Biology subjects

Fox, Z. R.

Publications and source records attributed to Fox, Z. R..

9 recordsLinked to original sources

bioRxiv2025

Prediction variability in physiologically based pharmacokinetic modeling of tissue disposition under deep uncertainty

Physiologically based pharmacokinetic (PBPK) models are increasingly invoked in virtual screening workflows for therapeutics. These mechanistic models project pharmacokinetic outcomes from molecular properties, with data-driven models acting as intermediaries to map molecular structure to PBPK input parameters. Errors in predicted parameters and unvalidated assumptions within PBPK models expose PK predictions to deep uncertainty. Herein, we examine how these uncertainties affect the prediction variability of dynamic, tissue-specific exposure. We validated four PBPK models against 1,854 experimental datapoints - to establish their predictive fidelity before introducing parameter uncertainty typical of property-prediction models. Depending on molecule properties and model choice, the coefficient of variation under parameter uncertainty ranged from 10-6 to 31 for predicted PK statistics. Further, we identified notable model disagreement for a subset of drug-like chemical space characterized by lipophilic, protonated molecules. Uncertainty quantification revealed biophysicochemical properties and parameter interactions that drove disagreement and highlighted model assumptions that exacerbated prediction variance. Our findings delineate the challenges presented by deep epistemic uncertainty in PBPK modeling.

systems biology↗

bioRxiv2023

A Structured Illumination Microscopy Framework with Spatial-Domain Noise Propagation.

Biological images captured by microscopes are characterized by heterogeneous signal-to-noise ratios (SNRs) due to spatially varying photon emission across the field of view convoluted with camera noise. State-of-the-art unsupervised structured illumination microscopy (SIM) reconstruction algorithms, commonly implemented in the Fourier domain, do not accurately model this noise and suffer from high-frequency artifacts, user-dependent choices of smoothness constraints making assumptions on biological features, and unphysical negative values in the recovered fluorescence intensity map. On the other hand, supervised methods rely on large datasets for training, and often require retraining for new sample structures. Consequently, achieving high contrast near the maximum theoretical resolution in an unsupervised, physically principled, manner remains an open problem. Here, we propose Bayesian-SIM (B-SIM), an unsupervised Bayesian framework to quantitatively reconstruct SIM data, rectifying these shortcomings by accurately incorporating known noise sources in the spatial domain. To accelerate the reconstruction process, we use the finite extent of the point-spread-function to devise a parallelized Monte Carlo strategy involving chunking and restitching of the inferred fluorescence intensity. We benchmark our framework on both simulated and experimental images, and demonstrate improved contrast permitting feature recovery at up to 25% shorter length scales over state-of-the-art methods at both high- and low-SNR. B-SIM enables unsupervised, quantitative, physically accurate reconstruction without the need for labeled training data, democratizing high-quality SIM reconstruction and expands the capabilities of live-cell SIM to lower SNR, potentially revealing biological features in previously inaccessible regimes.

biophysics↗

bioRxiv2023

Using mechanistic models and machine learning to design single-color multiplexed Nascent Chain Tracking experiments

mRNA translation is the ubiquitous cellular process of reading messenger-RNA strands into functional proteins. Over the past decade, large strides in microscopy techniques have allowed observation of mRNA translation at a single-molecule resolution for self-consistent time-series measurements in live cells. Dubbed Nascent chain tracking (NCT), these methods have explored many temporal dynamics in mRNA translation uncaptured by other experimental methods such as ribosomal profiling, smFISH, pSILAC, BONCAT, or FUNCAT-PLA. However, NCT is currently restricted to the observation of one or two mRNA species at a time due to limits in the number of resolvable fluorescent tags. In this work, we propose a hybrid computational pipeline, where detailed mechanistic simulations produce realistic NCT videos, and machine learning is used to assess potential experimental designs for their ability to resolve multiple mRNA species using a single fluorescent color for all species. Through simulation, we show that with careful application, this hybrid design strategy could in principle be used to extend the number of mRNA species that could be watched simultaneously within the same cell. We present a simulated example NCT experiment with seven different mRNA species within the same simulated cell and use our ML labeling to identify these spots with 90% accuracy using only two distinct fluorescent tags. The proposed extension to the NCT color palette should allow experimentalists to access a plethora of new experimental design possibilities, especially for cell signalling applications requiring simultaneous study of multiple mRNAs.

systems biology↗

bioRxiv2021

MicroMator: Open and Flexible Software for Reactive Microscopy

Microscopy image analysis has recently made enormous progress both in terms of accuracy and speed thanks to machine learning methods. This greatly facilitates the online adaptation of microscopy experimental plans using real-time information of the observed systems and their environments. Here we report MicroMator, an open and flexible software for defining and driving reactive microscopy experiments, and present applications to single-cell control and single-cell recombination.

systems biology↗

bioRxiv2020

Diverse cell stimulation kinetics identify predictive signal transduction models

The drive to understand cell signaling responses to environmental, chemical and genetic perturbations has produced outstanding fits of computational models to increasingly intricate experiments, yet predicting quantitative responses for new biological conditions remains challenging. Overcoming this challenge depends not only on good models and detailed experimental data but perhaps more so on how well the two are integrated. Our quantitative, live single-cell fluorescence imaging datasets and computational framework to model generic signaling networks show how different changing environments (hereafter kinetic stimulations) probe and result in distinct pathway activation dynamics. Utilizing multiple diverse kinetic stimulations better constrains model parameters and enables predictions of signaling dynamics that would be impossible using traditional step-change stimulations. To demonstrate our approachs generality, we use identified models to predict signaling dynamics in normal, mutated, and drug-treated conditions upon multitudes of kinetic stimulations and quantify which proteins and reaction rates are most sensitive to which extracellular stimulations.

systems biology↗

bioRxiv2019

Optimal Design of Single-Cell Experiments within Temporally Fluctuating Environments

Modern biological experiments are becoming increasingly complex, and designing these experiments to yield the greatest possible quantitative insight is an open challenge. Increasingly, computational models of complex stochastic biological systems are being used to understand and predict biological behaviors or to infer biological parameters. Such quantitative analyses can also help to improve experiment designs for particular goals, such as to learn more about specific model mechanisms or to reduce prediction errors in certain situations. A classic approach to experiment design is to use the Fisher information matrix (FIM), which quantifies the expected information a particular experiment will reveal about model parameters. The Finite State Projection based FIM (FSP-FIM) was recently developed to compute the FIM for discrete stochastic gene regulatory systems, whose complex response distributions do not satisfy standard assumptions of Gaussian variations. In this work, we develop the FSP-FIM analysis for a stochastic model of stress response genes in S. cerevisae under time-varying MAPK induction. We verify this FSP-FIM analysis and use it to optimize the number of cells that should be quantified at particular times to learn as much as possible about the model parameters. We then extend the FSP-FIM approach to explore how different measurement times or genetic modifications help to minimize uncertainty in the sensing of extracellular environments, and we experimentally validate the FSP-FIM to rank single-cell experiments for their abilities to minimize estimation uncertainty of NaCl concentrations during yeast osmotic shock. This work demonstrates the potential of quantitative models to not only make sense of modern biological data sets, but to close the loop between quantitative modeling and experimental data collection.

systems biology↗

bioRxiv2019

Computational design and interpretation of live-cell, single-RNA translation experiments

Advances in fluorescence microscopy have introduced new assays to quantify live-cell translation dynamics at single-RNA resolution. We introduce a detailed, yet efficient sequence-based stochastic model that generates realistic synthetic data for several such assays, including Fluorescence Correlation Spectroscopy (FCS), ribosome Run-Off Assays (ROA) after Harringtonine application, and Fluorescence Recovery After Photobleaching (FRAP). We simulate these experiments under multiple imaging conditions and for thousands of human genes, and we evaluate through simulations which experiments are most likely to provide accurate estimates of elongation kinetics. Finding that FCS analyses are optimal for both short and long length genes, we integrate our model with experimental FCS data to capture the nascent protein statistics and temporal dynamics for three human genes: KDM5B, {beta}-actin, and H2B. Finally, we introduce a new open-source software package, RNA Sequence to NAscent Protein Simulator (O_SCPCAPRC_SCPCAPSNAPO_SCPCAPSIMC_SCPCAP), to easily simulate the single-molecule translation dynamics of any gene sequence for any of these assays and for different assumptions regarding synonymous codon usage, tRNA level modifications, or ribosome pauses. O_SCPCAPRC_SCPCAPSNAPO_SCPCAPSIMC_SCPCAP is implemented in Python and is available at: https://github.com/MunskyGroup/rSNAPsim.git.\n\nAuthor summaryTranslation is an essential step in which ribosomes decipher mRNA sequences to manufacture proteins. Recent advances in time-lapse fluorescence microscopy allow live-cell quantification of translation dynamics at the resolution of single mRNA molecules. Here, we develop a flexible computational framework to reproduce and interpret such experiments. We use this framework to explore how well different single-mRNA translation experiment designs would perform to estimate key translation parameters. We then integrate experimental data from the most flexible design with our stochastic model framework to reproduce the statistics and temporal dynamics of nascent protein elongation for three different human genes. Our validated computational method is packaged with a simple graphical user interface that (1) starts with mRNA sequences, (2) generates discrete, codon-dependent translation models, (3) provides visualization of ribosome movement as trajectories or kymographs, and (4) allows the user to estimate how optical single-mRNA translation experiments would be affected by different genetic alterations (e.g., codon substitutions) or environmental perturbations (e.g., tRNA titrations or drug treatments).

systems biology↗

bioRxiv2018

Bayesian estimation for stochastic gene expression using multifidelity models

The finite state projection (FSP) approach to solving the chemical master equation has enabled successful inference of discrete stochastic models to predict single-cell gene regulation dynamics. Unfortunately, the FSP approach is highly computationally intensive for all but the simplest models, an issue that is highly problematic when parameter inference and uncertainty quantification takes enormous numbers of parameter evaluations. To address this issue, we propose two new computational methods for the Bayesian inference of stochastic gene expression parameters given single-cell experiments. We formulate and verify an Adaptive Delayed Acceptance Metropolis-Hastings (ADAMH) algorithm to utilize with reduced Krylov-basis projections of the FSP. We then introduce an extension of the ADAMH into a Hybrid scheme that consists of an initial phase to construct a reduced model and a faster second phase to sample from the approximate posterior distribution determined by the constructed model. We test and compare both algorithms to an adaptive Metropolis algorithm with full FSP-based likelihood evaluations on three example models and simulated data to show that the new ADAMH variants achieve substantial speedup in comparison to the full FSP approach. By reducing the computational costs of parameter estimation, we expect the ADAMH approach to enable efficient data-driven estimation for more complex gene regulation models.

systems biology↗

bioRxiv2018

The finite state projection based Fisher information matrix approach to estimate and maximize the information in single-cell experiments

Modern optical imaging experiments not only measure single-cell and single-molecule dynamics with high precision, but they can also perturb the cellular environment in myriad controlled and novel settings. Techniques, such as single-molecule fluorescence in-situ hybridization, microfluidics, and optogenetics, have opened the door to a large number of potential experiments, which begs the question of how best to choose the best possible experiment. The Fisher information matrix (FIM) estimates how well potential experiments will constrain model parameters and can be used to design optimal experiments. Here, we introduce the finite state projection (FSP) based FIM, which uses the formalism of the chemical master equation to derive and compute the FIM. The FSP-FIM makes no assumptions about the distribution shapes of single-cell data, and it does not require precise measurements of higher order moments of such distributions. We validate the FSP-FIM against well-known Fisher information results for the simple case of constitutive gene expression. We then use numerical simulations to demonstrate the use of the FSP-FIM to optimize the timing of single-cell experiments with more complex, non-Gaussian fluctuations. We validate optimal simulated experiments determined using the FSP-FIM with Monte-Carlo approaches and contrast these to experiment designs chosen by traditional analyses that assume Gaussian fluctuations or use the central limit theorem. By systematically designing experiments to use all of the measurable fluctuations, our method enables a key step to improve co-design of experiments and quantitative models.\n\nAuthor summaryA main objective of quantitative modeling is to predict the behaviors of complex systems under varying conditions. In a biological context, stochastic fluctuations in expression levels among isogenic cell populations have required modeling efforts to incorporate and even rely upon stochasticity. At the same time, new experimental variables such as chemical induction and optogenetic control have created vast opportunities to probe and understand gene expression, even at single-molecule and single-cell precision. With many possible measurements or perturbations to choose from, researchers require sophisticated approaches to choose which experiment to perform next. In this work, we provide a new tool, the finite state projection based Fisher information matrix (FSP-FIM), which considers all cell-to-cell fluctuations measured in modern data sets, and can design optimal experiments under these conditions. Unlike previous approaches, the FSP-FIM does not make any assumptions about the shape of the distribution being measured. This new tool will allow experimentalists to optimally perturb systems to learn as much as possible about single-cell processes with a minimum of experimental cost or effort.

systems biology↗