• Corpus ID: 244709358

Optimal Design of Experiments for Simulation-Based Inference of Mechanistic Acyclic Biological Networks

@inproceedings{Zaballa2021OptimalDO,
  title={Optimal Design of Experiments for Simulation-Based Inference of Mechanistic Acyclic Biological Networks},
  author={Vincent Zaballa and Elliot E Hui},
  year={2021}
}
Biological signaling pathways based upon proteins binding to one another to relay a signal for genetic expression, such as the Bone Morphogenetic Protein (BMP) signaling pathway, can be modeled by mass action kinetics and conservation laws that result in non-closed form polynomial equations. Accurately determining parameters of biological pathways that represent physically relevant features, such as binding affinity of proteins and their associated uncertainty, presents a challenge for… 

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References

SHOWING 1-10 OF 30 REFERENCES
A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation
TLDR
An approximate Bayesian computation framework and software environment, ABC-SysBio, which is a Python package that runs on Linux and Mac OS X systems and that enables parameter estimation and model selection in the Bayesian formalism by using sequential Monte Carlo (SMC) approaches is presented.
MASSpy: Building, simulating, and visualizing dynamic biological models in Python using mass action kinetics
TLDR
The Mass Action Stoichiometric Simulation Python (MASSpy) package is presented, an open-source computational framework for dynamic modeling of metabolism based on the mass action kinetics for each elementary step in an enzymatic reaction mechanism that enables the construction, simulation, and visualization of dynamic metabolic models.
Gradient-based Bayesian Experimental Design for Implicit Models using Mutual Information Lower Bounds
TLDR
A framework for Bayesian experimental design with implicit models, where the data-generating distribution is intractable but sampling from it is still possible, is introduced and a comprehensive empirical comparison of prominent lower bounds applied to the aforementioned tasks is provided.
Bayesian Optimization for Likelihood-Free Inference of Simulator-Based Statistical Models
TLDR
This paper proposes a strategy which combines probabilistic modeling of the discrepancy with optimization to facilitate likelihood-free inference and is shown to accelerate the inference through a reduction in the number of required simulations by several orders of magnitude.
Ligand-receptor promiscuity enables cellular addressing
TLDR
A general mathematical modeling framework based on the bone morphogenetic protein (BMP) pathway architecture finds that promiscuously interacting ligand-receptor systems allow a small number of ligands, acting in combinations, to address a larger number of individual cell types, each defined by its receptor expression profile.
Optimal experimental design for mathematical models of haematopoiesis
TLDR
This work has developed a novel Bayesian hierarchical framework for optimal design of perturbation experiments and proper analysis of the data collected, and uses a deterministic model that accounts for feedback and feedforward regulation on cell division rates and self-renewal probabilities.
Bayesian reaction optimization as a tool for chemical synthesis.
TLDR
The development of a framework for Bayesian reaction optimization and an open-source software tool that allows chemists to easily integrate state-of-the-art optimization algorithms into their everyday laboratory practices are reported, demonstrating that Bayesian optimization outperforms human decisionmaking in both average optimization efficiency and consistency.
Thermodynamic Analysis of Interacting Nucleic Acid Strands
TLDR
This dynamic program is based on a rigorous extension of secondary structure models to the multistranded case, addressing representation and distinguishability issues that do not arise for single-stranded structures.
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