Search for: All records

Abstract Cis-regulatory elements (CREs) control gene expression, orchestrating tissue identity, developmental timing and stimulus responses, which collectively define the thousands of unique cell types in the body^1–3. While there is great potential for strategically incorporating CREs in therapeutic or biotechnology applications that require tissue specificity, there is no guarantee that an optimal CRE for these intended purposes has arisen naturally. Here we present a platform to engineer and validate synthetic CREs capable of driving gene expression with programmed cell-type specificity. We take advantage of innovations in deep neural network modelling of CRE activity across three cell types, efficient in silico optimization and massively parallel reporter assays to design and empirically test thousands of CREs^4–8. Through large-scale in vitro validation, we show that synthetic sequences are more effective at driving cell-type-specific expression in three cell lines compared with natural sequences from the human genome and achieve specificity in analogous tissues when tested in vivo. Synthetic sequences exhibit distinct motif vocabulary associated with activity in the on-target cell type and a simultaneous reduction in the activity of off-target cells. Together, we provide a generalizable framework to prospectively engineer CREs from massively parallel reporter assay models and demonstrate the required literacy to write fit-for-purpose regulatory code. 

We developed a framework for characterizing an institution’s quantitative skills/reasoning support ecosystem to consider how various activities contribute to student success in areas connected to students’ quantitative preparation. Through discussions with faculty and staff stakeholders at eight selective small liberal arts colleges, we established that the quantitative skills/reasoning support ecosystem at these institutions consists of four domains: bridge programs with a quantitative component, assessment of readiness, curricular on-ramps, and supplementary support for courses that require quantitative skills/reasoning. The framework includes questions about each domain that can be used by stakeholders in different institutional positions to reflect on existing efforts to support student success in quantitative disciplines and identify opportunities to align or change their institutional quantitative skills/quantitative reasoning support systems to better meet student needs. 

We present the results of a survey of 220 faculty members at selective liberal arts colleges who teach introductory STEM courses. The survey was designed to learn how these faculty support student quantitative skills development in their introductory courses and what high-school-level quantitative skills are the most challenging for their incoming introductory students. In particular, we wanted to identify the skills that students struggled with across different disciplines in order to explore collaborative development of shared online modules to support student quantitative skills review and practice in many different introductory STEM courses. Five priority topic areas emerged – graphs and tables, descriptive statistics, exponents/logarithms, intercepts/slopes of lines, and confidence intervals/standard error – although there were significant disciplinary differences.