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Tutorial: Simple batch fermentation run

Introduction to Pica Photometry System


Disclaimer: This tutorial is only to illustrate how to use Pica Photometry System, not to provide proper protocols and methods for batch fermentation or other bioprocesses.

Two yeast growth curves showing different phases of the growth curve

Equipment required

  • Vessel adapter: Adapter suited for the reactor vessel, holds the sensor head in place
  • Sensor head: Backscattering sensor to measure cell density
  • Reactor: Vessel to hold the growth medium and cells
  • Stir bar: Magnetic stirrer bar to provide mixing for liquid (+ magnetic stirrer base to spin it)

Also, a Pica Readout is needed to perform measurements using the sensor head and interfacing with host PC.


Two yeast growth curves showing different phases of the growth curve

Assembled reactor

Besides the reactor itself, there are three crucial components:

  • Sensor head
    • Houses optical emitter and receiver used to perform biomass measurements
    • Should be placed so that the beam has unobstructed view of the broth
  • Tubing and connectors
    • Filling the reactor with growth media and later inserting the inoculant
    • As the growth medium fills the reactor, gasses (air) are displaced and must have a route to exit the reactor
  • Magnetic stirring
    • Stirring helps to keep the broth homogenous
    • Reactor needs to be placed on top of a magnetic stirrer base in order get the stirrer rod to spin
    • Speed of stirring should be adjusted so that vortex caused by stirring is not visible to sensor

Sensor head placement and measurement beam visualisation

Connecting to Readout

  • Sensor head to Readout
    • Readout controls the sensor head using analog signaling
    • Sensor can be installed to side of a transparent vessel or to a window
  • Readout to PC
    • Can be connected via USB or RS-485
    • Computer sends commands to Pica Readout and queries measurement results
    • Pica Readout acts as a virtual serial port, so there shouldn't be need for installing any drivers

Note: this tutorial provides Python examples, but a browser-based user interface is also available.


A Python script to check that the readout is working and connected to PC: 

import alshain
import serial
import sys

# Serial port as first command line parameter
com = serial.Serial( sys.argv[1], alshain.BAUDRATE, timeout = 0.25 )

dev = alshain.Pica( com, address = 1 )

# Read firmware version (to test communication between PC and readout)
print( hex( dev.read( alshain.Pica.Parameters.FIRMWARE_VER ) ) )

com.close()
Visualisation of what non-biology related factors contribute to backscattering measurement

Starting the experiment

Steps:

  • Fill the reactor with suitable growth media
  • Start the mixing and adjust mixing speed
  • Make sure that sensor head is positioned accordingly
  • Start logging backscattering signal, so difference between pre- and post-inoculation can be measured
  • Begin injecting the cells to reactor (inoculation)
  • Monitor signal levels and inoculant mixing

A Python script to log backscattering signal continuously: 


import alshain
import serial
import sys
import time

# Serial port as first command line parameter
com = serial.Serial( sys.argv[1], alshain.BAUDRATE, timeout = 0.25 )

dev = alshain.Pica( com, address = 1 )

# Enable photometer source
dev.write( alshain.Pica.Parameters.PULSE_ENABLE, 1 )   

# Start time
t0 = time.time()

# Loop ends with ctrl-c
try:
   # Poll results every 500ms
   with open( "output.csv", 'w' ) as handle:
      while True:
         value = dev.read( alshain.Pica.Parameters.RESULT )
         t = time.time() - t0
         
         # Store to file
         print( t, result, file = handle )
         
         # Print to terminal
         print( t, result )
         
         # Wait for 0.5s
         time.sleep( 0.5 )
except KeyboardInterrupt:
   pass


com.close()

Equation for components in measured signal

Measuring growth curve

Continue logging the backscattering signal

  • Depending on the type of cells and amount of resources available, timescale ranges from hours to days
  • Prominent features in the growth curve are:
    • Inoculation, curve spikes up when cells are introduced to system
    • Lag phase, cells adapt to their new environment, no growth
    • Log phase, number of cells increases rapidly
    • Stationary phase, growth slows down due to lack of resources
  • Parameters such as specific growth rate (µ) can be estimated from the log phase
  • Conversion from raw signal to more useful units (OD600, cell count etc.) requires calibration:
    • OD600: offline measurements using manual sampling and fitting a linear model to signal
    • Relative growth: backscattering signal is very close to linear regarding cell density, comparing measurements to signal change caused by inoculation gives a scale for relative growth

See also

Links