Computational Network Models for Quantifying Biological Perturbation Impact
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for assessing the impact of perturbations on biological systems are inadequate, as they lack predictive capabilities for long-term disease outcomes and often rely on unreliable animal testing or unethical human studies, failing to account for complex interactions within biological systems.
Innovation Solution
A computerized method that utilizes computational network models to quantify the response of biological systems to perturbations by generating biological impact factors (BIFs) based on measured activity data, incorporating multiple datasets and network models to represent interactions between biological entities and their relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If animal testing methods are used to assess biological perturbations, then experimental data can be obtained, but reliability and relevance to human biology deteriorate due to physiological differences between species
Solution Approach 1:
The patent uses in silico computational models that copy and simulate human biological systems, replacing animal testing with virtual human models. These computational models replicate human physiology and response to perturbations, eliminating species differences while maintaining reliability and human relevance.
Solution Approach 2:
The patent replaces the mechanical/biological animal testing system with an in silico computational system. By substituting physical animal experiments with computer-based simulations, the method eliminates the need for animal subjects while preserving the ability to assess perturbations reliably in a human-relevant context.
2Ease of manufacture
If in vitro cell and tissue-based methods are used, then animal testing can be reduced, but measurement of complex biological interactions deteriorates due to limited scope of specific cell and tissue mechanisms
Solution Approach 1:
The patent merges multiple in silico models representing different biological systems, pathways, and mechanisms into an integrated computational framework. This integration captures complex biological interactions that would be missed by isolated in vitro studies, while maintaining the ease and ethics of computational approaches.
Solution Approach 2:
The patent creates a universal in silico platform that can model diverse biological systems, from molecular pathways to organ-level functions. This multi-functional computational system replaces the need for multiple specialized in vitro experiments, capturing complex interactions across different biological scales while maintaining ease of implementation.
3Reliability
If traditional longitudinal epidemiological studies are used for risk assessment, then comprehensive human data can be collected, but ethical challenges and time requirements worsen due to decades-long study durations
Solution Approach 1:
The patent performs preliminary computational experiments in silico to predict long-term disease outcomes from short-term perturbation data. By conducting virtual studies that simulate decades of biological response in computational time, the method eliminates the need for lengthy longitudinal studies while maintaining reliable risk assessment.
Solution Approach 2:
The patent uses computational models that copy and accelerate the simulation of long-term biological processes. Instead of waiting decades for actual longitudinal data, the in silico system replicates decades of biological response in minutes or hours, eliminating time loss while preserving assessment reliability.
4Measurement precision
If phenotype-derived signatures are used for disease classification, then classification power is improved, but mechanistic understanding deteriorates due to lack of causal relationship between perturbations and signatures
Solution Approach 1:
The patent uses feedback from the in silico model to trace the causal chain from perturbation to phenotype. The computational system tracks how specific perturbations propagate through biological pathways to produce observed signatures, providing mechanistic understanding that complements the classification precision of phenotype-derived approaches.
Data Source
AI summary
Systems and methods are described for quantifying the response of a biological system to one or more perturbations. First and second datasets corresponding to a response of a biological system to first and second treatments are received. A plurality of computational network models that represent the biological system are provided, each model including nodes representing a plurality of biological entities and edges representing relationships between the nodes in the model. A first set of scores is generated, representing the perturbation of the biological system based on the first dataset and the plurality of models, and a second set of scores representing the perturbation of the biological system based on the second dataset and the plurality of computational models. One or more biological impact factors are generated based on each of the first set and second set of scores that represent the biological impact of the perturbation on the biological system.