Biology subjects

May, M. P.

Publications and source records attributed to May, M. P..

6 recordsLinked to original sources

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↗

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↗