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.

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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.

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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.

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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.

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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!

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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

How much energy is in a thought?

Sometime during the last months of grad school I was in the office late, polishing off one too many coffees, and dipping into my emergency ramen noodle stores. I was searching for that elusive (and perhaps illusory) moment of clarity that, one hopes, arrives to propel a manuscript forward. But, the long hours and coffee caused my mind to wander into distant realms of science. I had just finished teaching about neurons and action potentials and brain activity in my physiology class (100 billion neurons, forming 100 trillion neural connections—more connections than stars in our galaxy—sparking up right now allowing you to think this!) and I had a cool thought:

I am converting these cheap noodles directly into science and new insight. I am a biochemical machine that converts packs of 10 cent fake noodles into knowledge.

And then, the natural follow-up: at what rate? What is the cost of a thought? How many noodles does my brain burn to construct a statement? A paper? A dissertation?

Now that I do not have a dissertation submission deadline looming, I have some time to explore these thoughts—thankfully while burning some higher-grade fuel than emergency ramen! Warning: the calculations that follow are extremely ‘back of the envelope,’ and should be taken with a heaping helping of salt and skepticism. This is just a fun exploration.

How much energy is burned in a thought?

First, let’s gather some parameters. How much energy does the brain use? The short answer is: an incredible amount. Despite only accounting for 2% of the body’s weight, the brain uses 20% of the body’s energy (that figure is for an adult, in newborns it is 44%!!) The brain uses 2–3 times the amount of energy that the heart uses.

[Aside: the brain is extremely efficient at what it does—processing information using orders of magnitude less energy than the best supercomputers.]

So, let’s say that the brain uses 20% of the body’s basal metabolic rate, and the basal metabolic rate is 1500 kcal/day. That means the brain uses about 300 kcal/day, or 0.0035 kcal/second.

The next question is: what is a thought? How much time does one take, and what proportion of the brain’s energy is devoted to “thinking”? I don’t know! But, does anyone know? I don’t know that either. Since it is my blog, I am at liberty to define a thought. Let’s say, for the sake of argument (and feel free to argue in the comments) 100% of the brain’s energy is required for “a thought,” and all thoughts are created equal. And let’s also say that a thought is a statement, and that it takes as much time as one would take to think or read a sentence. For instance, here is a thought:

“Wow, I am thinking this thought about thinking; this is one of the things that hydrogen atoms do given 13.82 billion years of cosmic evolution, and it’s super cool.”

How long did it take to think that specific (extended) thought?  More than a couple of seconds, less than 10? Let’s say a substantial thought takes 5 seconds. At 0.0035 kcal/second, that’s about 0.02 kcal/thought!

So, how many ramen noodles are burned for a thought? At 400 kcal per block, and 150 noodles per block, we have 2.67 kcal per noodle. Assuming the average noodle is 33 cm long, we find that there are 0.08 kcal/cm of noodle—and every thought burns about 0.25 cm of ramen noodle! Your brain is incredibly efficient—no wonder that future AI are always super jealous and vindictive in sci-fi movies.

Now we can readily convert thinking-time into calories, and content creators can register their influence in energy. For instance, if 100 people read this blog post, consuming 5 minutes of calories thinking through the content, then about 100 calories would be burned on my words. 400 people and an entire block of ramen has been consumed by my words.

I wonder how much ramen has been burned by Shakespeare?