Charting a new frontier of science by integrating mathematical modeling to understand and predict complex biological systems

Biological systems are staggeringly complex. To untangle this complexity and make predictions about biological systems is a continuous goal of biological research. One approach to achieve these goals is to emphasize the use of quantitative measures of biological processes. Advances in quantitative biology data collection and analysis across scales (molecular, cellular, organismal, ecological) has transformed how we understand, categorize, and predict complex biological systems. Simultaneously, thanks to increased computational power, mathematicians, engineers and physical scientists -- collectively termed theoreticians -- have developed sophisticated models of biological systems at different scales. But there is still a disconnect between the two fields. This surge of quantitative data creates an opportunity to apply, develop, and evaluate mathematical models of biological systems and explore novel methods of analysis. The novel modeling schemes can also offer deeper understanding of principles in biology. In the context of this paper, we use “models” to refer to mathematical representations of biological systems. This data revolution puts scientists in a unique position to leverage information-rich datasets to improve descriptive modeling. Moreover, advances in technology allow inclusion of heterogeneity and variability within these datasets and mathematical models. This inclusion may lead to identifying previously undetermined variables driving or maintaining heterogeneity and diversity. Improved inclusion of variation may even improve biologically meaningful predictions about how systems will respond to perturbations. Although some of these practices are mainstream in specific sub-fields of biology, such practices are not widespread across all fields of biological sciences. With resources dedicated to better integrating biology and mathematical modeling, we envision a transformational improvement in the ability to describe and predict complex biological systems

Charting a new frontier of science by integrating mathematical modeling to understand and predict complex biological systems

Biological systems are staggeringly complex. To untangle this complexity and make predictions about biological systems is a continuous goal of biological research. One approach to achieve these goals is to emphasize the use of quantitative measures of biological processes. Advances in quantitative biology data collection and analysis across scales (molecular, cellular, organismal, ecological) has transformed how we understand, categorize, and predict complex biological systems. Simultaneously, thanks to increased computational power, mathematicians, engineers and physical scientists -- collectively termed theoreticians -- have developed sophisticated models of biological systems at different scales. But there is still a disconnect between the two fields. This surge of quantitative data creates an opportunity to apply, develop, and evaluate mathematical models of biological systems and explore novel methods of analysis. The novel modeling schemes can also offer deeper understanding of principles in biology. In the context of this paper, we use “models” to refer to mathematical representations of biological systems. This data revolution puts scientists in a unique position to leverage information-rich datasets to improve descriptive modeling. Moreover, advances in technology allow inclusion of heterogeneity and variability within these datasets and mathematical models. This inclusion may lead to identifying previously undetermined variables driving or maintaining heterogeneity and diversity. Improved inclusion of variation may even improve biologically meaningful predictions about how systems will respond to perturbations. Although some of these practices are mainstream in specific sub-fields of biology, such practices are not widespread across all fields of biological sciences. With resources dedicated to better integrating biology and mathematical modeling, we envision a transformational improvement in the ability to describe and predict complex biological systems

Charting a New Frontier Integrating Mathematical Modeling in Complex Biological Systems from Molecules to Ecosystems

Advances in quantitative biology data collection and analysis across scales (molecular, cellular, organismal, and ecological) have transformed how we understand, categorize, and predict complex biological systems. This surge of quantitative data creates an opportunity to apply, develop, and evaluate mathematical models of biological systems and explore novel methods of analysis. Simultaneously, thanks to increased computational power, mathematicians, engineers, and physical scientists have developed sophisticated models of biological systems at different scales. Novel modeling schemes can offer deeper understanding of principles in biology, but there is still a disconnect between modeling and experimental biology that limits our ability to fully realize the integration of mathematical modeling and biology. In this work, we explore the urgent need to expand the use of existing mathematical models across biological scales, develop models that are robust to biological heterogeneity, harness feedback loops within the iterative-modeling process, and nurture a cultural shift toward interdisciplinary and cross-field interactions. Better integration of biological experimentation and robust mathematical modeling will transform our ability to understand and predict complex biological systems

Eight Recommendations to Promote Effective Study Habits for Biology Students Enrolled in Online Courses

To achieve meaningful learning experiences in online classrooms, students must become self-regulated learners through the development of effective study habits. Currently, there is no set of recommendations to promote study habits in online biology learning environments. To fill gaps in our understanding, a working group associated with a research coordination network (Equity and Diversity in Undergraduate STEM, EDU-STEM) convened virtually in June 2021. We identify student barriers to self-regulated learning in online environments and present eight practical recommendations to help biology educators and biology education researchers apply and advance evidence-based study habits in online courses. As higher education institutions continue to offer online learning opportunities, we hope this essay equips instructors with the knowledge and tools to promote student success in online biology coursework.publishedVersio

Are synchronous chats a silver lining of emergency remote instruction? Text-based chatting is disproportionately favored by women in a non-majors introductory biology course

The coronavirus disease 2019 (COVID-19) pandemic has led to a reimagining of many aspects of higher education, including how instructors interact with their students and how they encourage student participation. Text-based chatting during synchronous remote instruction is a simple form of student-student and student-instructor interaction. The importance of student participation has been documented, as have clear disparities in participation between those well-represented and those under-represented in science disciplines. Thus, we conducted an investigation into who is texting, what students are texting, and how these texts align with course content. We focused on two sections of a large-enrollment, introductory biology class offered remotely during Fall 2020. Using an analysis of in-class chatting, in combination with student survey responses, we find that text-based chatting suggests not only a high level of student engagement, but a type of participation that is disproportionately favored by women. Given the multiple lines of evidence indicating that women typically under-participate in their science courses, any vehicle that counters this trend merits further exploration. We conclude with suggestions for further research, and ideas for carrying forward text-based chatting in the post-COVID-19, in-person classroom.publishedVersio