Boolean Network Master Regulator Identification for Cell-State Transitions
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Solution Overview
Problem
Identifying key molecules that determine cell fate and their regulation pathways is challenging due to the complexity of molecular networks, particularly in determining how to transition between stable cellular states using existing methods like LDOI and DEPC, which have limitations in accuracy and complexity.
Innovation Solution
A novel method using a Boolean network model and a combination of LDOI and DEPC methodologies to identify master regulators by perturbing nodes in diff-FBLs, defining a canalizing kernel, and determining key molecules that can alter cell fate through computer simulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing methods like LDOI and DEPC are used to identify key molecules, then the identification process can be performed, but the accuracy is insufficient and the computational complexity is high
Solution Approach 1:
The patent segments the complex molecular network into feedback loop units (FLUs) and further into minimal feedback loop units (minFLUs). This segmentation divides the large-scale network analysis into smaller, manageable units that can be processed independently, reducing computational complexity while maintaining identification accuracy through systematic evaluation of each unit's contribution to cell fate determination
Solution Approach 2:
The patent extracts and focuses specifically on feedback loops as the critical structural elements that determine cell fate. By taking out and analyzing only the feedback loop components (FBLs) rather than the entire molecular network, the method reduces the search space and computational burden while identifying the key regulatory mechanisms that drive cellular transitions
2Reliability
If the entire state space of the network is searched to find target nodes, then comprehensive coverage is achieved, but unnecessary targets are included and computational resources are wasted
Solution Approach 1:
The patent applies local quality by focusing analysis on specific regions of the network - the feedback loop units - rather than uniformly analyzing the entire state space. This localized approach identifies target nodes within the context of their feedback loop structures, ensuring comprehensive coverage of relevant regulatory mechanisms while avoiding waste of computational resources on unrelated network regions
Solution Approach 2:
The patent performs preliminary identification of feedback loop units and their minimal subsets before conducting the full target node search. By pre-structuring the network into minFLUs and identifying which feedback loops are active in transitions between cellular states, the method prepares the analysis framework in advance, reducing the computational time required for the actual target identification process
Data Source
AI summary
A method of identifying a cell state regulation path is described, including the steps of setting a state of each node constituting an expanded network generated from a Boolean network corresponding to a biomolecular network of a particular cell to a value of a predetermined given initial state of the Boolean network; selecting a test subject node from diff-FBLs having complementary states in the initial state of the Boolean network and in a given target state, among the Boolean network FBLs, and perturbing the selected test subject node by computer simulation; testing whether the perturbation changes the values of all nodes in the diff-FBLs from initial state values to target state values; and, if the test subject node passes the test, determining that the test subject node is a master regulator, which must be controlled to transition the Boolean network from the initial state to the target state.