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Characterization and Manipulation of Microbiomes From Arid Landfills for Improved Methane Production
This work begins by defining a working window of light intensity (LI). Wild-type and laurate-excreting Synechocystis required an LI of at least 5 µE/m2-s to sustain themselves, but are photo-inhibited by LI of 346 to 598 µE/m2-s.
Fixing electrons into valuable organic products, e.g., biomass and excreted laurate, is critical to success. Wild-type Synechocystis channeled 75% to 84% of its fixed electrons to biomass; laurate-excreting Synechocystis fixed 64 to 69% as biomass and 6.6% to 10% as laurate. This means that 16 to 30% of the electrons were diverted to non-valuable soluble products, and the trend was accentuated with higher LI.
How the Ci concentration depended on the pH and the nitrogen source was quantified by the proton condition and experimentally validated. Nitrate increased, ammonium decreased, but ammonium nitrate stabilized alkalinity and Ci. This finding provides a mechanistically sound tool to manage Ci and pH independently.
Independent evaluation pH and Ci on the growth kinetics of Synechocystis showed that pH 8.5 supported the fastest maximum specific growth rate (µmax): 2.4/day and 1.7/day, respectively, for the wild type and modified strains with LI of 202 µE/m2-s. Half-maximum-rate concentrations (KCi) were less than 0.1 mM, meaning that Synechocystis should attain its µmax with a modest Ci concentration (≥1.0 mM).
Biomass grown with day-night cycles had a night endogenous decay rate of 0.05-1.0/day, with decay being faster with higher LI and the beginning of dark periods. Supplying light at a fraction of daylight reduced dark decay rate and improved overall biomass productivity.
This dissertation systematically evaluates and synthesizes fundamental growth factors of cyanobacteria: light, inorganic carbon (Ci), and pH. LI remains the most critical growth condition to promote biomass productivity and desired forms of biomass, while Ci and pH now can be managed to support optimal productivity.
Cooperative cellular phenotypes are universal across multicellular life. Division of labor, regulated proliferation, and controlled cell death are essential in the maintenance of a multicellular body. Breakdowns in these cooperative phenotypes are foundational in understanding the initiation and progression of neoplastic diseases, such as cancer. Cooperative cellular phenotypes are straightforward to characterize in extant species but the selective pressures that drove their emergence at the transition(s) to multicellularity have yet to be fully characterized. Here we seek to understand how a dynamic environment shaped the emergence of two mechanisms of regulated cell survival: apoptosis and senescence. We developed an agent-based model to test the time to extinction or stability in each of these phenotypes across three levels of stochastic environments.