Elizabeth F. Ryder

Understanding microbial interactions within the gut microbiome is challenging due to the sheer number of microorganisms present. Each additional microbe introduces new layers of complexity, especially when studying the conditions and dynamics of infectious progression. To address this challenge, we have developed Enteroscape: an in silico agent-based model, grounded in previous research, laboratory experiments, and in vivo assays. Enteroscape enables simulation of diverse microbial interactions within a nematode host environment. In this study, we used Enteroscape to model the progression of pathogenic yeast infection and evaluate the effects of probiotic yeast treatment in the Caenorhabditis elegans intestine. Drawing upon empirical and literature-based behavioral rules for individual microbes, Enteroscape simulations produced emergent behaviors of the microbial population that mirror experimentally observed infection dynamics, including visual patterns of infectious progression, as well as the effect of probiotic treatment on the host lifespan. Enteroscape demonstrates how computational models can generate testable predictions that deepen our insight into microbial community interactions and their impact on host biology. Author Summary: The human microbiome is a complicated ecosystem, consisting of trillions of microbes of hundreds of different species that live in the human intestine. Understanding how different species interact to maintain a healthy balanced microbiological ecosystem that keeps pathogenic microbes at bay is critically important to human health. We have developed a simple experimental system that allows us to examine the interactions of just a few thousand microbes at a time in a living host, the microscopic nematode worm, C. elegans . In prior laboratory work, we have demonstrated how ingestion by the worm of the human pathogenic microbe C. albicans shortens the worm’s lifespan, while co-ingestion of various probiotic microbial species restores a longer lifespan. In this paper, we report the creation of a computational model of this system, which we named Enteroscape. We show that Enteroscape mimics the results of the experimental system, including the visual progression of infection by a pathogenic species, the effects on worm lifespan, and the amelioration of the pathogens’ effects by a probiotic species. Enteroscape will allow us to develop and test hypotheses about the mechanisms underlying remediation by probiotic microbes, with potential applications to better probiotic treatment of human infections.

As high-throughput methods become more common, training undergraduates to analyze data must include having them generate informative summaries of large datasets. This flexible case study provides an opportunity for undergraduate students to become familiar with the capabilities of R programming in the context of high-throughput evolutionary data collected using macroarrays. The story line introduces a recent graduate hired at a biotech firm and tasked with analysis and visualization of changes in gene expression from 20,000 generations of the Lenski Lab’s Long-Term Evolution Experiment (LTEE). Our main character is not familiar with R and is guided by a coworker to learn about this platform. Initially this involves a step-by-step analysis of the small Iris dataset built into R which includes sepal and petal length of three species of irises. Practice calculating summary statistics and correlations, and making histograms and scatter plots, prepares the protagonist to perform similar analyses with the LTEE dataset. In the LTEE module, students analyze gene expression data from the long-term evolutionary experiments, developing their skills in manipulating and interpreting large scientific datasets through visualizations and statistical analysis. Prerequisite knowledge is basic statistics, the Central Dogma, and basic evolutionary principles. The Iris module provides hands-on experience using R programming to explore and visualize a simple dataset; it can be used independently as an introduction to R for biological data or skipped if students already have some experience with R. Both modules emphasize understanding the utility of R, rather than creation of original code. Pilot testing showed the case study was well-received by students and faculty, who described it as a clear introduction to R and appreciated the value of R for visualizing and analyzing large datasets.

The purpose of the meeting described in this review was to decide how best to ensure the sustainability of the Network for Integrating Bioinformatics into Life Science Education (NIBLSE; pronounced “nibbles”). Biology research today generates large and complex datasets, and the analysis of these datasets is becoming increasingly critical to progress in the field. The long-term goal of NIBLSE is to address this need and achieve the full integration of bioinformatics into undergraduate life sciences education. Meeting participants supported several next steps for NIBLSE, including further development and dissemination of bioinformatics learning resources through our novel incubators and Faculty Mentoring Networks, vigorously pursuing assessment strategies for our learning resources, connecting learning resources with open educational resource (OER) textbooks, learning more about barriers to bioinformatics implementation for underrepresented groups, and developing future workshops and meetings. About half the participants at the meeting were newcomers to NIBLSE, a positive sign for the future. NIBLSE has many exciting opportunities available, and we welcome life science educators with any level of bioinformatics expertise as new members.

Understanding how anthropogenic disturbances affect plant–pollinator systems has important implications for the conservation of biodiversity and ecosystem functioning. Previous laboratory studies show that pesticides and pathogens, which have been implicated in the rapid global decline of pollinators over recent years, can impair behavioral processes needed for pollinators to adaptively exploit floral resources and effectively transfer pollen among plants. However, the potential for these sublethal stressor effects on pollinator–plant interactions at the individual level to scale up into changes to the dynamics of wild plant and pollinator populations at the system level remains unclear. We developed an empirically parameterized agent‐based model of a bumblebee pollination system called SimBee to test for effects of stressor‐induced decreases in the memory capacity and information processing speed of individual foragers on bee abundance (scenario 1), plant diversity (scenario 2), and bee–plant system stability (scenario 3) over 20 virtual seasons. Modeling of a simple pollination network of a bumblebee and four co‐flowering bee‐pollinated plant species indicated that bee decline and plant species extinction events could occur when only 25% of the forager population showed cognitive impairment. Higher percentages of impairment caused 50% bee loss in just five virtual seasons and system‐wide extinction events in less than 20 virtual seasons under some conditions. Plant species extinctions occurred regardless of bee population size, indicating that stressor‐induced changes to pollinator behavior alone could drive species loss from plant communities. These findings indicate that sublethal stressor effects on pollinator behavioral mechanisms, although seemingly insignificant at the level of individuals, have the cumulative potential in principle to degrade plant–pollinator species interactions at the system level. Our work highlights the importance of an agent‐based modeling approach for the identification and mitigation of anthropogenic impacts on plant–pollinator systems.

In this paper, we describe a system dynamics model that views cancer as a dysfunction of the cellular system rather than as an ailment of cells. Our experiments with the model replicate the propagation of the ailment and the impacts of the treatments. It presents a concept that deviates from the pervasive view of cancer as a cell malfunction that has led to treatments aiming to destroy the rogue cells. It points to more holistic treatment options aiming at reforming cell interaction so the system can contain the growth of cancer cells. Further research is needed to explore the details for such options.

The COVID‐19 pandemic has led to an urgent need for engaging computational alternatives to traditional laboratory exercises. Here we introduce a customizable and flexible workflow, designed with the SARS CoV‐2 virus that causes COVID‐19 in mind, as a means of reinforcing fundamental biology concepts using bioinformatics approaches. This workflow is accessible to a wide range of students in life science majors regardless of their prior bioinformatics knowledge, and all software is freely available, thus eliminating potential cost barriers. Using the workflow can thus provide a diverse group of students the opportunity to conduct inquiry‐driven research. Here we demonstrate the utility of this workflow and outline the logical steps involved in the identification of therapeutic or vaccine targets against SARS CoV‐2. We also provide an example of how the workflow may be adapted to other infectious microbes. Overall, our workflow anchors student understanding of viral biology and genomics and allows students to develop valuable bioinformatics expertise as well as to hone critical thinking and problem‐solving skills, while also creating an opportunity to better understand emerging information surrounding the COVID‐19 pandemic.

While it is essential for life science students to be trained in modern techniques and approaches, rapidly developing, interdisciplinary fields such as bioinformatics present distinct challenges to undergraduate educators. In particular, many educators lack training in new fields, and high‐quality teaching and learning materials may be sparse. To address this challenge with respect to bioinformatics, the Network for the Integration of Bioinformatics into Life Science Education (NIBLSE), in partnership with Quantitative Undergraduate Biology Education and Synthesis (QUBES), developed incubators, a novel collaborative process for the development of open educational resources (OER). Incubators are short‐term, online communities that refine unpublished teaching lessons into more polished and widely usable learning resources. The resulting products are published and made freely available in the NIBLSE Resource Collection, providing recognition of scholarly work by incubator participants. In addition to producing accessible, high‐quality resources, incubators also provide opportunities for faculty development. Because participants are intentionally chosen to represent a range of expertise in bioinformatics and pedagogy, incubators also build professional connections among educators with diverse backgrounds and perspectives and promote the discussion of practical issues involved in deploying a resource in the classroom. Here we describe the incubator process and provide examples of beneficial outcomes. Our experience indicates that incubators are a low cost, short‐term, flexible method for the development of OERs and professional community that could be adapted to a variety of disciplinary and pedagogical contexts.

Cellular Vehicle-to-everything (C-V2X) was introduced by 3GPP in Release 14, which supports direct vehicle-to-vehicle (V2V) communication using sidelink channels. There are two modes in C-V2X for supporting V2V communication: Mode 3 and Mode 4. Both modes support V2V communications but differ on their spectrum allocation methodology. In out-of-coverage scenario defined under Mode 4, vehicles reserve spectrum resources using semi-persistent scheduling to enable direct communication. Currently, Intelligent Transportation System (ITS) spectrum at 5.9 GHz is emerging as an immediate resource to facilitate vehicular communication which has already been assigned for IEEE 802.11p. However, these dedicated channels will not be sufficient to support the connected vehicles in densely populated cities. Leveraging the existing underutilized wireless spectrum elsewhere, such as in digital television spectrum band is a likely solution. An intelligent scheme is required which can make adaptive decisions based on channel environment to efficiently use the secondary spectrum. Bumblebee behavioral model possess evolved decision-making mechanisms to adaptively solve similar problems while foraging in environments containing multiple floral resources (channels). Simulation results using bumblebee-based algorithm have shown that a memory system based on the averaging of stored channel energy information dramatically increased VDSA performance over a memoryless system in both urban and highway scenarios by 52% and 20%, respectively. In this paper, we propose a vehicular dynamic spectrum access (VDSA) solution based on bumblebee behavioral model for C-V2X and evaluate its performance using OpenAirInterface (OAI) LTE-Sidelink platform.

Bioinformatics, a discipline that combines aspects of biology, statistics, mathematics, and computer science, is becoming increasingly important for biological research. However, bioinformatics instruction is not yet generally integrated into undergraduate life sciences curricula. To understand why we studied how bioinformatics is being included in biology education in the US by conducting a nationwide survey of faculty at two- and four-year institutions. The survey asked several open-ended questions that probed barriers to integration, the answers to which were analyzed using a mixed-methods approach. The barrier most frequently reported by the 1,260 respondents was lack of faculty expertise/training, but other deterrents—lack of student interest, overly-full curricula, and lack of student preparation—were also common. Interestingly, the barriers faculty face depended strongly on whether they are members of an underrepresented group and on the Carnegie Classification of their home institution. We were surprised to discover that the cohort of faculty who were awarded their terminal degree most recently reported the most preparation in bioinformatics but teach it at the lowest rate.