Week 3 HW: Lab Automation
๐ค Week 3 Homework: Lab Automation Find and describe a published paper utilizing automation for novel biological applications; describe automation tools for your final project.
๐ Overview โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โ ๐ค LAB AUTOMATION: PAPER + PROJECT ๐ค โ
โ โ
โ Part 1 Part 2 โ
โ โ โ โ
โ โผ โผ โ
โ [Microfluidics] [gumol + new-Clara] โ
โ Synthetic cells MD โ oxidative surrogate โ
โ (automation tool) (validation pipeline) โ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโThis week covers:
Part 1: Published paper โ synthetic cells via droplet-based microfluidicsPart 2: Automation project description โ gumol + ECSOD/MSC + new-Clara validation pipelinePart 1: Published Paper โ Automation for Novel Biological Applications Paper Citation Title: Synthetic cells and droplet-based microfluidics (review)Journal: Small DOI: 10.1002/smll.202400086 Year: 2024
Abstract Summary Synthetic cells function as biological mimics of natural cells by mimicking salient features such as metabolism, response to stimuli, gene expression, direct metabolism, and high stability. Droplet-based microfluidic technology presents the opportunity for encapsulating biological functional components in uni-lamellar liposome or polymer droplets. Verified by its success in the fabrication of synthetic cells, microfluidic technology is widely replacing conventional labor-intensive, expensive, and sophisticated techniques justified by its ability to miniaturize and perform batch production operations.
Droplet-based microfluidics โ lab-on-chip systems that automate encapsulation, mixing, and batch production of synthetic cell constructs. Microfluidics serves as the automation platform: it replaces manual, labor-intensive methods with reproducible, tunable, high-throughput workflows.
DROPLET MICROFLUIDICS: MANUAL โ AUTOMATED
Before (manual): After (microfluidic):
๐งช Hand pipetting โญโโโโโโฎ โญโโโโโโฎ โญโโโโโโฎ
tedious, variable โ โ โ โ โ โ โ โ โ โ โ โ โ droplets
batch-to-batch โฐโโโฌโโโฏ โฐโโโฌโโโฏ โฐโโโฌโโโฏ
โ โ โ
"Labor-intensive" โโโโโโโโโโผโโโโโโโโโ
โ
โผ
โโโโโโโโโโโโโโโ
โ CHIP โ โ reproducible
โ (automated)โ tunable
โโโโโโโโโโโโโโโ batch productionBiological Applications Synthetic Cell Type Description Lipid vesicles (liposomes) Uni-lamellar lipid bilayers encapsulating biological components Polymer vesicles (polymersomes) Polymer-based membranes for encapsulation Coacervate microdroplets Liquid-liquid phase separation compartments Colloidosomes Colloidal particle-stabilized droplets
The review discusses microfluidic chip design for synthetic cell preparation, the combination of microfluidics with bottom-up synthetic biology for reproductive and tunable construction, and advances in biosensors and biomedical applications .
Novel Aspects Reproducible, tunable construction โ Batch production from simple structures to higher hierarchical structuresMiniaturization โ Replaces conventional expensive techniquesIntegration โ Design, assembly, manipulation, and analysis within lab-on-chip devicesBiomedical relevance โ Biosensors, drug delivery, therapeutic applicationsWhy This Paper Fits the Assignment Microfluidics is an automation tool that achieves novel biological applications: it automates the fabrication of synthetic cells at scale, enabling research that would otherwise be labor-intensive and costly. The paper provides an overview of how this automation enables bottom-up synthetic biology and biomedical innovation.
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โ SYNTHETIC CELLS: MICROFLUIDICS AS AUTOMATION โ
โ โ
โ [Droplet microfluidics] โโโบ Liposome | Polymersome | โ
โ (automation tool) Coacervate | Colloidosome โ
โ โ โ โ
โ โโโโโโโโโโโโโโโโโโโโโโโโ โ
โ โ โ
โ โผ โ
โ Biosensors & biomedical applications โ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโProject Overview Project in development: A combined computationalโexperimental pipeline to study ECSOD (extracellular superoxide dismutase) overexpression from mesenchymal stem cells (MSCs) in acute radiation environments, with microfluidic validation serving as a surrogate for radiation exposure.
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โ ๐ฌ GUMOL + ECSOD + new-Clara PIPELINE ๐ฌ โ
โ โ
โ Rust MD engine Microfluidic validation โ
โ (radiation sim) (oxidative surrogate) โ
โ โ โ โ
โ โโโโโโโโโโโโโฌโโโโโโโโโโโโโโโโ โ
โ โผ โ
โ ECSOD from MSC โโโบ Correlation & validation โ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโPipeline Components Component Role gumol Custom MD simulation engine in Rust for molecular dynamics in acute radiation environments ECSOD / MSC Simulated overexpression of extracellular superoxide dismutase from MSC cells (mechanism still being refined) new-Clara Microfluidic system for controlled validation runs Surrogate model Microfluidic oxidative stress used as a surrogate for radioactive conditions
Workflow: Simulation โ Validation โ Correlation โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โ COMPUTATIONAL ARM โ EXPERIMENTAL ARM (AUTOMATION) โ
โ โ โ
โ gumol (Rust MD engine) โ new-Clara microfluidic system โ
โ โ โ โ โ
โ โผ โ โผ โ
โ Acute radiation environment โ Simulated oxidative environment โ
โ simulations โ (surrogate for radiation) โ
โ โ โ โ โ
โ โผ โ โผ โ
โ ECSOD overexpression from MSC โ Validation runs: controlled โ
โ (mechanism in refinement) โ oxidative stress delivery โ
โ โ โ โ โ
โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโ โ
โ โผ โ
โ CORRELATION & VALIDATION โ
โ (MD predictions โ microfluidic data) โ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโnew-Clara is the primary automation tool in this project. It provides:
Controlled oxidative stress โ Reproducible delivery of oxidative conditions as a surrogate for radiationPrecision and throughput โ Automated, repeatable runs instead of manual handlingData alignment โ Outputs that can be directly compared with gumol MD resultsBecause radiation experiments are costly and regulated, the microfluidic oxidative environment acts as a surrogate for acute radiation, enabling validation of computational predictions under safer, more accessible conditions.
SURROGATE VALIDATION: Radiation โ Oxidative stress
Radiation (expensive, regulated) Oxidative stress (accessible)
โ โ
โ "Same downstream damage โ
โ pathways (ROS, etc.)" โ
โ โ
โโโโโโโโโโโโโโโโฌโโโโโโโโโโโโโโโโโโโโโ
โ
โผ
โโโโโโโโโโโโโโโโโโโ
โ new-Clara โ โ controlled, reproducible
โ microfluidic โ surrogate runs
โโโโโโโโโโโโโโโโโโโWhat Will Be Automated Microfluidic runs โ new-Clara controls flow, dosing, and timing of oxidative stressData collection โ Automated or semi-automated readouts (e.g., fluorescence, viability) for correlation with MDParameter sweeps โ Systematic variation of oxidative stress levels to map doseโresponse and compare with simulationConnection to Part 1 (Synthetic Cells Paper) The synthetic cells / droplet microfluidics review supports this project by demonstrating how microfluidics enables:
Reproducible, tunable conditions โ Aligned with the need for controlled oxidative stressLab-on-chip workflows โ Similar to new-Clara’s role in validationBiosensor and biomedical applications โ Relevant to ECSOD and MSC-based therapies for radiation injuryCurrent Status & Next Steps gumol โ MD engine in Rust, in developmentECSOD/MSC mechanism โ Still being refinednew-Clara โ Microfluidic system for validation runsSurrogate design โ Oxidative stress protocol as radiation surrogateExample Pseudocode (Conceptual) # Pseudocode: new-Clara validation run aligned with gumol MD output
# Input: MD simulation predicts ECSOD protection at oxidative stress level X
# Output: Microfluidic validation at equivalent oxidative dose
def run_validation ( md_stress_level , n_replicates = 3 ):
"""
Map MD-predicted stress to microfluidic oxidative surrogate.
Run n_replicates for statistical correlation.
"""
oxidative_dose = map_md_to_oxidative_surrogate ( md_stress_level )
for rep in range ( n_replicates ):
new_clara . set_oxidative_conditions ( oxidative_dose )
new_clara . run_flow_protocol ()
data = new_clara . collect_readouts () # e.g., viability, ROS markers
log_for_correlation ( md_stress_level , oxidative_dose , data )
return correlate_with_md_predictions () Summary โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โ WEEK 3 HOMEWORK SUMMARY โ
โ โ
โ Part 1: Paper โ
โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ โ
โ โ Microfluidics โ synthetic cells (liposomes, etc.) โ โ
โ โ Automation for reproducible, tunable fabrication โ โ
โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ โ
โ โ
โ Part 2: Project โ
โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ โ
โ โ gumol (MD) โโโบ new-Clara (microfluidic) โโโบ validate โ โ
โ โ Oxidative surrogate for radiation; ECSOD/MSC focus โ โ
โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ โ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโPart Content Part 1 Synthetic cells via droplet microfluidics โ microfluidics as automation for reproducible, tunable biological fabrication Part 2 gumol (Rust MD) + ECSOD/MSC + new-Clara microfluidic validation โ oxidative surrogate for radiation, MDโexperiment correlation
This homework does not need to be tested on the Opentrons yet; it describes the intended automation workflow for the final project.