ML Inference for Biological Model Parameter Estimation
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Solution Overview
Problem
Biological models with non-closed form polynomial equations, such as the Bone Morphogenetic Protein (BMP) signaling pathway, face challenges in accurately determining parameters like binding affinity due to the lack of an explicit likelihood function, requiring extensive data from expensive perturbation-response experiments.
Innovation Solution
The use of machine learning algorithms, specifically the Simulation-Based Inference Design Of Experiment for Biological Mechanistic Acyclic Networks (SBIDOEMAN) algorithm, for automated biological model selection and likelihood-free inference, which trains machine learning models to estimate mutual information and design optimal experiments to identify and rank compounds modulating targeted cellular processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional perturbation-response experiments are used to fit model parameters, then parameter accuracy can be improved, but experimental cost and data requirements increase significantly
Solution Approach 1:
The patent replaces traditional mechanical/statistical parameter fitting methods with machine learning models that learn parameter distributions directly from limited experimental data. The ML approach substitutes conventional optimization algorithms, enabling accurate parameter estimation without requiring extensive perturbation-response data sets.
Solution Approach 2:
The patent uses simulation-based inference where virtual copies of the biological system are generated through computational models. These simulated data copies allow the system to learn parameter relationships without requiring numerous physical experiments, thereby reducing actual data collection needs while maintaining estimation accuracy.
2Reliability
If extensive perturbation-response experiments are conducted to parameterize biological models, then model accuracy improves, but time and resource consumption increase
Solution Approach 1:
The patent performs preliminary computational work by pre-training machine learning models on simulated data and pre-characterizing system behavior through computational experiments. This preliminary action reduces the need for extensive iterative wet-lab experiments, as the ML model can make informed predictions and guide subsequent experimental design with fewer iterations.
Solution Approach 2:
The patent introduces machine learning models as an intermediary between theoretical biological models and experimental data. This intermediary layer translates limited experimental observations into comprehensive parameter estimates, reducing the number of direct experimental iterations needed while maintaining model reliability.
3Adaptability or versatility
If traditional model selection methods are used without a priori information, then unbiased model comparison is achieved, but computational complexity and data requirements increase
Solution Approach 1:
The patent replaces traditional statistical model selection criteria (like AIC or BIC) with machine learning-based model comparison approaches. The ML framework substitutes conventional information-theoretic methods, providing robust model selection that does not rely on strong a priori assumptions while managing computational complexity through efficient training procedures.
Data Source
AI summary
This disclosure provides methods for optimal inference and design of experiments for mechanistic biological models to identify and/or rank compounds or agents that modulate a targeted cellular biological process to a statistically significant degree.