In Silico Model Parameter Estimation for Biological Systems
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Solution Overview
Problem
Current methods for modeling biological systems face challenges in accurately predicting behavior due to complex parameter dependencies and limitations in experimental data, particularly in measuring and accessing all relevant parameters, leading to biased models and a need for more robust and reliable data for accurate simulation of large models like whole cell systems.
Innovation Solution
A scalable experimental workflow using a culture system to maintain a steady state in a biological system, where measurement data is obtained at a physiological steady state with a substantially constant growth rate, allowing for the determination of parameter values for an in silico model, enabling accurate parameterization and simulation of biological systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physical experiments are conducted to obtain parameter values for biological models, then experimental data can be collected, but measurement accuracy and reliability are limited due to complex parameter dependencies and inaccessibility of many relevant parameters
Solution Approach 1:
The patent creates a computational copy (in silico model) of the biological system that replicates its behavior. This virtual model allows measurement of any parameter through simulation, eliminating the physical measurement limitations. The model is trained on available experimental data and then used to predict values for parameters that are difficult or impossible to measure directly in physical experiments.
2Reliability
If more parameters are included in the biological model to improve accuracy, then model predictive capability increases, but model complexity and difficulty of parameter estimation increase
Solution Approach 1:
Rather than simplifying the biological model, the patent creates a parallel computational model that can handle the full complexity. The in silico model is trained on experimental data and uses machine learning techniques to learn relationships between parameters, enabling it to predict values for complex models without requiring direct measurement of all parameters.
Solution Approach 2:
The patent replaces traditional mechanical/experimental measurement systems with a computational approach. Instead of using physical experiments and mathematical fitting methods to estimate parameters, the system uses a trained neural network model that can predict parameter values directly from available data, substituting computational intelligence for physical measurement and mathematical optimization.
3Adaptability or versatility
If experimental measurements are taken under varying growth conditions, then more biological scenarios can be covered, but data consistency and reliability decrease due to growth phase and environmental variations
Solution Approach 1:
The patent creates a dynamic model that can adapt to different growth conditions and phases. The in silico model is trained on data from various conditions and can predict parameters for any growth scenario. The model dynamically adjusts its predictions based on the input conditions, allowing versatile application across different experimental scenarios while maintaining consistency through the unified computational framework.
Data Source
AI summary
The present disclosure relates to a scalable experimental workflow that uses a culture system to maintain a steady state in a biological system, and techniques for identifying values for parameters in a in silico model based on experimental data obtained from the biological system. Particularly, aspects of the present disclosure are directed to obtaining measurement data for one or more characteristics of a biological system developed in a culture system, where the measurement data is indicative of each of the one or more characteristics at a physiological steady state where growth of the biological system is occurring at a substantially constant growth rate, determining a value for a parameter of a model of the biological system based on an growth formula, the measurement data, and the substantially constant growth rate, and parametrizing the model with at least the value determined for the parameter.