Biological Computing Units for Rare Cell Detection
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Solution Overview
Problem
Current diagnostic technologies, such as ELISA and flow cytometry, are inefficient in detecting rare cells or pathogens due to limitations in receptor-target binding kinetics, requiring extensive time and sensitivity, and lack hierarchical computing systems that process target-receptor interactions for enhanced accuracy.
Innovation Solution
Biological computing systems comprising computing units that interact with sample objects to generate output signals indicative of sample characteristics, utilizing surface-bound entities that recognize, produce, or degrade signal objects, and form computational clusters for enhanced detection and quantification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional diagnostic technologies (ELISA, flow cytometry) are used to detect rare cells or pathogens, then detection sensitivity can be achieved, but detection time and resource requirements become excessively long and extensive
Solution Approach 1:
The patent segments the detection task into multiple hierarchical levels: individual computing units (cells or particles) perform local target recognition, while populations of computing units collectively perform pattern recognition and decision-making. This segmentation allows parallel processing of multiple targets simultaneously, dramatically reducing detection time while maintaining sensitivity for rare cell detection
Solution Approach 2:
The patent changes the detection parameter from relying on high-affinity single-receptor binding (traditional immunology) to utilizing low-affinity receptor binding combined with population-level statistical analysis. By changing from individual high-specificity binding to collective pattern recognition, the system achieves both speed and sensitivity
2Reliability
If traditional immunological detection methods are used, then target-antibody binding can occur, but detection accuracy is limited by receptor-target binding kinetics
Solution Approach 1:
The patent introduces computational algorithms as intermediaries between the simple biological binding events and the final detection decision. The computing units process binding signals through logical operations and pattern recognition algorithms, transforming weak individual binding events into reliable collective detection signals, thereby improving accuracy without requiring complex high-affinity binders
3Measurement precision
If hierarchical immune computing systems are implemented, then detection accuracy and reliability are significantly increased, but system complexity increases
Solution Approach 1:
The hierarchical computing system is segmented into modular computing units that can function independently but also cooperate collectively. Each unit contains simplified logic for target recognition, while the hierarchy emerges from population-level interactions. This modular segmentation allows the complex hierarchical functionality to be built from simple, manufacturable components
Solution Approach 2:
The computing units are designed with universal functionality to perform multiple roles: they can individually bind targets, process signals through logical operations, communicate with other units, and contribute to collective decision-making. This multi-functionality reduces the number of specialized components needed, managing system complexity while maintaining high detection accuracy
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
These systems enable efficient detection and quantification of rare cells or pathogens by leveraging biological computing units that form clusters and process multivariate surface marker profiles, improving detection speed and specificity beyond traditional methods.
Implementation Method 1
Each of the one or more CUs is independently capable of binding to a cognate binding partner
Data Source
AI summary
The present invention provides biological computing systems comprising computing units that process input signals to produce an output signal. In particular, the computing units include, for example, cells and proteins that function to convert biological signals into a discernable output that provides information about a biological sample. Further provided are methods of using such biological computing systems, such as for the diagnosis of various diseases and conditions.