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

V Zaballa, E Hui - arXiv preprint arXiv:2111.13612, 2021 - arxiv.org
Quantitative Biology paper q-bio.QM Suggest

… LFI methods, also known as simulation-based inference (SBI), were recently benchmarked on various tasks and settings, and demonstrated reliably more efficient and effective in …

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BibTeX

@article{2111.13612v1,
Author        = {Vincent Zaballa and Elliot Hui},
Title         = {Optimal Design of Experiments for Simulation-Based Inference of
  Mechanistic Acyclic Biological Networks},
Eprint        = {2111.13612v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {q-bio.QM},
Abstract      = {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 biological models lacking an
explicit likelihood function. Additionally, parameterizing non-closed form
biological models requires copious amounts of data from expensive
perturbation-response experiments to fit model parameters. We present an
algorithm (SBIDOEMAN) for determining optimal experiments and parameters of
systems biology models with implicit likelihoods. We evaluate our algorithm
using simulations of held-out true parameter values and demonstrate an
improvement in the rate of accurate parameter inference over random and
equidistant experimental designs when evaluated on two simple models of the BMP
signaling pathway with an implicit likelihood function.},
Year          = {2021},
Month         = {Nov},
Url           = {http://arxiv.org/abs/2111.13612v1},
File          = {2111.13612v1.pdf}
}

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