CMSE 410/890: Bioinformatics and Computational Biology

Description

This course is an introduction to the inner-workings of methods in bioinformatics and computational biology: analytical techniques, algorithms, and statistical/machine-learning approaches developed to address key questions in biology and medicine.

In this course, students will also learn how to formulate problems for quantitative inquiry, design computational projects, think critically about data & methods, do reproducible research, and communicate findings.

Note
Open to both undergraduate and graduate students. Counts toward the CMSE minor, graduate certificates, and dual PhD. Please email Heather Johnson at john1451@msu.edu for an override.

Prerequisites
CMSE 201 or CMSE 301-304 or equivalent with programming experience and two semesters of introductory biology (LB 144 and 145 OR BS 161 and 162 OR BS 181H and 182H, or equivalent). Statistics at the level of STT 231 is strongly recommended.

Basically, it would be assumed that you:

know how to code in one of the mainstream languages like Python or R,
have an understanding of basic statistics and probability, and
have studied basic genetics, molecular biology, and cellular biology.

Instructor Contact Information

Arjun Krishnan | … :———— | :———— Affiliation | Dept. Computational Mathematics, Science, and Engineering</br>Dept. Biochemistry and Molecular Biology Office | 2507H Engineering Building Contact | Email: arjun@msu.edu</br>Twitter: @compbiologist</br>Website: https://thekrishnanlab.org

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Course Outline and Materials

Major Topics

Biological topics

Genome assembly & annotation
Sequence alignment & pattern finding
Comparative genomics; Phylogenomics
Genetic variation & quantitative genetics
Regulatory genomics
Functional genomics
Single-cell genomics
Molecular dynamics; Structure prediction
Modeling cellular pathways
Whole-cell models; Digital evolution
Biological networks

Computaitonal / Analytical topics

de Bruijin graphs; Hidden Markov models
Dynamic programming; Substitution matrices; Fast Local Alignment
Suffix trees; Tree construction
Regularized linear regression; Statistical inference, Multiple testing
Gibbs sampling; Expectation-Maximization
Two-sample tests; One-sided Fisher’s exact test; Intro to unsupervised and supervised learning
Missing value imputation; Dimensionality reduction; Trajectory inference
Molecular simulation; Maximum entropy modeling
Dynamical simulation, State Space, Bifurcation; Discrete/Boolean modeling
Wiring diagrams; Constraint-based modeling; Artificial life and other whole-cell models
Measuring associations; Network inference; Graph theory, Label propagation

Primers

Conducting a Bioinfo / CompBio Project: A Practical Primer in 3-parts:

Organizing and managing a CompBio project
Kickstarting and getting help in a CompBio project
Presenting data and results in a CompBio project

Recommended Materials

This document contains links to a bunch of excellent resources for brushing-up your Unix, Python/R, Statistics, and Biology.

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Schedule, Location, Calendar, and Office Hours

S/L | Info :———— | :———— Schedule | Mon, Wed, and Fri</br>11:00 am - 12:10 pm Location | 351 Natural Sciences Bldg

Calendar

This calendar contains the class schedule and the links to the lecture slides and reading materials. Download the detailed schedule as a PDF.

Day	Date	Module	Topic	Learning Materials
Day 01	Mon, Jan 06	Introduction, Overview, and Refreshers	Course overview	[Lecture]
Day 02	Wed, Jan 08	Introduction, Overview, and Refreshers	Refresher 1: Concepts in statistics & probability
Day 03	Fri, Jan 10	Introduction, Overview, and Refreshers	Refresher 2: Concepts in model building + Intro to Bioinformatics & Computational Biology	[Lecture]
Day 04	Mon, Jan 13	Genome assembly & annotation	Assembly with de Bruijin graphs	[Lecture]
Day 05	Wed, Jan 15	Genome assembly & annotation	Gene prediction with Hidden Markov models	[Lecture]
Day 06	Fri, Jan 17	Genome assembly & annotation	Paper discussion; HMM continued	Velvet: Algorithms for de novo short read assembly using de Bruijn graphs [Journal] [PDF]
	Mon, Jan 20	No class	Need an extra hour (or two 30-minute slots) to compensate
Day 07	Wed, Jan 22	Sequence alignment & pattern finding	Dynamic programming; Substitution matrices	[Lecture]
Day 08	Fri, Jan 24	Sequence alignment & pattern finding	Paper discussion; Basic Local Alignment Search Tool	Basic local alignment search tool [PDF]</br>Steps used by the BLAST algorithm [PDF]
Day 09	Mon, Jan 27	Comparative genomics; Phylogenomics	Whole genome alignment; Suffix trees	[Lecture]
Day 10	Wed, Jan 29	Comparative genomics; Phylogenomics	Molecular evolution; Tree construction	[Lecture]
Day 11	Fri, Jan 31	Comparative genomics; Phylogenomics	Paper discussion	Whole-genome alignment:</br>- MUMmer1: Alignment of whole genomes [Journal] [PDF]</br>- MUMmer2: Fast algorithms for large-scale genome alignment and comparison [Journal] [PDF]</br>- MUMmer3: Versatile and open software for comparing large genomes [Journal] [PDF]</br>- MUMmer4: A fast and versatile genome alignment system [Journal] [PDF]
Day 12	Mon, Feb 03	Genetic variation & quantitative genetics	GWAS, Regularized linear regression	[Lecture]
Day 13	Wed, Feb 05	Genetic variation & quantitative genetics	Statistical inference, Multiple testing	[Lecture]
Day 14	Fri, Feb 07	Genetic variation & quantitative genetics	Polygenic risk score; Paper discussion
Day 15	Mon, Feb 10	Regulatory genomics	ChIP-seq & Expectation-Maximization	[Lecture]
Day 16	Wed, Feb 12	Regulatory genomics	Expectation-Maximization & Gibbs Sampling	[Lecture]
Day 17	Fri, Feb 14	Regulatory genomics	Paper discussion	- What are DNA sequnence motifs? [Journal] [PDF]</br>- How does DNA sequence motif discovery work? [Journal] [PDF]</br>- What is the Expectation Maximization algorithm? [Journal] [PDF]</br>- Practical Strategies for Discovering Regulatory DNA Sequence Motifs [Journal] [PDF]
Day 18	Mon, Feb 17	Functional genomics	Distance measures; Clustering	[Lecture]
Day 19	Wed, Feb 19	Functional genomics	Differential expression; Functional enrichment analysis
Day 20	Fri, Feb 21	Functional genomics	Paper discussion	A module map showing conditional activity of expression modules in cancer [Journal] [PDF]
Day 21	Mon, Feb 24	Conducting a Bioinfo / CompBio Project: A Practical Primer in 3-parts	Organizing and managing a CompBio project	[Lecture]
Day 22	Wed, Feb 26	Conducting a Bioinfo / CompBio Project: A Practical Primer in 3-parts	Kickstarting and getting help in a CompBio project	[Lecture]
Day 23	Fri, Feb 28	Conducting a Bioinfo / CompBio Project: A Practical Primer in 3-parts	Kickstarting and getting help in a CompBio project	[Lecture]
	Mon, Mar 02	No class	Spring break
	Wed, Mar 04	No class	Spring break
	Fri, Mar 06	No class	Spring break
Day 24	Mon, Mar 09	Bioinformatics & Computational Biology Co-work Sessions	Diff. time: 10a–12:15p + Diff. location: Holmes Hall, Classroom W5
Day 25	Wed, Mar 11	Bioinformatics & Computational Biology Co-work Sessions	Diff. time: 10a–12:15p + Diff. location: Holmes Hall, Classroom W5
Day 26	Fri, Mar 13	Bioinformatics & Computational Biology Co-work Sessions	Diff. time: 10a–12:15p + Diff. location: Holmes Hall, Classroom W5
Day 27	Mon, Mar 16	Mid-course project presentations	Lightning talks
Day 28	Wed, Mar 18	Mid-course project presentations	Lightning talks
Day 29	Fri, Mar 20	Mid-course project presentations	Lightning talks
Day 30	Mon, Mar 23	Single-cell genomics	Dimensionality reduction	[Lecture]
Day 31	Wed, Mar 25	Single-cell genomics	Supervised machine learning	[Lecture]
Day 32	Fri, Mar 27	Single-cell genomics	Paper discussion	The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells [Journal] [PDF]
Day 33	Mon, Mar 30	Protein structure prediction & dynamics	Maximum entropy modeling	[Lecture]
Day 34	Wed, Apr 01	Protein structure prediction & dynamics	Molecular dynamics	[Lecture]
Day 35	Fri, Apr 03	Protein structure prediction & dynamics	Paper discussion	Evolutionarily conserved networks of residues mediate allosteric communication in proteins [Journal] [PDF]
Day 36	Mon, Apr 06	Modeling cellular pathways	Dynamical modeling	[Lecture]
Day 37	Wed, Apr 08	Modeling cellular pathways	State Space, Bifurcation	[Lecture]
Day 38	Fri, Apr 10	Modeling cellular pathways	Paper discussion	[Lecture]</br></br>Construction of a genetic toggle switch in E. coli [Journal] [PDF]
Day 39	Mon, Apr 13	Whole-cell models; Digital evolution	Genome-scale metabolic models; Constraint-based modeling	[Lecture]
Day 40	Wed, Apr 15	Whole-cell models; Digital evolution	Constraint-based modeling	[Lecture]
Day 41	Fri, Apr 17	Whole-cell models; Digital evolution	Paper discussion; Artificial life	- Network-based prediction of human tissue-specific metabolism [Journal] [PDF]</br>- Integration of expression data in genome-scale metabolic network reconstructions (Mini supplementary reading to help with the main paper) [Journal] [PDF]</br></br>[Lecture]
Day 42	Mon, Apr 20	Biological networks	Network representation and topology	[Lecture]
Day 43	Wed, Apr 22	Biological networks	Condition-specificity of networks; Network recontruction	[Lecture]
Day 44	Fri, Apr 24	Biological networks	Paper discussion; Network propagation	Genomic analysis of regulatory network dynamics reveals large topological changes [Journal] [PDF]</br></br> [Lecture]
Day 45	Thu, Apr 30	Final project poster presentations	Poster presentations; Diff. time: 12:45pm - 2:45pm

Project deadlines

Office Hours

Tue and Wed: 5–6p.

I will block these time from my schedule and be present in my office.

Couple of things to note:

While I’m happy to chat with you in person, many times, just sending me a message on Slack with your questions/concerns might work as well. So, if you have specific Qs in mind, just shoot me a message and let’s see if we can resolve it then and there.
If you would indeed like to meet in person, please try to meet me during this time. But, don’t worry if you can’t make it during this window for some reason. Again, just send me a message on Slack and we’ll find a time that works for both of us.

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Website and Communication

Course website

This GitHub repo will serve as the course website.

Communication

The primary mode of communication in this course (including major announcements), will be the course Slack account. All of you should have invitations to join this account in your MSU email.

Emails
Although the bulk of the communication will take place via Slack, at times (rarely), we will send out important course information via email. This email is sent to your MSU email address (the one that ends in “@msu.edu”). You are responsible for all information sent out to your University email account, and for checking this account on a regular basis.

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Course Activities

Assignments

For each topic, you will be given an assignment after the topic’s first “Lecture” class on Monday that you are required to work on and submit before beginning of the “paper discussion” class on Friday the same week. Links to the assignment will be posted on this page next to the topic on the Calendar and specific instructions will be posted on Slack.

Class Participation

In general:

Do the pre-class assignments and additional readings.
Show up to class.
Work in groups during in-class discussion sessions.
No one will have the perfect background: Ask questions about computational or biological concepts.
Correct me when I am wrong.

Paper discussion

You will also take turns to present the assigned paper during each topic’s “Paper discussion” class. Make sure you sign-up.

Two students together present each paper.
The presentation should focus on the computational/analytical parts, not necessarily on detailed biological background & conclusions.
1. What is the problem the authors are trying to solve? [description of the problem along with why it is important]
2. What are their claims about the then current practices and their limitations? [existing approaches to solve the problem & their pros-and-cons]
3. What’s their approach? What’s new in it and what is their rationale for it being potentially successful? [description of the new ideas, their merits in comparison to existing ones, and rationale]
4. What are the major contributions and limitations of this paper?
5. What are some open questions and next steps (for addressing the limitations)?

Semester Project and Presentation

A major goal of this course is to prepare you for performing original research in computational biology, and for effectively presenting your ideas and research. The semester project will serve as the most practical way to do exactly that.

Projects can take any one of the following flavors:

Design and implement a new computational method for a task in biology
Improve an existing method
Perform an evaluation of several existing methods
Develop a fully-reproducible documentation and codebase for an existing analysis in a paper

The outcomes of this semester-long project should include:

Well-documented code to:
- download and process the data
- perform the computational analysis and generate all the results
- visualize the results as various plots
Detailed final report containing the following sections:
- Abstract
- Introduction
- Data and Methods
- Results and Discussion
- Limitations and Future Directions
- References
- Glossary
A poster that describes your project - motivation, exact problem, approach, results, discussion & conclusions, limitations & future direcrtions, acknowledgements.

There are several project deadlines throughout the course that will help you stay on track, enabling you to complete a substantial project.

Describe your previous research, areas of research interest in bioinformatics / computational-biology, type of project that best fits your interests. Post this description in a profile that lets your classmates know you. Project profile due Wed, Jan 15.
Discuss with Arjun (and any other PI) and read recent papers. Briefly describe project ideas. Project topic due Fri, Jan 31.
Prepare a two-page pre-proposal (Page1: text; Page2: figures & references). Project pre-proposal due Fri, Feb 07.
Write full proposal. Project proposal due Wed, Feb 19.
- Length: 5-pages (incl. figures & ref; sections listed below)
- Sections:
  - Background, goals, & significance (what is the problem you are hoping to address; what is the current approach & its limitations; what will you do & why is it likely to succeed; if successful, what is the broader impact)
  - Datasets (what datasets will you use; where are they from; what exactly do they contain; how are they formatted)
  - Computational methods/approach (what are the analytical methods; what are the specific software implementations you’ll use; include your flowchart here)
  - Evaluation plan (how will you evaluate the results that you get; think in terms of how to test if a) your approach is working correctly without errors and b) your results make quantitative/biological sense and are meaningful)
  - Potential challenges & alternative approaches (what are some assumptions you are making that can fail; what are some potential limitations of your dataset or approach that might prevent you from achieving your aforementioned goals; what will you do as alternatives if you hit those limitations)
  - Specific milestones (what is the list of specific results/outcomes you will work on getting)
Review proposals. Discuss proposal with Arjun. Reviews due Fri, Feb 28.
Mid-course project presentations on Mon, Mar 16, Wed, Mar 18, and Fri, Mar 20.
- Address peer evaluations, revise aims, scope, and list of final goals & deliverables. Meet with Arjun about reviews, revised plan, and progress.
- In addition to the usual things – background, problem, approach, etc. – I would like you to also present the following:
  - Clear flowchart of approach.
    - Raw data → Preprocessing & quality control → Preliminary/exploratory analysis → Analysis/Model-building steps → Expected outcomes.
  - Thorough exploration and sanity checks of data.
    - Tables & plots to showcase various aspects of your datasets/problem.
  - Method/software.
    - Usage & I/O format for each.
  - Preliminary analysis
    - With simple baselines, samples datasets, and toy examples.
Continue making substantial progress on proposed milestones. Write mid-course project report. Mid-course project report due Fri, Mar 27.
Complete milestones, finalize results, figures, write-up in conference publication format. As part of the report, comment on your overall project experience. Final project report due Fri, Apr 26.
Final project presentations will take place on Thu, Apr 30 12:45pm – 2:45pm in 351 Natural Sciences Bldg.

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Grading Information

Grading scale

Point | Percentage —-: | ———: 4.0 | ≥ 90% 3.5 | ≥ 85% 3.0 | ≥ 80% 2.5 | ≥ 75% 2.0 | ≥ 70% 1.5 | ≥ 65% 1.0 | ≥ 60% 0.0 | < 60%

Note: Grades will not be curved. Your grade is based on your own effort and progress, not based on competition with your classmates.

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Attendance, Conduct, Honesty, and Accommodations

Class Attendance & Presence

This class is heavily based on material presented and worked on in class, and it is critical that you attend and participate fully every week! Therefore, class attendance is absolutely required. An unexcused absence will result in zero points for the day. Arriving late or leaving early without prior arrangement with the instructor of your session counts as an unexcused absence. Note that if you have a legitimate reason to miss class (such as job, graduate school, or medical school interviews), you must arrange this ahead of time to be excused from class. Three unexcused absences will result in the reduction of your grade by one step (e.g., from 4.0 to 3.5), with additional absences reducing your grade further at the discretion of the course instructor.

Code of Conduct

All conduct should serve the singular goal of sustain a friendly, supportive, and fun environment where we can do our best work and have a great time doing it.

Do work that you’re proud of, from the smallest piece of code to the entire project.
Be supportive of your classmates; respect each others’ strengths, weaknesses, differences, and beliefs.
Communicate openly & respectfully with everyone in the class.
Ask for help; at the same time, respect and appreciate others’ time and effort.

Respectful and responsible behavior is expected at all times, which includes not interrupting other students, turning your cell phone off, refraining from non-course-related use of electronic devices, and not using offensive or demeaning language in our discussions. Flagrant or repeated violations of this expectation may result in ejection from the classroom, grade-related penalties, and/or involvement of the university Ombudsperson.

I am unequivocally dedicated to providing a harassment-free experience for everyone, regardless of gender, gender identity and expression, age, sexual orientation, disability, physical appearance, body size, race, or religion (or lack thereof). We will not tolerate harassment of colleagues in any form. Behaviors that could be considered discriminatory or harassing, or unwanted sexual attention, will not be tolerated and will be immediately reported to the appropriate MSU office (which may include the MSU Police Department).

Academic honesty

Intellectual integrity is the foundation of the scientific enterprise. In all instances, you must do your own work and give proper credit to all sources that you use in your papers and oral presentations – any instance of submitting another person’s work, ideas, or wording as your own counts as plagiarism. This includes failing to cite any direct quotations in your essays, research paper, class debate, or written presentation. The MSU College of Natural Science adheres to the policies of academic honesty as specified in the General Student Regulations 1.0, Protection of Scholarship and Grades, and in the all-University statement on Integrity of Scholarship and Grades, which are included in Spartan Life: Student Handbook and Resource Guide. Students who plagiarize will receive a 0.0 in the course. In addition, University policy requires that any cheating offense, regardless of the magnitude of the infraction or punishment decided upon by the professor, be reported immediately to the dean of the student’s college.

It is important to note that plagiarism in the context of this course includes, but is not limited to, directly copying another student’s solutions to in-class or homework problems; copying materials from online sources, textbooks, or other reference materials without citing those references in your source code or documentation, or having somebody else do your pre-class work, in-class work, or homework on your behalf. Any work that is done in collaboration with other students should state this explicitly, and have their names as well as yours listed clearly.

More broadly, we ask that students adhere to the Spartan Code of Honor academic pledge, as written by the Associated Students of Michigan State University (ASMSU): “As a Spartan, I will strive to uphold values of the highest ethical standard. I will practice honesty in my work, foster honesty in my peers, and take pride in knowing that honor is worth more than grades. I will carry these values beyond my time as a student at Michigan State University, continuing the endeavor to build personal integrity in all that I do.”

Accomodations

If you have a university-documented learning difficulty or require other accommodations, please provide me with your VISA as soon as possible and speak with me about how I can assist you in your learning. If you do not have a VISA but have been documented with a learning difficulty or other problems for which you may still require accommodation, please contact MSU’s Resource Center for People with Disabilities (355-9642) in order to acquire current documentation.

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Going Online

Folks, hope all of you are doing ok in these surely difficult circumstances. I hope you are taking all the precautions to keep you and everyone around you safe and unaffected:

If you think you came in contact with anyone sick, please self-quarantine for a couple of weeks.
Try to maintain 2-3m distance from your neighbors. Avoid handshakes/hugs.
Use hand-sanitizers and wipe surfaces with disinfectant wipes when outside.
Wash hands frequently and thoroughly with soap, each time for 20+s. Do not touch your face (eyes, nose, mouth) or food until you do so.
Eat and drink healthy – keep your immune system strong!
All this is also to avoid being carriers and re-infecting others, especially people more vulnerable than you.

Since we are going completely online for the rest of our semester including the finals, I am making a bunch of adjustments to deliver this class as effectively possible and I will work on helping you master the remaining course material as best I (and you) can in these circumstances. There is a lot in here but read through these carefully.

1. Important Links

Permanent Zoom Meeting ID

The link is available in the #GoingOnline post on our class Slack.

All Schedules

The google sheet shared on Slack contains the schedule/assignments for:

Paper presentations
Midterm/Final project presentations
Office hours (appointments)

2. Zoom Best Practices

If you haven’t done so already, please register your MSU Zoom account http://msu.zoom.us/
Please use a headphone (if you can) so that we can avoid audio feedback.
All your microphones will be muted and video will be turned off by default.
Use the chat feature to ask questions or provide comments. I will make sure I look and respond to them.
You can “raise your hand” on Zoom when you wish to talk and I will unmute you.

3. Midterm Presentations – March 16, 18, 20

We are going to use the Zoom meeting room.

Check the “Mid-term Project Presentations” sheet in the google sheet with the schedule for the details on who is presenting when.
Please log-in in a timely manner because we are on a tight schedule.
- Each of you gets 6 min to talk and another 3 min for Q&A.
By default, everyone will be on mute except the speaker.
When it is your turn to present, I will unmute you and you can share your screen to present.
The rest of us will listen and post questions/comments on chat. The speaker will look through the chat and answer questions one by one.
Remember: presenting remotely over Zoom sitting all by yourself is pretty odd and lonely. So, it is up to the rest of us to encourage each other by engaging with the speaker as much as possible via questions/comments.

4. Lectures

We are going to use the Zoom meeting room.

By default, everyone will be on mute except me.
I will share my screen with the lecture slides.
I will also share the lecture slides beforehand on the class website so that you can follow along better.
It is more important now (online) than before (in-person) to stop me and ask questions and/or provide your thoughts. If you’re confused about something I’m presenting, it is extremely likely that others are also confused about that. So, you will be helping a lot of people by stopping me to ask questions and clarify things.
As and when you think of them, post your questions/comments on chat. I will look through the chat window and answer questions as they come.
As usual, I will pause several times to ask questions. When I do so, I will expect many of you to chime-in via chat to give me brief answers. Based on what I see, I may umute and ask specific individuals to elaborate.
I will keep my input volume high to ensure that what I’m saying is clear. If I’m at a high volume, you can always turn down your audio; it’s harder for you to turn it up enough if I speak too softly.
Finally, since it is hard to pay attention to online/zoom lectures on a computer screen, I will also record my lectures as I present them via zoom and share them with you at the end of the day. But, I’m not going to do any slick editing. So, you’ll be subject to the entire zoom session as-is.

5. Student Paper Presentations on Fridays

We are going to use the Zoom meeting room and the style will be similar to how you did your mid-term presentations.

Check the “Paper Presentations” sheet in the google sheet with the schedule.
By default, everyone will be on mute except the speaker.
When it is your turn to present, I will unmute you and you can share your screen to present.
The rest of us will listen and post questions/comments on chat. The speaker will look through the chat and answer questions one by one.
Remember: presenting remotely over Zoom sitting all by yourself is pretty odd and lonely. So, it is up to the rest of us to encourage each other by engaging with the speaker as much as possible via questions/comments.

6. Class Participation

I hope you understand that I’m looking for ways to ensure you’re present and paying attention.

Please log-in in a timely manner. I would like to see you listed as one of the participants.
During each lecture, I would like to receive from each of you, via chat, at least:
- One question to clarify something I’m saying, and
- One answer to a question I’m posing.
Remember that presenting remotely over Zoom sitting all by yourself is pretty odd, lonely, and lacking in nonverbal feedback (nodding heads, puzzled looks, drowsy eyes, etc.). So, when someone is presenting, as listeners, it is our responsibility to encourage the speaker by engaging with the them as much as possible via questions/comments/answers.
Finally, I am cognizant of the fact that you are at home/residence and each of you will have a unique circumstance in terms of convenience of working environment, other family-members/roommates, childcare/pet-care needs, spotty internet, etc. All of us will fully accommodate any of this.

7. Office Hours

We are going to use Slack video calls to do all our office hours.

I have added an extra hour during the week. So, now we’ll hold virtual office hours from 5–6 pm on Tuesdays, Wednesdays, and Thursdays.
Go to the “Office hours” sheet in the google sheet with the schedule and put your name down on the day/time of your choice. Naturally, this will be on a first-come-first-serve basis. I will give you a call at the chosen time.
We will use this same sheet every week. At the end of each week, I will clear this table and it’ll be ready for the next week.

8. Final Presentations – April 30

Some preliminary thoughts here. This will be solidified soon.

Use Zoom to record a 5 min video of your poster presentation and submit by the due date.
Assigned reviewers will view the video and give scores. A detailed rubric will be provided ahead of time.
All of us will then meet via Zoom during the Finals hour on April 30 to discuss all the projects. During this time, three others who are not assigned reviewers will ask questions they have prepared.

9. Class Timeline

At the outset:

The class timeline, including deadlines, remain intact.
The only change is that we’re going to do everything – my lecture discussions, your Friday paper presentations, office hours, and midterm/final project presentations – via Zoom and Slack.

However, I want to qualify this wish:

My point is that we should strive to keep the structure the class as much as possible and prevent the class from straying far from what it was intended to be and what you signed-up for.
I am also aware that, during these difficult circumstances, we all need a simultaneous dose of structure and flexibility.
So, as always, I am very happy to work with you on a case-by-case basis to figure out different assignments and due dates based on your specific circumstances/needs.

Finally, all the physical distancing we are doing to mitigate the spread of infection can lead to a lot of stress. The isolation is not great for mental health and the confinement is not great for physical health. To help you a little in mitigating these effects, here are some additional things we will do as a class.

10. Virtual Meetings with Your Learning Group

Take advantage of the in-class learning groups you are part of. I am asking you to meet remotely with your learning group once every week – perhaps on Thursday or before class on Friday – over a video call. You can coordinate the time works for all of you and how you will do the video call. When you meet online, you can:

Go over the lecture slides for that week and ask/answer questions.
Discuss the assignment due on Friday.
Bring unresolved questions to the Friday online class and ask me those Qs.
Or, just say HI and continue to do your work in the company of your peers.

11. Scrum

I have created a #scrum channel in our class Slack. Let’s use this channel to do a daily scrum to help each other keep their work – any school/professional work, not just what’s related to this class – on-track. Include studying, preparation, reading, etc.

At the beginning of each day you’re working, post brief but specific answers to the following three Qs in this channel:
1. What did you do yesterday? [“Yesterday, I …“]
2. What will you do today? [“Today, I’m going to …“]
3. Are there any impediments in your way?
Note that scrum is not for:
- Problem-solving or issue resolution.
- Status updates or evaluating progress.
- Checking on anyone.
All issues raised here should be discussed further elsewhere, perhaps one-on-one or in group channels and with the relevant people.

12. Health & Workout

I have created a #health-workout channel on our class Slack. Let’s use this channel to share our ideas and post our daily plans/goals for keeping up our mental and physical health.

You can post something at the beginning of the day to motivate yourself and others, and to get help from others to hold you accountable.
Doesn’t have to 100 pushups. Like it is for me, even stretching for 10 min and drinking 1.5L of water is a worthy goal!!

13. Fun

I have created a #fun channel on our class Slack. Let’s use this channel to share your ideas/plans for engaging in a hobby or just disengaging with a Netflix show.

Keep them coming as when you find/take-on something new – yoga, online multiplayer games, new TV series, anything!

14. Frequently Asked Questions

Q. Can I connect to zoom by dialing a phone number?
A. Yes, additional details are in the #GoingOnline post on Slack.

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