Designing Automated, High-throughput, Continuous Cell Growth Experiments Using eVOLVER
May 19th, 2019
13.1K views
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.
Overview
The eVOLVER framework allows for high-throughput continuous microbial culture with dynamic control over experimental parameters. This protocol guides users in programming automated control over multiple cultures while measuring and interacting with data in real-time.
Key Study Components
Area of Science
- Microbial culture
- Automated feedback systems
- Experimental design
Background
- The eVOLVER system provides flexibility in designing culture growth experiments.
- It allows for customization of both software and hardware components.
- Proper setup and understanding of components are crucial for successful experiments.
- It can be adapted for various culturing conditions, including anaerobic fermentation.
Purpose of Study
- To demonstrate the setup and operation of the eVOLVER platform for continuous culture experiments.
- To enable real-time monitoring and control of microbial cultures.
- To facilitate the study of fitness variations among different microbial strains.
Methods Used
- Preparation of media and sterilization of equipment.
- Setup of the eVOLVER system and initialization of experiments.
- Real-time data monitoring through an interactive dashboard.
- Analysis of growth rates and fitness values of microbial strains.
Main Results
- Successful automation of continuous culture experiments.
- Real-time monitoring of optical density (OD) and temperature.
- Quantitative fitness values derived from growth rate data.
- Identification of performance differences among microbial variants.
Conclusions
- The eVOLVER system enhances experimental control and flexibility.
- Real-time data allows for immediate intervention in experiments.
- It supports the engineering of new strains based on experimental outcomes.
Frequently Asked Questions
What is the eVOLVER framework?
The eVOLVER framework is a system for high-throughput continuous microbial culture that allows for dynamic control over experimental parameters.
How can the eVOLVER system be customized?
Users can customize both the software in Python and the hardware by modifying the smart sleeves that hold sensors for each culture.
What types of cultures can be maintained using eVOLVER?
eVOLVER is designed for aerobic culturing of bacteria and yeast but can be modified for anaerobic fermentation or mammalian cell cultures.
What is the importance of proper setup?
Proper setup ensures that all components are functioning correctly, preventing issues such as overflow or clogs that could ruin experiments.
How does real-time monitoring work in eVOLVER?
The eVOLVER dashboard allows users to monitor OD values and temperature in real-time, enabling immediate adjustments if necessary.
What can be done with samples taken from eVOLVER?
Samples can be sequenced to check for genomic alterations, which can inform the engineering of new strains or systems.
How does eVOLVER support experimental design?
eVOLVER allows researchers to apply unique selection pressures in each experiment, facilitating tailored experimental designs.
Executive Industry Relevance
The eVOLVER platform enables high-throughput, automated continuous culture with real-time monitoring of optical density and temperature, supporting scalable fitness landscape mapping for strain engineering and selection studies. Its open-source software and modular hardware allow rapid reconfiguration for multiparameter experimental designs, reducing manual intervention and increasing reproducibility in microbial strain characterization. This positions eVOLVER as a reusable discovery tool for de-risking target validation and lead identification in antimicrobial, metabolic, and synthetic biology programs.
Strategic Applications in Biopharma R&D
Early Discovery & Target Validation
- Scientific Value: Enables quantitative fitness profiling of mutant libraries under defined selection pressures to interrogate gene function and pathway essentiality.
- Operational Value: Automates continuous culture with feedback control, reducing manual sampling and enabling long-term, unattended experiments.
- Scientific Value: Supports mechanistic de-risking by linking genotype to fitness under controlled environmental conditions.
Screening & Assay Development
- Scientific Value: Generates quantitative growth rate and fitness metrics from real-time OD and temperature data for comparative strain analysis.
- Operational Value: Standardizes culture conditions across hundreds of parallel vessels via programmable fluidic and sensor arrays.
- Scientific Value: Facilitates assay readiness by providing reproducible, dynamically controlled environments for library selection and evolution experiments.
Translational & Preclinical Research
- Scientific Value: Enables longitudinal tracking of genomic adaptations in evolving populations, informing strain engineering for therapeutic production.
- Operational Value: Supports scalable preparation of evolved strains for downstream phenotypic validation in disease-relevant models.
- Scientific Value: Enhances predictive confidence in strain performance by measuring competitive fitness across environmental gradients.
Pipeline & Workflow Integration
eVOLVER integrates into early discovery workflows by enabling hypothesis-driven fitness screening, assay development for evolution-based selection, and translational strain optimization prior to preclinical validation.
- Discovery Biology: Supports hypothesis testing through precise environmental control and quantitative fitness readouts across genetic variants.
- Screening: Enables high-throughput, reproducible growth selection assays with real-time monitoring and automated feedback.
- Analytics: Provides real-time OD, temperature, and derived fitness metrics for longitudinal comparison of culture conditions.
- Translational Research: Connects discovery-phase evolution to preclinical strain engineering by identifying adaptive mutations under selection.
- Enterprise Reuse: Functions as a reconfigurable platform for multiple campaigns, reducing need for bespoke culture systems per project.
Operational & Enterprise Impact
- Scientific Value: Predictive fitness modeling, target validation via competitive growth, reduction of phenotypic ambiguity.
- Operational Value: Standardization, automation, and scalability of continuous culture across environmental gradients.
- Strategic Value: Improved go/no-go decisions in strain selection, reduced biological risk in development candidates.
- Portfolio Impact: Risk-adjusted prioritization of engineered strains based on quantitative fitness landscapes.
Implementation Considerations
- Requires expertise in microbial culture, microfluidics, and Python-based software customization.
- Needs perfusion pumps, temperature control units, smart sleeves, and real-time data monitoring infrastructure.
- Demands cross-team standardization of protocols for media preparation, sterilization, and sampling.
- Requires adaptation of fluidic layouts and sensor configurations for different organism types and selection pressures.
- Limited by clogging risks in fluid lines and the need for regular maintenance to prevent experimental failure.
Biopharma FAQs
Why does OD monitoring matter for fitness assessment?
Real-time optical density measurements enable calculation of growth rates and competitive fitness values by tracking population dynamics over generations, supporting quantitative strain comparison under selection.
How does temperature control enable selection pressure application?
Precise, programmable temperature regulation across eVOLVER vials allows researchers to define thermal gradients that quantify fitness effects of temperature-sensitive mutations.
What does real-time data access enable during experiments?
Live dashboard access to OD and temperature trends permits immediate detection of anomalies, allowing intervention to maintain culture integrity and experimental validity.
Why are replication requirements important for strain comparison?
Replicate vials per condition ensure statistical robustness of fitness measurements, reducing variability and increasing confidence in comparative strain performance rankings.
What statistical analysis is needed before fitness conclusions?
Linear regression on growth rate data across generations is required to derive quantitative fitness values, with a minimum of three points recommended for reliable slope fitting.
Chapters in this video
0:04
Title
0:58
Sterilizing the System
2:56
Loading the Culture Vials
3:51
Configuring eVOLVER Software and Programming Algorithmic Culture Routines
4:48
Inoculating and Monitoring Experiment in Real-time
5:47
Results: Fitness Surface Construction
6:35
Conclusion
