GRIPS – Dr. PJ Utz’s Talk

The man. The myth. The legend. He once came to my school where his son was a student at the time and gave an amazing talk. The next few summers, I began researching in my first biology lab, then began researching at Stanford, and then heard the man speak again!

It was a great talk about careers. I learned a lot, but my biggest take away from his talk would be the key takeaway point is to identify four values most important to you when you get your job. If you keep that in mind, that’s a pretty good formula not just for success but for happiness, too.

GRIPS – Tour of SLAC

SLAC is huge and beautiful.

This is where the real money lies. Apparently, it costs a burrito a second this linear accelerator operates. They’re doing research on a ton of different things including dark matter. Quantum physics is super cool! Smashing atoms and subatomic particles almost at the speed of light must be pretty fun.

Thoughts on Population Growth

I have been posting about research on my blog quite a bit. We’re mostly operating at the molecular or cellular level here or talking about CS heavy stuff like SQL/Django/ for the sake of biological applications. Yes, both biological research at the molecular level and CS applied to biology (think big data/machine learning/AI/neural networks/huge databases like Encode which is what I’m working with at the moment) are important for the future of this world (progress in medicine).

That being said, I would like to think about an even bigger picture here for a little bit today, population growth. The world contains a finite amount of resources, so Malthus, you weren’t entirely wrong I guess. Incidentally, research at the molecular level and CS applications to biology fall under Stage 4 countries in the demographic transition model, but I digress… The main take home point today is that certain indicators can tell us a lot of interesting things about where population growth is headed.

Why should you care? Good question. Aside from global warming, the finite amount of resources coupled with exponential population growth (ok fine it’s been slowing down a little so not quite exponential) could also spell the end of the world as we know it in the far future (or maybe not so far ahead). I guess it’s good that we have people like Elon Musk pushing us to colonize Mars even though it does sound a little far fetched right now. While Elon Musk figures that one out, we can use key indicators (more on that later) to try to make sure that the population growth does not spiral out of control by making informed public policy changes. Ok, let’s jump into this stuff!

Key Indicators

GDP is a measure of how well the economy is doing. The key thing to know about TFR is that if it’s around 2, a population will be able to sustain itself. If it’s bigger or smaller than 2, it’s going to increase or decrease respectively. Natural increase rate is basically the overall population growth. The other indicators should hopefully be intuitive.

Even though this isn’t a key indicator, the demographic transition model will be important for our case study where we see these key indicators in practice.

Note that there aren’t any countries in Stage 1 anymore. After the Agriculture Revolution thousands of years ago, hunters and gathers were able to settle down and improve their farming techniques to consistently have a supply of food (which also led to specialization of labor, the very foundation of our modern world as we know it). The Industrial Revolution really helped the transition to Stage 2 though. By mass producing various kinds of items with specialized labor, resources became plentiful in several departments. Medical advancements also happened, ultimately starting to lower the death rate. The transition to stage 3 happens when women’s literacy rates (a good measure of education) rise. As a result, more women work in the workforce and gain access to contraceptives. Brith rates start to drop more. The transition to stage 4 happens when the industrialized economy shifts more to a service based economy while education levels continue to rise. Birth rates roughly equalize with death rates resulting in minimal population growth. Some countries like Japan are facing negative population growth due to an aging population where death rates are slowly outpacing birth rates (Stage 5). In order to combat this, birth rates would have to increase to steady the population or grow it a little bit.

Case Study

Country	GDP Per Capita	GDP Per Capita (world ranking)	Female Literacy Rate (%)	Infant Mortality Rate (per thousand births)	Total Fertility Rate (number of children)	Life Expectancy (in years)	Natural Increase Rate (%)
Pakistan	$2,600	171	36	67.36	3.43	65.26	1.555
Spain	$33,700	38	97.2	4.21	1.31	80.05	0.72
Indonesia	$4,000	155	86.8	21	2.3	69	1.1
Philippines	$3,300	162	92.7	22	2.9	69	1.4
Kazakhstan	$11,800	94	99.3	9	2.7	73	1.3
United Kingdom	$35,200	34	99	4	1.8	81	0.6
Australia	$38,000	23	99	3	1.8	82	1.6

Pakistan and Spain

Take a look at the indicators for Pakistan and Spain. What are the differences? How can you account for them?

Pakistan’s GDP is significantly lower than that of Spain. This means that Pakistan can devote less of its economic resources towards medical care, meaning that Pakistan’s infant mortality rate and life expectancy will be significantly higher and lower than that of Spain’s respectively. Pakistan’s low female literacy rate as opposed to Spain’s high female literacy rate will correlate with a high and low total fertility rate for Pakistan and Spain respectively. This is because women in Pakistan tend to be less educated than in Spain. This means that women in Spain will probably be more out in the workforce and have knowledge about and access to contraceptives, which women in Pakistan will not be as educated about and probably will not have access to. Pakistan would probably be a stage 2 country in the demographic transition model while Spain would probably be a stage 4 country given that Pakistan’s total fertility rate is quite high and Spain’s is relatively low (which would correspond to a high and low natural increase rate for Pakistan and Spain respectively). 

Philippines and the United Kingdom

Looking at those two countries, what public policies would you advocate to stabilize their population levels? What are some other concerns that you would worry about with their current population levels and trends?

For the Philippines, I would advocate education about contraceptives and providing free access to them, so the country can keep its population growth in check and not have too many people and too less resources. For the United Kingdom, I would advocate to increase its population either by putting incentives for more immigrants to come to the country or women to have more children to boost the total fertility rate. This is because I would want to avoid the long-term scenario of too few workers trying to support too many retired people, which could have negative economic consequences. 

A Final Challenge For You

Look up some data for these indicators for other countries. Based on what you’ve learned, hopefully you realize the different correlations between various indicators and why they exist. Taking the GDP Per Capita, GDP Per Capita (World Ranking), and Female Literacy Rate (%), can you predict the Infant Mortality Rate (per thousand births), Total Fertility Rate (number of children), Life Expectancy (in years), and Natural Increase Rate (%)? How close can you get to the actual values? Did you notice any interesting anomalies? How can you account for them? Happy conjecturing!

GRIPS – CRISPR-Cas9 Talk

It’s hard to imagine genome editing before CRISPR-Cas9, using TALENs and ZFNs. The older technologies were difficult to produce and use while CRISPR-Cas9 is relatively easy and taking the field by storm. Dr. Josh Tyco talked about the ethics including the notorious example of a Chinese scientist editing the DNA of viable human embryos, gene drives (using CRISPR-Cas9 to eradicate mosquitoes is one such possibility), and the mechanics of it (I found the fact that the length of the sgRNA not necessarily correlating with the specificity of the CRISPR-Cas9 experiment most fascinating).

GRIPS – Tour of the Sequencing Center

I learned about DNA sequencing and went on a tour of the sequencing center. The million dollar Illumina machines and the exciting rise of other technologies like sequencing using a nanopore bode well for the future of precision medicine. It cost billions of dollars to sequence the first human genome, and now it’s only $100. Stanford researchers want to get it down to $10, which is crazy and awesome!

GRIPS – Heatmap Update

I created an SQL table (similar to the one above) in MySQLWorkbench. I also wrote a few Python scripts to insert some values into the SQL table. From there, I was able to use the django framework to get the SQL table values and display them on the server. Now, I’m working on dynamically inputting values into the SQL table. Later, I’ll read those values from the server and display them in the JS heatmap (front end).

I also learned about dockers, and they are pretty easy to use. I successfully dockerized my project. I’m also learning about CircleCI and industry management of code deployment, which is pretty awesome as well.

Transfected Cells!

There’s nothing better on a Thursday than transfecting cells! (Ok, there are better things to do on a Thursday, but this one is still pretty high up there for me.)

Mixing the plasmid with the PBS and the PEI with the PBS for some reason just felt really good. Pulse vortexing the combination of those three times was even better. The best part was dumping the transfection solution into the cell culture flasks and swirling them afterward. I’ll see how much protein I get tomorrow.

Cells, death, more cells!

It’s fun culturing cells! It’s not fun when you inject Trypan blue, and it penetrates your dead cells coloring them blue. Looking at blue cells and floating cellular debris under the light microscope on the hemocytometer feels pretty weird actually. That’s what happened with my batch of 50 mL cells, but fortunately, I got a new batch. That looks much better, and I’m growing it up. Last time, it was sitting at 180 mL of 1.9 million cells per mL. Tomorrow, hopefully, I’ll have around 700 mL near 2 million cells per mL, so I can transfect 2 batches of 300 mL cell cultures and make more of my protein of interest.

GRIPS – Ancestry Talk

Last Friday, Natalie Telis from Ancestry gave a really interesting talk explaining how Ancestry works to find out who we are related to. Ancestry uses the genomes of people living in areas all over the world for generations as references to look for certain patterns with machine learning (specifically topic modeling) as indicative that you would be from that area. There are data biases (like the reference people being sequenced usually live near universities to get their DNA sequence).

Ancestry also uses other sources like existing family trees, records, and DNA sequences from previous people who have used the DNA kit. Through genetic clustering, they can group people into various communities. They can further narrow down the search by using annotations in the datasets, which include items like the place of birth and also government records which often have details about family migrations.

GRIPS – Research Update

Last week, I was working on creating a heatmap using deepTools in Python to display Pearson/Spearman correlations of ChIP-Seq data. The goal is to implement it onto the Encode database at some point (e.g. show heatmaps of ChIP-Seq data across various tissues of a human donor or for a particular tissue across multiple human donors). I was able to generate a heatmap of all the different time points for cells treated with 100 nM of dexamethasone. Now, I’m working on integrating the backend with the frontend (displaying the heatmap using highcharts and react in JS).