Biology subjects

Munsky, B. E.

Publications and source records attributed to Munsky, B. E..

6 recordsLinked to original sources

bioRxiv2026

The Stochastic System Identification Toolkit (SSIT) to model, fit, predict, and design experiments

Biological data is prone to both intrinsic and extrinsic noise and variability between experimental replicas. That same stochasticity and heterogeneity can carry information about underlying biochemical mechanisms but, if not incorporated in modeling and probabilistic inference, can also bias parameter estimates and misguide predictions and, subsequently, experiment design. Mechanistic inference typically requires lengthy simulations (e.g., the Stochastic Simulation Algorithm (SSA)); approximations to chemical master equation (CME) solutions that lack rigorous error tracking; or deterministic averaging that lacks the complexity necessary to reflect the data. We introduce the Stochastic System Identification Toolkit (SSIT) - a fast, flexible, and open-source software package available on GitHub that makes use of MATLABs efficient and diverse computational architecture. The SSIT is designed for building, simulating, and solving chemical reaction models using ODEs, moments, SSA, Finite State Projection truncations of the CME, or hybrid methods; sensitivity analysis and Fisher information quantification; parameter fitting using likelihood- or Bayesian-based methods; handling of experimental noise and measurement errors using probabilistic distortion operators; and sequential experiment design that empowers users to save time and resources while gaining the most information possible out of their data. The SSIT also offers advanced modeling tools, including model reduction methods for increased efficiency and joint fitting of models and datasets with overlapping reactions or parameters. To facilitate the ease and speed of use, the SSIT provides a graphical user interface and ready-made, adaptable pipelines that can be run in the background from commandline or high-performance computing clusters. We demonstrate features of the SSIT on two experimental datasets: the first consists of published mRNA count data that reflect Saccharomyces cerevisiae yeast cell response to osmotic shock using single-cell single-molecule fluorescence in situ hybridization; the second consists of single-cell RNA sequencing measurements of 151 activating genes in breast cancer cells following treatment with dexamethasone. Author summaryWe present the Stochastic System Identification Toolkit (SSIT) to model, fit, and predict any data that can be interpreted as changing populations or counts through time, including but not limited to single-cell experiments, economics, epidemiology, ecology, sociology, agriculture, and biotechnology. The SSIT was constructed particularly for stochastic modeling, which is important for systems whose states may experience significant fluctuations from mean behavior, thus affecting the inference of the underlying rate parameters and predictions of subsequent behavior. The SSIT provides statistical inference tools for parameter estimation; sensitivity analysis and information calculation; handling of distortions to probability distributions caused by experimental or measurement processes (e.g., dropout in single-cell RNA sequence data and total fluorescence intensities versus spot counting/puncta analysis); and quantitative design of experiments. The SSIT also offers a variety of complex modeling tools, including model reduction methods and fitting of combined models/datasets that share some behavior but remain distinct (e.g., different genes responding a single stimulus). The SSIT generates pipelines for easy, efficient analyses to run in the MATLAB environment, in the background on commandline, or on high-performance computing clusters, thus facilitating users to make informed, time- and cost-effective decisions about their next set of experiments.

bioinformatics↗

bioRxiv2024

The Transcriptional Gradient in Negative-Strand RNA Viruses Suggests a Common RNA Transcription Mechanism

We introduce a novel model of nonsegmented negative-strand RNA virus (NNSV) transcription. Previous models have relied on polymerase behavioral differences in the highly conserved intergenic sequences. Our model hypothesizes the transcriptional gradient in NNSVs is explained through a simple model with two parameters associated with the viral polymerase. Most differences in expression can be attributed to the processivity of the polymerase while additional attenuation occurs in the presence of overlapping genes. This model reveals a correlation between polymerase processivity and genome length, which is consistent with the universal entry of polymerases through the 3 end of the genome. Using this model, it is now possible to predict the transcriptional behavior of NNSVs from genotype alone, revolutionizing the design of novel NNSV variants for biomedical applications.

bioinformatics↗

bioRxiv2021

Designing single-cell experiments to harvest fluctuation noise while rejecting measurement noise

Despite continued technological improvements, measurement errors will always reduce or distort the information that any real experiment can provide to quantify cellular dynamics. This problem becomes even more serious in the context of cell signaling studies that are specifically designed to quantify heterogeneity in single-cell gene regulation, where important RNA and protein copy numbers are themselves subject to the inherently random fluctuations of biochemical reactions. It is not clear how measurement noise should be managed in addition to other experiment design variables (e.g., sampling size, measurement times, or perturbation levels) to ensure that collected data will provide useful insights on signaling or gene expression mechanisms of interest. To address these fundamental single-cell analysis and experiment design challenges, we propose a computational framework that takes explicit consideration of measurement errors to analyze single-cell observations and Fisher Information Matrix-based criteria to decide between experiments. Using simulations and single-cell experiments for a reporter gene controlled by an HIV promoter construct, we demonstrate how our approach can analyze and redesign experiments to optimally harvest fluctuation information while mitigating the effects of image distortion.

systems biology↗

bioRxiv2020

A parallel implementation of the Finite State Projection algorithm for the solution of the Chemical Master Equation

Stochastic reaction networks are a popular modeling framework for biochemical processes that treat the molecular copy numbers within a single cell as a continuous time Markov chain, whose forward Chapman-Kolmogorov equation is known in biochemistry literature as the chemical master equation (CME). The solution of the CME contains extremely useful information that can be compared to experimental data in order to improve the quantitative understanding of biochemical reaction networks within the cell. However, this solution is costly to compute as it requires integrating an enormous system of differential equations that grows exponentially with the number of chemical species. To address this issue, we introduce a novel multiple-sinks Finite State Projection algorithm that approximates the CME with an adaptive sequence of reduced-order models with an effecient parallelization based on MPI. The implementation is tested on models of sizable state spaces using a high-performance computing node on Amazon Web Services, showing favorable scalability.

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↗

bioRxiv2020

Quantifying the spatiotemporal dynamics of IRES versus Cap translation with single-molecule resolution in living cells

Viruses use IRES sequences within their RNA to hijack translation machinery and thereby rapidly replicate in host cells. While this process has been extensively studied in bulk assays, the dynamics of hijacking at the single-molecule level remain unexplored in living cells. To achieve this, we developed a bicistronic biosensor encoding complementary repeat epitopes in two ORFs, one translated in a Cap-dependent manner and the other translated in an IRES-mediated manner. Using a pair of complementary probes that bind the epitopes co-translationally, our biosensor lights up in different colors depending on which ORF is being translated. In combination with single-molecule tracking and computational modeling, we measured the relative kinetics of Cap versus IRES translation and show: (1) Two non-overlapping ORFs can be simultaneously translated within a single mRNA; (2) EMCV IRES-mediated translation sites recruit ribosomes less efficiently than Cap-dependent translation sites but are otherwise nearly indistinguishable, having similar mobilities, sizes, spatial distributions, and ribosomal initiation and elongation rates; (3) Both Cap-dependent and IRES-mediated ribosomes tend to stretch out translation sites; (4) Although the IRES recruits two to three times fewer ribosomes than the Cap in normal conditions, the balance shifts dramatically in favor of the IRES during oxidative and ER stresses that mimic viral infection; and (5) Translation of the IRES is enhanced by translation of the Cap, demonstrating upstream translation can positively impact the downstream translation of a non-overlapping ORF. With the ability to simultaneously quantify two distinct translation mechanisms in physiologically relevant live-cell environments, we anticipate bicistronic biosensors like the one we developed here will become powerful new tools to dissect both canonical and non-canonical translation dynamics with single-molecule precision. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=177 HEIGHT=200 SRC="FIGDIR/small/900829v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@a44259org.highwire.dtl.DTLVardef@651org.highwire.dtl.DTLVardef@19ebc26org.highwire.dtl.DTLVardef@527822_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗