In MicroBiology class we’re still a long way from our immunology unit, but we have started talking about some basic principles of the immunity and vaccination, including the idea of ‘herd immunity’. This is the notion that even incomplete vaccination may be sufficient to prevent the spread of an infection through an entire population.
In this video, Scientific American’s Dina Fine Maron explains Herd Immunity very simply.
Interestingly, as I started writing this, I stumbled upon the old crap movie, Outbreak – I’ll also be calling this film crap on my film blog, 100FilmIn100Days.
The Smithsonian Magazine has an article this week proposing that we consider Mars as the origin of Terrestrial Life. This notion stems from Steven Benner’s Four Paradoxes: The Tar Paradox, The Water Paradox, The Single Biopolymer Paradox and The Probability Paradox. Each of these is described in the abstract of his work, and do add up to a possible alternative for life’s origin. However, as compelling as his arguments may be, the origin of life will always be a mystery veiled in time. Even if we were to find evidence of life on Mars that is very much like that on Earth, it would be difficult to say whether Terrestrial life was the origin of Martian life, or vice versa.
Another problem I have with tracing the origins of life off-planet is that it does not solve anything, but merely relocates the source. So it’s not that I feel that Benner’s work is uninteresting or unworthy of consideration, but presently, Ockham’s razor precludes seriously considering extra-terrestrial origins without a good deal more hard evidence. Further, relocating the source or life’s origin does little to change how we think about origins. Regardless or where life started, it is still highly probably that it began with RNA, a unique molecule in that even today it serves dual roles as an information-carrying molecule and a structural one that often has enzymatic function. And, that the addition of the more stable , DNA molecule as the primary source of information happened later – as adding protein synthesis also did for providing an alternative structural / functional molecule.
There’s a chance I may be getting more takers for my ‘Codecademy’ – based coding club at FSCC soon. Several students have shown interest and I look forward to opening up the class towards becoming a more open space with students (including myself) pursuing a number of projects simultaneously.
If anyone (local, at least) is interested in joining our group, please feel free. We take all comers and look forward to building our numbers with anyone interested in learning, teaching or challenging themselves.
If you’re not local, I’d still be interested in hearing from you if you’d like to start an online learning community tied to codecademy, code school, or any other online resource.
I was looking around online and found this site that has a virtual lab for doing streak plates. Since we will likely be doing these very soon, I thought you might find this to be a pretty neat preview from Michigan State University.
It took a little playing around before I got it, but click on the ‘module’ button to get started and then drag the inoculating loop to the burner. I think once you do that, the rest is pretty intuitive. There is a pdf of instructions as well to get you started.
The purpose of streak-plating, you should recall, is that it allows you to pick individual colonies that have grown out from individual cells.
I mentioned in my classes that I sometimes post extra credit hints here – but not always. Just enough to make it worth checking once in a while and help buoy my stats.
In class last Thursday someone mentioned that they heard I liked to post extra credit questions about Twin Peaks (a favorite series), but that was last semester. I’m sure I won’t be able to resist throwing a couple in at some point. And, after all it’s always a good idea to see that TV doesn’t always have to be predictable and mundane – sometimes it can be awesome.
If I am giving any hints, it’s that this year, I’ve been watching a lot of movies- especially ones from the 70s and 80s – and writing reviews on my other blog, the now-incorrectly titled, 100FilmsIn100Days. So perhaps, we’ll see a little of that nice Dutch Colonial on Long Island from time to time.
But not all of my extra credit questions come from incidental materials. Sometimes, they’re serious, about subjects like the Measles outbreak I discuss below or material that I think is cool, but too detailed or tangential to be tested on for credit.
I look forward to getting back in class. See you soon.
A new Measles outbreak erupted in Tarrant County, TX when a visitor to the Eagle Mountain International Church infected members of the congregation, staff and the daycare. Although the church’s pastor, Terri Pearsons, has been critical of vaccines in the past, she has fortunately changed her outlook and is now urging her congregation to get immunized now to prevent further spread of the disease.
Measles is a highly contagious virus, that infects ~90% of those who are exposed (and unimmunized). Since the introduction of the vaccine in 1963, cases have fallen from the hundreds of thousands per year to near eradication levels. However, global travel and the recent rise in anti-vaccine rhetoric has allowed for the past several years to see higher numbers of cases in the ‘post-vaccine era’.
2013 Measles Map
Although it is imperfect (due to incomplete and sometimes redundant data), I put together this map of the 2013 outbreaks in the US, presently amounting to ~135 cases (using data from Vincent Iannelli, M.D.’s report to About.com.) With four and a half months remaining in the year, 2013 stands a chance of reaching or surpassing the 2011 (modern) record of 220 cases.
This map helps to highlight that Measles is considered to be eradicated in the United States, however the disease continues to be introduced by travelers and spread in short bursts amongst unimmunized individuals.
“The majority of measles cases were unvaccinated (65%) or had unknown vaccination status (20%). Of the 911 reported measles cases, 372 (40%) were importations (on average 34 importations/year), 239 (26%) were epidemiologically linked to these importations, 190 (21%) either had virologic evidence of importation or had been linked to those cases with virologic evidence of importation” says the CDC.
The CDC encourages parents (and all Citizens) to remain vigilant and follow these recommendations to help maintain herd immunity and prevent introduced cases from becoming endemic:
vaccinating children at age 12-15 months with a first dose of MMR vaccine,
ensuring that school-age children receive a second dose of MMR vaccine,
vaccinating high-risk groups, such as health care personnel and international travelers including infants aged 6 to 11 months,
maintaining measles awareness among health care personnel and the public, and
working with US Government agencies and international agencies, including World Health Organization (WHO), on global measles mortality reduction and elimination goals.
The letstalkaboutscience blog posted this great infographic illustrating the abundance of each of the elements in the universe, the oceans, etc. I’ve brought that here – click to enlarge and explore in greater detail.
This week, we talked about the various bonds that atoms engage in to satisfy the octet rule. I also found this great, simple illustration of Na and Cl forming ions and bonding through their difference in charge.
On Tuesday we’ll finish up discussing bonding and talk about the four basic molecules of life (proteins, nucleic acids, fats and carbohydrates). Then we’ll finish up with this brief overview of chemistry by talking about how it fits into the big picture.
Yesterday was the first day of my Bio and MicroBio classes for the Fall semester and I’m trying some new things including using iPads as clicker devices and media delivery. I’ve had some problems moving my iBooks onto the devices, but with some help from Apple’s technical services, I hope to have that worked out soon.
Won’t get Fooled Again
One problem I did stumble into was getting used to the eClicker interface while trying to keep my cool at the same time. Worse, in my fluster I confused the approach of 11 o’clock with 12, and rushed my way into ending class a full hour early.
in the event that anyone reading this is a student in my class, I want to mention that I would like you all to read the first chapter of the textbooks and write out the end-of-chapter questions (MC&TF for Micro, Testing Yourself for GenBio). You should also read the first chapter of the complementary book by Thursday, Aug 29.
All in all, I think everything went fine and I’m eager to get working on core material in the coming classes.
In population genetics there are two equations that allow us to estimate the frequency of alleles within a population and also to estimate the number of homozygotes vs heterozygotes for a recessive trait. These equations are known today as the Hardy-Weinberg equations because they were simultaneous proposed by two independent scientists. Like many equations, they assume a model that is not exactly reflective of the real world, however they do lend us an understanding of the rules of the system.
The two equations are:
q + p = 1
q2 +2pq +q2 = 1
It’s that easy. In each of these equations p stands for the frequency of one allele in a population and q stands for the frequency of the other allele. Assuming there are only two alleles, they must add up to 100%, represented by the decimal number 1 here.
In order to use these equations, certain conditions must be adhered to.
No gene flow (immigration / emigration)
No sexual selection
No survival selection
No mutations
No genetic drift
The last one is the one that has been interesting me lately.
What is genetic drift? What it describes are statistical anomalies, like a run of ‘Red’ on the Roulette Wheel or an unexpectedly long string of ‘Heads’ when tossing a coin.
What happens during genetic drift is that one allele becomes favored just because of such a statistical swing. But unlike roulette or coin tosses, when an allele loses out for a number of generations, it stands a diminishing chance of being seen again. The statistical anomaly becomes ‘hard-coded’ and self-reinforcing, such that eventually alleles disappear.
The key is that small samples allow genetic drift to happen more often, while larger populations tend to not see this occur. Using out coin toss example, if you toss a coin ten times, it is not especially surprising when you get 8 ‘heads’ and 2 ‘tails’. Whereas, in a toss of 1000 coins, getting 800 ‘heads’ is nearly inconceivable.
I encountered this while coding a genetics simulation program (note: my simulation uses a Wright-Fisher model that has distinct, non-overlapping generations). I wrote the program and started testing it by allowing random breeding to occur over 100 generations or so. I started using only 100 animals in my simulation, but regularly saw one allele outcompete all others, meaning that the population had lost diversity.
Below is an example with 100 organisms with four alleles for the gene breeding randomly for 200 generations.
I was sure it was a problem with my algorithm. Then I started increasing the number of animals and the ‘problem’ went away.
Here’s a second experiment at the other end of the spectrum using 50,000 animals also with four alleles breeding for 200 generations. I’ve forced Excel to graph this out on the same axis.
All this, just to demonstrate to myself that the prohibition against genetic drift is actually another way of saying, “This only works with large populations.”
What interested me is how to know whether your population is large enough to ‘resist’ genetic drift. And, how quickly will genetic drift drive alleles to fixation / loss?
“The expected number of generations for fixation to occur is proportional to the population size, such that fixation is predicted to occur much more rapidly in smaller populations.”
Not surprisingly, there is an equation designed to predict the time (# of generations) before an allele is lost by drift.
The expected time for the neutral allele to be lost through genetic drift can be calculated as
where T is the number of generations, Ne is the effective population size, and p is the initial frequency for the given allele.
Sometimes having a computer simulation comes in handy to help get a better look at how these rules apply given different populations. I’d like to get this simulation built into a simple app for either desktop or mobile device to make public, but I have been having a lot of difficulty making the leap from a program running in the console to something worth sharing.
DownHouseSoftware 