Bioelectric Tissue Simulation for Voltage Pattern Control
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Solution Overview
Problem
Current technologies lack a flexible and powerful platform for modeling bioelectric patterns and dynamics in multicellular tissues, hindering the development of effective interventions for controlling voltage distributions in biomedical applications and synthetic biology.
Innovation Solution
A finite volume method multiphysics simulator that models ion channel, ion transporter, and gap junction activity to predict bioelectric patterns and their spatio-temporal dynamics, enabling the simulation of ion concentration, voltage, and electric fields in tissues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a comprehensive model of bioelectric patterns in multicellular tissues is developed, then the ability to control voltage distributions for biomedical interventions is improved, but the complexity of the model increases
Solution Approach 1:
The patent divides the tissue into discrete cellular compartments, each with its own membrane potential and ion concentration variables. This segmentation allows the complex continuous bioelectric system to be modeled as a collection of manageable discrete units that can be simulated independently and then integrated, reducing overall model complexity while maintaining comprehensive control capabilities.
Solution Approach 2:
The computational model is designed to be universally applicable across different tissue types and biomedical scenarios. The same core framework can simulate various bioelectric patterns, test different interventions, and accommodate different tissue configurations, making the model versatile without requiring separate specialized models for each application.
2Measurement precision
If the simulator models ion channel, ion transporter, and gap junction activity in detail, then the accuracy of bioelectric pattern prediction is improved, but the computational time and complexity increase
Solution Approach 1:
The model dynamically adjusts the level of detail for different cellular components based on the specific simulation requirements. Ion channels, transporters, and gap junctions can be modeled with varying degrees of complexity depending on whether the focus is on rapid signal propagation or steady-state bioelectric patterns, allowing accurate predictions without unnecessary computational overhead.
Solution Approach 2:
The simulator allows changing key parameters such as ion concentration gradients, channel conductance values, and membrane capacitance to optimize the balance between accuracy and computational efficiency. By adjusting these parameters, the model can achieve sufficient prediction accuracy for a given application while reducing computational time by simplifying less critical aspects of the bioelectric system.
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
Enables deep understanding and control of local and long-range bioelectrical dynamics, facilitating the development of interventions for morphogenesis, tissue regeneration, and bioelectric pattern control.
Implementation Method 1
calculates a membrane- and an extracellular-ion flux over time, t; and updates, by the processor, the intracellular ion concentration and extracellular ion concentration of each of the cells based on the calculated membrane- and extracellular-ion fluxes
Implementation Method 2
calculates an extracellular ion concentration gradient and a voltage gradient
Implementation Method 3
calculates an extracellular ion concentration gradient and a voltage gradient
Implementation Method 4
calculates a membrane voltage
Implementation Method 5
calculates a voltage sensitive membrane permeability
Implementation Method 6
cells in a tissue are not isolated, but are electrochemically connected (and, therefore, communicating) in several ways, including intracellular channels known as gap junctions
Data Source
AI summary
A differentiable bioelectric tissue simulator is provided. Methods of using a bioelectric tissue simulator are also provided.


