We thank B. Stafford for his assistance in design of the system, and H. Khalil, A. Soltanianzadeh, A. Sun, S. Pipe, and A. Cavale for help with construction of the system. We acknowledge the Electronics Design Facility (EDF), Engineering Product Innovation Center (EPIC), and the Software &#38; Application Innovation Lab (SAIL) at the Hariri Institute for Computing at Boston University for their services. This work was supported by an NSF CAREER Award (MCB-1350949 to A.S.K.), and DARPA grants HR0011-15-C-0091 and HR0011-18-2-0014 (to A.S.K.). A.S.K. also acknowledges funding from the NIH Director&#8217;s New Innovator Award (1DP2AI131083-01), DARPA Young Faculty Award (D16AP00142), and NSF Expeditions in Computing (CCF-1522074).

1. Preparing Media, Vials, and Inoculum
NOTE: This protocol assumes users have already calibrated the eVOLVER system and are using the Smart Sleeve configuration described in prior work16. Smart Sleeves are easily redesigned or modified, but setup details of volume and control parameters may differ for alternative configurations.
<ol>
	<li>The day before the experiment, prepare 2 mL overnight liquid cultures of S. cerevisiae BY4741 reference strain and variant strains in YPD media (Yeast Peptone Dextrose) at 30 &#176;C in a shaking incubator set to at least 300 r.p.m.</li>
	<li>Transfer sterile YPD media into clean, autoclaved bottles fitted with tubing. Alternatively, media may be autoclaved directly in bottles pre-fitted with tubing. 
	NOTE: Bottles are fit with tubing by drilling a hole into the cap and running tubing through it with connectors to secure it in place. See Table of Materials.</li>
	<li>Add stir bar to each vial and screw on a vial lid equipped with influx and efflux straws. Ensure that all components are clean by visual inspection.</li>
	<li>Place vials in an autoclavable rack, cover with foil, and autoclave on a gravity or vacuum cycle with a 20-min sterilization step.</li>
</ol>
2. Setting Up an Experiment
<ol>
	<li>In three large beakers, prepare 500 mL of 10% bleach, 200 mL of 10% bleach, and 300 mL of 70% ethanol.</li>
	<li>Using the switches on the eVOLVER device, turn on the 5 V power supply on the eVOLVER, wait for 5 s, then turn on the 12 V power supply.</li>
	<li>If running several vials from the same media bottle, connect multiple media input lines with tubing splitters. Custom tubing splitters can be constructed with Luer components.</li>
	<li>Submerge the media input lines in the first bleach beaker, and the media efflux lines in the second bleach beaker. Place the downstream end of the media input lines in the second beaker with the efflux lines. Place waste lines into waste carboy.</li>
	<li>Add 1-2 L of bleach into large empty waste carboy, which will sterilize waste generated during experiment. Consult with a safety coordinator to ensure proper disposal of waste.</li>
	<li>Using the touchscreen on the eVOLVER, navigate to the setup section, and run all pumps for 20 s to fill fluid lines with 10% bleach. While running, ensure by visual inspection that all lines have been filled and that pumps are operating normally. Allow bleach to sit in the lines for at least 30 min to sterilize.</li>
	<li>Run pumps again using the touchscreen app until lines are no longer submerged, pushing air through the lines to get as much of the bleach out as possible.</li>
	<li>Place media input lines in the ethanol beaker and repeat as in above, filling the lines with ethanol and flushing with air. 
	NOTE: As ethanol kills quickly, there is no need to wait 30 min for sterilization.</li>
	<li>Attach media input lines to the media bottles with the Luer connectors and run the pumps until the media fully runs through the lines, flushing out any residual ethanol.</li>
	<li>Partially insert sterilized vials into the eVOLVER Smart Sleeves and hook up the lines to the proper positions, according to the color coding on the lines. Start by attaching input lines to the short influx straw, and then waste lines to the long efflux straw. 
	CAUTION: It is critical to check for loose connections or incorrectly routed lines. Failure to do so will cause overflows and potentially damage the Smart Sleeve.</li>
	<li>Run all pumps in 10 s increments to fill the vials with media. Ensure by visual inspection efflux pumps are efficiently removing media through the efflux straws, to prevent overflows. If the efflux straws do not appear to be functioning efficiently, inspect the fluidic line connections and the peristaltic pump. Correct or replace parts as needed.</li>
	<li>Push vials down until fully encased by the Smart Sleeve.</li>
	<li>Set the initial conditions for experimental parameters using the eVOLVER touchscreen on the interactive setup page. For all vials, set the temperature to 30 &#176;C and the stir to 10. This can be done for all vials at once by dragging a finger across the touchscreen to select all of the vials.</li>
</ol>
3. Configuring eVOLVER Software and Programming Algorithmic Culture Routines
<ol>
	<li>In the eVOLVER dashboard on a computer, navigate to the experiment manager page. Select the base experiment in the experiment navigator panel or an existing experiment as a starting point and click clone new. 
	NOTE: It is possible to use experiments from other groups by pasting the GitHub link to the repo where the experimental definition is saved into the experiment manager.</li>
	<li>Specify the experiment name and the eVOLVER device that the experiment will be run on. Be sure that the desired calibration settings have been selected by modifying these fields on the page.</li>
	<li>Use the vial selector and parameter definition panes to set experimental conditions. For example, to set the temperature for vials 0-4 to 30 &#176;C, select the vials in the vial selector, set the parameter definition to 30, and click Set. Repeat this for OD to set an upper and lower threshold for each vial.</li>
	<li>After experimental parameter definitions complete, save the experiment by clicking Save and click Start to run.</li>
</ol>
4. Initiating Experiment and Monitoring in Real-time
<ol>
	<li>Once the experiment is underway, navigate to the real-time data panel in the interactive dashboard. For each vial, check that OD values are holding at zero, and that temperatures are at or progressing towards programmed levels by browsing through the graphs.</li>
	<li>To prepare inoculum, measure the OD of overnight cultures, calculate the desired fold dilution assuming a vial volume of 25 mL, and pipette calculated volume of inoculum into vials through the sampling port. For example, to reach a desired starting OD of about 0.05 in the culture vial from overnight cultures that are at OD 2.0, 625 &#956;L of cells should be added to each vial.</li>
	<li>Check the graphs on the dashboard to see that the OD graphs have been updated accordingly. An increase to near the desired starting OD should be seen in the graphs.</li>
	<li>Track different data types (such as OD, temperature, growth rate, generations, and media consumption) throughout the experiment by browsing through the corresponding graphs in the dashboard. These values may inform experimental decisions by the user (e.g. schedule timepoints) or even used in functions to perform automated feedback.</li>
	<li>To replace media bottles mid experiment, pause the experiment in the experiment manager panel to prevent pump events from occurring. Quickly unscrew the media line from the empty bottle and connect it to the new bottle. Avoid touching the ends of tubing to prevent contamination. Resume the experiment in the experiment manager.</li>
</ol>
5. Sampling Yeast Cells from eVOLVER Vials
<ol>
	<li>Prepare a cycloheximide solution to inhibit protein synthesis and fix yeast cells for flow cytometry analysis. In a 15 mL conical tube, add 12 mL of PBS and 12 &#956;L of 20 mg/mL cycloheximide and vortex for 5 s.</li>
	<li>Using a multichannel pipette, aliquot 100 &#956;L into each well of a 96-well round bottom plate.</li>
	<li>Wrap the plate in aluminum foil to block out light and keep at 4 &#176;C until needed.</li>
	<li>To sample, pipette through the sampling port on the vial lid using extended length 200 &#956;L tips.</li>
	<li>Pipette 100 &#956;L from a vial into a well in the fixation plate, mixing 2-3x by pipetting up and down, then recover in foil and return the plate to 4 &#176;C.</li>
</ol>
6. Experiment Break Down and Clean Up
<ol>
	<li>Prepare 1 L of 10% bleach and two 500 mL DI water solutions in large beakers.</li>
	<li>Stop the experiment by sending the stop command in the experiment manager panel. The data is still stored in the cloud-enabled database, and can still be viewed later in the real-time data panel of the dashboard, or downloaded for offline analysis.</li>
	<li>Remove vials from Smart Sleeves and place them in an autoclave rack to avoid overflows in subsequent steps.</li>
	<li>Unscrew the media input lines from the media bottles and place them into the beaker of 10% bleach. Run pumps from the eVOLVER user interface as in setup to sterilize lines and vials.</li>
	<li>Disconnect lines from vials and submerge lines in DI water. Run pumps to flush all bleach out of the system first with DI water, then with air.</li>
	<li>Turn off eVOLVER by first switching off the 12 V power supply, waiting 5 s, and then turning off the 5 V power supply.</li>
	<li>Soak stir bars and caps in 10% bleach, then rinse with DI water. Be sure to rinse the influx and efflux straws of each cap by running DI water through them. Scrub vials using test tube brush if film has formed on walls.</li>
	<li>Dispose of waste appropriately and rinse waste containers thoroughly. 
	CAUTION: Waste containers will contain a mixture of 10% bleach, sterilized cell culture waste, and trace amounts of ethanol. Consult a safety coordinator for proper handling and disposal.</li>
</ol>
7. Quantifying Fitness Using Flow Cytometry Analysis of Fractional Populations
<ol>
	<li>Analyze collected samples from section 5 using a flow cytometer equipped with the appropriate fluorescence channel and detectors16.</li>
	<li>Acquire at least 10,000 events per sample, and gate for intact cells using the forward and side scatter data.</li>
	<li>Gate based on the appropriate fluorescence channel (e.g. GFP) to determine the fractional distribution of each population.</li>
	<li>On a computer, save data to desired location from the Experiment Manager page by clicking the &#8220;Export&#8221; button.</li>
	<li>From the eVOLVER data files, determine the number of generations completed by each timepoint for each vial. In the eVOLVER code, generations are calculated by first segmenting the OD data between each dilution event, then each segment is fit with a basic exponential of the form <img alt="Equation 1" src="/files/ftp_upload/59652/59652eq1.jpg" /> to each segment, yielding r, the growth rate16. The number of generations over a time period is then calculated using the following formula: 
	<img alt="Equation 2" src="/files/ftp_upload/59652/59652eq2v2.jpg" /></li>
	<li>Plot the log-ratio of the labelled and unlabeled populations from the flow cytometry data vs generation number from the above calculations at the time samples were taken. Identify the linear region and fit the slope according to the following equation. This slope is commonly defined as the competitive fitness18,19. 
	<img alt="Equation 3" src="/files/ftp_upload/59652/59652eq3v2.jpg" /></li>
</ol>

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The authors, Brandon G. Wong and Ahmad S. Khalil, are co-founders of Fynch Bioscience Inc., which develops the eVOLVER platform used in this Article.

Growth selection is an indispensable tool in biology, broadly used to generate and characterize phenotypic differences between cellular populations. While batch cultures do permit growth selection in a limited way, continuous culture techniques dramatically expand the degree of control and predictability of these experiments, by exerting precise regulation over the form and dynamics of selection to generate repeatable, quantitative results22. Continuous culture has been employed to rigorously control selection for high-diversity libraries20,23,24,25, and to implement sophisticated adaptive regimes in experimental and directed evolution11,12,26,27. Continuous culture also enables precise characterization of cells across an array of quantitatively controlled conditions to better understand complex genetic systems and optimize engineered bioproduction strains9,14,28.
However, there is no universal protocol for continuous culture, as subtle changes to the selective conditions can lead to dramatic changes in biological outcomes4,29,30. Experimenters must be able to choose between selection regimes and adapt experimental protocols and equipment accordingly. In addition to offering a choice between control parameters, such systems would ideally be sophisticated enough to&#160;independently manage several parameters simultaneously in highly-parallel experiments that are needed to decipher interacting inputs in complex biological systems (e.g. epistasis). eVOLVER addresses this challenge by enabling users to arbitrarily program feedback control between culture conditions and fluidic functions in order to specify highly specialized environmental niches.
To overcome limitations in the current setup and expand or change control parameters, the Smart Sleeve could easily be redesigned to add new sensors or actuators. Additionally, reducing vial volume would decrease media expenditures, which can be significant in continuous culture. While the current design permits measurement and control of temperature, culture agitation, light induction, turbidity, and fluidics, other parameters must be measured externally by sampling from the vials. Current work includes incorporating the ability to monitor enzymatic activity via luciferase and regulate dissolved oxygen and pH directly in eVOLVER cultures. Additionally, while not demonstrated in this work, eVOLVER can interface with novel millifluidic multiplexing devices16 that draw on principles of large-scale integration (originating from electronics and adopted by microfluidics) in order to inexpensively enable more complex fluidic handling (e.g. multiplexed fluidic inputs, vial-to-vial transfers). These wetware modules may be designed and manufactured completely in the lab, allowing users to route fluids by programmatically actuating different combinations of valves in automated fluidic routines. This allows users to overcome the rigid fluidic designs traditionally used in continuous culture, but also to scale fluidic capabilities to high-throughput with a smaller number of costly control elements (e.g. peristaltic pumps). Lastly, we are hoping to incorporate an autosampling platform which will utilize these millifluidics and DIY components, overcoming the limitation of manual interaction during longer and larger experiments where sampling cultures would be cumbersome.
In addition to physical modifications to the platform, the web-based software opens new degrees of freedom by allowing users to write, edit, and share custom eVOLVER scripts, generating fully automated, feedback-enabled culture programs (e.g., turbidostat). Users may programmatically sweep across parameter ranges in subtle variations on the same selection scheme or connect control algorithms in novel combinations to specify any number of sophisticated selection schemes. Furthermore, the ability to easily monitor cultures in real-time transforms the way in which experiments are conducted. With real-time monitoring, users may 1) check for consistency between runs, a critical feature for bioproduction applications and high-throughput experiments, and 2) intervene during experiments if necessary, to troubleshoot challenging strains that exhibit poor growth or biofilm formation, or diagnose user errors (e.g., contamination). Finally, with multiple data streams being collected and interpreted in real-time for each individual culture, eVOLVER generates a high density of data, which may facilitate machine learning approaches for novel downstream analysis.
Beyond demonstrated uses for fitness characterization, library selection, and laboratory evolution, we view a number of related fields as ripe for implementation in eVOLVER with integrated fluidics. eVOLVER experiments with microbiome samples could assay community stability in controlled environments31,32, explore microbiota composition using culturomics techniques33, or dynamically mix species to interrogate ecological dynamics of colonization or invasion34,35. Numerous methods for continuous directed evolution of biomolecules could easily be implemented on the device as well26,36,37, greatly increasing accessibility and throughput of these systems. The ability to optimize growing conditions such as media composition, temperature, and strains in a dynamic, high throughput nature can aid in optimization efforts for industrial biomanufacturing applications9. We further envision vertically integrating eVOLVER with other analysis techniques such as microscopy and flow cytometry in a closed loop fashion, providing a fully automated system for growth and analysis of cellular cultures at both single cell and population levels. Moreover, with some hardware modifications to the Smart Sleeve such as sealing the vessel and controlling gas content, eVOLVER could potentially be adapted to support the growth of a wider range of cell types, such as suspension mammalian cells. It is also feasible to place the entire framework into an anaerobic chamber for anaerobic cell culture. Looking forward, we aim to build our software framework into a centralized cloud infrastructure and believe this would allow users to easily configure, analyze, and share their data remotely without needing to physically be present in the lab. Functioning as a data curator, the cloud infrastructure would also lend itself to large-scale meta analyses across experiments. We anticipate that eVOLVER and these future advances will greatly expand the scope of possible growth selection experiments by facilitating automation and innovation in continuous culture.

Continuous cell culture techniques, first developed nearly 70 years ago1,2, are enjoying a recent revival3,4. This is due to a confluence of factors. First, the development of high throughput -omics techniques, which have made it possible to read out and generate large numbers of genotypes5,6, has created a concomitant demand for experimental techniques that facilitate well-controlled cell growth and phenotyping. To this end, continuous culture represents a powerful experimental approach to capitalize on emergent genomic advances. By facilitating growth selections/experiments on cellular populations in precisely controlled (and dynamic) environmental conditions, continuous culture provides a means to rigorously map genotypes to phenotypes7,8, quantitatively characterize engineered strains and organisms9, and track adaptive genetic changes in laboratory evolution studies10,11,12.
Second, the recent emergence of accessible prototyping techniques, such as additive manufacturing and open-source hardware and software elements, has enabled a wider set of users to design and build their own cost-effective forms of continuous culture systems directly in the laboratory. All of this has led to an exciting array of do-it-yourself (DIY) devices that perform continuous culture functionalities, such as the chemostat13, turbidostat14, or morbidostat15. Unfortunately, though successful in addressing specific (niche) problems for which they were designed, these ad hoc solutions generally lack the ability to scale in throughput and/or experimental design complexity.
The eVOLVER system was designed with the goal of creating a single platform that can accommodate the growing experimental needs of continuous culture and match the speed and scale of emergent genomic techniques16 (Figure 1A). eVOLVER&#8217;s design implements common tenets underlying highly scalable technologies from other disciplines17, including standardized footprints, modular components, and open-source design principles. Thus, solutions for new niche applications can be designed without major modifications to the system. Comprised of highly modular and open-source wetware, hardware, electronics, and web-based software, eVOLVER is the first automated continuous culture system that can be cost-effectively and readily re-configured to carry out virtually any type of high-throughput growth experiment. Through modular and programmable Smart Sleeves which house all the sensors and actuators needed to control individual cultures, eVOLVER uniquely enables scaling of both throughput and individual control of culture conditions. Moreover, as a web-based platform, eVOLVER exchanges data and information with remote computers in real-time, permitting simultaneous monitoring of hundreds of individual cultures and automated culture perturbations through arbitrarily defined control algorithms.
In previous work16, eVOLVER&#8217;s robust performance was demonstrated in long-term experiments over hundreds of hours of operation, and its ability to cultivate various organisms, from E. coli and S. cerevisiae to undomesticated microbes. A series of distinct growth selection experiments were performed, in which programmatically defined multi-dimensional selection gradients were applied across an array of individual culture conditions and the resulting cellular fitness landscapes were quantified. Here, the objective is to provide eVOLVER users with a description of how to use the system to design and these types of experiments. As an illustrative example, methods quantifying the fitness landscape of S. cerevisiae mutants across a two-dimensional environmental gradient composed of temperature and osmotic stress are presented. The protocol guides users through configuring the eVOLVER framework for this experiment both programmatically, in using the software to set customized turbidity and temperature control routines for each of 16 parallel continuous cultures, and physically, through the fluidics layout to appropriately route medias of different salt concentrations. This protocol should serve as a general rubric for configuring eVOLVER to execute a wide variety of automated continuous culture experiments for diverse studies and disciplines.

<table><tbody><tr><td>5 Gallon Plastic Hedpack with cap</td><td>Midwest Brewing and Winemaking Supplies</td><td>45-56Y8-E2FR</td><td>For waste collection</td></tr><tr><td>a-D(+)-Glucose</td><td>Chem-Impex</td><td>00805</td><td>For YPD Medium</td></tr><tr><td>Attune NxT Autosampler</td><td>Thermo Fisher</td><td></td><td>Allows Flow Cytometer to run samples from 96 well plate</td></tr><tr><td>Attune NxT Flow Cytometer</td><td>Thermo Fisher</td><td></td><td>Used to determine population fractions via single cell fluoresence</td></tr><tr><td>Bacto Peptone</td><td>Fisher Scientific</td><td>DF0118-07-0</td><td>For YPD Medium</td></tr><tr><td>Carbenicillin</td><td>Fisher Scientific</td><td>BP2648250</td><td>For YPD Medium</td></tr><tr><td>Chemical-Resistant Barbed Tube Fitting Tee Connector, for 1/8&quot; Tube ID, 250&amp;deg;F Maximum Temperature</td><td>McMaster- Carr</td><td>5121K731</td><td>For media input branching</td></tr><tr><td>Chloramphenicol</td><td>Fisher Scientific</td><td>BP904-100</td><td>For YPD Medium</td></tr><tr><td>CLOROX GERMICIDAL Bleach 8.25</td><td>Fisher Scientific</td><td>50371500</td><td>For Sterilization of fluidic lines</td></tr><tr><td>Custom eVOLVER vial lid</td><td>FynchBio</td><td></td><td>Lid has ports for sampling and fluidic input/output</td></tr><tr><td>Cycloheximide</td><td>Fisher Scientific</td><td>ICN10018301</td><td>For flow cytometry sampling plates</td></tr><tr><td>Ethanol, Anhydrous (Histological)</td><td>Fisher Scientific</td><td>A405P-4</td><td>For sterilization of fluidic lines</td></tr><tr><td>eVOLVER Unit</td><td>FynchBio</td><td></td><td></td></tr><tr><td>Fisherbrand Extended-Length Tips (Lift Off Rack; 1 to 200 ul)</td><td>Fisher Scientific</td><td>02-681-420</td><td>For vial sampling</td></tr><tr><td>Fisherbrand Octagon Spinbar Magnetic Stirring Bars</td><td>Fisher Scientific</td><td>14-513-57</td><td>Diameter: 4.5 mm, Length, 12 mm</td></tr><tr><td>Fisherbrand Reusable Glass Media Bottles with Cap</td><td>Fisher Scientific</td><td>FB8002000</td><td>Must be fitted with tubing</td></tr><tr><td>High-Temperature Silicone Rubber Tubing Semi-Clear White, Durometer 70A, 1/8&quot; ID, 1/4&quot; OD</td><td>McMaster- Carr</td><td>51135K73</td><td>For media bottles</td></tr><tr><td>Mac Mini</td><td>Apple</td><td></td><td>For running the experiment/collecting data</td></tr><tr><td>Phosphate Buffered Saline (PBS)</td><td>Fisher Scientific</td><td>BP243820</td><td>For flow cytometry sampling plates</td></tr><tr><td>Pipettes</td><td>Eppendorf</td><td></td><td></td></tr><tr><td>Plastic Quick-Turn Tube Coupling, Plugs, for 1/16&quot; Barbed Tube ID, Polypropylene</td><td>McMaster- Carr</td><td>51525K141</td><td>For media bottles</td></tr><tr><td>Plastic Quick-Turn Tube Coupling, Plugs, for 5/32&quot; Barbed Tube ID, Polypropylene</td><td>McMaster- Carr</td><td>51525K144</td><td>For media bottles</td></tr><tr><td>Plastic Quick-Turn Tube Coupling, Sockets, for 1/16&quot; Barbed Tube ID, Polypropylene</td><td>McMaster- Carr</td><td>51525K291</td><td>For media bottles</td></tr><tr><td>Plastic Quick-Turn Tube Coupling, Sockets, for 5/32&quot; Barbed Tube ID, Polypropylene</td><td>McMaster- Carr</td><td>51525K294</td><td>For media bottles</td></tr><tr><td>SCREW CAPS, OPEN TOP, WITH PTFE FACED SILICONE SEPTA, LAB-PAC, SEPTUM. Screw thread size: 24-400, GREEN</td><td>Chemglass</td><td>CG-4910-04</td><td>Culture vials</td></tr><tr><td>Sodium Chloride (NaCl)</td><td>Fsher Scientific</td><td>S271-3</td><td>For YPD Medium</td></tr><tr><td>SpectraMax M5 Multi-Mode Microplate Reader</td><td>Molecular Devices</td><td></td><td>For measuring OD600 of overnight cell cultures</td></tr><tr><td>Vial Only, Sample, 40 mL, Clear, 28 mm x95 mm, GPI 24-400</td><td>Chemglass</td><td>CG-4902-08</td><td>Culture vials</td></tr><tr><td>Yeast Extract</td><td>Fisher Scientific</td><td>BP1422-500</td><td>For YPD Medium</td></tr></tbody></table>

designing automated, high-throughput, continuous cell growth experiments using evolver

Continuous culture methods enable cells to be grown under quantitatively controlled environmental conditions, and are thus broadly useful for measuring fitness phenotypes and improving our understanding of how genotypes are shaped by selection. Extensive recent efforts to develop and apply niche continuous culture devices have revealed the benefits of conducting new forms of cell culture control. This includes defining custom selection pressures and increasing throughput for studies ranging from long-term experimental evolution to genome-wide library selections and synthetic gene circuit characterization. The eVOLVER platform was recently developed to meet this growing demand: a continuous culture platform with a high degree of scalability, flexibility, and automation. eVOLVER provides a single standardizing platform that can be (re)-configured and scaled with minimal effort to perform many different types of high-throughput or multi-dimensional growth selection experiments. Here, a protocol is presented to provide users of the eVOLVER framework a description for configuring the system to conduct a custom, large-scale continuous growth experiment. Specifically, the protocol guides users on how to program the system to multiplex two selection pressures &#8212; temperature and osmolarity &#8212; across many eVOLVER vials in order to quantify fitness landscapes of Saccharomyces cerevisiae mutants at fine resolution. We show how the device can be configured both programmatically, through its open-source web-based software, and physically, by arranging fluidic and hardware layouts. The process of physically setting up the device, programming the culture routine, monitoring and interacting with the experiment in real-time over the internet, sampling vials for subsequent offline analysis, and post experiment data analysis are detailed. This should serve as a starting point for researchers across diverse disciplines to apply eVOLVER in the design of their own complex and high-throughput cell growth experiments to study and manipulate biological systems.

The protocol described here was used to construct fitness maps for genetic variants of S. cerevisiae across a two-dimensional stress gradient of temperature and salinity. Specifically, for each variant strain, we used eVOLVER to conduct pairwise competition experiments against a fluorescently-labeled reference strain (S. cerevisiae strain BY4741 with an integrated constitutive mNeonGreen reporter) in 16 different combinations of these stresses (Figure 1). For variants, two knockout strains were selected each predicted to be sensitive to one of the stress condition axes: &#916;PRX1 which has been reported to have fitness defects in high temperatures20&#160;and &#916;PBS2 with fitness defects at high salt concentrations21. As a control variant, an unlabeled wild-type BY4741 strain was used, expected to perform similarly to the reference strain. In total, these three 72-hour competition experiments were conducted simultaneously in 48 vials across three eVOLVER (16-vial) devices all run from a single computer.
The eVOLVER interactive dashboard is used to navigate the large amount of data that is generated during each experiment (Figure 2A). Representative OD traces across all 16 vials of a single eVOLVER device are shown in Figure 2B. Each trace displays a characteristic sawtooth pattern from the turbidostat control algorithm, which uses feedback on OD to trigger dilutions and maintain cultures in a narrow density range (0.2-0.3 in this case). OD and temperature data are continuously measured, streamed to the cloud-enabled database, and updated in real-time in the eVOLVER interactive dashboard. From continuously streamed data, higher-order metrics (e.g. growth rate, cumulative generations) may be calculated and plotted in the dashboard (Figure 2C) and even used as feedback parameters in different selection schemes (e.g., morbidostat).
To generate fitness maps, we measure the rate at which the reference strain outcompetes the variant strain by incorporating both flow cytometry measurements and eVOLVER growth data. Specifically, population fractions are calculated as the log-ratio of variant strain over reference strain using flow cytometry data from each timepoint sample. For each culture and time point, the elapsed number of generations is interpolated from eVOLVER growth rate data in each dilution period. Plotting the log-ratios against generations, qualitative differences between conditions begin to emerge (Figure 3A). To calculate fitness, a slope is fit through the linear portion of each plot18,19, yielding a quantitative fitness value (with both sign and rate) that describes how the variant strain competes against the reference strain (Figure 3B). In this experiment, negative fitness values indicate that the variant strain is outcompeted by the reference strain. Constructing heatmaps from all the fitness calculations permits visualization of the two-dimensional fitness landscape, revealing subtle quantitative differences in performance between strains and conditions (Figure 3C).
From the fitness heatmaps, we see that each variant strain exhibits fitness defects that manifest in different ways. &#916;PRX1 is primarily sensitive to high temperatures, but salt stress appears to have an additive effect, increasing the fitness defect. Conversely, &#916;PBS2 shows fitness defects primarily in response to high salt concentrations, with minimal differences along the temperature axis. These results highlight the utility of high-resolution selection experiments particularly for deciphering interactions of multiple environmental stressors, which could have additive, synergistic, or epistatic effects.
Finally, it is important to note that in some cases, particularly for severe stress conditions, fitness calculations can lead to exaggerated or skewed results. For example, &#916;PBS2 appears to show a steep positive slope in the 39 &#176;C/1 M NaCl condition relative to the reference strain (Figure 3A). This is attributed to poor growth of both strains, which is reflected in the wild type data and limits the utility of this particular metric in this condition. An insufficient number of generations results in 1) poor separation of timepoints which interferes with slope fitting, and 2) sensitivity to stochastic effects originating from lag phase. While we did not elect to do so in this study, the real-time growth data collected by eVOLVER during the experiment could have been used to inform the decision to extend the experiment and collect additional timepoints for this condition with little effort. In follow-up experiments, starting ratios could also be modulated to extend the length of the linear region before saturation occurs.
 
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/59652/59652fig1v3.jpg" /> 
Figure 1: Overview of the eVOLVER framework and experimental design. (A) eVOLVER is an automated, continuous culture platform allowing multiparameter control of culture conditions. The platform consists of Smart Sleeves, which house the sensors and actuators for controlling culture conditions, a peristaltic pump array for flowing media and waste in and out of the cultures, a touchscreen for interacting manually with the device, and a computer used for interacting with the cloud infrastructure where data from the platform is streamed. (B) eVOLVER can be configured in multiple ways: physically, through cell and media inputs, and programmatically by adjusting the algorithms controlling Smart Sleeve culture vial modules. This can be utilized to program multidimensional selection/environmental gradients at high-resolution, with outputs consisting of physically collected cells at defined time points and real-time streaming of culture data. (C) Configuring eVOLVER to conduct a growth fitness experiment across a two-dimensional selection gradient, composed of temperature and osmotic stress. eVOLVER culture vials were seeded with equal proportions of a reference and variant yeast strain. A turbidostat routine was programmed to maintain all the cultures in exponential phase in a defined OD window (OD 0.2-0.3). Temperature and media conditions were varied across the Smart Sleeve array to form two independent stress gradients. Base media was composed of YPD (2% glucose) + 100 &#181;g/mL carbenicillin + 25 &#181;g/mL chloramphenicol as a precaution against bacterial contamination. Media compositions were varied by adding supplemental NaCl to 0 M, 0.6 M, 0.8 M, or 1.0 M. Vial temperatures were programmed to one of four values: 30 &#176;C, 35 &#176;C, 37 &#176;C, or 39 &#176;C. (D) Examples of raw output for culture OD and population fractions from three tested conditions. The competition experiment was maintained for 72 h, sampling at 24 h and subsequently every 12 h until the conclusion of the experiment. <a href="https://www.jove.com/files/ftp_upload/59652/59652fig1v3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/59652/59652fig2.jpg" /> 
Figure 2: eVOLVER real-time data analysis dashboard. (A) eVOLVER data is processed and streamed in real time via cloud-based software to an interactive dashboard. Through the dashboard, users can define and initiate their own culture routines on a connected eVOLVER, monitor currently running experiments across all eVOLVER units, and vary experimental conditions manually on an individual vial or set of vials if needed. (B) OD traces for each vial across the matrix of conditions tested for the entire experiment duration. Traces can be viewed in real-time to ensure experiment is running as desired and used post-experiment for further analysis. (C) Growth rates and cell generations can be calculated from OD traces on the fly to track experiment progress. <a href="https://www.jove.com/files/ftp_upload/59652/59652fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/59652/59652fig3.jpg" /> 
Figure 3: Construction of fitness surfaces. (A) Cell population fractions natural log (variant strain/reference strain) plotted against cell generations. Color indicates the temperature of selection while dashes distinguish salt concentrations. (B) To quantify relative fitness of the variant to the reference strain, a fitness metric is derived from the rate at which the cellular population fraction changes over generations. This metric is calculated from the linear region of the cellular fraction plots using a minimum of three points. (C) The relative fitness metrics are displayed in a heatmap to construct a fitness surface over the environmental stress gradients. The top right corner condition (39 &#176;C/1.0 M NaCl) for the wild type and &#916;PBS2 are not included in this analysis as the cultures went through insufficient generations (see Representative Results). <a href="https://www.jove.com/files/ftp_upload/59652/59652fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Designing Automated, High-throughput, Continuous Cell Growth Experiments Using eVOLVER at JoVE.com

Designing Automated, High-throughput, Continuous Cell Growth Experiments Using eVOLVER

The eVOLVER framework enables high-throughput continuous microbial culture with high resolution and dynamic control over experimental parameters. This protocol demonstrates how to apply the system to conduct a complex fitness experiment, guiding users on programming automated control over many individual cultures, measuring, collecting, and interacting with experimental data in real-time.

Continuous culture methods enable cells to be grown under quantitatively controlled environmental conditions, and are thus broadly useful for measuring ...

designing-automated-high-throughput-continuous-cell-growth

Research

JoVE Journal

Genetics

11.9K Views.  Boston University. eVOLVER geeft onderzoekers experimentele vrijheid, de mogelijkheid om hun eigen cultuurgroeiexperimenten te definiëren met geautomatiseerde feedback op meerdere parameters en deze te schalen naar welke grootte ze nodig hebben. eVOLVER is flexibeler dan andere apparaten. Gebruikers kunnen eenvoudig software aanpassen in Python en de hardware, door de slimme mouwen aan te passen die sensoren voor elke cultuur bevatten.Het systeem kan op het eerste gezicht ingewikkeld lijken met meerdere...

Video: Designing Automated, High-throughput, Continuous Cell Growth Experiments Using eVOLVER

Design and Use of a Low Cost, Automated Morbidostat for Adaptive Evolution of Bacteria Under Antibiotic Drug Selection

We describe a low cost, configurable morbidostat for characterizing the evolutionary pathway of antibiotic resistance. The morbidostat is a bacterial culture device that continuously monitors bacterial growth and dynamically adjusts the drug concentration to constantly challenge the bacteria as they evolve to acquire drug resistance. The device features a working volume of ~10 ml and is fully automated and equipped with optical density measurement and micro-pumps for medium and drug delivery. To validate the platform, we measured the stepwise acquisition of trimethoprim resistance in Escherichia coli MG 1655, and integrated the device with a multiplexed microfluidic platform to investigate cell morphology and antibiotic susceptibility. The approach can be up-scaled to laboratory studies of antibiotic drug resistance, and is extendible to adaptive evolution for strain improvements in metabolic engineering and other bacterial culture experiments.

We describe a low cost, configurable morbidostat that enables the characterization of antibiotic drug resistance by dynamically adjusting the drug concentration. The device can be integrated with a multiplexed microfluidic platform. The approach can be scaled up for laboratory antibiotic drug resistance studies.

We describe a low cost, configurable morbidostat for characterizing the evolutionary pathway of antibiotic resistance. The morbidostat is a bacterial ...

A High-throughput Automated Platform for the Development of Manufacturing Cell Lines for Protein Therapeutics

The fast-growing biopharmaceutical industry demands speedy development of highly efficient and reliable production systems to meet the increasing requirement for drug supplies. The generation of production cell lines has traditionally involved manual operations that are labor-intensive, low-throughput and vulnerable to human errors. We report here an integrated high-throughput and automated platform for development of manufacturing cell lines for the production of protein therapeutics.
The combination of BD FACS Aria Cell Sorter, CloneSelect Imager and TECAN Freedom EVO liquid handling system has enabled a high-throughput and more efficient cell line development process. In this operation, production host cells are first transfected with an expression vector carrying the gene of interest 1, followed by the treatment with a selection agent. The stably-transfected cells are then stained with fluorescence-labeled anti-human IgG antibody, and are subsequently subject to flow cytometry analysis 2-4. Highly productive cells are selected based on fluorescence intensity and are isolated by single-cell sorting on a BD FACSAria. Colony formation from single-cell stage was detected microscopically and a series of time-laps digital images are taken by CloneSelect Imager for the documentation of cell line history. After single clones have formed, these clones were screened for productivity by ELISA performed on a TECAN Freedom EVO liquid handling system. Approximately 2,000 - 10,000 clones can be screened per operation cycle with the current system setup.
This integrated approach has been used to generate high producing Chinese hamster ovary (CHO) cell lines for the production of therapeutic monoclonal antibody (mAb) as well as their fusion proteins. With the aid of different types of detecting probes, the method can be used for developing other protein therapeutics or be applied to other production host systems. Comparing to the traditional manual procedure, this automated platform demonstrated advantages of significantly increased capacity, ensured clonality, traceability in cell line history with electronic documentation and much reduced opportunity in operator error.

A high-throughput, automated platform of manufacturing cell line development for producing protein therapeutics is described. Implementation of BD FACS Aria Cell Sorter, CloneSelect Imager and TECAN Freedom EVO liquid handling system has demonstrated significantly increased processing capacity in cell line development with improved cell line quality and high reproducibility.

The fast-growing biopharmaceutical industry demands speedy development of highly efficient and reliable production systems to meet the increasing ...

Medicine

A Versatile Automated Platform for Micro-scale Cell Stimulation Experiments

Study of cells in culture (in vitro analysis) has provided important insight into complex biological systems. Conventional methods and equipment for in vitro analysis are well suited to study of large numbers of cells (&ge;105) in milliliter-scale volumes (&ge;0.1 ml). However, there are many instances in which it is necessary or desirable to scale down culture size to reduce consumption of the cells of interest and/or reagents required for their culture, stimulation, or processing. Unfortunately, conventional approaches do not support precise and reproducible manipulation of micro-scale cultures, and the microfluidics-based automated systems currently available are too complex and specialized for routine use by most laboratories. To address this problem, we have developed a simple and versatile technology platform for automated culture, stimulation, and recovery of small populations of cells (100 - 2,000 cells) in micro-scale volumes (1 - 20 &mu;l). The platform consists of a set of fibronectin-coated microcapillaries ("cell perfusion chambers"), within which micro-scale cultures are established, maintained, and stimulated; a digital microfluidics (DMF) device outfitted with "transfer" microcapillaries ("central hub"), which routes cells and reagents to and from the perfusion chambers; a high-precision syringe pump, which powers transport of materials between the perfusion chambers and the central hub; and an electronic interface that provides control over transport of materials, which is coordinated and automated via pre-determined scripts. As an example, we used the platform to facilitate study of transcriptional responses elicited in immune cells upon challenge with bacteria. Use of the platform enabled us to reduce consumption of cells and reagents, minimize experiment-to-experiment variability, and re-direct hands-on labor. Given the advantages that it confers, as well as its accessibility and versatility, our platform should find use in a wide variety of laboratories and applications, and prove especially useful in facilitating analysis of cells and stimuli that are available in only limited quantities.

We have developed an automated cell culture and interrogation platform for micro-scale cell stimulation experiments. The platform offers simple, versatile, and precise control in cultivating and stimulating small populations of cells, and recovering lysates for molecular analyses. The platform is well suited to studies that use precious cells and/or reagents.

Study of cells in culture (in vitro analysis) has provided important insight into complex biological systems. Conventional methods and equipment for in ...

Bioengineering

Daily Transfers, Archiving Populations, and Measuring Fitness in the Long-Term Evolution Experiment with Escherichia coli

The Long-Term Evolution Experiment (LTEE) has followed twelve populations of Escherichia coli as they have adapted to a simple laboratory environment for more than 35 years and 77,000 bacterial generations. The setup and procedures used in the LTEE epitomize reliable and reproducible methods for studying microbial evolution. In this protocol, we first describe how the LTEE populations are transferred to fresh medium and cultured each day. Then, we describe how the LTEE populations are regularly checked for possible signs of contamination and archived to provide a permanent frozen "fossil record" for later study. Multiple safeguards included in these procedures are designed to prevent contamination, detect various problems when they occur, and recover from disruptions without appreciably setting back the progress of the experiment. One way that the overall tempo and character of evolutionary changes are monitored in the LTEE is by measuring the competitive fitness of populations and strains from the experiment. We describe how co-culture competition assays are conducted and provide both a spreadsheet and an R package (fitnessR) for calculating relative fitness from the results. Over the course of the LTEE, the behaviors of some populations have changed in interesting ways, and new technologies like whole-genome sequencing have provided additional avenues for investigating how the populations have evolved. We end by discussing how the original LTEE procedures have been updated to accommodate or take advantage of these changes. This protocol will be useful for researchers who use the LTEE as a model system for studying connections between evolution and genetics, molecular biology, systems biology, and ecology. More broadly, the LTEE provides a tried-and-true template for those who are beginning their own evolution experiments with new microbes, environments, and questions.

Daily Transfers, Archiving Populations, and Measuring Fitness in the Long-Term Evolution Experiment with Escherichia coli

This protocol describes how to maintain the Escherichia coli Long-Term Evolution Experiment (LTEE) by performing its daily transfers and periodic freeze-downs and how to conduct competition assays to measure fitness improvements in evolved bacteria. These procedures can serve as a template for researchers starting their own microbial evolution experiments.

The Long-Term Evolution Experiment (LTEE) has followed twelve populations of Escherichia coli as they have adapted to a simple laboratory environment for ...

Design and Implementation of an Automated Illuminating, Culturing, and Sampling System for Microbial Optogenetic Applications

Optogenetic systems utilize genetically-encoded proteins that change conformation in response to specific wavelengths of light to alter cellular processes. There is a need for culturing and measuring systems that incorporate programmed illumination and stimulation of optogenetic systems. We present a protocol for building and using a continuous culturing apparatus to illuminate microbial cells with programmed doses of light, and automatically acquire and analyze images of cells in the effluent. The operation of this apparatus as a chemostat allows the growth rate and the cellular environment to be tightly controlled. The effluent of the continuous cell culture is regularly sampled and the cells are imaged by multi-channel microscopy. The culturing, sampling, imaging, and image analysis are fully automated so that dynamic responses in the fluorescence intensity and cellular morphology of cells sampled from the culture effluent are measured over multiple days without user input. We demonstrate the utility of this culturing apparatus by dynamically inducing protein production in a strain of Saccharomyces cerevisiae engineered with an optogenetic system that activates transcription.

We designed a continuous culturing apparatus for use with optogenetic systems to illuminate cultures of microbes and regularly image cells in the effluent with an inverted microscope. The culturing, sampling, imaging, and image analysis are fully automated so that dynamic responses to illumination can be measured over several days.

Optogenetic systems utilize genetically-encoded proteins that change conformation in response to specific wavelengths of light to alter cellular ...

Use of High-Throughput Automated Microbioreactor System for Production of Model IgG1 in CHO Cells

Automated microscale bioreactors (15 mL) can be a useful tool for cell culture engineers. They facilitate the simultaneous execution of a wide variety of experimental conditions while minimizing potential process variability. Applications of this approach include: clone screening, temperature and pH shifts,&#160;media and supplement optimization. Furthermore, the small reactor volumes are conducive to large Design of Experiments that investigate a wide range of conditions. This allows upstream processes to be significantly optimized before scale-up where experimentation is more limited in scope due to time and economic constraints. Automated microscale bioreactor systems offer various advantages over traditional small scale cell culture units, such as shake flasks or spinner flasks. However, during pilot scale process development&#160;significant care must be taken to ensure that these advantages are realized. When run with care, the system can enable high level automation, can be programmed to run DOE&#39;s with a higher number of variables&#160;and can reduce sampling time when integrated with a nutrient analyzer or cell counter. Integration of the expert-derived heuristics presented here, with current automated microscale bioreactor experiments can minimize common pitfalls that hinder meaningful results. In the extreme, failure to adhere to the principles laid out here can lead to equipment damage that requires expensive repairs. Furthermore, the microbioreactor systems have small culture volumes&#160;making characterization of cell culture conditions difficult. The number and amount of samples taken in-process in batch mode culture is limited as operating volumes cannot fall below 10 mL. This method will discuss the benefits and drawbacks of microscale bioreactor systems.

A detailed protocol for the concurrent operation of 48 parallel cell cultures under varied conditions in a microbioreactor system is presented. Cell culture process, harvest and subsequent antibody titer analysis are described.

Automated microscale bioreactors (15 mL) can be a useful tool for cell culture engineers. They facilitate the simultaneous execution of a wide variety of ...

Automated Microbial Cultivation and Adaptive Evolution using Microbial Microdroplet Culture System (MMC)

Conventional microbial cultivation methods usually have cumbersome operations, low throughput, low efficiency, and large consumption of labor and reagents. Moreover, microplate-based high-throughput cultivation methods developed in recent years have poor microbial growth status and experiment parallelization because of their low dissolved oxygen, poor mixture, and severe evaporation and thermal effect. Due to many advantages of micro-droplets, such as small volume, high throughput, and strong controllability, the droplet-based microfluidic technology can overcome these problems, which has been used in many kinds of research of high-throughput microbial cultivation, screening, and evolution. However, most prior studies remain&#160;at the stage of laboratory construction and application. Some key issues, such as high operational requirements, high construction difficulty, and lack of automated integration technology, restrict the wide application of droplet microfluidic technology in microbial research. Here, an automated Microbial Microdroplet Culture system (MMC) was successfully developed based on droplet microfluidic technology, achieving the integration of functions such as inoculation, cultivation, online monitoring, sub-cultivation, sorting, and sampling required by the process of microbial droplet cultivation. In this protocol, wild-type Escherichia coli (E. coli) MG1655 and a methanol-essential E. coli strain (MeSV2.2) were taken as examples to introduce how to use the MMC to conduct automated and relatively high-throughput microbial cultivation and adaptive evolution in detail. This method is easy to operate, consumes less labor and reagents, and has high experimental throughput and good data parallelity, which has great advantages compared with conventional cultivation methods. It provides a low-cost, operation-friendly, and result-reliable experimental platform for scientific researchers to conduct related microbial research.

Automated Microbial Cultivation and Adaptive Evolution using Microbial Microdroplet Culture System MMC

This protocol describes how to use the Microbial Microdroplet Culture system (MMC) to conduct automated microbial cultivation and adaptive evolution. MMC can cultivate and sub-cultivate microorganisms automatically and continuously and monitor online their growth with relatively high throughput and good parallelization, reducing labor and reagent consumption.

Conventional microbial cultivation methods usually have cumbersome operations, low throughput, low efficiency, and large consumption of labor and ...

Design and Use of Multiplexed Chemostat Arrays

Chemostats are continuous culture systems in which cells are grown in a tightly controlled, chemically constant environment where culture density is constrained by limiting specific nutrients.1,2 Data from chemostats are highly reproducible for the measurement of quantitative phenotypes as they provide a constant growth rate and environment at steady state. For these reasons, chemostats have become useful tools for fine-scale characterization of physiology through analysis of gene expression3-6 and other characteristics of cultures at steady-state equilibrium.7 Long-term experiments in chemostats can highlight specific trajectories that microbial populations adopt during adaptive evolution in a controlled environment. In fact, chemostats have been used for experimental evolution since their invention.8 A common result in evolution experiments is for each biological replicate to acquire a unique repertoire of mutations.9-13 This diversity suggests that there is much left to be discovered by performing evolution experiments with far greater throughput. 
We present here the design and operation of a relatively simple, low cost array of miniature chemostats&mdash;or ministats&mdash;and validate their use in determination of physiology and in evolution experiments with yeast. This approach entails growth of tens of chemostats run off a single multiplexed peristaltic pump. The cultures are maintained at a 20 ml working volume, which is practical for a variety of applications. It is our hope that increasing throughput, decreasing expense, and providing detailed building and operation instructions may also motivate research and industrial application of this design as a general platform for functionally characterizing large numbers of strains, species, and growth parameters, as well as genetic or drug libraries.

We developed and validated a small-footprint array of miniature chemostats built from readily available parts for low cost. Physiological and experimental evolution results were similar to larger volume chemostats. The ministat array provides a compact, inexpensive, and accessible platform for traditional chemostat experiments, functional genomics, and chemical screening applications.

Chemostats are continuous culture systems in which cells are grown in a tightly controlled, chemically constant environment where culture density is ...

Biology

Setting Up PRANCE: A Comprehensive Guide for Robotics-Accelerated Evolution

A Practical Guide to Phage- and Robotics-Assisted Near-Continuous Evolution

Robotics-accelerated Evolution techniques improve the reliability and speed of evolution using feedback control, improving the outcomes of protein and organism evolution experiments. In this article, we present a guide to setting up the hardware and software necessary to implement Phage- and Robotics-assisted Near-continuous Evolution (PRANCE). PRANCE combines fast phage-based molecular evolution with the ability to run hundreds of independent, feedback-controlled evolution experiments simultaneously. This paper will describe the hardware requirements and setup for PRANCE, including a liquid-handling instrument, a plate reader, auxiliary pumps, heaters, and 3D-printed containers. We describe how to configure the liquid handling robot to be compatible with Python-based open-source software. Finally, we provide suggestions for the first two experiments that can be conducted with a newly constructed PRANCE system that exercises its capabilities and validates that the system is ready to conduct multiplexed evolution. This guide is intended to serve as a handbook for navigating the considerable equipment setup associated with conducting robotics-accelerated evolution.

Author Spotlight: Advancing Protein Engineering &#8211; Harnessing Evolution Through PRANCE and Lab Automation

Phage- and Robotics-assisted Near-continuous Evolution (PRANCE) is a technique for rapid, robust protein evolution. Robotics allows the parallelization of experiments, real-time monitoring, and feedback control.

Robotics-accelerated Evolution techniques improve the reliability and speed of evolution using feedback control, improving the outcomes of protein and ...

Precise, High-throughput Analysis of Bacterial Growth

Bacterial growth is a central concept in the development of modern microbial physiology, as well as in the investigation of cellular dynamics at the systems level. Recent studies have reported correlations between bacterial growth and genome-wide events, such as genome reduction and transcriptome reorganization. Correctly analyzing bacterial growth is crucial for understanding the growth-dependent coordination of gene functions and cellular components. Accordingly, the precise quantitative evaluation of bacterial growth in a high-throughput manner is required. Emerging technological developments offer new experimental tools that allow updates of the methods used for studying bacterial growth. The protocol introduced here employs a microplate reader with a highly optimized experimental procedure for the reproducible and precise evaluation of bacterial growth. This protocol was used to evaluate the growth of several previously described Escherichia coli strains. The main steps of the protocol are as follows: the preparation of a large number of cell stocks in small vials for repeated tests with reproducible results, the use of 96-well plates for high-throughput growth evaluation, and the manual calculation of two major parameters (i.e., maximal growth rate and population density) representing the growth dynamics. In comparison to the traditional colony-forming unit (CFU) assay, which counts the cells that are cultured in glass tubes over time on agar plates, the present method is more efficient and provides more detailed temporal records of growth changes, but has a stricter detection limit at low population densities. In summary, the described method is advantageous for the precise and reproducible high-throughput analysis of bacterial growth, which can be used to draw conceptual conclusions or to make theoretical observations.

Quantitative evaluation of bacterial growth is essential to understanding microbial physiology as a systems-level phenomenon. A protocol for experimental manipulation and an analytical approach are introduced, allowing for precise, high-throughput analysis of bacterial growth, which is a key subject of interest in systems biology.

Bacterial growth is a central concept in the development of modern microbial physiology, as well as in the investigation of cellular dynamics at the ...

Designing Automated, High-throughput, Continuous Cell Growth Experiments Using eVOLVER

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