning by modulating plasticity in striatum. Model-based learning may also be affected by these striatal effects, or by other dopaminergic effects elsewhere, notably on prefrontal working memory function. Indeed, prominent demonstrations linking striatal dopamine to putatively model-free learning did not rule out model-based effects, whereas other studies have reported dopaminergic modulation of verifiably model-based learning, but without distinguishing a prefrontal versus striatal locus. To clarify the relationships between dopamine, neural systems, and learning strategies, we combine a genetic association approach in humans with two well-studied reinforcement learning tasks: one isolating model-based from model-free behavior and the other sensitive to key aspects of striatal plasticity. Prefrontal function was indexed by a polymorphism in the COMT gene, differences of which reflect dopamine levels in the prefrontal cortex. This polymorphism has been associated with differences in prefrontal activity and working memory. Striatal function was indexed by a gene coding for DARPP-32, which is densely expressed in the striatum where it is necessary for synaptic plasticity. We found evidence for our hypothesis that variations in prefrontal dopamine relate to model-based learning, whereas variations in striatal dopamine function relate to model-free learning.Decisions can stem reflexively from their previously associated outcomes or flexibly from deliberative consideration of potential choice outcomes. Research implicates a dopamine-dependent striatal learning mechanism in the former type of choice. Although recent work has indicated that dopamine is also involved in flexible, goal-directed decision-making, it remains unclear whether it also contributes via striatum or via the dopamine-dependent working memory function of prefrontal cortex. We examined genetic indices of dopamine function in these regions and their relation to the two choice strategies. We found that striatal dopamine function related most clearly to the reflexive strategy, as previously shown, and that prefrontal dopamine related most clearly to the flexible strategy. These findings suggest that dissociable brain regions support dissociable choice strategies.

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Beyond the Central Dogma: Model-Based Learning of How Genes Determine Phenotypes.

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