It's all about science!

The phrase 'global warming' brings a slight froth to both far-leftists (who I will affectionately call by their not-at-all preferred name, moonbats) and far-rightists (who will likewise be referred to by that timeless endearment, wingnuts). Moonbats, who recently received instructions from their hive mind to start using the phrase 'climate change' instead and will take great exception to any attempt to use the older term, get a bit religiously frothy about it -- there is no god but Al Gore, and I am his Prophet! Raise the specter of doubt to these rabid fans of (certain kinds of politically correct) science and like as not you'll be faced with a self-appointed Clarence Darrow thundering righteously against a frightening amalgamation of George W. Bush, Adolf Hitler, Ned Ludd, and William Jennings Bryan (also known as 'you'). Wingnuts, on the other hand, will egg you on into their pre-prepared verbal mine-field, citing misleading popular sources and asking probing but utterly loaded questions. The worst of these folks are ace debaters who wouldn't know science from scientology if Tom Cruise picked them up and strangled them with it.

But who cares, right? Someone recently enthusiastically recommended I read an anti-global warming tract written by some guy. I forget his name, but I looked it up at the time. He wasn't a climatologist, or even a scientist. He was a lawyer with a conservative advocacy group. I pointed out to my mine laying interlocutor that it seemed a bit odd to try and refute the significant library of peer-reviewed scientific literature on global warming with a non-technical, non-scientific policy tract written by a conservative lawyer. He responded, and I quote:

"This is not about science." (emphasis his)

It is, though. You've absolutely got to design policy from a knowledgeable standpoint of the underlying issue. What should our policies be with regard to climate change? Yes, it's a cost/benefit analysis -- but those costs and benefits can only be determined if you understand the process under consideration.

So, what does the science say? Here's my take:

There's three distinct issues here: first, do we observe warming, second, can we draw a reasonable conclusion that warming is anthropogenic, and third, how much can we trust the general circulation models? So, let me state that I don't trust the GCMs. A lot of my own research involves dynamical simulations, so I'm wary of trying to make forward predictions for such a complex system. With a GCM, you can't build-a-little-test-a-little with controlled experiments, so you're stuck saying, Well, this matched previous data pretty well. But that can just be curve fitting: you bounced around parameter space until you found a set that fit pretty well, but that doesn't guarantee your parameters are physically meaningful. If they're not, will that model work for making forward predictions? Probably not. In particular, I think there's so many external factors that are not taken into consideration in the GCMs, as well as parametrizations for factors that are included but that we know we're not able to model accurately (the effects of cloud cover, surface albedo, etc.), that quantitative in silico predictions about climate change shouldn't be taken too seriously. It's important to differentiate between doubts about GCM accuracy, which can be well-founded, and saying The observed global temperature data is wrong!, which isn't.

That said, back to the empirical question: have we observed statistically significant warming? Yes, and perhaps more importantly, the observed warming is nonlinear: recent years have seen it accelerate, and the per-continent surface temperature average increases are consistent with increased sulfate aerosols and greenhouse gases. Furthermore, although ocean temperature has been increasing at a slower rate (about half) of the land surface warming, it absolutely is increasing, and this observation isn't limited to the ocean surface -- temperature increases are observed down to depths of several thousand feet. The issue of the lack of a warming trend in Antarctica illustrates two important points -- that global and local temperature trends are often conflated, by people who should know better, and also that Antarctica (and parts of the tropics) has substantial gaps in its historical temperature data set. This data has been 'filled in' with data interpolation and averaging techniques, but in any consideration of Antarctic temperature trends, it's important to keep this caveat in mind.

Second, is this observed warming trend anthropogenic? This is tricky, because you need to de-couple it from natural climate forcings -- for example, obviously the Medieval Warm Period wasn't caused by man-made aerosols. So, you're trying to draw a statistical correlation between anthropogenic forcings (GHGs, aerosols) and temperature, and you've got GCMs to make this link -- and as I mentioned before, I'm leery of the predictive power of these models.

Using Statistical Mechanics to Link the Sequence and Dynamics of a Genetic Circuit

Bacteria can be reprogrammed with new genetic commands encoded in synthetic DNA. These programs require a signal processing circuit to analyze sensory input and control the cell's response. Genetic circuits have been developed that function as toggle switches, oscillators, pulse generators, and band-pass filters. This circuitry is needed to write the complex instructions necessary for applications such as nanoscale manufacturing, metabolic engineering, programmed therapeutics, and embedded intelligence in materials.

Genetic circuit assembly is challenging because genes are specific to their native systems. There is currently no method to predict the spatiotemporal dynamics of a genetic circuit directly from its DNA sequence, and coupling components from different systems requires the tedious trial-and-error adjustment of the components' kinetic characteristics. My objective is to apply biophysical models of gene regulation to predict the DNA sequence of genetic circuits in silico for a desired dynamical behavior.

Natural components of biological systems have widely varying gene expression levels. To effectively design large or complex genetic programs, we will need a detailed biophysical link between DNA sequence and gene expression dynamics. Gene expression is controlled by the transcription of DNA to produce mRNA, and the translation of mRNA to produce proteins. The rates of these processes are controlled by the DNA sequence around the expressed gene, so it is possible to tune the dynamical expression of the gene by adjusting these sequences. The promoter and ribosome binding site (RBS) sequences can be used to modify the transcription and translation rates, respectively.

Quantitative biophysical models of bacterial transcription and translation initiation have recently been developed, and their predictions are consistent with experimental data.^1,2 These models present a starting point to connect the dynamics of a genetic circuit directly to its DNA sequence. Genetic circuits can utilize a variety of sensory input signals, including chemicals, light, and temperature; here I will consider a single transcription factor input processed by a genetic inverter circuit in Escherichia coli³, shown schematically in Fig. 1. The inverter's dynamics are well-characterized for many promoter and RBS sequences, making it an ideal test circuit.

Aim 1: Predict the dynamics of a genetic inverter circuit from its DNA sequence.

Using the models referenced above, I will calculate transcription and translation rates from the DNA sequence. The equilibrium thermodynamic model of translation predicts the free energy change of ribosome binding to the mRNA, which is proportional to the translation initiation rate.

The rate-limiting step in transcription initiation is open complex formation. Prediction of transcription rate from the promoter sequence is done by computing the rate of open complex formation. However, the initiation rate is adjusted by the equilibrium binding probability of RNA polymerase to the promoter DNA. This permits the use of a statistical thermodynamic approach to model how transcription factor concentrations affect the circuit: calculating the system's partition function provides a way of adjusting the predicted transcription rates according to the population of each discrete system configuration.⁴

These predicted rates will be incorporated into a dynamical mathematical framework: a system of differential equations describing the rates of change of the inverter’s internal concentrations. This system of equations will be solved numerically to update the concentrations of the inverter's components. The result of this model will be a transfer function (Fig. 2) showing the predicted dependence of the inverter’s output, a fluorescent protein, on the concentration of its input signal, a transcription factor. Comparison of the in silico transfer functions with previous experimental data will provide a convenient way to assess and modify the model described here.

Aim 2: Forward engineer the sequence of an inverter circuit for a specified dynamical behavior.

I will wrap this model with an optimization routine to search parameter space for optimal transcription and translation rates for a given transfer function. The unknown shape of the parameter space makes a Monte Carlo simulation well-suited for this problem. The dynamical mathematical model described in aim 1 quantitatively links these parameters to the promoter and RBS DNA sequences. This link provides a systematic way to search for optimal DNA sequences, given a known parameter list.

I will generate in silico transfer functions by mutating each nucleotide in the promoter and RBS sequences, followed by experimental construction of these sequences using site-directed mutagenesis. Analysis of the in silico transfer functions should provide guidelines for efficient mutagenesis, by identifying nucleotides predicted to significantly alter the transfer function.

Verification and stress testing will be done by generating in silico promoter and RBS sequences for diverse transfer functions, then comparing the requested transfer function shape to an empirical transfer function measured using flow cytometry. These tests will focus on quantitative adjustment of the transfer function's shape, in particular, the curve's steepness (how well it approximates a digital output signal) and its gain (the range between its on and off states).

Impact: This modeling strategy is useful because it can be generalized to more complex genetic systems. Applications of this method include automated tuning of existing genetic components as well as guiding the assembly of new, more complex genetic circuits: synthetic constructs to perform arithmetic and other logical operations, such as conditionals and control logic. Automated in silico control of the dynamical behavior of synthetic genetic circuits will help synthetic biology mature into a practical and useful engineering discipline.

References:
1. Salis H, Mirsky E, Voigt C. “Designing synthetic ribosome binding sites.” Submitted 11/2008.
2. Djordjevic M, Bundschuh R. 2008. Biophys J 94.
3. Yokobayashi Y, Weiss R, Arnold FH. 2002. Proc Natl Acad Sci USA 99.
4. Bintu L et al. 2005. Curr Opin Genes Dev 15.

Snake oil

Sirtris has developed a new wonder drug, it's an all-in-one caloric restriction mimetic and no-effort-required weight loss program! It's like resveratrol but 1000 times better!!!

...

Wait, is there any actual evidence of resveratrol extending lifespan in metazoans? Even the yeast and C. elegans evidence is unconvincing; to my knowledge, no one outside his lab has ever been able to duplicate Sinclair's results. Since the lifespan assay is so prone to experimenter-introduced bias, Linda Partridge went through and did a more thorough analysis of the putative lifespan extension, and didn't find anything. (Being a veteran kool-aid drinker, I actually ran an experiment myself using wild type C. elegans and some other worms treated with dsRNA to give them an unusual germ-cell 'cancerous' phenotype...didn't help with aging, or the cancer, for that matter.)

I'd be pretty leery about using large amounts of resveratrol (which is effectively what this new compound is) as a supplement. People have picked up all kinds of low-affinity (~micromolar) targets for it, with a variety of mechanisms, which isn't surprising, given its structure. In particular, it seems to hit the adrenergic receptors and affect Wnt signaling, which is ok in small amounts but you wouldn't want to deluge your system with this stuff.

Obamania!

I hovered over the Barr/Root checkbox for a long moment, but I ended up voting for Barack Obama. I have many reservations about him, but ultimately, for me at least, the combination of McCain's temperament, age, and astonishing bad judgment in choosing Sarah Palin as his running mate did it for me. And, let's face it, Bob Barr just plain sucks.

Here's hoping he governs well...