Search for: All records

Not AvailableThe building and simulation of biological models is a valuable skill that can deepen student knowledge and promote systems thinking. Signal transduction networks are complex biological communication systems that regulate many interactions between an organism and its surrounding environment, creating dynamic behaviors. Bacterial chemotaxis exemplifies the basic principles of signal transduction and demonstrates core biology concepts like feedback inhibition, systems, and transfer and utilization of information. This system is ideal for learning about modeling. It contains a small number of components while still demonstrating key aspects of signal transduction: how an environmental signal is received and translated into a mechanical behavior and how feedback loops give rise to nonlinear dynamics. Using Cell Collective, we developed a model- and simulation-based lesson to help students grow their computational modeling skills while developing knowledge of these core concepts. Cell Collective and the lesson design allow students to build and simulate a model without extensive background knowledge of the technology or computer programming. It also targets common student misconceptions about the features of complex systems like emergent behaviors and randomness. The lesson contains all resources, assessment questions, and instructions needed for teaching signal transduction and having students practice modeling and system thinking. 

This study provides practical suggestions for the features to be prioritized in spending limited resources to create and improve educational technology like Cell Collective. The results suggest a need to prioritize features improving the learning rather than the teaching side to motivate instructors more effectively to adopt and use the technology. 

Teachers’ integration of the Next Generation Science Standards and corresponding Science and Engineering Practices (SEPs) illustrate current science education reform in the United States. Effective teacher professional development (PD) on SEPs is essential for reform success. In this study, we evaluated the Nebraska STEM Education Conference, a PD program for middle school, high school, and first- and second-year post-secondary STEM teachers. This SEP-oriented PD program focused predominantly on the SEPs ‘developing and using models’ and ‘using mathematics and computational thinking.’ An electronic survey was used to measure participants’ (n = 45) prior integration of SEPs, influential factors and barriers to using SEPs, and changes to interest and confidence in using SEPs as a result of attending the PD program. Our results showed that teachers had limited prior use of SEPs in their teaching. Student interest and learning outcomes were the factors found to be most influential to teachers’ use of SEPs, while limited knowledge, confidence, and resources were the most commonly identified barriers. As a result of attending the PD program, participants significantly improved their confidence and interest to incorporate SEPs. We recommend continued SEP-oriented PD to foster successful NGSS integration and to advance reforms in science education. 

Computational models are often employed in systems biology to study the dynamic behaviours of complex systems. With the rise in the number of computational models, finding ways to improve the reusability of these models and their ability to reproduce virtual experiments becomes critical. Correct and effective model annotation in community-supported and standardised formats is necessary for this improvement. Here, we present recent efforts toward a common framework for annotated, accessible, reproducible and interoperable computational models in biology, and discuss key challenges of the field. 

Abstract Computational simulation experiments increasingly inform modern biological research, and bring with them the need to provide ways to annotate, archive, share and reproduce the experiments performed. These simulations increasingly require extensive collaboration among modelers, experimentalists, and engineers. The Minimum Information About a Simulation Experiment (MIASE) guidelines outline the information needed to share simulation experiments. SED-ML is a computer-readable format for the information outlined by MIASE, created as a community project and supported by many investigators and software tools. The first versions of SED-ML focused on deterministic and stochastic simulations of models. Level 1 Version 4 of SED-ML substantially expands these capabilities to cover additional types of models, model languages, parameter estimations, simulations and analyses of models, and analyses and visualizations of simulation results. To facilitate consistent practices across the community, Level 1 Version 4 also more clearly describes the use of SED-ML constructs, and includes numerous concrete validation rules. SED-ML is supported by a growing ecosystem of investigators, model languages, and software tools, including eight languages for constraint-based, kinetic, qualitative, rule-based, and spatial models, over 20 simulation tools, visual editors, model repositories, and validators. Additional information about SED-ML is available at https://sed-ml.org/ .