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Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging
Published on: October 4, 2024
L1 regularization facilitates detection of cell type-specific parameters in dynamical systems
Bernhard Steiert1, Jens Timmer2, Clemens Kreutz3
1Institute of Physics.
Motivation:
A major goal of drug development is to selectively target certain cell types. Cellular decisions influenced by drugs are often dependent on the dynamic processing of information. Selective responses can be achieved by differences between the involved cell types at levels of receptor, signaling, gene regulation or further downstream. Therefore, a systematic approach to detect and quantify cell type-specific parameters in dynamical systems becomes necessary.
Results:
Here, we demonstrate that a combination of nonlinear modeling with L1 regularization is capable of detecting cell type-specific parameters. To adapt the least-squares numerical optimization routine to L1 regularization, sub-gradient strategies as well as truncation of proposed optimization steps were implemented. Likelihood-ratio tests were used to determine the optimal regularization strength resulting in a sparse solution in terms of a minimal number of cell type-specific parameters that is in agreement with the data. By applying our implementation to a realistic dynamical benchmark model of the DREAM6 challenge we were able to recover parameter differences with an accuracy of 78%. Within the subset of detected differences, 91% were in agreement with their true value. Furthermore, we found that the results could be improved using the profile likelihood. In conclusion, the approach constitutes a general method to infer an overarching model with a minimum number of individual parameters for the particular models.
Availability And Implementation:
A MATLAB implementation is provided within the freely available, open-source modeling environment Data2Dynamics. Source code for all examples is provided online at http://www.data2dynamics.org/
Contact:
bernhard.steiert@fdm.uni-freiburg.de.
Insights
This study presents a new computational method using nonlinear modeling and L1 regularization to identify cell type-specific parameters in dynamic systems. This approach accurately detects differences crucial for targeted drug development.
Area of Science:
- Systems Biology
- Computational Biology
- Pharmacology
Background:
- Drug development aims to target specific cell types, requiring understanding of cellular information processing.
- Cellular responses to drugs depend on dynamic processes and cell-type specific differences in receptors, signaling, and gene regulation.
- A systematic method is needed to detect and quantify cell type-specific parameters in dynamical systems.
Purpose of the Study:
- To develop and validate a computational approach for identifying cell type-specific parameters in dynamical systems.
- To enable precise quantification of molecular differences between cell types for targeted drug development.
Main Methods:
- Nonlinear modeling combined with L1 regularization for parameter detection.
- Implementation of sub-gradient strategies and truncation for optimization.
- Likelihood-ratio tests and profile likelihood for determining regularization strength and improving accuracy.
Main Results:
- The method accurately recovered parameter differences (78% accuracy) in a benchmark model.
- Within detected differences, 91% agreed with true values.
- The approach yields a sparse solution with a minimal set of cell type-specific parameters.
Conclusions:
- The developed approach provides a general method for inferring overarching models with minimal individual parameters.
- This technique aids in understanding and exploiting cell type-specific differences for targeted therapies.
- The MATLAB implementation is available in the open-source Data2Dynamics environment.
