Using energy budget models to understand evolutionary trait reversion in a green alga

Supervisors: Sinead Collins, John Raven

Project description:

While evolutionary adaptation is often framed in terms of organisms adapting to deteriorated or sub-optimal environments, many environmental changes are, from the point of view of evolving lineages, improvements. For example, many photosynthetic organisms respond favourably to increases in CO2, temperature and nutrients, which are hallmarks of global change. This means that many populations are going to evolve in improved rather than deteriorated environments. Over several years of microbial evolution experiments with photosynthetic microalgae in high CO2 and other enriched environments, the following pattern has emerged: First, the initial response to the new rich environment involves massive phenotypic change, including increased growth rates, that can last up to a few hundred generations. However, after hundreds of generations in the rich environment many of these changes reverse, often leading to much lower growth in the rich environment than expected. Our data so far are consistent with this trait reversion being part of a strategy to deal with increased oxidative damage associated with more rapid metabolism and growth. However, this is only part of the story, and understanding why some traits revert during evolution while others do not will require a more nuanced understanding of how traits are integrated at the level of the whole organism (whole cell, for single-celled microbes).

The PhD project offered here will use energy budget models based on data collected in previous experiments to build “virtual algae cells” with a goal of understanding trait evolution in high CO2 or other environments that promote rapid growth.  There are available well-defined starting points for algal Dynamic Energy Budget (DEB) models in enriched CO2 environments [1]), a stripped-down DEB model applicable to Chlamydomonas (a model single celled alga) [2], a DEB-related module describing damage from oxidative stress [3], and a prototype evolutionary model [4]. Based on the interests and abilities of the student, the project could then progress to further modeling, or using laboratory experiments to improve existing models. The successful candidate on this project will gain a detailed understanding of energy budget models, individual-based simulations, and microbial evolution, as well as learn to work on an international, highly collaborative project. The Collins group as a whole works on evolution of primary producers in aquatic environments under various global change scenarios; all members of the group necessarily learn about diverse aspects of ocean global change biology with a focus on evolutionary processes and collaborations with biological oceanographers.

An undergraduate degree in evolutionary biology, microbiology, or plant sciences is recommended, but other undergraduate degrees where relevant courses in these fields have been undertaken will be considered. Experience with scientific computation and some understanding of modelling is an asset, but excellent applicants willing to learn these skills will also be considered. Please contact Sinead Collins if you require further information.

This project will be done in collaboration with Roger Nisbet (University of California Santa Barbara).

References:

1.  Muller, E. B., and R. M. Nisbet. (2014). Dynamic energy budget modeling reveals the potential of future growth and calcification for the coccolithophore Emiliania huxleyi in an acidified ocean. Global Change Biology 20:2031-2038.

2.  Lorena, A., G. M. Marques, S. A. L. M. Kooijman, and T. Sousa. (2010). Stylized facts in microalgal growth: interpretation in a dynamic energy budget context. Philosophical Transactions of the Royal Society B-Biological Sciences 365:3509-3521.

3.  Klanjscek, T., E. B. Muller, and R. M. Nisbet. 2016. Feedbacks and tipping points in organismal response to oxidative stress. Journal of Theoretical Biology 404:361‐374.

4. Collins, S. (2016). Growth rate evolution in improved environments under Prodigal Son dynamics. Evolutionary Applications. 9:1179-1188.

 

 

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