PhotoSynthetic Biology a la carte

Project

PhotoSynthetic Biology a la carte

Photosynthetic bacteria have evolved for over 2.5 Billion years in the free-living state, can cope with most wavelengths of visible and infrared light, and have large metabolic diversity as they are oxygenic or anoxygenic phototrops.

In addition, several bacteria and archaea have developed  photoheterotrophic growth and contain retinal-based photoproteins, termed rhodopsins, that are in fact light-driven proton or sodium pumps. Moreover, new members of these phototrophic prokaryotes continue to be discovered and many have be characterized at the metagenome level. This fantastic diversity allows not only an enormous flexibility in light energy harvesting potential but provides the option to exploit a wide array of systems for the light-induced production of ATP, with or without the production of reducing equivalents. We here designate these systems photobricks. By using synthetic biology approaches, it becomes now possible to design photosynthetic bacteria à la carte by incorporating multiple and synergistic photobricks into a single transformable host. The concept of photosynthetic biology à la carte is proposed here as a novel approach to generate production systems with a negative carbon footprint. In this approach, the photoreactive systems of the photobricks – embedded in an suitable microbial chassis - supply the appropriate ratio of reducing equivalents and energy that is optimal for converting carbon dioxide into relevant high value model compounds, such as amino acids or their precursors. In this photosynthetic biology cycle we foresee the following tasks: (1) Designing the best combination of photobricks and chassis hosts for the production of high value model compounds; (2) Predicting the flux dynamics and light wavelengths needed to run the system by using a combination of biochemical, metabolic and biophysical models; (3) Constructing and validating the relevant parts of this system, and (4) Incorporating the experimental data into the models and optimizing these, closing the photosystems biology cycle.

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