In Silico Pathway Prediction for Recombinant Cell Design
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Solution Overview
Problem
Existing methods for modifying cells to exhibit computational features, such as those used in compunomics, can inadvertently interfere with desirable proteomic pathways, leading to unforeseen effects.
Innovation Solution
The development of methods to predict and control the impact of recombinant nucleic acids on endogenous pathways in cells, allowing for the precise regulation of gene expression and the creation of recombinant cells with desired traits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If recombinant nucleic acids are incorporated into endogenous pathways to create compunomic features, then computational functionality is improved, but interference with desirable proteomic pathways occurs
Solution Approach 1:
The invention performs in silico pathway modeling and prediction analysis before actually incorporating recombinant nucleic acids into cells. This preliminary computational assessment identifies potential pathway interferences and allows selection of recombinant nucleic acids that achieve desired computational functionality while minimizing interference with endogenous proteomic pathways.
Solution Approach 2:
The invention uses in silico pathway models as an intermediary between the design of compunomic features and their actual implementation in living cells. These computational models predict pathway behavior and serve as a bridge that allows optimization of recombinant nucleic acid designs before cellular incorporation, reducing the risk of harmful interference.
2Reliability
If in silico pathway modeling is performed to predict effects, then pathway interference is reduced, but computational resources and time are increased
Solution Approach 1:
The invention implements pathway modeling at multiple levels of detail, performing comprehensive in silico analysis only for critical pathways where interference would be most harmful, while using simplified models or heuristic assessments for less critical pathways. This selective approach maintains high reliability for important predictions while reducing overall computational time and resources.
Data Source
AI summary
Described herein are methods of making recombinant cells using an in silico-generated pathway map of an endogenous pathway in a cell of interest to determine, in silico, a predicted effect of incorporating a recombinant nucleic acid into the endogenous pathway; and based on the predicted effect, incorporating the recombinant nucleic acid into the endogenous pathway in the cell of interest such that activation of the endogenous pathway regulates expression of the recombinant nucleic acid.