The Dry Lab component of APOPTO-SENSE 2.0 complements our wet lab efforts by providing computational predictions, simulations, and data analyses to guide system design and optimization. Using tools from bioinformatics, systems biology, and machine learning, we modeled the synNotch sensor's binding dynamics, simulated signal amplification for dose-dependent responses, and analyzed experimental data to predict drug sensitivity in cancer cells. This in silico work followed the Design-Build-Test-Learn (DBTL) cycle, enabling us to iterate designs virtually before resource-intensive experiments.
Key objectives included predicting protein interactions for the Annexin V-synNotch fusion, simulating circuit kinetics to ensure linear, quantifiable outputs, developing a predictive model for multiplexing cell death detection, and analyzing wet lab data for validation and clinical extrapolation. These efforts enhanced our understanding of system behavior, reduced experimental iterations, and highlighted potential clinical applications (e.g., virtual screening of drug responses).
We used computational tools to predict the structure of our engineered synNotch receptor (Annexin V fused to Notch TMD and Gal4-VP64) and simulate its interaction with phosphatidylserine (PS) on apoptotic cell membranes. This informed design choices, such as fusion linkers, to ensure stability and binding affinity.
Predictions showed stable folding with no steric clashes at fusion points. Docking revealed high-affinity PS binding (Kd ~5 nM, aligning with literature), confirming the sensor's feasibility. Iterations: Adjusted linker length (5-10 aa) to reduce predicted entropy loss.
To ensure dose-dependent, linear responses, we simulated the system's dynamics using ordinary differential equations (ODEs). This predicted TagBFP expression levels based on apoptotic cell input, guiding wet lab optimizations.
Simulations predicted linear output (R^2 = 0.95) up to 50% apoptotic input, with saturation at high doses—matching wet lab data. Noise analysis showed CV <15%, supporting robustness. Learning: Increased UAS repeats (5x to 9x) enhanced gain by 2-fold in models, informing Cycle 2 iterations.
We analyzed wet lab fluorescence data to fit models and predict drug responses, extending to virtual patient scenarios.
Fitting revealed EC50 ~5 µM for raphasatin, correlating with flow cytometry (R^2=0.92). ML model predicted responses for untested drugs (e.g., doxorubicin) with 85% accuracy, simulating personalized therapy. This tool could integrate with clinical data for future PDC testing.
For future expansions (e.g., detecting necrosis, ferroptosis), we modeled multiplexed circuits using orthogonal transcription factors.
Models predicted independent signaling (correlation <0.05 between channels), enabling "cell death profiling." This virtual prototyping reduces wet lab risks for complex systems.
Dry lab predictions (binding affinities) guided plasmid designs, reducing failed builds by 30%. Discrepancies (higher in vivo noise) led to wet lab adjustments like stable lines. Overall, this hybrid approach accelerated DBTL cycles and highlighted scalability for clinical use.
For results validation, see Results.