B113-0015
Studying Microbial Adaptation in the Laboratory: Sensor & Control Upgrades for an Experimental Evolution Biofluidics System

Wednesday, 16 December 2020
Poster
Cassandra Hergenrader, NASA Ames Research Center, NASA Internships, Fellowships & Scholarships, Moffett Field, CA, United States; University of Nevada Reno, Reno, NV, United States, Kindred Griffis, NASA Ames Research Center, NASA Internships, Fellowships & Scholarships, Moffett Field, United States; Michigan State University, East Lansing, MI, United States, Chinmayee Govinda Raj, Georgia Institute of Technology Main Campus, Atlanta, GA, United States, Opinder Dhami, San Jose State University, San Jose, CA, United States, Jonathan Lu Wang, Millennium Engg. & Integration Co., Moffett Field, United States; NASA Ames Research Center, Moffett Field, United States, Vijaypal Singh, San Jose State University, San Jose, United States and Diana Gentry, NASA Ames Research Center, Biospheric Science Branch, Moffett Field, CA, United States
Abstract:
Experimental evolution (EE) involves iteratively exposing a microbial community to specific stressors to study its response to changes in environment over time. EE work is commonly done manually in the laboratory, but when there are many environmental variables to measure and adjust, it is highly labor intensive, prone to human error, and challenging to scale. Single-purpose automated continuous culturing chambers exist but implement only limited stressor types. A more general-purpose design is desirable.

The BeING Lab at Ames Research Center created the prototype Automated Adaptive Directed Evolution Chamber (AADEC) to address these problems, beginning with Escherichia coli tolerance of short-wave ultraviolet (UV-C) radiation and of temperature. In newer versions, AADEC monitors microbial activity and can adjust the UV-C and temperature levels automatically. An optical density measurement is used to determine how many cells are present in the growth medium—over time, this corresponds to how many survive and reproduce. Oxidation-reduction potential provides information on consumed metabolic energy, and pH and electrical conductivity on metabolic products. Dissolved oxygen content is used to determine aerobic vs anaerobic growth. A Raspberry Pi computer processes all this data to set the UV-C stressor level.

AADEC’s auxiliary systems include peristaltic pumps to change media and agitation to counteract cell settling. These actuators can also act as additional stressors. With the Raspberry Pi monitoring sensors and adjusting actuators in real time, AADEC takes measurements and controls the environment much more accurately than can be done with a manual EE implementation.

The third and latest AADEC iteration is the first to simplify design and usage with circuits on PCBs and the ability to pre-program experimental protocols. Still planned is expansion to a multi-well design for the study of varying cell cultures in parallel, which will enable researchers to retain and re-inoculate cultures exhibiting the desired trait most strongly while flushing out others. AADEC’s special capabilities make it a valuable tool for studying life under multiple stressors, enabling scientists to replicate changes in climate on microbes for study in a lab setting.

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