MET/EMT Transcriptional Network Modeling from Single-Cell Data
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Solution Overview
Problem
Existing technologies struggle to accurately characterize dynamic cell state transitions in cancer cells due to the high dimensional nature of single-cell data and computational challenges, which hinders understanding of metastasis and therapy-resistant disease mechanisms.
Innovation Solution
A method using a neural network to calculate a continuous trajectory of target cells based on single-cell data, interpolating a gene regulatory system, including gene expression profiles and transcription factors, to model transitions between mesenchymal and epithelial states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If single-cell technologies are used to characterize cell states, then detailed static cell state characterization is achieved, but dynamic cell state transitions cannot be elucidated
Solution Approach 1:
The patent performs preliminary computational actions by training neural networks on static single-cell data to predict dynamic transition probabilities. The system pre-calculates trajectory inference models and gene regulatory network dynamics before actual dynamic analysis is needed, enabling subsequent dynamic transition elucidation from static snapshots.
Solution Approach 2:
The patent replaces direct mechanical observation of dynamic cell transitions with computational modeling using neural networks. Instead of physically tracking cells over time, the system uses machine learning algorithms to infer dynamic trajectories from static data points, substituting computational prediction for direct mechanical observation.
2Loss of information
If longitudinal patient samples are analyzed, then insight into metastasis and therapy-resistant disease mechanisms is gained, but computational challenges increase
Solution Approach 1:
The patent segments the complex computational problem into distinct neural network modules: one for trajectory inference, another for gene regulatory network dynamics, and a third for transition probability prediction. This segmentation allows each module to handle specific aspects of the computational challenge independently, reducing overall complexity.
Solution Approach 2:
The patent introduces neural networks as intermediary computational layers between raw longitudinal patient sample data and disease mechanism insights. These intermediary models process and transform complex high-dimensional data into interpretable trajectory inferences and transition probabilities, bridging the gap between raw data and biological understanding.
3Reliability
If transcriptional networks are targeted to guide cancer cell state, then therapy effectiveness is improved, but identification of these networks is difficult
Solution Approach 1:
The patent implements feedback loops where neural network predictions about gene regulatory dynamics are continuously refined based on observed cell state transitions. The system uses observed transitions to update and improve its transcriptional network models, creating a feedback mechanism that enhances both network identification accuracy and subsequent therapy guidance reliability.
Solution Approach 2:
The patent changes the parameter space from directly observing gene expressions to inferring transcriptional network dynamics through neural network transformations. By transforming the problem parameters from raw expression data to predicted network interactions and transition probabilities, the system makes transcriptional networks detectable and measurable.
Data Source
AI summary
Aspects of the present invention relate to a method of determining a gene regulatory system within a cell that transitions from a first state to a second state including providing, to a neural network, a set of single-cell data of a target cell that is transitioning from a first state to a second state, calculating, via the neural network, a continuous trajectory of the target cell from the first state to the second state based on the single-cell data set, and interpolating a gene regulatory system of the target cell based on the calculated continuous trajectory, wherein the gene regulatory system includes a gene expression profile of at least one gene and at least one transcription factor that regulates expression of the at least one gene. Further, a system for determining a gene regulatory profile of a cell comprising at least one neural network is described.