Virtual Tissue Modeling with Adaptive Emergent Functionality
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Solution Overview
Problem
Current models for simulating biological tissues lack fidelity in representing tissue response to external stimuli and internal processes, relying on incomplete and static assumptions that fail to capture emergent properties and complex biological relationships, leading to inadequate simulation of development, metabolism, and disease.
Innovation Solution
A method and system that incorporate biologically-derived primitives into a computational framework, focusing on cellular processes and the multicellular body plan, using a developmental engine and genetic operators to simulate cellular function, differentiation, and self-repair, with a bottom-up approach to modeling that captures emergent functionalities like self-repair and adaptability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If top-down systems engineering approach is used to model tissue structure and elasticity, then computational verification and feedback control are improved, but emergent properties and biological realism are lost
Solution Approach 1:
The patent inverts the conventional top-down modeling approach by implementing a bottom-up approach where simple cellular automata agents autonomously generate complex tissue structures and behaviors. Instead of programming macro-level tissue properties directly, the system uses micro-level cell rules that emergently produce macro-level phenomena such as tissue growth, pattern formation, and mechanical properties, thereby capturing both computational verification and emergent properties.
Solution Approach 2:
The cellular automata agents in the patent operate autonomously without external control, making decisions based on local rules and environmental conditions. This self-service capability enables the system to generate complex tissue behaviors and adapt to changing conditions naturally, resolving the contradiction between computational control and emergent adaptability.
2Ease of operation
If deterministic top-down modeling is used to specify tissue features, then design control is improved, but simulation of development and internal processes is degraded
Solution Approach 1:
The patent transitions from static deterministic models to dynamic stochastic cellular automata systems. The model incorporates randomness and probabilistic transitions that allow tissues to develop naturally through cellular processes. This dynamic approach enables accurate simulation of development and internal processes while maintaining design control through configurable rule sets and initial conditions.
Solution Approach 2:
The patent segments the tissue into individual cellular automata agents, each capable of independent computation and interaction. This segmentation allows the system to simulate detailed cellular processes and development mechanisms that would be impossible to model as a monolithic deterministic system, thereby improving simulation fidelity while maintaining operational control through the collective behavior of segmented units.
3Device complexity
If static assumptions are used in current models, then model simplicity is maintained, but simulation of tissue response and development is degraded
Solution Approach 1:
The cellular automata system in the patent operates continuously, with cells constantly interacting, dividing, differentiating, and responding to environmental changes. This continuous dynamic process replaces static assumptions, enabling reliable simulation of tissue response and development. The system maintains manageability through discrete time steps and localized interactions, avoiding excessive complexity while capturing continuous biological processes.
Data Source
AI summary
A method system, and apparatus for virtual modeling of biological tissue yields virtual multicellular individuals that exhibit adaptive emergent functionality in response to environmental stimuli. Virtual environmental parameters and cells with genomes are generated, and modified by genetic operations. Cells are developed into generations of multicellular individuals, which are evaluated and selected via evolutionary search according to fitness criteria, and individuals exhibiting adaptive emergent functionality, such as self-repair, are developed and identified.