Books for new faculty (part 2) and also for new students (part 1)

Earlier in the summer I wrote a post on “Books for new faculty” wherein I detailed all the books that current profs recommended to me as I start my new faculty journey (including an editable google doc, so go add some books if you have some in mind!). Now that the semester is a few days away I thought it would be appropriate to throw in my two cents. Over the last year I have read two outstanding books that made me say “wow, I wish I read that earlier!” One book, on the importance of sleep, would have been super appropriate to read as a freshman in college. The other book, on maintaining bouts of deep concentration in this world of constant distraction, would have been very useful on the journey from late college through my postdoc.

Why we sleep

https://www.goodreads.com/book/show/34466963-why-we-sleep

Back in college we would brag about how little we slept. The all-nighters we pulled in the Genesee Hall common rooms before the orgo exams. The late-night caffeine-fueled Milne library study sessions. The respect we would bestow upon one another after hearing about marathon weeks (“they only sleep 3 hours a night!”).

If only I had read Dr. Walker’s Why We Sleep beforehand—

“Sleep enriches a diversity of functions, including our ability to learn, memorize, and make logical decisions and choices. Benevolently servicing our psychological health, sleep recalibrates our emotional brain circuits, allowing us to navigate next-day social and psychological challenges with cool-headed composure.” –Dr. Walker in Why We Sleep

Enriches our ability to learn and memorize and recalibrate our emotional brain circuits allowing us to navigate social challenges? Sounds like sleep is exactly what the doctor ordered for a new college student!

Besides overviewing (with plenty of experiments and evidence to back it up—Dr. Walker is Director of UC Berkeley’s Sleep and Neuroimaging Lab) everything that sleep enriches, the author also details plenty of aspects of our life that a lack of sleep alters.

Sleep loss inflicts such devastating effects on the brain, linking it to numerous neurological and psychiatric conditions (e.g., Alzheimer’s disease, anxiety, depression, bipolar disorder, suicide, stroke, and chronic pain), and on every physiological system of the body, further contributing to countless disorders and disease (e.g., cancer, diabetes, heart attacks, infertility, weight gain, obesity, and immune deficiency). –  Dr. Walker in Why We Sleep

One message from the book that has stuck with me is how we develop a sleep deficit, a debt of sleep we owe our bodies, with any prolonged period of reduced sleep. A few nights of 5–6 sleep hours a night instead of 7–8 hours builds up this deficit, and then we live our waking hours with reduced functionality (decreased cognitive abilities, reaction timing, immune system function, etc. etc. etc.). And, perhaps most worrisome, is that driving with a sleep deficit is just as bad as driving drunk, and it is much more prevalent.

In summary: get some sleep!  Now, you may find yourself asking “how could I possible sleep 8 hours a night when I am taking 16 credit hours and working a part time job and figuring out what to do with my life?”

Well, perhaps Deep Work has some answers.

Deep Work

https://www.goodreads.com/book/show/25744928-deep-work

I think my social media usage is pretty cyclic. It typically follows the following pattern:

  1. Wow, I’m spending a lot of time on my phone mindlessly scrolling through Twitter. I should cut down. I’ll log off and only sign in for important reasons.
  2. Wow, I’m missing out on conference updates, new manuscripts, and amazing opportunities to share my research. I should check into Twitter more often.
  3. See (1).

In Deep Work Dr. Cal Newport chronicles our ever-increasing connectivity to one another  (or, more appropriately, to our phones and the attention-grabbing algorithms therein) and how this is affecting our ability to concentrate intensely for long periods of time and do meaningful deep work. “Deep work”, he argues, is the bread-and-butter of our information economy. The author then prescribes several rules and ideas to help us recapture our attention span. Here are some that stuck with me:

  1. Structure your day. Make an hour-by-hour schedule for yourself, and defend your time blocks reserved for your meaningful work. Be aware that breaks for “shallow work” eat into your attention reservoirs and make getting back into deep work all the more difficult.
  2. Have a time of the day where you stop working, and I mean really stop working. After you finish your work for the day, have a shutdown sequence routine that signifies the end of the work day. Do not check email at home. Let your mind reset and it will be more efficient and ready to work deeply tomorrow.
  3. Quit social media. Dr. Newport suggests we view social media as a tool, and as any good farmer knows, you need to evaluate the necessity and economic benefit of any tool (not just blindly adopt the usage of a tool because everyone else is doing it!). Contrary to current popular opinion, just because something increases connectivity and has potential to be useful does not mean it will create more benefits than damage for everyone. (Note to students: He does recognize that social media may be very useful for new students that are looking to meet new friends).

I recommend this book to anyone who is finding themselves a little too connected with attention-grabbing algorithm-driven content streams and a not connected enough with work they find meaningful.

A common message

How do we find the time to do meaningful intellectual work in a world saturated in algorithms designed to grab and hold our attention? Well, if my recent reads have anything to say about it, the first thing we need to do is get enough sleep so that we have a healthy bedrock for our concentration to take hold. Next, we need to reserve blocks of time to a single meaningful task—and within this block do everything possible to keep our concentration on that single task. And by “we” I mean “me” because it is time to practice what I preach and get to work (the semester starts tomorrow!) Good luck everyone, remember to sleep!

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Books for new faculty

I have spent a sizable chunk of my life in a profession that requires 1) not knowing things, and then 2) reading works from people that know those things until I know those things too. So, with my faculty position looming on the horizon, and with it the amorphous and exciting batch of new responsibilities that role brings, I feel the need to read some articles/books on best practices.

I sent a tweet out fishing for advice

and received a lot of feedback! Thanks twittersphere! I made a Google doc of the recommendations, and I hope it is useful for other new faculty. Please add more!

The editable Google doc is here: Books for new faculty.

UPDATE: Books for new faculty part 2 (and new students part 1) here.

An interactive evolutionary game

A little while ago I was looking for an active way to teach about the evolutionary dynamics occurring within each of us. So, finding the perfect excuse to learn some shiny, I built a simulation of an evolving stem cell niche that students can control—a fun evolutionary game to play!

Give it a shot! Either on the shiny website while my account can support the simulation or try it yourself straight from the github source page.

Screen Shot 2019-06-20 at 12.01.10 PM

The goal here is to understand how inherently “random” dynamics—cells are chosen to divide based on a die roll and chosen to leave the system based on the flip of a coin—can manifest in outcomes that are predictable. For instance, it turns out that neutral variants, i.e., mutations that do not affect the relative division rate of cells, have a knowable probability of “fixing” in the population (taking over) and consequently a knowable probability of going extinct. You can adjust the starting size of the mutant population or the starting size of the entire population and see how this changes the probability of fixation.

Screen Shot 2019-06-20 at 11.59.23 AM

You can also adjust the relative division rate of “mutant” cells, and see how this changes probability that the mutant lineage takes over the system. This difference in fixation probability is the intensity by which the mutant is naturally selected to survive.

Screen Shot 2019-06-20 at 12.06.26 PM.png

In other words, if you have information about the actual rate of fixation of variants, and the expected rate of fixation of the variants if they were neutral with respect to selection, you can calculate the differential intensity of selection for these variants, and you can understand which variants give the largest boost to cellular division and survival. These are the same sort of tools we use to understand which molecular variants are driving cancers! And, of course, the evolutionary dynamics occurring in the small populations that constitute our bodies are hugely important!

I built this simulation hoping that others can use it in their classrooms as well  Please let me know if you think of any ways to improve the simulation, the code, or anything at all!

 

 

People always change

72 days from reading this, more than half of the cells in your body will be completely different cells than the cells in your body today. 

Don’t ever let someone tell you “people never change.”

People are always changing. Sitting there, reading this, you—the mass of writhing, wiggling, cooperating, and competing cells that constitute your corporeal self—are changing. You are in flux. Millions of your cells have just died, and millions have just been born!

In talks, and on this blog, and in my papers, I often discuss this personal turnover because it leads to interesting biological questions. But I always paint this picture in the light of specific tissues. A recent conversation had me wondering—what about the entire body? What percent of our total cell number are different after a day? A week? A month?

Time for some more back of the envelope calculations!

Let’s say that 25 trillion (25 with 12 zeros after it, 25,000,000,000,000!) out of the 30 trillion of the cells in your body are red blood cells, as estimated by Sender et al. (2016). These red blood cells have an average lifetime—marking the time they are born until they are eventually recycled—of 120 days.  This turnover is a continuous process that keeps our blood fresh and functional each day.

So, every day, about \frac{1}{120} of the 84\% of our cells are renewed, or \frac{1}{120} \times \frac{84}{100} = 0.007 , i.e. at least 0.7\% of our total cells are renewed daily! I stress at least because this estimate just includes the turnover of our red blood cells… our skin, our intestinal epithelium, and many other tissues that account for the 5 trillion cells that we didn’t include in the above calculation are continually renewed as well.

How long until half of the 30 trillion cells in your body are different from today? Again, just thinking about red blood cells, we need to calculate how long 15 trillion of these cells take to be recycled. 15 trillion is \frac{15}{25} = 0.6 = 60\% of the total 25 trillion blood cells, and if the full batch of blood cells is renewed every 120 days, this means that 60\% of the blood cells will be renewed in 0.6 \times 120 = 72 days!

 

 

A biological calendar

I recently read a bit of Why Evolution is True by Jerry Coyne and stumbled upon a fun fact I needed to dig into.

Hundreds of millions of years ago, there were corals, just like today. And, just like today, they grew by depositing a ring of calcium carbonate onto their outer skeleton every day*—similar to the growth patterns in the trunk of a tree. When you look at these growth patterns in living corals, taking into account changes of deposition with seasons, you can see annual growth patterns and, as one might expect, about 365 daily rings per year. When you look at fossil corals from 400 million years ago you see over 400 daily rings per year!

Scientists have long predicted that the rotation of the Earth must be slowing down due to tidal friction—the motion of the tides have been dampening our angular momentum (it’s stolen by the moon!). Not by much, about 1 second gets added to the day every 50,000 years. But, over millions and millions of years, these seconds add up. 600 million years ago, a day was 21 hours long, and over 410 of these days elapsed before the Earth could complete its annual journey around our sun. In 1963 Prof. John W. Wells used a biological calendar—fossilized corals—to corroborate astronomical predictions about our lengthening days.

Anyway, it was the perfect storm of fun facts. The days are getting longer, coral living 400 million years ago experienced over 400 days a year, and we can see this in a biological record.

P.S. Modern corals are still keeping a record. Using “coral chronometers” we have a record of variations in temperature, cloudiness, and even nuclear activity (some bands in corals coinciding with nuclear tests are radioactive). Maybe 400 million years from now somebody (something?) will find corals from today and see the impact of the human era. At least 400 million years from now the postdoc doing this research will have ~27 hours in a day to write up the results.

* I also just read Jurassic Park, hence the Mr. DNA adaptation.

Bill Nye the Science Guy meets Vin Can the Science Man

OK, so I need to work on my stage name.

Back in May 2017, I tweeted at Bill Nye:

And it turns out that Bill, and the great writers and producers and everyone else behind Bill Nye Saves the World, were paying attention. Shortly after the tweet I was contacted by a producer of the show and asked if I would like to come on and give a demonstration about the evolution of “super-bugs”, i.e. antibiotic-resistant bacteria.

An opportunity for science outreach involving Bill Nye? Yes, please.

In this post, I first want to talk about the science in my 5 minutes (at the end of Season 2 Episode 3 of Bill Nye Saves the World). Then, I’ll touch on the experience of being on the show.

The Science

I wanted to convey three things in my demo:

  1. Antibiotics work really well!
  2. So does natural selection. In the presence of an antibiotic, bacteria resistant to that antibiotic survive and proliferate more than non-resistant bacteria, leading to the spread of the information conferring that resistance (i.e. the evolution of “super-bugs”).
  3. And that’s why it is important to be judicious about antibiotic use.

all while conveying how scientists can use models of reality to study biology.

So, for my demo I created a model of the evolutionary dynamics of bacterial strains within a person. In this model, bacteria either replicate or die—similar to a common mathematical model used to study evolutionary dynamics called a “birth-death process”. If there is more birth than death, the bacteria grow too big and overflow from the host—the infection spreads to other hosts. If there is more death than birth (as in a typical situation where the immune system does a good job), the bacteria die off —the infection is cleared.

What makes this a model of evolution is that we can introduce two different bacterial strains into the model and observe how the relative abundance of these two strains change within the total bacterial population over time. Let’s say one strain has a mutation in their genome that makes them resistant to antibiotics, and the other strain is still susceptible to antibiotics.

Let’s also assume that the host’s immune system is compromised, all strains are growing more than they are dying. The person goes to their physician, and gets some antibiotics that decrease the birth rate of only the susceptible strain. Growth of the susceptible strain is stopped, but the resistant strain grows and grows, and when the model “overflows” it is the resistant strain that spreads to other hosts.

By continually providing selective pressures favoring resistance, we drive susceptible strains to extinction. As the model suggests, we would expect the spread of antibiotic resistant bacteria to be especially prevalent in areas that have a high concentration of individuals with compromised immune systems that take antibiotics, such as hospitals and nursing homes.

But, there is hope! Many of the mechanisms of resistance are actually costly to bacteria when antibiotics are not present. It may be possible to reverse many of the mechanisms of resistance (select for non-resistant strains) by being extremely judicious about when to apply antibiotics. The original focus of the demo was on how to reverse resistance through exploiting this cost of resistance, however due to time constraints I refocused on the emergence of resistance.

My Experience

Everything was awesome. I had no idea just how much went on behind the scenes to get a show produced. From the props people helping with my demo, to the writers and producers working around the clock anticipating every little thing that will happen. Everyone really cared about being true to the science and explaining the information in an accessible and exciting way. Especially Bill Nye, who was extremely genuine and kind throughout the whole experience. I’m very grateful for the opportunity to help #savetheworld!

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P.S.

For those who arrived at my blog curious about my current research: I use mathematical models and simulations to investigate how tumors evolve from our tissues, how evolution has structured our tissues to minimize the risk of cancer, the effects of mutations in growing tumors, and how cancers evolve resistance to chemotherapy. Relating to pathogen evolution, during graduate school, I was part of a team that used mathematical models to study the evolutionary dynamics of pathogens and their hosts.

Dark selection from spatial cytokine signaling networks — Theory, Evolution, and Games Group

Check out a post I wrote over at the Theory, Evolution, and Games Group blog on some of our work at the 2016 Integrated Mathematical Oncology (IMO) Workshop! The link for that post is at the bottom of this post.

It details a really neat model we created to interrogate a system of cytokine signaling and cancer treatment. For those unfamiliar with the IMO Workshop/competition, five teams of a dozen or so researchers, all from different backgrounds, are formed at the beginning of the week, and quickly decide on an interesting research problem they can tackle. Each team has a few physicians and scientists stationed at the Moffitt Cancer Center, where the competition is held, that act as mentors. The groups spend the four days working and researching and planning ahead, and on the last day they all present their completed and proposed work. Oh, did I mention that $50,000 of future funding is on the line? The winning team gets the $$ to complete their proposed research.

This sets the stage for an awesome week-long hackathon, where longer and longer workdays culminate in an inevitable all-nighter as mathematicians and computational biologists and physicians and new colleagues perfect their models and presentations.

So, there we were, 35 hours or so away from the final presentation, when we all decided we needed a spatially-explicit model of cytokine diffusion and cell response. I had created spatially-explicit simulations of cell turnover before, so I volunteered to lead the analysis. And, like the scientist in an action movie rushing to find the vaccine for the zombie virus before the meteor strikes (or something), I worked overnight in my hotel room, and all the next day, and delivered this video and results right before the final presentation:

(For more information on what the video is showing, check out the post linked below or our preprint.)

It was only 2 slides worth of work within our whole presentation, just to give you a sense of how much everyone in the group accomplished during the week. But it was actually a ton of fun rushing to get everything together and connected. And, we won the competition!

imo2016_teamorange_awardphoto

Greetings, Theory, Evolution, and Games Group! It’s a pleasure to be on the other side of the keyboard today. Many thanks to Artem for the invite to write about some of our recent work and the opportunity to introduce myself via this post. I do a bit of blogging of my own over at vcannataro.com […]

via Dark selection from spatial cytokine signaling networks — Theory, Evolution, and Games Group