Information

Simple computational biology project for AP Biology class. Ideas?


Our teacher assigned us a project to do with an extremely open-ended prompt and it should be completed within a month. I was planning on doing something related to computation and evolution in terms of biology although I'm not really sure where to start. I say simple because I'm not a professional programmer, rather, extremely interested in the field and I hope to use this as an opportunity to stretch by skills. Any ideas on a project or where to start would be greatly appreciated.


You can use BLAST or NCBI resources to do an analysis of something like the duplication of a gene in the HIV virus, for example. If you're looking for a place to start, I would recommend just learning the NCBI database resources.


In addition to what Ham Radio suggested, checking out Galaxy might not be a bad idea for doing any sort of computation/analysis that would otherwise have to be done on the command line. In particular, check out tools for sequence alignment (BWA and Bowtie), genome assembly (Velvet, Spades, IDBA-UD), and variant analysis (Freebayes, Mpileup, GATK). Good luck!


Simple computational biology project for AP Biology class. Ideas? - Biology

A list of project ideas with associated class members (and their contact info) is posted here.

An advanced optimization software is now available for use for the final projects. If your project involves the development and solution of large-scale optimization models (for example, flux balance analysis (FBA)), see here for details.

1) The full written project in the form of an article or grant proposal as well as the figures ready for (MS Powerpoint or Word) presentation is due Dec 2 at noon to the [email protected] account. We recommend that you start choosing a topic and team before the end of Oct.

2) The oral presentation will be limited to 6 minutes per person on each project team. This will give us 2 minutes (per person) for questions at the end. The presentations will be loaded on the computer in order of the schedule below, unless special requests are made.

3) Each team must have at least one computational "result". This can be as simple as checking a table in a published article or as complex as a new computational-biology algorithm and associated graphics.

4) There should be critical assessment of at least one previous relevant article.

5) Please cite and link pubmed or web references wherever possible.

6) The role that each member played in the team should be clearly stated in the written version. Each team member should present a substantial contribution orally, not merely introduce the final speaker(s).

7) The overall course grade will be 14% per problem set and 30% for the project.

8) The late policy is 5% (of 100%) off per day after the deadline of Dec 2 at noon. (If you are in the first group, you should get your slides emailed to us and confirm functioning in our hands by 9 AM Dec 2.)

9) The total email disk limit per project is 1 Mbyte for presentation (MS-word or ppt) and text (MSWord). If this is a problem, the figures can typically be reduced to an appropriate size with MSpaint or adobephotoshop and the whole file compressed in zip format. The recommended word limit is 1000 to 3000 words per project (excluding figure legends and references).

Tips for avoiding font problems in presentations

1. On PCs in PowerPoint, you can use the "Pack and Go" option located under the File menu. This will compress your presentation and include all non-standard fonts with the compressed file. E-mail the file (or transfer) and the accompanying expander executable to the destination computer and unpack. You should be set to go.

2. (Slightly more involved but more reliable) Take a screen shot of the text/graphics you wish to display and insert this into your Powerpoint presentation. On PCs, use the PrintScreen button to capture the whole screen or Alt-PrintScreen to capture the active window. On Macs, use Apple-3 (aka clover-3) to capture the screen. In both cases, it is in your best interest to open the image in an image editing program, crop the image, and save as JPG or GIF. Then import/paste the image into your presentation.


Research Projects

The new era of large-scale experimental methods in molecular biology has transformed it into an information-based science, making bioinformatics an integral part of genomic research. The research focus of the Laboratory of Bioinformatics and Functional Genomics is the development of integrated computational and experimental technologies for the study of gene function and regulation in biological systems through analysis, modeling, and visualization of heterogeneous biological data. The is is a joint laboratory with the Department of Computer Science and the Lewis-Sigler Institute for Integrative Genomics.

CASS: Content-Aware Search System

This project investigates how to build an efficient, high-quality content-based similarity search engine for feature-rich (non-text) data, which has dominated the increasing volume of digital information. The research topics include sketch construction, indexing for similarity search, distance functions for different feature-rich data types, integration with attribute-based search tools, content-addressable and searchable storage system, and Memex systems. The current toolkit is used to construct search systems for four data types including audio recordings, digital photos, 3D shapes, and genomic micro-array data.

CertiCoq: Principled Optimizing Compilation of Dependently Typed Programs

The CertiCoq project aims to build a proven-correct compiler for dependently-typed, functional languages, such as Gallina—the core language of the Coq proof assistant. A proved-correct compiler consists of a high-level functional specification, machine-verified proofs of important properties, such as safety and correctness, and a mechanism to transport those proofs to the generated machine code. The project exposes both engineering challenges and foundational questions about compilers for dependently-typed languages.

Computational Molecular Biology

My group develops algorithms for a diverse set of problems in computational molecular biology. We are particularly interested in predicting specificity in protein interactions and uncovering how molecular interactions and functions vary across context, organisms and individuals. We leverage high-throughput biological datasets in order to develop data-driven algorithms for predicting protein interactions and specificity for analyzing biological networks in order to uncover cellular organization, functioning, and pathways for uncovering protein functions via sequences and structures and for analyzing proteomics and sequencing data. An appreciation of protein structure guides much of our research.

Computational Neuroscience

The Seung Lab uses techniques from machine learning and social computing to extract brain structure from light and electron microscopic images.

Cryptocurrencies and blockchains

Cryptocurrencies and blockchains

Enterprise and data-center networks

Enterprise and data-center networks

Epigenome-wide association studies

We are currently developing methods for performing epigenome-wide scans for association of methylation status with phenotypes of interest.

Eyewire

EyeWire is a game to map the brain from Seung Lab at MIT. Anyone can play and you need no scientific background. Over 130,000 people from 145 countries already do. Together we are mapping the 3D structure of neurons advancing our quest to understand ourselves.

Fairness and ethics in computing

Fairness and ethics in computing

FCMA: Full Correlation Matrix Analysis of Human Brains

FCMA: Full Correlation Matrix Analysis of Human Brains

Geo-replicated cloud storage

Scalable causal consistency for wide-area data replication

ImageNet

ImageNet is an image database organized according to the WordNet hierarchy (currently only the nouns), in which each node of the hierarchy is depicted by hundreds and thousands of images. Currently we have an average of over five hundred images per node. We hope ImageNet will become a useful resource for researchers, educators, students and all of you who share our passion for pictures.

Internet architecture

Internet policy

Least-privilege web services

Inferring and Enforcing Security Policies in Web Applications

Liberty Research

The Liberty Computer Architecture Research Group exploits unique opportunities exposed by considering the interaction of compilers and architectures to increase performance, to improve reliability, to reduce cost, to lower power, and to shorten the time to market of microprocessor systems. This objective is accomplished by providing critical computer architecture and compiler research, expertise, and prototypes to the community.

Natural Algorithms

For much of my professional life, designing algorithms had been my thing. Then, one day, I watched a majestic flock of geese fly over Carnegie Lake and it dawned upon me that it had been their thing, too. Having been at it for 100 million years, even longer than I had, naturally their algorithmic genius surpassed mine. Undaunted, I resolved to catch up. The premise of my current research is that interpreting biological or social self-organized systems as "natural algorithms" brings upon them a fresh, new perspective ripe for inquiry. I believe that only the algorithm has the expressive power to model complex self-adaptive systems at the right levels of abstraction. Algorithms are the differential equations of the 21st century. Beyond its trite catchiness, this line serves to remind us that mankind's grasp of PDEs vastly exceeds its mastery of algorithms. The first order of business, therefore, is to build new analytical tools for natural algorithms.

Network Programming and Verification

The Network Programming Initiative supports research on languages, algorithms, and tools for network programming, and facilitates closer interactions with partners in industry and government.

Painting with Triangles

Although vector graphics offer a number of benefits, conventional vector painting programs offer only limited support for the traditional painting metaphor. We propose a new algorithm that translates a user’s mouse motion into a triangle mesh representation. This triangle mesh can then be composited onto a canvas containing an existing mesh representation of earlier strokes. This representation allows the algorithm to render solid colors and linear gradients. It also enables painting at any resolution. This paradigm allows artists to create complex, multi-scale drawings with gradients and sharp features while avoiding pixel sampling artifacts.


Terms and Concepts

To do this type of experiment you should know what the following terms mean. Have an adult help you search the internet, or take you to your local library to find out more!

  • Allele - Alleles are different forms of the same gene. Allelic variation in a gene arises through mutation of the DNA sequence defining the gene and may or may not be associated with trait variation (e.g., height, eye color).
  • Amino acid - The molecules that make up a protein. Each codon encodes for a specific amino acid.
  • Bioinformatics - The collection, classification, storage, and analysis of large volumes of biological information (e.g., genomic, metabolomic, proteomic) using computers.
  • Codon - Three bases in a DNA or RNA sequence which specify a single amino acid.
  • DNA (Deoxyribonucleic Acid) - DNA is the chemical that forms a basic molecular code for how a living being should operate. DNA is the biological heredity material passed down from parent to child. Four bases called adenine (A), guanine (G), cytosine (C), and thymine (T) constitute DNA. It is present in the nucleus of almost all cells in an organism.
  • Exons - Exons are sequences of DNA that code information for protein synthesis that are transcribed to messenger RNA, which in turn are translated into at least a portion of a protein.
  • Gene - DNA sequences that contain a code that can be translated into a particular protein. For example, CFTR gene has the information that is necessary for a cell to make the CFTR protein.
  • Genome - The DNA sequence of the entire organism's chromosomes. e.g., Human Genome
  • Genomics - The study of the entire human genome. Genomics explores not only the actions of single genes, but also the interactions of multiple genes with each other and with the environment.
  • Genotype - People inherit one allele for a gene from each parent such that they have two copies of each gene. The pair of alleles defines a person's genotype. For a gene that has two alleles in the population (e.g., an A allele and a G allele), there are three possible genotypes&mdashAA, AG, and GG.
  • HapMap - A partnership of scientists and funding agencies from Canada, China, Japan, Nigeria, the United Kingdom, and the United States to develop a public resource that will help researchers find genes associated with human disease and response to pharmaceuticals. HapMap's goal is to ultimately develop a haplotype map of the human genome and identify haplotype blocks.
  • Homozygous - A genotype in which the two copies of the gene that determine a particular trait are the same.
  • Heterozygous - Possessing two different forms of a particular gene, one inherited from each parent.
  • Introns - Introns are segments of a genes situated between exons that are removed before translation of messenger RNA and do not function in coding for proteins or protein fragments.
  • Junk DNA/Non-Coding Region - A region of the genome where the DNA has no known function (i.e., it does not code for a protein, regulatory sequence, or other functional elements). These regions usually consist of repeating DNA sequences. The majority of the human genome has no known function only 2 percent to 5 percent of the DNA sequence codes for genes.
  • Locus - The position of a gene on a chromosome. This term is a classical genetic concept used to understand gene order, gene distance, and gene function before gene and genomic DNA sequences were known.
  • mRNA - Messenger RNA, or a single-stranded molecule of ribonucleic acid that is transcribed from the DNA and then translated into protein.
  • Mutation - A mutation is a change in a DNA sequence. If the mutation occurs during the development of an egg or a sperm (i.e., gametes), then it becomes a heritable mutation. If the mutation occurs in any other body cell (i.e., part of the soma), then it is called a somatic mutation and it is not heritable. Somatic mutations are a cause of cancer. Mutations can be of many different types-substitutions, deletions, or insertions. Mutations in the DNA can be synonomous, e.g., not having any effect on the translated protein, or non-synonomous, causing amino acid changes in the translated protein.
  • Non-synonymous change - If a SNP results in a change in the protein sequence.
  • RNA (Ribonucleic Acid) - A chemical found in the nucleus and cytoplasm of cells it transcribes the protein-coding instructions of DNA into a code that the protein-building ribosomes of a cell can understand. The chemical structure of RNA is similar to DNA-RNA also contains adenine (A), guanine (G), and cytosine (C), but instead of thymine (T), RNA contains uracil (U).
  • SNPs (Single Nucleotide Polymorphisms) - Currently, there is estimated to be about 6 million positions in the human genome where a mutation occurred at a single nucleotide (A, T, C, or G) and both its alleles are now greater than 1 percent prevalent in the population. These SNPs are important for studies of genetic or genomic associations with disease because the alleles are common in the population.
  • Synonymous change - If a SNP does not result in a change in the protein sequence. It is also known as a silent change.

Questions

This science project is based on research that provides often inconclusive but strongly correlative evidence that associates SNPs to risk of disease. The notion is that, with the availability of information about the complete human genome, we would be able to predict the risk of an individual contracting a disease or identify individuals with specific qualitative traits ('smart' genes, 'criminal' genes, 'intuition' genes etc.). One outcome of such advance would be personalized medicine where it is possible to treat each individual with a custom-made drug or even perform preventive therapy. However, on the flip side, ethical concerns need to be addressed with respect to individual human rights (The Minority Report movie debate).

Here are some questions that you will be thinking about while doing this science project:

  • What is FASTA format?
  • If two genes are homologous, are they similar?
  • What are different types of mutations and how do they affect protein function?
  • What is the probability of a single base mutation affecting protein function?

Methods

Curriculum Development

Two e CS ite fellows (computer science graduate students) with research experience in computational biology worked closely with two AP biology teachers in order to introduce computational methods into required AP biology lessons. Beginning in September, they met weekly to brainstorm possible units in an effort to minimize time taken away from required content. In addition, the two e CS ite fellows met with each other on a weekly basis to coordinate the planning with both teachers, and monthly with an e CS ite co-director whose research interests are in computational biology to help guide the content. There were periodic meetings of all involved (the e CS ite fellows, AP Biology teachers, and co-director). Curriculum was developed during the fall semester and delivered to the classes during the spring semester.

The Curriculum

Our curriculum consisted of three basic lessons: an introduction to algorithms, a basic discussion of the BLAST algorithm [6], and algorithms to construct phylogenetic trees.

The first lesson, an introduction to algorithms, was designed to introduce students to the basic vocabulary involved with algorithms and get students thinking algorithmically. We started by introducing students to algorithms for every day tasks, such as making coffee, then asking them to write their own algorithms to make peanut butter sandwiches. The next activity had students participating in a “living computer” algorithm: each student was given a numbered instruction that represented one step in the algorithm. Students then stood in line in front of the class in the order that the steps would be performed in the computer. Each student performed their step and handed the “output” to the next student in line until the final step had been reached. After each step was acted out, it was executed on a computer using the same inputs so that the students could see the same output coming from the computer as their “living computer”. The students executed the algorithm, and then were shown the computer code that did the same thing. This allowed students to see what sort of basic steps should be included in an algorithm and how those steps could be translated to computer code. Finally, we had students write algorithms to create Punnett squares, diagrams to predict the outcome of a cross-breed given the genotypes of the parents. Students were already familiar with the creation of Punnett squares, so this helped students think about biological problems in algorithmic terms.

The second lesson focused on DNA sequence comparison using BLAST. This lesson had two parts. In the first part of the lesson, we explained BLAST in terms of a word search. We gave students three word search puzzles containing various vocabulary words from biology and genomics (see Figure 1 ). The first was a “perfect” word search, simulating the problem of finding an unmutated gene in another genome. In the second puzzle, students were given a “wrong key” word search created by someone who would occasionally hit the wrong key, so some letters might be wrong. This served as a simulation of the problem in a genome with single nucleotide polymorphism (SNP) mutations. The third puzzle was designed to simulate the problem in a genome that contains both SNP and insertion/deletion (indel) mutations. Students were given a 𠇍itz” word search that contained not only wrong letters, but missing or added letters, as if the person writing the word search may have skipped parts of the puzzle or forgotten what he was doing and started typing in a shopping list or telephone message. The second part of the lesson introduced students to an implementation of the BLAST algorithm. We asked students to choose a disease with a genetic basis, then search for that gene in the National Center for Biotechnology Information (NCBI) database. Once they obtained the DNA sequence for that gene, they were asked to run it through the BLAST implementation on the NCBI website to find similar genes in humans and other species. Students were then asked to answer several questions about the BLAST results, including: give the scientific and common names of species that have similar genes, give the percent of base pairs that could be matched in a given alignment, and give an example of an alignment that contains an indel.

Students are introduced to the problems inherent in searching the genome with a series of word search puzzles designed to model a genome with no mutations, a genome with SNPs, and a genome with both SNPs and indels.

The third lesson was building phylogenetic trees. Students were given sequence data on the COX15 gene from eight different species. These genes were identified using only first names (𠇊lex”, 𠇌hris”, etc.) without any information about what species they came from. Students were then asked to construct a phylogenetic tree by running pairwise BLAST to compute a similarity score between every pair of genes, and to cluster based on these results. Students were taught a simple hierarchical agglomerative clustering algorithm [7], and different students were given different metrics for determining distance between clusters: some students were asked to cluster by considering the distance between clusters to be the distance between the closest two points in the clusters (single linkage), some students were asked to consider the distance between clusters to be the distance between the furthest two points in the clusters (complete linkage), and the remainder were asked to consider the distance to be the average distance between all points in the clusters (average linkage). Students then compared the results of their different clustering algorithms to see that seemingly minor differences in algorithm implementation can result in different biological conclusions, reinforcing the notion that biologists need to understand details of the computational tools they use, even if they aren't themselves developing the tools. Finally, students used BLAST to identify species from their sample genomes. Once species were identified, students could compare their results to their predictions of species relatedness.

Teaching

This unit was taught in three different classes in two different high schools: Centaurus High School and Monarch High School. Centaurus is a high school located in Lafayette, Colorado, with 28% of students receiving free or reduced lunches. Monarch is a high school in Louisville, Colorado, with 4% of students receiving free or reduced lunches (data is from the Boulder Valley School District website at http://www.bvsd.org/and is based on numbers collected in October 2009). In Centaurus High School, the unit was taught to a combined AP/IB Standard Level (SL) Biology class composed mostly of high school juniors with some seniors. At Monarch High School, it was taught to an AP Biology class and a dedicated Biotechnology science elective class. There was little overlap between students in the Biotechnology class and those taking any AP science classes. Due to scheduling constraints in the schools, the lessons in the unit were spread out over a period of approximately three months. Lessons were taught primarily by the e CS ite fellows in collaboration with the classroom teachers. For various reasons, not all activities were done in all classrooms.

Lessons were evaluated through discussion with the classroom teachers. In formal and informal discussions, the teachers reported to the fellows their observations about which part of the lessons the students had enjoyed, which parts they had struggled with, and whether or not students were able to connect these lessons to other lessons from biology class. Teachers also reported on the general types of comments, questions, and complaints they had received from students about the curriculum.


Student Responsibilities

  • Attend nearly all of the lectures and lab meetings
  • Do the coding exercises in class when they are assigned
  • Regularly update your course webpage with completed and partially completed assignments
  • Study the code of your classmates on their webpages to see how they have solved the same problems you are working on
  • Annotate your code so it is useful to you and others
  • Surf the internet to find solutions to programming problems
  • Share code and ideas, and help others who are struggling
  • Use the course work to complete some concrete work that will go towards your thesis

Convolutional designs

More recent work using convolutional neural networks (CNNs) allowed direct training on the DNA sequence, without the need to define features (Alipanahi et al, 2015 Zhou & Troyanskaya, 2015 Angermueller et al, 2016 Kelley et al, 2016 ). The CNN architecture allows to greatly reduce the number of model parameters compared to a fully connected network by applying convolutional operations to only small regions of the input space and by sharing parameters between regions. The key advantage resulting from this approach is the ability to directly train the model on larger sequence windows (Box 2 Fig 2B).


Facts, Fiction and Computational Biology

If you take a look at any heart diagram for children in school biology textbooks, you will find a very clear and basic idea about what a human heart resembles. Although the theme for the unit studies might be a science subject, they still have many pursuits that cover the rest of the subjects. It’s often harder for the biological parent to do because they’re more emotionally involved but that’s exactly why it must be that manner.


Day to Day Focus

– Single nucleotie polymorphism

Tentative career focus

university scientists (academic career)
business
law (ethics, patents, policy, etc)
pharmaceutics/biotechnology (careers in industry)

Each group will pitch a biotechnology busines plan based on the week’s research.

Refund Policy

The $100 administrative fee will not be refunded once the camp application is accepted.

If a student withdraws, the camp fee paid minus the administrative fee will be refunded if we are notified by April 25, 2021.

If a student withdraws between April 26 and three weeks before the camp starting date, we will refund 50 percent of the camp fee paid.


Introduction

In the spring of 2020, nearly all academic institutions went to some level of shutdown/quarantine in order to slow the spread of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the virus that causes Coronavirus Disease 2019 (COVID-19). For many universities, courses were moved online, laboratory-based research was required to slow or stop, and most on-site work shifted to telework. Optimistically, many academics thought initially that this might lead to a surge in research productivity. Indeed, by this point, we suspect that readers have heard that Isaac Newton apparently figured out calculus while in isolation during the plague. Consistent with this, some of the authors experienced or observed messaging from department chairs, center leaders, or mentors telling principal investigators (PIs) that the pandemic situation has likely created “extra time” for them to focus on writing grants and developing new ideas. Further, well-intentioned suggestions included ideas to shift research projects from experimental to computational questions however, such shifts may represent a major research pivot for members of the lab group and require substantial support from the PI. Even as the pandemic persists and university leaders consider how to safely reopen labs and return to fall courses, which will result in new upheaval to our lives, there are still messages that quarantine might give scientists time to pursue new interests or work on long-forgotten projects [1].

However, if scientific fields are seeing increased output due to time focused on writing/computer-based tasks (e.g., publication submission, patent applications, and grant proposals), all indications suggest that this has been a benefit for men in science, and not women [2–4]. Discussions of these data have focused primarily on the fact that women do a disproportionate amount of house and childcare [5–7], and options used to provide support for this unpaid work have essentially evaporated (e.g., limiting outside workers into the home for cleaning, day cares not accessible to children of nonessential workers, and school and summer camp closures). Indeed, productivity gaps are being observed for working women in many industries and at a broader level have been suggested to be an issue for working parents regardless of gender due to the lack of school/day care options [8].

While we recognize that the impacts of COVID-19 are particularly acute for women with significant childcare/eldercare duties, we note that women PIs in academia carry disproportionately higher teaching and service loads [9–11]. These roles have been repeatedly emphasized by university leadership as essential in supporting students through the pandemic. For example, the shift to online teaching requires faculty to develop new teaching methods and provide additional support to students facing challenges with remote learning. Increases in service work have been noted by authors who serve in general advisory roles to undergraduate/graduate students (e.g., program directors). Additional work has been required from various research committees (e.g., biosafety and human subjects research) to support a safe return to research. While undoubtedly true that this work is essential, there has not been recognition that this requires more time from PIs and further disadvantages women from maintaining their research activity on par with male colleagues. Through a series of online discussions over the past few months, the authors have tried to identify “10 Simple Rules” to help women PIs navigate the pandemic. We recognize that these rules will not adequately address the additional challenges affecting women who also may suffer impacts due to other aspects of their identity (e.g., Black, Latina, and Indigenous women or LGBTQIA individuals).

Throughout this piece we will use several terms that may have slightly different meanings in different university structures. Our suggestions are aimed toward women PIs, meaning women who have an independent research position these women may or may not lead a larger group or have a didactic teaching role. We use the term “students” to refer to undergraduate or graduate students that are being taught in a didactic setting and “trainee” to refer to undergraduate/graduate students or postdocs being mentored in a research setting. Research staff would include lab personnel such as technicians and scientists that are not independent PIs. Many PIs conduct research with both trainees and research staff therefore, the term “group” implies all people that the PI mentors and manages. Finally, the term “staff” refers to administrative staff, whether in support of the research or teaching missions of the university.

Rule 0: There are literally no rules

That’s right—the authors are going to acknowledge up front that there are no hard rules for a situation that has been described as “unprecedented” an unprecedented amount of times. Quite simply, the challenges the authors are facing may not reflect challenges others are facing because of differences in career stage, family situation, health stresses, degree that COVID-19 has impacted their locality, or many other variables. We instead are offering 10 suggestions that we hope will be useful or adaptable to others. In addition, we present suggestions to university leadership regarding institutional policies that can better support women PIs—now and in the future. Many of these suggestions are presented in more depth in [12]—we are indebted to the University of Wisconsin Caregiving Taskforce for authorizing us to amplify their efforts.

Suggestion 1: Find a peer group of women to provide professional support

It is well established that peer networks are important for women in science, technology, engineering, and mathematics (STEM) [13]. The authors are all members of an online group for women PIs in biomedical engineering, which has historically served as a sounding board for professional concerns. However, many have their own smaller networks composed of women in their department/college/university, from similar racial/ethnic backgrounds, or with similar family situations (e.g., single parents, children with special needs, and eldercare responsibilities). We have found that maintaining these connections has been essential over the course of our careers and are even more important during this quarantine period. Examples to maintain connectivity included having a virtual happy hour/lunch, sending messages/e-mails to show support or to share your latest frustrations, or having a socially distant get-together. It is important to keep an eye out for women who seem to be struggling (e.g., not engaging in normal exchanges and sharing their stress) and reach out to them likewise, if you are struggling, you should engage your network. Knowing you aren’t alone and that your concerns are valid is worth the time, as hard as it is to find the time.

Institutional suggestions.

Create and/or support professional groups for women PIs. Examples include Association for Women in Science (AWIS), Society of Women Engineers (SWE), or individualized programs such as the Women Faculty Mentoring Program at the University of Wisconsin-Madison [14]. Provide financial support or relief from service to enable women to participate in such groups. Wherever possible, these groups should provide networks based on both professional interests and personal/identity concerns.

Suggestion 2: Say no to requests to do anything outside of your main responsibilities

Despite the suggestion that you have gained time from things like losing your commute, recognize that you have lost far more time from challenges teaching online, working with your research team remotely, loss of childcare, and simple annoyances like finding toilet paper. So cut yourself some slack, and realize you won’t be able to do as much as you normally do. Perhaps you say no to peer review requests. Maybe you pass on a request to serve on a new committee. As things normalize, you can return to these tasks if they are important to you. Alternatively, if you can’t do them now, you can offer a time when you might be able to do them the requesting party will need to recognize that your future schedule could go through upheaval as surges of the disease hit different areas and restrictions change. See S1 File for example responses.

Institutional suggestions.

Cancel all nonessential service such as assessment reports that can be resumed after the pandemic with minimal effect. Suspend the tenure clock for probationary faculty. Support women PIs who need to cancel external service work (e.g., editing and reviewing) by making clear this will not be weighed in future promotion decisions. Conduct assessments of teaching and service loads within departments to achieve equity.

Suggestion 3: Drop something

Recognizing that you have had to take on extra work (e.g., mentoring graduate students dealing with isolation and teaching third grade to your child), something has to give. In the best case, you would look at your workload and see something that really isn’t that important, takes up a lot of time, and you don’t enjoy. If so, drop this task! Alternatively, perhaps there are tasks that are necessary but could be delegated to a group member or staff who needs telework assignments. Provided your expectations are reasonable and you are able to provide mentoring support, accelerating others’ competency in tasks you normally reserve for yourself (e.g., proposal writing and budgeting) has the potential benefits of giving group members and staff a greater sense of ownership of the research mission, while reserving your time for critical tasks. Similarly, if your class has a teaching assistant (TA), engage them to help you improve the transition to virtual learning—insight from their own experiences taking such classes could be very beneficial. More often, this will mean not completing something you wanted to do but is not mission essential—unfortunately for many women, this has meant not submitting a half-finished proposal or pushing off writing a paper. So, we encourage you to look at your teaching/service load to see if there are items that can be put off or not done you may find the questions posed in [15] useful as you determine what to keep or take on as your career progresses.

Or, consider adjusting your expectations for what your finished manuscript looks like—as we know, “perfect is the enemy of submitted.” Likewise, look at your homelife and do the same. True, your house might not be as clean as you would like, and you may be eating more frozen pizza than you normally would tolerate. Remember that you are not the only person accepting this as your “new normal.” We can’t forget that the goal during a pandemic is survival—if you are keeping yourself physically and mentally healthy, you are more than succeeding. Acknowledging these changes consciously and trying to make choices about where you make cuts might provide a semblance of control over a situation that has felt out of control.

Institutional suggestions.

Reevaluate tenure/promotion expectations, extend the tenure clock, and formalize changes in writing. Provide adequate TA/grader support for classes. Make course evaluations, which are often susceptible to bias, optional. Alternatively, if course evaluations are done, make them developmental rather than evaluative in nature. Suspend requirements for probationary faculty to undergo peer teaching evaluations.

Suggestion 4: When you have energy to do more than the minimum, use that in support of women and underrepresented groups

This may seem in conflict with prior suggestions—and it is—but women know they are able to pursue their scientific careers due to the hard-fought battles by the women before them [16]. Losing ground due to COVID-19 is a real possibility. We recognize that advocacy of this nature is a privilege, and not everyone is able to do so safely. If you are in the position to support women and underrepresented groups and have the energy, pick a cause and lean into it. Also, recognize that this action can take many forms, some of which may be a better fit for your individual situation. As examples of larger/more public actions, you could lobby your institution for policies to address the pandemic gender-related gap due to caregiving burdens [17], or push funding agencies to close racial disparities [18].

Remember that small actions add up—perhaps you don’t have the mental focus right now for a big battle or a position on a new committee. But if you are able to send an e-mail to colleagues and administrators pushing for more equitable policies from the university, you are contributing. You can share the names of scientists who are women, immigrants, and members of underrepresented groups with your trainees to make sure their work gets recognized and cited (for example, see the soon to be launched http://citeblackauthors.com). Take the time to incorporate Black and other underrepresented scientists in your teaching and research group meetings so the next generation recognizes their accomplishments. And for white men and women reading this piece, remember that service related to diversity and equity too often falls on the small number of PIs of color [19].

Institutional suggestions.

Ensure that faculty and staff who contribute to diversity, equity, and inclusion initiatives are compensated for their time and effort in these areas, and this work is included in considerations of overall workload. Value these efforts in tenure and promotion decisions. Invite women and underrepresented PIs to present their work and provide an honorarium to cover caregiving costs for the time they will need to prepare and present virtually so that they can fully engage.

Suggestion 5: Remember, you know yourself best

Maybe you thought you would have time to pick up a new hobby (or finish the many unfinished projects you already have). Six months (or more) into COVID-19, you have likely accepted that this is unrealistic. However, you know the things that have historically helped you relieve stress. Make a list of 10 of them. Some of them may not be an option during quarantine (oh, how some of us miss writing in coffee shops). But for those that are an option, try to do 1 of them every now and then. Maybe you like baking—bake cookies and drop them off on your colleague’s doorstep as the baking bandit (or just eat them all, no judging). Perhaps a daily walk is your relief, or you can join a yoga class online. You may personally find that taking a break from social media is calming. It doesn’t have to be every day, it doesn’t have to be long periods of time—but you have to find time for your mental health.

In the same manner, you know your group’s research strengths best. It is understandable during a global pandemic to feel called to shift your research priorities to the urgent problems at hand. Depending on your research skills, this may be a logical area for you to pursue and well worth the investment. For example, 1 of the coauthors was doing a sabbatical during the outbreak at a small biotech and participated in the development of a new diagnostic test. While not her original plan, the skills and collaborations she gained during this shift have opened new areas for her academic work. However, if your expertise is not relevant to infectious disease, diagnostics, personal protective equipment (PPE) testing, or other COVID-19 topics, it is perfectly appropriate to maintain your research focus. After all, there are many important problems that will remain even once the pandemic ends—and in some cases, they may have reached new levels of importance based on lack of preventative care during the pandemic or complications found in COVID-19 survivors.

Institutional suggestions.

Offer employees access to programming that supports wellness, including aspects of both physical and mental health. Maintain a balance of support for research related to COVID-19 as well as previously established priorities.

Suggestion 6: It’s OK to push back

Academia often seems to demand that you should be working at all times. And for those in academia who do not have significant demands at home due to childcare or eldercare, it is possible that the pandemic might be a time of productivity due to relief from some of the daily interruptions of working on-site. However, perpetuating the myth that we can all work to the same degree (or better!) than we did a few months ago is very damaging to many women PIs. When you hear statements such as “everyone is writing more grants now” and “since we have more time, let’s have a virtual conference about this topic,” it’s more than OK to push back that this is not your reality, regardless of the reason. You are likely to hear your voice amplified by others who were nervous to speak up. It is time for us to instead ask the person stating this to take on some of the work you have had to pass on (Suggestions 2 and 3), as a more equitable working environment should be a goal for all in science (Suggestion 4). If they balk, this is a perfect time to engage your network to vent your frustration (Suggestion 1). We have provided some sample responses we have used (S1 File).

Institutional suggestions.

Provide training for department chairs and supervisors underscoring the strains that women and primary caregivers will face during the fall and spring semester, paying particular attention to how this crisis will be amplified for single parents, people of color, and others at the intersections of marginalized identities. Facilitate an education campaign for colleagues and students highlighting the immense strains on women and caregivers this fall, to improve empathy for delays in responses or slower completion of tasks. Normalize that children may appear during lectures or meetings. Be flexible about meeting times and attendance for committee service since faculty may be juggling multiple responsibilities at home.

Suggestion 7: Remember, you have some flexibility to make your own schedule

If there are pockets of time where you find yourself able to focus better than others, do your best to protect them. Block these times on your calendar—both in the near future and in the upcoming months by declining invitations for extraneous responsibilities (Suggestions 2 and 3). Keep a “to-do” list of small tasks handy for those times that you have just a few minutes or you have time but not the mental energy to tackle a major project. You may find that knowing you will “get to it” is enough to clear some mental bandwidth to deal with your stress.

Conversely, if there are times your family needs your attention, put it on your calendar to prevent meetings from being scheduled in your family time. You may have colleagues that approach their telework or family scheduling very differently from you do not put pressure on yourself if your approach radically deviates from those of your peers. Again, trust yourself to make the schedule and decisions that work best for you (see also Suggestion 5). Respect this right in your trainees as well—we can change the scientific culture if we reflect our values. Allow them to set their own schedules to fall outside the traditional workday window if that is a better fit for their situation (indeed, with social distancing in many labs, this may even be a necessity). And yes, it is okay to decline meetings that are requested at short notice (S1 File).

Institutional suggestions.

Offer remote work and online teaching options for all faculty, instructors, and staff. Provide explicit policies that detail how faculty and instructors who teach face to face can pivot to asynchronous teaching modes and flexible work-from-home policies if an emergency arises. Utilize polling methods to identify meeting times rather than relying on meeting times from prior semesters when childcare was available during standard business hours—and remember that meal times and bed times may be particularly challenging for caregivers.

Suggestion 8: Whatever help you can get, take it

This might seem obvious, but sometimes when we are overwhelmed, it’s hard to see the options that are there. Help with work tasks may come in the form of engaging staff who have limited telework—providing them with work may help them to avoid furlough, teach them new skills, and potentially lead to lasting support. For those with caregiving, help might come in the form of a family member who can provide childcare or screen time–based rewards that give you focused time to work. With many partners also working from home, discussions about the distribution of domestic and childcare responsibilities may be warranted to ensure equity and the ability of both partners to pursue their careers. Perhaps your kids are old enough that they can even help with some of your work—1 of the authors tried (unsuccessfully) to engage her son in doing analysis on ImageJ (NIH, Bethesda, Maryland). Another purchased a 3D printer and recruited her daughter to help print parts for an OpenSPIM setup. Maybe this is the time to get your children more involved in housework and cooking. Of course, these kinds of changes may be an uphill fight so take them on in slow steps when you are ready to deal with the next battle. Until then, refer to Suggestion 3 as often as needed.

Institutional suggestions.

Create solutions beyond Family and Medical Leave Act (FMLA) for emergency leave and workload reductions. Offer a combination of creative solutions such as a 1-semester teaching release or course reduction, a 50% work option, etc. Repurpose travel funds to subsidize emergency childcare or eldercare. Offer a sick-day bank that allows others to contribute excess sick days to those in need. Utilize human resources (HR) and School of Education to provide a database of local resources for caregivers and resources for families homeschooling, tutoring, and support for families with children who need additional accommodations.

Suggestion 9: Do your best to remember that others are struggling too—be empathetic and work to build a community

In times of stress, human beings are wired to focus on themselves—the fight-or-flight instinct is not about saving the community after all. This built-in response, coupled with the natural isolation of a quarantine, is a serious challenge to overcome. Finding virtual or socially distanced ways to maintain a sense of community with your friends and family are essential at this time. As a PI, you may want to arrange such events for your research group many of the authors have done so and have found these to be appreciated by our groups even though the activities were not elaborate or lengthy. However, as you interact with others, it can be easy to fall into the pattern of comparing your stresses and deciding that you either have it “worse” or feeling guilty because you have it “better.” For example, while the authors have stresses related to the pandemic and gender equity imbalances, we acknowledge that our Black colleagues are dealing with racism and microaggressions on a daily basis. Women with different family situations (e.g., single parenting) will be dealing with different challenges. All of these feelings of stress are real and valid. Given the complex nuances of each individual’s situation, we will all benefit from trying to be more considerate as we work together.

So how do you remain considerate without falling into the trap of taking on the tasks that your lab group or staff should be responsible for? This is indeed a challenge that many woman PIs are dealing with—additional directives from university administration combined with lower productivity from their group and staff are putting the PI into a squeeze. We suggest that each situation is approached with empathy, while maintaining your standards and accountability. For example, empathy may mean that when you assign a task to a group member or staff, you ask them whether the timeline is feasible. If it’s not, that may be a sign that in the future you should aim to give them more advance notice. If a pattern of not completing work continues, it is then time to ask for an explanation. Recognize that just as no one is fully aware of your situation, you are not completely aware of your colleague’s situation. Those with younger children may struggle with finding focused time to work, while colleagues with older children may be dealing with remote school and developmental challenges that are unique to socially distanced teenagers. Perhaps you can help the group member or staff using some of the suggestions above to help them carve out work time and focus on the most important items. This is also a point to examine your role in this dynamic—are your expectations reasonable, or should they be modified? This may be an area where your network from Suggestion 1 can provide you honest feedback.

However, if you find that the group member or staff is not able to work through their challenges, it may be time to ask them to consider the impact of this situation on their mental health. Depression and anxiety often manifest in the inability to initiate or complete work tasks at the level an individual can typically perform at. Some group members and staff may share their struggles with you indeed, studies have shown that students expect women faculty to be more accommodating of student challenges than men [20]. We know from our experiences that this takes up significant time/mental energy, and we suggest that you direct students, group members, and staff to university and community resources. If the situation persists, the next step may be to work with your HR to arrange for a leave (ideally, one that maintains benefits for the person).

Institutional suggestions.

Provide leave options that maintain benefits for people at all levels at the university. Support and expand mental health care options, and regularly advertise these resources. Create funds to subsidize childcare costs for trainees.

Suggestion 10: Don’t lose your sense of humor

We know, there is nothing funny about this situation. Many of us have needed or will need space to grieve deeply. However, our experience is that where you can share a laugh, you should. In that spirit, we offer a few of the more “tongue in cheek” suggestions that our larger peer group shared during discussion of this paper (S2 File).

Institutional suggestions.

Participate in social media in ways that build community and provide moments of occasional levity.


Create Python projects for kids

Practicing Python does not have to be boring. We hope your child uses these project ideas to create something truly unique while improving their Python skills along the way. After mastering the basics, your child can use Python to tackle cutting edge problems such as artificial intelligence, data science, cloud computing and computational biology!

Written by Brandon Lim, a Create & Learn instructor and curriculum developer. Brandon also works full-time as a software engineer and holds a BS in Computer Science from Johns Hopkins University.


Watch the video: Ap Biology Big Ideas Project (January 2022).