As part of my recent fascination with plant biology, I read a paper called “A chemical genetic roadmap to improved tomato flavor“, by Tieman et al. This led me down a rabbit hole of a surprisingly large literature on the topic of tomato flavor. Of course it’s obvious why crop breeders would want to make better tomatoes (some background on this from the extensive media coverage). What I find really interesting, though, are the broader lessons about genetics and bioengineering that come out of tomatoes as a model system.1
I’m particularly struck by the theme of tradeoffs. In some sense, this is the key challenge for all of modern crop genetics. Why do intensively bred crop varieties, which have undergone selection for yield and market appeal, often taste worse than their ancestor plants? Is it possible to make a convenient and attractive tomato that is also delicious? Or, is the tradeoff due to fundamental biological constraints and therefore can’t be overcome by breeding or genetic engineering?
Recently I’ve become fascinated1 by crop breeding and plant genetics, after realizing that these are, in a sense, the oldest fields of biological engineering. Even though we’ve only been able to manipulate genetic circuits and metabolic pathways for a few decades, we’ve been influencing whole-organism traits through selective breeding for a whole lot longer. You could say that humans were engineering plants hundreds of years before we even knew there were such things as genes!
Of course, this all applies to animal breeding as well, but I think plant science is particularly relevant right now as an applied field. In biology, at least, I’m not sure there are more important advances than those being made in crop genetics, photosynthesis, and carbon/nitrogen fixation for allowing human society to continue in its current material form in a future without fossil fuels.
I’m also just curious about plant biology because I was never taught (or even exposed to) it during my formal training — this still surprises me. Continue reading
I wrote this privately 2 years ago, reflecting on my first publication. Later I saw that others had already been having a similar discussion (see this piece by Yarden Katz), so I thought this would be good to share here.
My first first-author paper was recently accepted to a journal, a paper I’ve worked on, in some form, for almost 3 years. Writing this paper was my first experience writing a full-length scientific manuscript, and I knew it would be hard work from start to finish. However, what I thought would be challenging were the scientific or logical aspects of writing: framing the argument, explaining it clearly, and conveying the significance of the findings to the readers. These things were indeed challenging, but I was surprised to encounter another difficulty, the problem of “storytelling” during writing. This is a problem I wish I didn’t have to deal with, and I think one of the most stressful parts of doing science. I think the reason it is stressful, which I’m not sure many people could even articulate explicitly, is that it is in essence an ethical problem. Continue reading
This is a tutorial on how to do Bulk Segregant Analysis (BSA) in yeast, a particular way of doing Quantitative Trait Locus (QTL) mapping. The general aim is to identify the mutations underlying a trait difference between individuals–in this case, different yeast strains. A large literature exists on this topic, but it’s still often time-consuming to figure out how to actually analyze data. BSA is quite simple in concept, so software packages like R/qtl are a bit overkill but the “homebrew” scripts you get from most papers are not quite commented enough to easily learn from. My hope with this tutorial is to fill the gap in between so you can get started, and provide a jumping off point to useful references.
Throughout grad school I’ve had many ideas I wanted to write about, or tools I’d developed that I wanted to explain and share, but these never seemed quite “serious” enough to spend time on. So, for the most part, I abandoned my previous hobby of blogging in favor of spending more time in lab.
But some of these ideas seemed fun or maybe could have led to interesting research directions. At the very least, they might have helped others with technical tasks. So it always felt remiss to not air them out somehow.
Now that I’ve finished my PhD, and at least for now will not be writing many more papers, I want to make a more serious effort to communicate my mini-ideas. I’ve also entered a new field of biology, and am trying to make sense a lot of new and fascinating literature that I’d like to share with a broader audience (or, well, I’d be happy with “beyond my own head”).
In the spirit of change, I will call this new blogging project “Diauxic Shift”, after the phenomenon, discovered by Monod and Jacob, where microbes pause growth between consuming different nutrients so they can induce the appropriate metabolic genes. Continue reading
Growth curve experiments are used to study the physiology of bacteria, yeast, or other micro-organisms. You inoculate cells in a nutrient medium, let them grow, and record the optical density of the culture over time with a spectrophotometer. Automated plate readers can do thousands of growth curves in a single experiment, giving a detailed view of how environmental conditions affect cells.
I’ve spent many hours analyzing growth curves during my PhD, and almost as many hours teaching others to do the same, so I am going to describe a basic growth curve analysis here and try to highlight some quantitative principles and programming techniques along the way. Hopefully this can save you some time if you are new to growth curves and/or programming.
If you do experiments on microorganisms, you are probably familiar with fitness assays, where you study a mutant strain by comparing its growth rate to that of the wildtype in various environments. If, like me, you have learned the method by reading papers, you may have missed an important fact: there are two different ways of presenting fitness data. Microbial fitness can be reported as either a selection coefficient or a relative growth rate difference, and although these are mathematically related, they are not equivalent. Moreover, they have different conceptual meanings. This point may be obvious to some, but is subtle enough that I thought the two quantities were equal until I had read (and failed to understand) multiple explanations to the contrary.
Here I will try to help other confused experimentalists by explaining this difference in practical terms, as it arises in the analysis of a competitive growth assay. None of this material is original, but I hope my presentation clarifies ideas that aren’t as transparent elsewhere. I borrow especially heavily from Greg Lang’s competition assay protocol, the Crow and Kimura population genetics textbook, and this article by Luis-Miguel Chevin that makes the same point I make here, but at a more conceptual level.
If you’re the impatient type, everything below can be summarized in 3 points:
- A 1% difference between two strains in the number of offspring per generation (selection coefficient) is not the same as a 1% difference in exponential growth rate (relative growth rate difference).
- A 1% difference in number of offspring per generation is approximately equal to a 0.69% difference in exponential growth rate.
- You should report fitness as a selection coefficient if you can. If you must use a different metric, state this prominently so people like me don’t get confused.