🦠Brighter Autonomous Bioluminescence🦠 I would love to improve the intensity of the glow that is emitted from autonomous bioluminescent organisms whether natural or synthetic.
There are several different organisms that produce bioluminescence through various forms of luciferases (the enzyme that catalyzes the light emitting reaction) and luciferins (the substrate). However, most of them require the addition of the substrate to the growing medium to induce bioluminescence, typically coelenterazine or D-Luciferin. This to me just does not seem like the most convenient way to do this, so I am more interested in autonomous bioluminescent systems, such as Lux (bacterial luciferase) and Luz (fungal luciferase). These systems are the only two bioluminescent systems that have been fully elucidated. This means that they are fully genetically encoded, cells express luciferase and the enzymes necessary for substrate synthesis. This enables continuous supply of substrate without having to worry about adding the substrate to the growing medium or tissues to produce a glow.
Lab Automation Article of Interest: Deep reinforcement learning for the control of microbial co-cultures in bioreactors This study uses an automation tool in the form of AI-based process control, deep reinforcement learning. Instead of manually tuning bioreactor conditions, the authors train an algorithm to make control decisions that regulate nutrient inputs and maintain stable microbial populations in co-culture. The novel biological application is dynamic control of multi-species microbial communities, which is a major challenge in synthetic biology and biomanufacturing because species can outcompete each other or become unstable over time. The paper shows that reinforcement learning can effectively stabilize co-cultures and optimize bioprocess performance in silico, demonstrating a promising path toward autonomous bioreactor operation. This is significant because reliable co-culture control could improve production efficiency and enable more complex engineered biological systems.
Why are there only 20 natural amino acids?
There aren’t only 20 amino acids. There are just 20 that biology standardized early on in evolution. Proteins are built using translation. Once that system had evolved changing it was difficult because every protein in every organism depended on it. That creates evolutionary lock-in often referred to as a “frozen standard.” The current amino acids were selected due to their component atoms, functional groups, biosynthetic cost, use in a protein core or on the surface, solubility and stability. There are reasons for the selection of every amino acid. 2. Where did amino acids come from before enzymes that make them, and before life started?
Subsections of Homework
Week 1 HW: Principles & Practices
🦠Brighter Autonomous Bioluminescence🦠
I would love to improve the intensity of the glow that is emitted from autonomous bioluminescent organisms whether natural or synthetic.
There are several different organisms that produce bioluminescence through various forms of luciferases (the enzyme that catalyzes the light emitting reaction) and luciferins (the substrate). However, most of them require the addition of the substrate to the growing medium to induce bioluminescence, typically coelenterazine or D-Luciferin. This to me just does not seem like the most convenient way to do this, so I am more interested in autonomous bioluminescent systems, such as Lux (bacterial luciferase) and Luz (fungal luciferase). These systems are the only two bioluminescent systems that have been fully elucidated. This means that they are fully genetically encoded, cells express luciferase and the enzymes necessary for substrate synthesis. This enables continuous supply of substrate without having to worry about adding the substrate to the growing medium or tissues to produce a glow.
The first bioluminescent organism I ever cultivated was the fungus Panellus stipticus. The culture was given to me by a mycologist I was working with at the time. In order to get P. stipticus to glow I was directed to subculture onto bread crumb agar (simply agar and bread crumbs from the grocery store). Once cultured on the bread crumb agar the P. stipticus cultures did glow! However, this is where it should be noted that the glow that is produced from bioluminescence is typically not very bright. I will include a picture that I had taken of my first plate, the glow seems brighter than what it actually appears in person due to a longer exposure time on my iPhone camera settings.
While in undergrad we transformed cells to produce GFP in an Advanced Microbiology Lab course.
You can see the difference in how bright the glow is even with the long exposure time on the photo for the bioluminescent fungus. However, it should be noted in order to get organisms to produce fluorescence with GFP light is used. Whereas the glow from the bioluminescence may not be as bright there is no need for any other light sources to see the glow. So, I would prefer to use bioluminescence as there is no need to use light.
Then most recently, about a week ago, I transformed E. coli, on my own, with pVIB to produce bioluminescent cells. Again, I found myself with the same feeling that I had all those years ago when I first cultured P. stipticus, this is amazing but not exactly what I had imagined.
So now through this class I would like to take the opportunity to see if it would be possible to improve brightness/intensity of the glow produced from bioluminescent organisms!
Governance Goals
Goal 1: Ensure Safety & Prevent Harm
Biocontainment Standards: Making sure that containment strategies are in place that require genetic safeguards (e.g., inhibiting reproduction, auxotrophy or kill switches). This would prevent survival outside of intended environments. Safeguards should be validated and verified before deployment or release.
Operational Biosafety Protocols: Establish biosafety training and certification for anyone working with engineered bioluminescent organisms, including DIY biologists. This would mirror existing requirements in higher-biosafety labs and reduce risks posed by accidental exposure or poor technique.
Goal 2: Equity & Access
Equitable Access to Research Tools: Support public funding or bioluminescence kits for educational institutions and community labs with built-in safety guidelines, so that whoever is interested can participate responsibly in this area of science.
Ethics Education: Integrate ethics and governance training into the curriculum for any program funding or advising engineered organism projects, helping ensure researchers understand broader societal impact and their responsibilities.
Governance Actions
1) Community Driven Codes of Conduct for DIY and Institutional Labs
A community code of conduct would address the current gap between informal safety norms and clear expectations for work with engineered organisms. A coalition of academic groups, community labs, and DIYbio chapters could create a public code of conduct for responsible biodesign describing standards for organism handling, transparent documentation, and peer review, with voluntary adoption by spaces that host workshops or shared laboratories. This approach assumes that practitioners care about reputation and will align behavior with visible community values, though voluntary norms may struggle to reach groups that want independence over guidance. The main risk of failure is adoption without real behavioral change, while success could normalize safer habits and provide newcomers with a clear ethical baseline that reduces harm from inexperience.
2) Community Lab Micro-Grant Network
A micro-grant network would expand access to research by providing small, flexible funding to community labs, schools, and independent creators who lack institutional backing. Foundations, universities, or regional science hubs could distribute grants of $500–$2,000 paired with basic safety mentorship and simple reporting requirements, allowing projects in education, art, and environmental sensing to flourish. This approach assumes that modest resources can meaningfully broaden participation. The risk is that funds could be captured by already privileged groups or used without adequate guidance, but success would mean a more diverse ecosystem of synthetic biologist and ensure that the future of synthetic biology reflects many voices.
3) Peer Audit & Recognition Program for Safety Practices
A voluntary peer audit network would replace external enforcement with collaborative review, drawing on models from open source software and engineering design critique. Community organizations, DIYbio groups, iGEM teams, and university clubs could review one another’s safety documentation, containment strategies, and workflows, with participating projects receiving a public “Safety Verified” recognition. The model assumes that practitioners will invest time in mutual review and that social trust can motivate improvement, which may not hold equally across all groups. Audits could fail if reduced to paperwork exercises, yet success would foster a culture where safety is shared, allowing many eyes to strengthen emerging technologies.
Scoring of Governance Actions Against Policy Goals
Scale: 1 = best alignment, 2 = moderate, 3 = weak/indirect, N/A = not applicable
Goals & Sub-Goals
1) Codes of Conduct
2) Community Micro-Grants
3) Peer Audit & Recognition
Goal 1: Ensure Safety & Prevent Harm
Support biocontainment standards & safety-by-design
1
3
2
Encourage operational biosafety protocols
1
2
1
Reduce accidental exposure or misuse
1
2
1
Goal 2: Equity & Access
Equitable access to research tools
2
1
2
Integrate ethics & governance education
1
2
1
Broaden participation beyond elite labs
2
1
2
Other Considerations
Minimize costs and burdens
1
1
2
Feasibility
2
1
2
Not impede research
1
1
1
Promote constructive applications
1
1
1
Drawing on the scoring table, I would prioritize a combined approach centered on Community Driven Codes of Conduct, the Community Lab Micro-Grant Network, and Peer Audit & Recognition. The Codes of Conduct scored highest for fostering safety and preventing harm by establishing shared expectations around biocontainment and biosafety without restricting research. The Micro-Grant Network performed best for equity and access, directly lowering barriers for schools, community labs, and independent creators while including mentorship to support responsible practice. Peer Audit & Recognition complements both by reinforcing operational safety through collaborative review and practical ethics learning, helping translate norms into everyday lab behavior.
This combination reflects key trade-offs and it relies on voluntary incentives rather than formal enforcement, assuming that reputation, community trust, and access to resources could meaningfully shape the community. The approach may struggle if some actors do not value these social levers, and micro-grants could still be captured by already privileged groups. However, for audiences such as MIT leadership, community lab networks, and nonprofit funders, this strategy offers a feasible path that protects safety, expands participation, and promotes constructive uses of bioluminescence while keeping burdens on researchers low and innovation open.
Week 2 Prep Questions
Homework Questions from Professor Jacobson:
Nature’s machinery for copying DNA is called polymerase. What is the error rate of polymerase? How does this compare to the length of the human genome. How does biology deal with that discrepancy?
The error rate of polymerase is 1:10^6. The length of the human genome is 3.2 Gbp. Biology deals with this discrepancy by incorporating proofreading capabilities within the DNA polymerase. Certain DNA polymerases contain 3’ to 5’ exonuclease activity allowing them to remove any incorrect DNA bases. There are also other mismatch repair systems such as the MutS system that detect mismatched DNA bases and fixes them.
How many different ways are there to code (DNA nucleotide code) for an average human protein? In practice what are some of the reasons that all of these different codes don’t work to code for the protein of interest?
An average human protein can be encoded by an enormous number of different DNA sequences because most amino acids are specified by multiple codons. Even though DNA sequences could encode the same amino-acid chain, they are not functionally the same because for example, different codons are translated at different speeds due to tRNA abundance.
Homework Questions from Dr. LeProust:
What’s the most commonly used method for oligo synthesis currently?
Solid-phase syntheis using the phosphoramadite method.
Why is it difficult to make oligos longer than 200nt via direct synthesis?
Each step in the phosphoramadite method has an efficiency of about 99%, which means that even at an oligo length of just 200nt most products will be truncated or contain deletions and therefore not be usable.
Why can’t you make a 2000bp gene via direct oligo synthesis?
Once oligos start to reach this length there are issues with the strand starting to fold back on itself as well as the error issue mentioned in the answer to the previous question.
Homework Question from George Church:
What are the 10 essential amino acids in all animals and how does this affect your view of the “Lysine Contingency”?
Histidine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan, valine, and arganine. Lysine is an essential amino acid which means that it cannot be synthesized by human or other animal cells. Therefore, it has to be obtained from the diet. This means the “Lysine Contingency” that was referenced within the Jurassic Park series makes no sense, as animals already do not synthesize lysine. So this edit would not do anything at all lol.
There are multiple codons that code for a single amino acid. Every organism has certain tRNAs associated with codons that will be more abundant than others. If an organism runs into a codon that it does not commonly see and has a low abundance of the associated tRNA then it can cause the production of the associated protein to stall. This is where codon optimization comes in. Codon optimization takes a DNA sequence and converts it into a sequence that contains codons that would be more commonly found in the host organism.
I chose to optimize the codon sequence for yeast (Saccharomyces cerevisiae) as this is a model microorganism used for synthetic biology and I would first just like to test if this would work to produce fungal luciferase.
The DNA sequence can now be sent to a DNA synthesis company such as, Twist Biosciences. Twist can provide either DNA fragments or clonal genes that already have the DNA sequence of interest inserted into a vector. If receiving DNA fragments instead of clonal genes, first the fragments will have to be inserted into a vector. Once the fragments have been inserted into a vector, or if clonal genes were ordered instead, this can then be introduced into an organism. For yeast a heat shock or electroporation are used to introduce the DNA into the cells. Once the yeast cells have successfully taken up the DNA, the cells will then proceed to transcribe the DNA into RNA and then translate that RNA into the protein of interest.
How does it work in nature/biological systems?
Describe how a single gene codes for multiple proteins at the transcriptional level.
In eukaryotic systems a single gene can code for multiple proteins because eukaryotic genes contain exons and introns. The exons are the segments of the gene that will end up in the final mRNA product and encode the final protein. When mRNA is being processed the exons can be spliced together in different combinations creating different proteins.
Try aligning the DNA sequence, the transcribed RNA, and also the resulting translated Protein!!!
Prepare a Twist DNA Synthesis Order
Fully annotated Benchling insert fragment
Twist cloning vector
DNA Read/Write/Edit
DNA Read
(i) What DNA would you want to sequence (e.g., read) and why?
I would want to sequence the DNA of all known bioluminescent fungi just to see all the different variations of this system that nature has come up with. Then compare how those differences impact the intensity of the glow or maybe certain variations would work better in certain cirumstances/organisms.
(ii) What technology or technologies would you use to perform sequencing on your DNA and why?
I would want to use use nanopore sequencing technology because it is a relatively low-cost sequencing method. Nanopore sequencing is a third-generation sequencing method because it can read long single DNA molecules directly in real time. The input for nanopore sequencing is typically DNA, RNA, amplicons, or cDNA. First the DNA/RNA is extracted then sequencing adapters are attached to the ends of the strands. The sequencing adapters are oligonucleotides that are loaded with a motor protein. The motor protein associates with the nanopore in the flow cell and controls the DNA or RNA strand movement through the nanopores at a defined speed. Samples are then ready to be loaded and sequenced.
The flow cells used for sequencing the samples contain ion-permeable nanopores embedded in an electrically-resistant membrane enabling an ionic current to pass through the nanopore when a voltage is applied across the membrane. This creates a measurable current that is disrupted when a strand of DNA or RNA passes through the nanopore. The disruption of current is measured and is used to identify the bases passing through the nanopore. The disruption produces a characteristic ‘squiggle’. The squiggle is then decoded using basecalling algorithms to determine the DNA or RNA sequence in real time.
DNA Write
(i) What DNA would you want to synthesize (e.g., write) and why?
I would synthesize the cluster of genes that are involved in the bioluminescence pathway in the fungus Neonothopanus nambi. There are four genes involved in the autonomous biolumiscent pathway in N. nambi. These are hispidin synthase (HispS), hispidin-3-hydroxylase (H3H), luciferase (Luz), and caffeylpyruvate hydrolase (CPH). Here are their associated genetic sequences:
(ii) What technology or technologies would you use to perform this DNA synthesis and why?
I would use chip-based oligonucleotide synthesis followed by assembly because it enables parallel production of many DNA fragments at relatively low cost. Here are the steps for chip-based oligonucleotide synthesis:
Coupling with phosphoramidite - a protected phosphoramidite is added to the unprotected 5’ OH end of the DNA strand
Capping unreacted sites - unreacted 5’ OH are acetylated to prevent further chain extension
Oxidation - oxidation of phosphite triester to phosphate using aqueous iodine
Deprotection - acid catalyzed removal of protective group to allow for subsequent base addition
Accuracy is the biggest technical limitation. Each base addition has a small failure probability. This means only shorter maximum reliable length DNA can be synthesized. Speed can also be a limitation as the chemistry is cycle-based, one nucleotide added per cycle, so the physical synthesis time scales with sequence length no matter how many sequences are on the chip. You can make millions at once, but you cannot make any single one faster than the chemistry allows.
DNA Edit
(i) What DNA would you want to edit and why?
I would want to edit the DNA of various organisms so that they would become bioluminescent. I would start with microorganisms such as E. coli and yeast. Then I would like to move to plants, I have not thought about modifying any other eukaryotic organisms beyond that. My main interest is just to see if this would work well in various organisms to produce autonomous bioluminescence.
(ii) What technology or technologies would you use to perform these DNA edits and why?
I would use CRISPR-Cas9 to make the edits. CRISPR-Cas9 works across kingdoms and it enables precise editing and insertion of DNA. CRISPR-Cas9 has the ability to find and cut specific DNA targets based on the sequence of its guide RNA. The guide RNA can be designed to be complementary to a particular target sequence in the genome. Cas9 will search the whole genome, eventually finding its target site and making a double-stranded break. The double-stranded break can then be repaired through homology-directed repair allowing for the donor DNA to be incorporated into the host organisms genome.
To prepare to edit with CRISPR-Cas9 the first step is to design a donor DNA template flanked by homology arms matching the target locus and guide RNA complementary to the region. Plasmids would then need to be constructed to express Cas9 and the guide RNA. All in all, the inputs required for the editing would include the Cas9 nuclease, guide RNA, donor DNA template, plasmids, primers, and the host cells to be edited. Additional components such as enzymes for DNA assembly and transformation or transfection reagents would also be required to introduce the editing system into the cells.
One limitation of CRISPR-Cas9 editing is the possibility of off-target effects. This is where Cas9 cuts at sites that are similar but not identical to the target sequence and could induce unintended mutations. Another limitation is that cells prefer to do non-homologous end joining over homology-directed repair, which can introduce insertions or deletions. The CRISPR-Cas9 editing system is also limited by the delivery problem, which involves getting the CRISPR-Cas9 system to the target cells. Some organisms or cell types are more difficult to transform or transfect, which can reduce the number of successfully edited cells.
Week 3 HW: Lab Automation
Lab Automation Article of Interest:
Deep reinforcement learning for the control of microbial co-cultures in bioreactors
This study uses an automation tool in the form of AI-based process control, deep reinforcement learning. Instead of manually tuning bioreactor conditions, the authors train an algorithm to make control decisions that regulate nutrient inputs and maintain stable microbial populations in co-culture. The novel biological application is dynamic control of multi-species microbial communities, which is a major challenge in synthetic biology and biomanufacturing because species can outcompete each other or become unstable over time. The paper shows that reinforcement learning can effectively stabilize co-cultures and optimize bioprocess performance in silico, demonstrating a promising path toward autonomous bioreactor operation. This is significant because reliable co-culture control could improve production efficiency and enable more complex engineered biological systems.
Final Project Automation
For my final project, I intend to use automation tools to identify the best construct architecture (single plasmid vs. multi-plasmid system, promoter/RBS combinations, and coding sequence variants) needed to make the fungal bioluminescence pathway (FBP) + BRET system function across multiple host organisms.
My goal is to build a scalable design-test-learn workflow rather than test only a few manual designs. I will use lab automation to generate and evaluate many candidate sequence/plasmid combinations in parallel, then iteratively improve designs using data from each round.
Planned automation workflow
Design Phase
Build a combinatorial library of FBP + BRET constructs (promoters, copy number, linkers, fluorescent acceptors, plasmid architecture).
Build Phase
Use automated liquid handling (or cloud-lab style protocols) for DNA assembly setup, transformation setup, and plate preparation.
Test Phase
Measure luminescence, fluorescence, and growth (OD) in microplate format.
Use standardized imaging conditions and, if needed, a simple 3D-printed holder/dark-box insert for reproducible camera-based signal capture.
Learn Phase
Use Python-based analysis to rank designs by brightness, brightness/OD, and BRET signal ratio.
Select top performers for the next iteration and/or for testing in additional hosts.
Optional scale-up plan (Ginkgo Nebula)
If available, I would use Ginkgo Nebula to scale beyond local throughput: submit top construct sets for higher-throughput build/test cycles across multiple organisms and feed those results back into my design loop.
Overall, automation is central to my project because it enables systematic, reproducible, and data-driven optimization of a complex FBP + BRET system across diverse biological hosts.
Final Project Aims
Aim 1.
Build an automated design-build-test workflow and demonstrate baseline fungal bioluminescence pathway expression in E. coli and yeast (S. cerevisiae).
Include: at least a small construct panel (e.g., 4-12 variants)
Success metric:
Reproducible luminescence with automated assay + analysis pipeline working end-to-end
Aim 2.
Add BRET module and use automation to identify a better-performing construct architecture (single vs multi-plasmid and promoter/linker combinations) in both hosts.
Success metric:
Measurable spectral shift and improved BRET ratio vs donor-only control; identify at least one top-ranked architecture
BRET luminescence/fluorescence improvement >20% vs bioluminescence alone
Aim 3
Design and pilot multi-host optimization strategy (with Ginkgo Nebula as scale-up path)
Success metric:
Transfer top designs to additional hosts and improve brightness/OD + stability through multiple rounds
Week 4 HW: Protein Design Part I
1. Why are there only 20 natural amino acids?
There aren’t only 20 amino acids. There are just 20 that biology standardized early on in evolution. Proteins are built using translation. Once that system had evolved changing it was difficult because every protein in every organism depended on it. That creates evolutionary lock-in often referred to as a “frozen standard.” The current amino acids were selected due to their component atoms, functional groups, biosynthetic cost, use in a protein core or on the surface, solubility and stability. There are reasons for the selection of every amino acid.
2. Where did amino acids come from before enzymes that make them, and before life started?
Abiotic chemistry on early Earth. Amino acids are chemically natural products when carbon, nitrogen, hydrogen, oxygen, and energy mix. Meteorites can also contain amino acids, therefore, some could have come to Earth from space. Geochemical environments like hydrothermal vents, mineral surfaces, metal ions, heat gradients, and pH differences can drive reactions that form amino acids from simpler molecules. Before enzymes chemistry did the job.
3. If you make an α-helix using D-amino acids, what handedness (right or left) would you expect?
A helix made from D-amino acids will form a left-handed α-helix.
4. Can you discover additional helices in proteins?
Yes there are algorithms that can scan protein structures and assign different helices based on hydrogen-bond patterns and geometry. Proteins contain more than just the regular α-helix. There are also rarer helices such as 3₁₀ helices, π helices, polyproline helices, and collagen triple helices. With computational design or mutation experiments, you can often convert loops or disordered regions into helices.
5. Why are most molecular helices right-handed?
Most molecular helices are right-handed because the building blocks of life are chiral molecules, and biology chose one handedness early on. Once that choice locked in, the geometry of bonding and steric constraints naturally favor right-handed helices for those particular molecular configurations. A right-handed α-helix lets hydrogen bonds line up cleanly while avoiding atomic collisions.
A left-handed α-helix is theoretically possible but energetically unfavorable with L-amino acids.
6. Why do β-sheets tend to aggregate?
A β-sheet is a protein secondary structure where the backbone is stretched out into strands that sit next to each other, stabilized by hydrogen bonds between the backbone carbonyl and amide groups. The hydrogen-bond donors and acceptors often remain partially unsatisfied at the sheet edges. When another β-strand comes nearby, it can complete those hydrogen bonds. So strands stack. Then stacks stack. Then you get fibrils.
What is the driving force for β-sheet aggregation?
β-sheet aggregation is driven by the combination of unsatisfied backbone hydrogen bonds seeking partners, hydrophobic interactions between sheet faces, favorable side-chain packing, and nucleation-dependent polymerization that lowers free energy as aggregates grow.
7. How many molecules of amino acids do you take with a piece of 500 grams of meat? (on average an amino acid is ~100 Daltons)
