Biology subjects

Vo, H. D.

Publications and source records attributed to Vo, H. D..

8 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↗

bioRxiv2022

High-fidelity dendritic sodium spike generation in human layer 2/3 neocortical pyramidal neurons

Dendritic computations have a central role in rodent neocortical circuit function, which are signaled into neuronal output by the initiation and propagation of regenerative dendritic spikes. However, it remains poorly explored whether these mechanisms are operational in the human neocortex. To directly examine the dendritic computational properties of the most numerous class of pyramidal neuron in the human and rat neocortex we made simultaneous electrical recordings from the soma and apical dendrites of layer 2/3 pyramidal neurons maintained in acute brain slices of analogous cortical areas under identical experimental conditions. In both species correlated dendritic excitatory input led to the initiation of sodium channel-mediated dendritic spikes with conserved biophysical properties. Dendritic sodium spikes in human and rat layer 2/3 pyramidal neurons could be generated across a similar wide input range, exhibited a similar frequency range of activation, and forward propagated with high fidelity to drive neuronal output to implement stereotyped computations. Our findings therefore reveal that the dendritic computational properties of human layer 2/3 pyramidal neurons are phylogenetically conserved.

neuroscience↗

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

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

Targeted mutagenesis of the Arabidopsis GROWTH-REGULATING FACTOR (GRF) gene family suggests competition of multiplexed sgRNAs for Cas9 apoprotein

Genome editing in plants typically relies on T-DNA plasmids that are mobilized by Agrobacterium-mediated transformation to deliver the CRISPR/Cas9 machinery. Here, we introduce a series of CRISPR/Cas9 T-DNA vectors for minimal lab settings, such as in the classroom or citizen science projects. Spacer sequences targeting genes of interest can be inserted as annealed short oligonucleotides in a single straightforward cloning step. Fluorescent markers expressed in mature seeds enable reliable selection of transgenic as well as transgene-free individuals using a combination of inexpensive LED lamps and colored-glass alternative filters. Testing these tools on the Arabidopsis GROWTH-REGULATING FACTOR (GRF) gene family, we found that Cas9 expression from an EGG CELL1 (EC1) promoter resulted in tenfold lower mutation rates than expression from a UBIQUITIN10 (UBQ10) promoter. A collection of bona fide null mutations in all nine GRF genes could be established with little effort. Finally, we explored the effects of simultaneously targeting two, four and eight GRF genes on the rate of induced mutations at each target locus. Multiplexing caused strong interference effects: while mutation rates at some loci remained consistently high, mutation rates at other loci dropped dramatically with increasing number of single guide RNA species. Our results suggest potential detrimental genetic interaction between induced mutations as well as competition of CRISPR RNAs for a limiting amount of Cas9 apoprotein.

plant 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↗

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

3D Reconstruction of Bird Flight Using a Single Video Camera

Video cameras are finding increasing use in the study and analysis of bird flight over short ranges. However, reconstruction of flight trajectories in three dimensions typically requires the use of multiple cameras and elaborate calibration procedures. We present an alternative approach that uses a single video camera and a simple calibration procedure for the reconstruction of such trajectories. The technique combines prior knowledge of the wingspan of the bird with a camera calibration procedure that needs to be used only once in the lifetime of the system. The system delivers the exact 3D coordinates of the position of the bird at the time of every full wing extension and uses interpolated height estimates to compute the 3D positions of the bird in the video frames between successive wing extensions. The system is inexpensive, compact and portable, and can be easily deployed in the laboratory as well as the field.

animal behavior and cognition↗