Computational GRN Training for Non-Invasive Drug Response Prediction
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Solution Overview
Problem
Current approaches in biomedicine for controlling gene regulatory networks (GRNs) are limited by the need for physical rewiring or genomic editing, which is impractical and invasive, and there is a lack of understanding of how GRNs can change their behavior based on prior experiences or memories.
Innovation Solution
A computational approach is taken to treat GRNs as learning agents, using a software suite to identify and manipulate memory types in GRNs, allowing for the prediction of drug responses and the development of stimuli protocols to train GRNs for specific dynamics without physical rewiring or gene therapy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If physical rewiring or genomic editing is used to control GRNs, then the ability to change GRN behavior is improved, but the invasiveness and practicality worsen
Solution Approach 1:
The patent replaces physical/genetic manipulation (mechanical-biological intervention) with computational stimulation protocols. Instead of physically rewiring or editing genes, the system uses patterned stimuli to train GRNs to learn and adapt their behavior, substituting invasive biological intervention with non-invasive computational control.
Solution Approach 2:
The patent introduces computational models and software suites as intermediaries between the researcher and the GRN. These computational tools simulate and predict GRN behavior, allowing indirect control through identified stimulation patterns rather than direct physical intervention in the biological system.
2Reliability
If chronic drug exposure is used to treat diseases, then the therapeutic effect is improved, but the side effects and drug tolerance worsen
Solution Approach 1:
The patent applies periodic or pulsed drug administration patterns instead of continuous chronic exposure. By training GRNs with specific temporal stimulation patterns, the system identifies optimized dosing schedules that maintain therapeutic effects while reducing cumulative side effects and preventing drug tolerance.
Solution Approach 2:
The patent uses computational modeling to predict and prepare optimized drug regimens before actual treatment. The software suite simulates GRN responses to various dosing patterns in advance, allowing selection of treatment protocols that minimize side effects before administering drugs to patients.
3Adaptability or versatility
If individualized treatment is provided based on GRN memory properties, then the treatment effectiveness is improved, but the complexity of prediction and analysis worsens
Solution Approach 1:
The patent creates computational copies or models of individual patients' GRNs. These virtual replicas simulate and predict how each patient's unique GRN will respond to different treatments, allowing individualized medicine without requiring direct complex experimentation on each patient's biological system.
Solution Approach 2:
The patent develops a universal software suite that can analyze and predict GRN behavior across different diseases and patients. This multi-functional tool handles various types of GRN analysis, memory property detection, and treatment optimization in a single integrated platform, reducing overall system complexity.
Data Source
AI summary
Disclosed are methods for exploiting memory properties of biological systems such as gene regulatory networks (GNRs). The disclosed methods may be utilized in order to treat diseases and disorders and in order to promote health.