Switching Process Analysis Using Discretized Time-Series Graphs
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Solution Overview
Problem
Existing methods for analyzing gene regulatory networks face limitations in interpreting single-cell sequencing data using the Waddington landscape, as they lack the ability to accurately measure real-time series of states and transitions in evolving reaction systems, leading to questionable interpretability and limited explanatory power.
Innovation Solution
A method is developed to analyze switching and regulation processes by measuring parameters in reaction systems, creating time series, discretizing these measurements into microstates, generating a directed graph, and performing structural analysis to determine time-correlated changes in system components, using tools like Petri nets to model these processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If single-cell sequencing data is analyzed using traditional Waddington landscape approaches, then cell differentiation states can be visualized, but the interpretability and explanatory power are limited due to inability to accurately measure real-time series of states and transitions
Solution Approach 1:
The patent applies dynamics by transforming static single-cell sequencing data into dynamic time series representations. It models gene regulatory networks as evolving systems where gene expression states change over time, capturing temporal dynamics through sequential sampling and time-series analysis. This allows the system to track state transitions and regulatory processes dynamically rather than as static snapshots.
Solution Approach 2:
The patent introduces mathematical models and computational frameworks as intermediaries between raw sequencing data and biological interpretation. These models serve as mediators that transform discrete gene expression measurements into continuous temporal trajectories, enabling the reconstruction of developmental paths and transition probabilities without directly observing continuous biological processes.
2Adaptability or versatility
If sequential sampling from individual cells is performed to expand relevance and scope, then more comprehensive data can be obtained, but the complexity of analyzing switching and regulation processes increases
Solution Approach 1:
The patent applies segmentation by decomposing the complex analysis of gene regulatory networks into distinct computational modules: time series construction from sequential sampling, state space discretization, transition probability calculation, and trajectory reconstruction. Each module handles a specific aspect of the analysis, making the overall complex process more manageable and applicable to diverse biological systems.
Solution Approach 2:
The patent creates a universal analytical framework that can be applied across different cell types, developmental stages, and experimental conditions. The mathematical models and computational methods are designed to be system-agnostic, allowing the same approach to analyze various gene regulatory networks and switching processes regardless of specific biological context, thereby expanding versatility.
3Ease of operation
If discrete gene expression states are used to model cell differentiation, then the Waddington landscape can be constructed, but the ability to distinguish concurrent and sequential processes is limited
Solution Approach 1:
The patent adds a temporal dimension to the traditional discrete state model by constructing time series from sequential sampling. Instead of analyzing single time-point snapshots, the method trajectories through state space over time, enabling distinction between concurrent processes (simultaneous state changes) and sequential processes (ordered state transitions) through temporal pattern recognition in the expanded state-time space.
Data Source
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AI summary
The subject matter of the present invention is a method for analyzing switching and regulatory processes, comprising the following steps: a) providing at least one reaction system, b) measuring and selecting at least one parameter on at least one reaction system from step a), c) repeating step b) at least once, d) creating at least one time series using at least one parameter selected in step b), e) discretizing the measured parameter values from step b) in the time series from step d) to generate a sequence of microstates, f) generating a directed graph of macrostates from the at least one discretized time series in step e), g) structural analysis of the directed graph from step f) to combinatorially determine the number of time-correlated changes of the microstates in a subset of randomly selected system components.