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Contribution: Giving Back to the SynBio Community

Our project was built on the foundations laid by the countless scientists and iGEM teams who came before us. Their open sharing of knowledge, parts, and ideas made it possible for us to explore epigenetic regulation. We believe that science grows best when knowledge is shared freely. Throughout our journey, we focused on how our efforts could serve as valuable stepping stones for future teams.

The EPIC Toolkit​

(Epigenetic Programmable Intervention and Control System)

Synthetic biology has traditionally relied on "hardware-like" genetic circuits. With EPIC, we introduce the software upgrade, epigenetic programming. Our toolkit enables programmable gene silencing and activation through targeted DNA methylation using a dCas9–Dam fusion system.

We successfully designed and cloned a recombinant dCas9-Dam construct using in-fusion cloning, which was verified through sequencing. This construct enables site-specific methylation of promoter regions, allowing for the creation of reversible and heritable genetic states in E. coli.

To make this tool accessible, we standardized the cloning workflow, established troubleshooting guidelines, and characterized critical parameters, ensuring that future users can reproduce and adapt the construct easily for their own applications.

Standardized Workflow for gRNA Cloning​

We established a detailed and optimized workflow for guide RNA (gRNA) cloning into the pdCas9 backbone, covering every step from oligo annealing to ligation and colony PCR screening.

Our initial trials faced challenges due to expired reagents and non-specific primers, but through systematic troubleshooting (adjusting ligation ratios, digestion conditions, temperatures for the digestion and purification steps), we arrived at a robust and reproducible protocol.

This standardized guide RNA cloning protocol is designed for modularity, allowing any promoter or gene of interest to be targeted with minimal changes, thereby simplifying CRISPR-based epigenetic circuit construction for future teams.

The dCas9-Dam Plasmid Construction​

We developed and validated a recombinant plasmid expressing dCas9-Dam, a key element of our epigenetic memory mechanism.

We carefully documented and optimised the protocol to develop the plasmid, involving PCR amplification of the Dam gene and the subsequent infusion cloning into pdCas9. We verified the presence of positive recombinants through sequencing and restriction digestion analysis. The optimized cloning and validation workflow is shared in our protocols, enabling future iGEM teams to reproduce or extend this construct. We also hope to verify the production of the stable fusion protein using SDS-PAGE and Western blotting.

Co-Transformation and Compatibility Testing​

To demonstrate compatibility and potential for modular regulation, we co-transformed E. coli with two plasmids: dnaAP2-GFP and dCas9-Dam. This experiment laid the foundation for testing targeted methylation effects on a reporter system. Our findings contribute to the design of programmable promoter memory systems, where gene expression can be toggled through the methylation of specific states.

Competent Cell Preparation and Resource Standardization​

We optimized and validated competent cell preparation protocols for both E. coli XL1-Blue and Dam-negative strains, ensuring high transformation efficiency and reproducibility. These stocks provide reliable chassis strains for dCas9-Dam experiments and methylation-sensitive studies, serving as a long-term resource for future teams.

Future Directions and Applications​

Potential applications of our system include:

  • Memory-encoded biomanufacturing: maintaining induced states without constant inducers.
  • Programmable biosensors that record environmental stimuli through methylation patterns.
  • Epigenetic silencing in industrial microbes to reduce byproduct formation.

ECHO​

Developed ECHO: a dual-model software framework that combines ElasticNet regression (for interpretable site-level importance) with an Adaptive Regressive CNN (for accurate expression prediction). This hybrid design allows researchers to both understand which CpG sites matter and predict the effects of methylation changes.

Introduced a novel site-selection strategy by using ElasticNet-derived weights as a heuristic to guide methylation targeting, ensuring faster convergence and fewer experimental interventions compared to CNN-only approaches.

Implemented multiple methylation algorithms (sequential, circular, gradCAM-guided, ElasticNet-guided) and benchmarked them, demonstrating that the ElasticNet sequential algorithm provides the fastest, most reliable convergence with strong biological interpretability.

Validated biological relevance of predictions by cross-referencing ElasticNet weight peaks with annotated enhancer/silencer regions in NCBI, and further explaining long-range peaks with DNA-looping mechanisms described in literature.

ENIGMA​

We developed a deep learning–driven framework that links DNA methylation to gene expression and metabolism. It accurately predicts cancer-related gene regulation and simulates resulting metabolic reprogramming, offering a new way to study epigenetic effects.

Our adaptive regressive CNN-based deep learning model is able to capture and explain the variation in gene expression based only on its methylation profile with great accuracy.

The model predicts accurately, the upregulation/downregulation of genes implicated in cancer (SDHB, SLC7A5, LDBH) as supported by experimental data.

We formulated a new algorithm to ingeniously translate the changes in gene expression to those in reaction fluxes.

Our team employed three different algorithms (GIMME, iMAT and our novel pruning method) to integrate the predicted gene expression into the human metabolic model (Human1). Through this, we reproduced, in silico, aberrant behaviours of pathways like the Warburg Effect and Glycolytic Activity which are shown to be core hallmarks of cancer.

We developed a user-friendly command line implementation of the entire pipeline named ENIGMA which serves as a useful software tool to study effects of epigenetic modifications on reactions of interest.