Week 1: Principles and Practices Class Assignment 1. Application or Tool to Develop & Why Application: “The Bio-Puzzle” – A hardware-agnostic DNA assembly toolkit using split-protein reporters (e.g., Split-GFP) as physical “checksums” for long-sequence construction.
Week 3: Lab Automation Lab Automation assignment This week, we explored lab automation and its applications in synthetic biology.
Subsections of Homework
Week 1 HW: Principles and Practices
Week 1: Principles and Practices
Class Assignment
1. Application or Tool to Develop & Why
Application:“The Bio-Puzzle” – A hardware-agnostic DNA assembly toolkit using split-protein reporters (e.g., Split-GFP) as physical “checksums” for long-sequence construction.
Why: For students and DIY biologists, the most immediate “biosecurity” threat is unintentional human error. Assembling long DNA (2,000bp+) from short, affordable oligo pools is difficult and error-prone. Currently, verifying these assemblies requires expensive and slow sequencing.
The “Bio-Puzzle” transforms biosecurity from a restrictive policy into a helpful engineering tool. By engineering DNA fragments to produce a visual signal only when assembled in the correct order, we provide a real-time, “at-the-bench” verification system.
2. Governance/Policy Goals
Goal: To foster a culture of “Integrity by Design” in decentralized research environments.
Sub-goal A: Minimize human error in DNA synthesis by providing immediate physical feedback.
Sub-goal B: Establish a community norm where long-sequence assembly is inherently linked to transparency and verification.
3. Potential Governance Actions
Option 1: The Split-Reporter Standard (Technical): Standardize “Verification Tags” at DNA junctions.
Option 2: Orthogonal Overhang Library (Policy): Create open-source library of “puzzle teeth” for safe connection.
Lysine constraint: Lysine can be supplied externally, so the constraint isn’t absolute and could undergo reversion mutations.
Week 2 HW: DNA Read, Write, and Edit
Week 2: DNA Read, Write, and Edit
Part 1 & 2: Gel Art
Describe your process of creating Gel Art using Benchling and restriction digests.
1.1 In-silico Design (Benchling)
Enzymes used: HindIII, SacI, KpnI
Design Concept: For this project, I designed a “Gel Art” pattern inspired by a European circular arch bridge. I wanted to use the different migration speeds of DNA fragments to recreate the structural elegance of an ancient stone bridge.
Inspiration:
Simulated Gel Pattern:
1.2 Lab Execution
Protocol followed: “Gel Art: Restriction Digests and Gel Electrophoresis”
Results: (Awaiting wet-lab execution results)
Part 3: DNA Design Challenge
3.1 Protein Choice
Chosen Protein: LuxR (Aliivibrio fischeri)
Why: I chose LuxR because it is a fundamental component for building genetic circuits. LuxR/LuxI system is one of the most well-characterized Quorum Sensing systems, used to create sophisticated biological logic gates and population-controlled behaviors in synthetic biology.
Reverse Translation Process: Starting from the LuxR protein sequence, I determined the original nucleotide sequence of the luxR gene from Aliivibrio fischeri. I used the Gene Corner Reverse Translate Tool for this process. In synthetic biology, this “reverse” process allows us to understand how nature codes for the protein, providing a baseline for synthesis.
Optimization Tool: I utilized the IDT Codon Optimization Tool to adapt the sequence for high expression in E. coli.
Why Optimize?
Codon optimization is crucial because different organisms prefer different “synonymous” codons to represent the same amino acid. This is known as codon usage bias. In E. coli, rare codons (like those found in A. fischeri) can lead to:
Low Expression: Ribosomes stalling at rare codons, reducing protein yield.
Truncated Proteins: Ribosomes falling off the mRNA before finishing.
Misfolding: The timing of translation speed affects how the protein folds.
Additionally, optimization helps remove internal restriction sites (like BsaI, BbsI) and strong secondary structures that might interfere with DNA synthesis or translation.
Codon-Optimized DNA Sequence for E. coli:
ATG AAG AAC AAT ATT AAA AAC TAC GCA TTT CTG CTG CTG TTT TTT ATC ATT
TTC ATC AAC CCG AAA AAT AAC TCA GCC AAG CTG GAT AAA ATT AAA GCG TAT
AAT ACA ATT GTC GAA AAA GTG GAG GGC AAC GAA TTT GAT TTG GCG CTT TTT
GCC TAC ATC CAC TTG GCG CTG TTG CTG AAT AAA ATT AAT AAT AAA TTG TTT
ATT AAA GGC GAC AAG ATT TCG CTG GTC GGT TTC CCG TGC GTG GAC AAC GGC
CTG TGC TCA ACT GGT ATT ATC TTT TCA CAT GTC AAT GAT CTT GTA GTG AAT
GAT TAT ATC TTT TAT ATT GAC AAT AAA GAA AAT GAG AGT ATT AAG CTG ATT
GAC CTT TTC GAG AAG TCC GTA GAG GAA GTG AAG GCC ATT TAT AAT TAT TAC
AAG AAA ATC AAC GAA AAG AAT TAT CTG ATT TTG GAC TCA AAA ATC TCG TTC
TAT AAA TTA CAC GAT TCT TAT AAA AAG CTT TAC ATC CTG TCG CTG AAC ATC
ATC CCG TTG TCT TTT GAA AAA AAG GAA CTG TGT ATT CTG AAA AAA CTG ATC
CAC GAA ACC CTG AGT AAA TTT AAA ATT GAG AAG TCT TAC GTT AAC CTG GAT
AAA TTA ATT GAT AAA AAC ATT CAG TTG ATC AAG ATT GAA CAA AAC GAT TTC
AAC GAT AGC ATT TAT TCG TAC AAA AAG TTA ATT AGC ATC ATT CTG CTG CCC
TTG ACA TAT TTT GAA TAA
3.4 Production Technology
Process (Cell-Free Protein Synthesis - CFPS):
I am particularly interested in producing this protein using cell-free methods, such as the PURE (Protein synthesis Using Recombinant Elements) system or TX-TL (Transcription-Translation) cell extracts (e.g., from E. coli).
How it works:
In a cell-free system, instead of transforming the DNA into a living cell, we simply mix the DNA template with a cocktail of “biological machinery” in a test tube. This cocktail includes:
RNA Polymerase: To transcribe the DNA into mRNA.
Ribosomes: To translate the mRNA into a protein.
tRNAs & Amino Acids: The building blocks and adapters for protein assembly.
Energy Sources: ATP and regeneration systems to fuel the process.
Why Cell-Free?
Speed: Protein can be produced in hours rather than days, as there is no need for cell growth or transformation.
Direct Prototyping: We can use linear DNA (like the codon-optimized sequence I designed) directly without cloning it into a plasmid.
Safety & Control: Since there are no living cells, it is easier to study proteins that might be toxic to a host cell, and we have precise control over the reaction environment.
Dual-Method Compatibility:
Ultimately, this design is highly versatile: it is possible to use both cell-free (in vitro) and cell-dependent (in vivo) methods. While cell-free systems offer rapid prototyping and a controlled environment, the same optimized sequence can be cloned into a plasmid and transformed into E. coli for stable, large-scale production. This flexibility allows us to choose the most appropriate method depending on the experimental goals.
3.5 [Optional] Central Dogma in Nature
Transcription and Translation: In nature, a single gene can sometimes code for multiple proteins through mechanisms like alternative splicing (in eukaryotes) or overlapping open reading frames (ORFs) (in viruses and some bacteria like our lux operon). For example, in the lux system, the organization of genes allows for coordinated expression of the entire bioluminescence machinery from a single promoter.
Strategy: This vector was chosen for its high copy number in E. coli, ensuring high yields of the plasmid for downstream applications. The cassette is flanked by cloning sites for easy extraction if needed. You can view the full annotated sequence on Benchling here.
Final Plasmid Map:Visual representation of the LuxR_Expression_Cassette inserted into the pTwist Amp High Copy vector.
Part 5: DNA Read/Write/Edit
5.1 DNA Read (Sequencing)
(i) Target: I want to sequence environmental DNA and synthesized constructs in real-time at the “bench” or in the field. This is crucial for early detection of unintended mutations or environmental contamination.
(ii) Technology:Oxford Nanopore Technologies (ONT)
Input & Preparation: Long-read DNA. Minimal preparation is key; using ONT’s Rapid Sequencing Kits, we can perform transposase-based fragmentation and adapter ligation in under 10 minutes.
Essential Steps: DNA molecules pass through a protein nanopore embedded in a membrane. As they pass, they disrupt the electrical current.
Base Calling: The changes in current are decoded using neural networks (base-callers like Guppy or Dorado) into a sequence of A, T, C, and G.
Output: FASTQ files of “long reads,” allowing me to see entire genetic circuits in a single piece.
Why ONT? It is highly portable (MinION) and provides data in real-time, which is essential for the “Biosecurity by Design” concept below.
5.2 DNA Write (Synthesis)
(i) Target: Synthetic genetic circuits based on quorum sensing regulators like LuxR (as designed in Part 3 and 4). These would be distributed as part of “safe-to-use” biological toolkits for decentralized research.
(ii) Technology:Phosphoramidite Synthesis (Modern Array-based)
Essential Steps: Controlled coupling of A, T, C, and G nucleotides onto a substrate (like a silicon chip for Twist Bioscience), followed by deprotection and oxidation cycles.
Limitations: While accurate and scalable for fragments, constructing multi-kb circuits still requires hierarchical assembly (Gibson or Golden Gate). Errors increase with length, requiring the biosecurity measures described in Part 5.3.
5.3 DNA Edit (Biosecurity Kill Switch)
(i) Target: I want to edit the Host Genome (E. coli or cell-free chassis) to include an autonomously triggered “Biosecurity Kill Switch.”
(ii) Technology:CRISPR-Cas9 System
The Concept: If the ONT sequencer (Read) detects a specific error, unintended mutation, or the presence of a hazardous sequence, it triggers the expression of a specialized CRISPR-Cas system.
Mechanism: I would design the system where a guide RNA (gRNA) targets essential genomic sequences or the synthetic circuit itself. Once triggered, Cas9 creates double-strand breaks in the genome, effectively “self-destructing” the cell or the DNA pool to prevent the spread of a dangerous or dysfunctional agent.
Preparation & Input: Requires a plasmid carrying the Cas9 gene under a conditional promoter and specialized gRNAs designed for high precision.
Limitations: The primary limitation is “escaper” mutants—cells that survive by mutating the CRISPR target site. To mitigate this, multiple essential sites must be targeted simultaneously (multiplexing).
Week 3 HW: Lab Automation
Week 3: Lab Automation
Lab Automation assignment
This week, we explored lab automation and its applications in synthetic biology.
1. Assignment: Python Script for Opentrons Artwork
I designed a “Pac-Man” themed piece of Bio-Art for the Opentrons OT-2 robot. The design features Pac-Man and several ghosts, mapped onto a circular canvas representing the output of the lab’s liquid handling.
Python Script Logic:
The script iterates through a matrix of coordinates, assigning specific colored liquids (Yellow for Pac-Man, Red/Blue/Cyan/Orange for ghosts) to designated wells. I used the Opentrons Python API (v2) to handle the aspirate and dispense operations with the P300 single-channel pipette.
2. Post-Lab Questions
1. Published Paper Review
Paper: Direct SARS-CoV-2 detection using a portable, open-source robotic platform (Yue et al., Matter, 2021)
Summary: This research describes the development of a fully automated, portable diagnostic platform for SARS-CoV-2 using the Opentrons OT-2 robot. It integrates CRISPR-Cas12a based detection (a “novel biological application”) with robotic liquid handling to perform sensitive and rapid virus screening.
Novelty: The platform automates the entire process from sample preparation to fluorescence readout. By using an open-source robot like the OT-2, the authors created a system that is significantly more affordable and flexible than traditional diagnostic workstations. This demonstrates how automation can be deployed in “the field” or in resource-limited settings for high-stakes biological monitoring.
2. Automation for Final Project
For my final project, I am interested in optimizing LuxR-based biosensors. I intend to use automation to:
High-Throughput Screening: Characterize dozens of LuxR variants against a library of AHL (Acyl-homoserine lactone) analogs.
Serial Dilutions: Automatically perform precise serial dilutions of AHLs to calculate dose-response curves (EC50) for each variant.
Combinatorial Mixing: Mix different signaling components to study crosstalk in multi-channel quorum sensing circuits.
Pseudocode Idea:
# Pseudo-code for LuxR sensitivity screeningconcentrations=[0,1e-9,1e-8,1e-7,1e-6,1e-5]# Molarvariants=['LuxR_WT','LuxR_V1','LuxR_V2']forvariantinvariants:forconcinconcentrations:mix_variant_with_ahl(variant,conc)measure_fluorescence()# Integrated with reader
3. Final Project Ideas
As part of the assignment, I have proposed the following three directions for my individual final project:
AI-Guided Evolution of LuxR Biosensors:
Using the LLR (Log-Likelihood Ratio) and sequence analysis I’ve been working on to predict mutations.
Using automation to screen these variants for shifted specificity.
Automated Cell-Free Prototyping of Quorum Sensing Circuits:
Bypassing the cell transformation loop by testing LuxR-responsive promoters in a cell-free expression system.
Leveraging Opentrons for setup.
Biological “Log” Memory via DNA Synthesis:
Designing a DNA synthesis cassette (like the one I designed for LuxR) that “records” chemical exposures into a DNA sequence that can be read out later.
