(13/13)

This work left us with another thought-provoking observation: the way these activation energies correlate is *not* evocative of an aboslute upper limit to the adaptation of microbial growth to high temperature... If it exists, such an absolute limit has to be caused by some other process!

(12/13)

the observed correlation between the maximal and optimal growth temperature which seems to hold universally in microbes. Such a pattern is expected if activation energies of both constructive and destructive processes increase with growth temperature while metabolic scaling does not.

(11/13)

This scaling remains relatively constant over different temperatures, signaling that it may not be as important a determinant of maximal growth temperature as activation energies. Together with the correlation found among activation energies, this sheds light on a thought provoking pattern:

(8/13)

Likewise, a phenomenon called the 'enthalpy-entropy' compensation was thought to apply at the cell scale within the family of models that we study, explaining some observed correlations within parameters of the original formulation of the model by Hinshelwood in 1946.

(5/13)
-surprisingly, the rate of 'constructive' processes is also inhibited in high temperature growing archaea. This could be the manifestation of a longstanding hypothesis: the enzymatic activity-stability tradeoff, which states that increasing enzyme stability decreases activity.

(4/13)
Linking empirical optimal and maximal growth temperatures to parameter values in Archaea showed that
-organisms growing at higher temperatures have more inhibited rates for destructive processes, which makes perfect sense in the context of adaptation to high temperatures

(3/13)
We created a basic version of this family of models, suitable for parameter inference using data where the growth rate of an organism is reported at various temperatures. This allowed us to turn a database of measured growth rates into one of estimated parameter values.

(2/13)
Often, microbial growth is represented as the sum of a positive ("constructive") and negative ("destructive") temperature-dependent rate, reproducing the typical asymmetrical shape of microbial thermal growth curves. How does adaptation change the parameters defining these terms?

Taking a Bayes factor approach, we estimate that sampling ~25 exoplanets should allow us to answer this question. Target yields for near-future telescope designs are spot on to learn about exoplanet habitability 🎯.

OK, but will future telescopes observing Earth-sized exoplanets in the HZ be able to determine which of our two scenarios is most common? How likely detection of atmospheric CO2 is differs between our scenarios.

Lo and behold! The distributions of atmospheric CO2 as a function of orbit/luminosity are qualitatively different depending on the carbon cycle scenario (left: Earth-like; right:SL).

To tackle this question, we tried to set expectations for the atmospheric composition of exoplanets under two scenarios of carbon cycling: Earth-like and 'stagnant-lid' (SL) where atmospheric control of CO2 is weaker. Writing and running this model has been my toughest challenge so far.

We just got some new research published last week about exoplanet habitability : doi.org/10.3847/1538-3881/ada384
Earth's carbon cycle sustained by plate tectonics is unique in the solar system.
Are terrestrial exoplanets in the HZ like the Earth? Or like Mars and Venus ?