Biology subjects

Munsky, B.

Publications and source records attributed to Munsky, B..

At least 19 recordsLinked to original sources

bioRxiv2026

Integrating Single-Cell Experiments and Stochastic Models to Understand and Predict Glucocorticoid Receptor Transport and DUSP1 mRNA Expression Dynamics

Glucocorticoids activate the glucocorticoid receptor (GR) to suppress inflammation, yet it remains unclear how GR transport dynamics and downstream gene regulation are coordinated within single cells. We combine immunocytochemistry (ICC) and single-molecule fluorescence in situ hybridization (smFISH) to quantify endogenous GR transport and DUSP1 transcription dynamics across thousands of individual cells following dexamethasone (Dex) stimulation. Performing multiple rounds of statistical inference based on Chemical Master Equations (CME), we determine the most likely mechanisms and reaction rates for Dex-driven GR nuclear import; compartment-specific GR degradation; GR-dependent control of the DUSP1 promoter; and DUSP1 transcription, elongation, transport, and degradation. Our inferred model suggests that nuclear GR degradation is the dominant mechanism of receptor clearance, that GR primarily regulates promoter activation, and that time-dependent AU-rich element (ARE)-mediated mRNA degradation contributes heavily to DUSP1 clearance. With these mechanisms, the fully-parameterized model quantitatively predicts joint distributions of GR translocation and decay dynamics, DUSP1 transcription site activity, and nuclear and cytoplasmic DUSP1 mRNA heterogeneity among clonal cells as functions of time and across seven orders of magnitude for Dex induction concentrations. Our results establish an integrated quantitative framework to link receptor dynamics to gene expression heterogeneity and predict single-cell hormone-responsive transcription programs.

cell biology↗

bioRxiv2025

MicroLive: An Image Processing Toolkit for Quantifying Live-cell Single-Molecule Microscopy

Advances in live-cell fluorescence microscopy have enabled us to visualize single molecules (such as mRNAs and nascent proteins) in real time with high spatiotemporal resolution. However, these experiments generate large datasets that require complex computational processing pipelines to derive meaningful and quantitative information, which is a technical barrier for many researchers. To address this barrier, here, we introduce MicroLive, an open-source Python-based application for quantifying live-cell microscopy images. MicroLive provides an interactive Graphical User Interface (GUI) to perform key tasks, including cell segmentation, photo-bleaching correction, single-particle detection/tracking, spot intensity quantification, inter-channel colocalization, and time-series correlation analysis. As a ground-truth testing dataset, we used synthetic live-cell imaging data generated with the rSNAPed toolkit, demonstrating accurate extraction of biologically relevant parameters. Microscopy images of U-2 OS cells expressing a gene construct smHA-KDM5B-BoxB-MS2 were used to demonstrate the use of this software. Availability and implementationMicroLive is distributed under a GPLv3 license and available on GitHub. https://github.com/ningzhaoAnschutz/microlive. Contactning.zhao@cuanschutz.edu.

biophysics↗

bioRxiv2024

Sequential design of single-cell experiments to identify discrete stochastic models for gene expression.

Control of gene regulation requires quantitatively accurate predictions of heterogeneous cellular responses. When inferred from single-cell experiments, discrete stochastic models can enable such predictions, but such experiments are highly adjustable, allowing for almost infinitely many potential designs (e.g., at different induction levels, for different measurement times, or considering different observed biological species). Not all experiments are equally informative, experiments are time-consuming or expensive to perform, and research begins with limited prior information with which to construct models. To address these concerns, we developed a sequential experiment design strategy that starts with simple preliminary experiments and then integrates chemical master equations to compute the likelihood of single-cell data, a Bayesian inference procedure to sample posterior parameter distributions, and a finite state projection based Fisher information matrix to estimate the expected information for different designs for subsequent experiments. Using simulated then real single-cell data, we determined practical working principles to reduce the overall number of experiments needed to achieve predictive, quantitative understanding of single-cell responses.

systems biology↗

bioRxiv2023

Exploiting Intrinsic Noise for Heterogeneous Cell Control Under Time Delays and Model Uncertainties

The majority of previous research in synthetic biology has focused on enabling robust control performance despite the presence of noise, while the understanding for how controllers may exploit that noise remains incomplete. Motivated by Maxwells Demon, we previously proposed a cellular control regime in which the exploitation of stochastic noise can break symmetry between and allow for specific control of multiple cells using a single input signal (i.e., single-input-multiple-output or SIMO control). The current work extends that analysis to include uncertain stochastic systems where system dynamics are are affected by time delays, intrinsic noises, and model uncertainty. We find that noise-exploiting controllers can remain highly effective despite coarse approximations to the models scale or incorrect estimations or extrinsic noise in key model parameters, and these controllers can even retain performance under substantial observer or actuator time delays. We also demonstrate how SIMO controllers could drive multi-cell systems to follow different trajectories with different phases and frequencies. Together, these findings suggest that noise-exploiting control should be possible even in the practical case where models are always approximate, where parameters are always uncertain, and where observations are corrupted by errors.

synthetic biology↗

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↗

bioRxiv2022

Optogenetic Maxwell Demon to Exploit Intrinsic Noise and Control Cell Differentiation Despite Time Delays and Extrinsic Variability

The field of synthetic biology focuses on creating modular components which can be used to generate complex and controllable synthetic biological systems. Unfortunately, the intrinsic noise of gene regulation can be large enough to break these systems. Noise is largely treated as a nuisance and much past effort has been spent to create robust components that are less influenced by noise. However, extensive analysis of noise combined with smart microscopy tools and optognenetic actuators can create control opportunities that would be difficult or impossible to achieve in the deterministic setting. In previous work, we proposed an Optogenetic Maxwells Demons (OMD) control problem and found that deep understanding and manipulation of noise could create controllers that break symmetry between cells, even when those cells share the same optogenetic input and identical gene regulation circuitry. In this paper, we extend those results to analyze (in silico) the robustness of the OMD control under changes in system volume, with time observation/actuation delays, and subject to parametric model uncertainties.

synthetic biology↗

bioRxiv2022

Biochemical noise enables a single optogenetic input to control identical cells to track asymmetric and asynchronous reference signals

Optogenetics is a powerful technology to control synthetic gene circuits using external and computer-programmable light inputs. Like all biological processes, these systems are subject to intrinsic noise that arises from the stochastic process of gene regulation at the single-cell level. Many engineers have sought to mitigate this noise by developing more complex embedded bio-circuits, but recent work has shown that noise-exploiting stochastic controllers could enable new control strategies that take advantage of noise, rather than working against it. These noise-exploiting controllers were initially proposed to solve a single-input-multi-output stationary control problem, where symmetry was broken in a means reminiscent to the concept of Maxwells Demon. In this paper, we extend those results and show through computation that transient, asymmetric, and asynchronous stochastic control of the single-input-multi-output (SIMO) control problem is posible to achieve by cycling through different controllers in time. We show that such a method is able control two cells to two different periodic fates with different frequencies and different phases despite the use of only one control input.

synthetic biology↗

bioRxiv2021

Exploiting noise, nonlinearity, and feedback to differentially control multiple synthetic cells with a single optogenetic input

Synthetic biology seeks to develop modular bio-circuits that combine to produce complex, controllable behaviors. These designs are often subject to noisy fluctuations and uncertainties, and most modern synthetic biology design processes have focused to create robust components to mitigate the noise of gene expression and reduce the heterogeneity of single-cell responses. However, deeper understanding of noise can achieve control goals that would otherwise be impossible. We explore how an "Optogenetic Maxwell Demon" could selectively amplify noise to control multiple cells using single-input-multiple-output (SIMO) feedback. Using data-constrained stochastic model simulations and theory, we show how an appropriately selected stochastic SIMO controller can drive multiple different cells to different user-specified configurations irrespective of initial condition. We explore how controllability depends on cells regulatory structures, the amount of information available to the controller, and the accuracy of the model used. Our results suggest that gene regulation noise, when combined with optogenetic feedback and non-linear biochemical auto-regulation, can achieve synergy to enable precise control of complex stochastic processes.

synthetic biology↗

bioRxiv2020

Visualization and Modeling of Inhibition of IL-1β and TNFα mRNA Transcription at the Single-Cell Level

IL-1{beta} and TNF- are canonical immune response mediators that play key regulatory roles in a wide range of inflammatory responses to both chronic and acute conditions. Here we employ an automated microscopy platform for the analysis of messenger RNA (mRNA) expression of IL-1{beta} and TNF- at the single-cell level. The amount of IL-1{beta} and TNF- mRNA expressed in a human monocytic leukemia cell line (THP-1) is visualized and counted using single-molecule fluorescent in-situ hybridization (smFISH) following exposure of the cells to lipopolysaccharide (LPS), an outer-membrane component of Gram-negative bacteria. We show that the small molecule inhibitors MG132 (a 26S proteasome inhibitor used to block NF-{kappa}B signaling) and U0126 (a MAPK Kinase inhibitor used to block CCAAT-enhancer-binding proteins C/EBP) successfully block IL-1{beta} and TNF- mRNA expression. Based upon this single-cell mRNA expression data, we screened 36 different mathematical models of gene expression, and found two similar models that capture the effects by which the drugs U0126 and MG132 affect the rates at which the genes transition into highly activated states. When their parameters were informed by the action of each drug independently, both models were able to predict the effects of the combined drug treatment. From our data and models, we postulate that IL-1{beta} is activated by both NF-{kappa}B and C/EBP, while TNF- is predominantly activated by NF-{kappa}B. Our combined single-cell experimental modeling efforts shows the interconnection between these two genes and demonstrates how the single-cell responses, including the distribution shapes, mean expression, and kinetics of gene expression, change with inhibition.

systems biology↗

bioRxiv2020

Probabilistic Ranking Of Microbiomes with Taxa Selection to discover and validate microbiome function models for multiple litter decomposition studies

The overwhelming complexity of microbiomes makes it difficult to decipher functional relationships between specific microbes and ecosystem properties. While machine learning analyses have demonstrated an impressive ability to correlate microbial community composition with macroscopic functions, mechanisms that dictate model predictions are often unknown, and predictions often lack an assigned metric of uncertainty. In this study, we apply Bayesian networks to build on prior feature selection analyses and construct easy-to-interpret probabilistic models, which accurately predict levels of dissolved organic carbon (DOC) from the relative abundance of soil bacteria (16S rRNA gene profiles). In addition to standard cross-validation, we show that a Bayesian network model trained using samples from a pine litter decomposition study accurately predicts DOC of samples from an independent oak litter decomposition study, suggesting that mechanisms driving variation in soil carbon storage may be conserved across different types of decomposing plant litter. Furthermore, the structure of the resulting Bayesian network model defines a minimal set of highly informative taxa, whose abundances directly constrain the probability of high or low DOC conditions. Significant accuracy of the Bayesian network model with independent data sets supports the validity of the identified relationships between taxa abundance and DOC. SummaryUnderstanding the interplay between microbiomes and the environments they inhabit is a daunting task. While recent advances in gene sequencing technology provide a means of profiling the relative abundance of microbial species, the resulting data are noisy, sparse, and limited to small sample sizes. Despite these challenges, machine learning approaches have demonstrated a promising ability to discover patterns linking the microbiome with macroscopic behavior. However, most machine learning models applied to microbiome data do not estimate prediction uncertainty and provide little insight regarding how predictions are made. In this study, we couple machine learning approaches for feature reduction with Bayesian networks to model the relationship between the soil microbiome and dissolved organic carbon (DOC). We show that Bayesian networks are accurate and provide a transparent link between microbial abundance and DOC. To validate Bayesian networks, we demonstrate accurate predictions for held-out testing data and with data from independent decomposition experiments.

microbiology↗

bioRxiv2020

Live-cell imaging reveals the spatiotemporal organization of endogenous RNA polymerase II phosphorylation at a single gene

The carboxyl-terminal domain of RNA polymerase II is dynamically phosphorylated during transcription in eukaryotic cells. While residue-specific phosphorylation has been mapped with exquisite spatial resolution along the 1D genome in a population of fixed cells using immunoprecipitation-based assays, the timing, kinetics, and spatial organization of phosphorylation along a single-copy gene have not yet been measured in living cells. Here, we achieve this by combining multi-color, single-molecule microscopy with fluorescent antibody-based probes that specifically bind to unphosphorylated and phosphorylated forms of endogenous RNAP2 in living cells. Applying this methodology to a single-copy HIV-1 reporter gene provides live-cell evidence for heterogeneity in the distribution of RNAP2 along the length of the gene as well as clusters of Serine 5 phosphorylated RNAP2 that form around active genes and are separated in both space and time from nascent mRNA synthesis. Computational models fit to our data determine that 5 to 40 RNAP2 cluster around the promoter of a gene during typical transcriptional bursts. Rapid imaging demonstrates nearly all RNAP2 in the cluster acquire Serine 5 phosphorylation within 3-6 seconds of arrival. Transcription from the cluster appears to be highly efficient, with nearly half of the clustered RNAP2 ultimately escaping the promoter in 1.5 minutes on average to elongate a full-length mRNA in approximately five minutes. The highly dynamic and spatially organized concentrations of RNAP2 we observe support the notion of highly efficient transcription clusters that form around promoters and contain high concentrations of RNAP2 phosphorylated at Serine 5.

molecular 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↗

bioRxiv2019

Machine learning to predict microbial community functions: An analysis of dissolved organic carbon from litter decomposition.

Microbial communities are ubiquitous and often influence macroscopic properties of the ecosystems they inhabit. However, deciphering the functional relationship between specific microbes and ecosystem properties is an ongoing challenge owing to the complexity of the communities. This challenge can be addressed, in part, by integrating the advances in DNA sequencing technology with computational approaches like machine learning. Although machine learning techniques have been applied to microbiome data, use of these techniques remains rare, and user-friendly platforms to implement such techniques are not widely available. We developed a tool that implements neural network and random forest models to perform regression and feature selection tasks on microbiome data. In this study, we applied the tool to analyze soil microbiome (16S rRNA gene profiles) and dissolved organic carbon (DOC) data from a 44-day plant litter decomposition experiment. The microbiome data includes 1709 total bacterial operational taxonomic units (OTU) from 300+ microcosms. Regression analysis of predicted and actual DOC for a held-out test set of 51 samples yield Pearsons correlation coefficients of .636 and .676 for neural network and random forest approaches, respectively. Important taxa identified by the machine learning techniques are compared to results from a standard tool (indicator species analysis) widely used by microbial ecologists. Of 1709 bacterial taxa, indicator species analysis identified 285 taxa as significant determinants of DOC concentration. Of the top 285 ranked features determined by machine learning methods, a subset of 86 taxa are common to all feature selection techniques. Using this subset of features, prediction results for random permutations of the data set are at least equally accurate compared to predictions determined using the entire feature set. Our results suggest that integration of multiple methods can aid identification of a robust subset of taxa within complex communities that may drive specific functional outcomes of interest.

bioinformatics↗

bioRxiv2018

Using Flow Cytometry and Multistage Machine Learning to Discover Label-Free Signatures of Algal Lipid Accumulation

--Most applications of flow cytometry or cell sorting rely on the conjugation of fluorescent dyes to specific biomarkers. However, labeled biomarkers are not always available, they can be costly, and they may disrupt natural cell behavior. Label-free quantification based upon machine learning approaches could help correct these issues, but label replacement strategies can be very difficult to discover when applied labels or other modifications in measurements inadvertently modify intrinsic cell properties. Here we demonstrate a new, but simple approach based upon feature selection and linear regression analyses to integrate statistical information collected from both labeled and unlabeled cell populations and to identify models for accurate label-free single-cell quantification. We verify the methods accuracy to predict lipid content in algal cells (Picochlorum soloecismus) during a nitrogen starvation and lipid accumulation time course. Our general approach is expected to improve label-free single-cell analysis for other organisms or pathways, where biomarkers are inconvenient, expensive, or disruptive to downstream cellular processes.

systems biology↗

bioRxiv2018

Live-cell single RNA imaging reveals bursts of translational frameshifting

Ribosomal frameshifting during the translation of RNA is implicated in both human disease and viral infection. While previous work has uncovered many mechanistic details about single RNA frameshifting kinetics in vitro, very little is known about how single RNA frameshift in living systems. To confront this problem, we have developed technology to quantify live-cell single RNA translation dynamics in frameshifted open reading frames. Applying this technology to RNA encoding the HIV-1 frameshift sequence reveals a small subset (~8%) of the translating pool robustly frameshift in living cells. Frameshifting RNA are preferentially in multi-RNA \"translation factories,\" are translated at about the same rate as non-frameshifting RNA (~2 aa/sec), and can continuously frameshift for more than four rounds of translation. Fits to a bursty model of frameshifting constrain frameshifting kinetic rates and demonstrate how ribosomal traffic jams contribute to the persistence of the frameshifting state. These data provide novel insight into retroviral frameshifting and could lead to new strategies to perturb the process in living cells.

biochemistry↗

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↗