Modeling environmental disturbances with the chemical master equation

Yeung, Enoch; Beck, James L.; Murray, Richard M.

doi:10.1109/cdc.2013.6760076

Cited by 5 publications

(3 citation statements)

References 22 publications

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“…In both systems and synthetic biology, discovering (or verifying) the network of a (engineered) biological system is an important problem. However, discovering an entire biochemical reaction network is typically ill-posed, since many network dynamics occur simultaneously from host or environmental context [50], loading effects [51], or unanticipated retroactivity effects [7,[51][52][53][54]. Even without these effects, the reconstruction problem is equivalent to finding a unique realization for the dynamical system from direct measurements of every state of the system.…”

Section: Representing Network Interactions In Partiallymentioning

confidence: 99%

Data-driven network models for genetic circuits from time-series data with incomplete measurements

Yeung

Kim

Yuan³

et al. 2021

J. R. Soc. Interface.

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Synthetic gene networks are frequently conceptualized and visualized as static graphs. This view of biological programming stands in stark contrast to the transient nature of biomolecular interaction, which is frequently enacted by labile molecules that are often unmeasured. Thus, the network topology and dynamics of synthetic gene networks can be difficult to verify in vivo or in vitro , due to the presence of unmeasured biological states. Here we introduce the dynamical structure function as a new mesoscopic, data-driven class of models to describe gene networks with incomplete measurements of state dynamics. We develop a network reconstruction algorithm and a code base for reconstructing the dynamical structure function from data, to enable discovery and visualization of graphical relationships in a genetic circuit diagram as time-dependent functions rather than static, unknown weights. We prove a theorem, showing that dynamical structure functions can provide a data-driven estimate of the size of crosstalk fluctuations from an idealized model. We illustrate this idea with numerical examples. Finally, we show how data-driven estimation of dynamical structure functions can explain failure modes in two experimentally implemented genetic circuits, a previously reported in vitro genetic circuit and a new E. coli -based transcriptional event detector.

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Section: Representing Network Interactions In Partiallymentioning

confidence: 99%

Data-driven network models for genetic circuits from time-series data with incomplete measurements

Yeung

Kim

Yuan³

et al. 2021

J. R. Soc. Interface.

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“…In this area, finding or verifying the network of a biological system is an important problem. However, discovering the entire chemical reaction network is typically an ill-posed problem, since additional reactions may be introduced due to host or environmental context [46], loading effects, or unanticipated retroactivity effects [47][48][49][50]7]. Even without these effects, the reconstruction problem is equivalent to finding a unique realization for the dynamical system, which is ill-posed without measurements of every chemical species in the system.…”

Section: Representing Network Interactions In Partially Measured Biological Networkmentioning

confidence: 99%

Data-Driven Network Models for Genetic Circuits From Time-Series Data with Incomplete Measurements

Yeung

Yuan

et al. 2021

Preprint

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Synthetic biological gene networks are typically conceptualized and visualized as static graphs with nodal and edge dynamics that are time invariant. This conceptualization of biological programming stands in stark contrast to the transient nature of biological dynamics, which are driven by labile biomolecules. Here we demonstrate the use of dynamical structure function theory to evaluate and visualize network dynamics within synthetic biological circuits. We introduce the theory of dynamical structure functions as a tool for understanding network dynamics in synthetic gene networks. We show in particular, that canonical biological crosstalk and resource loading effects in synthetic biology can be quantified directly using dynamical structure functions from simulation and experimental data. We illustrate the importance of knowing these loading effects through several example systems, showing that crosstalk imbalance in feed-forward loops can explain circuit failure or performance limitations. Finally, we show how dynamical structure functions can be used to diagnose crosstalk and network imbalance to explain failure modes in two types of synthetic biocircuits: an in vitro genelet repressilator and an E. coli based transcriptional event detector. We show that dynamical structure functions can be used as a form of inverse modeling, to pinpoint biological parts within a complex biological circuit that need revision or improvement.

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“…In [55], spectral analysis based on the CME is explicitly performed for a specific parametric gene expression model. Effects of exogenous or unmodelled dynamics on the statistics of a reaction network are treated with various approaches in [24,31,56]. A Langevin approximation for the frequency-domain analysis of noise in genetic circuits is proposed in [48,10].…”

Section: Introductionmentioning

confidence: 99%

Stochastic reaction networks with input processes: Analysis and application to gene expression inference

Cinquemani

2019

Automatica

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Stochastic reaction network models are widely utilized in biology and chemistry to describe the probabilistic dynamics of biochemical systems in general, and gene interaction networks in particular. Most often, statistical analysis and inference of these systems is addressed by parametric approaches, where the laws governing exogenous input processes, if present, are themselves fixed in advance. Motivated by reporter gene systems, widely utilized in biology to monitor gene activation at the individual cell level, we address the analysis of reaction networks with state-affine reaction rates and arbitrary input processes. We derive a generalization of the so-called moment equations where the dynamics of the network statistics are expressed as a function of the input process statistics. In stationary conditions, we provide a spectral analysis of the system and elaborate on connections with linear filtering. We then apply the theoretical results to develop a method for the reconstruction of input process statistics, namely the gene activation autocovariance function, from reporter gene population snapshot data, and demonstrate its performance on a simulated case study.

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Modeling environmental disturbances with the chemical master equation

Cited by 5 publications

References 22 publications

Data-driven network models for genetic circuits from time-series data with incomplete measurements

Data-driven network models for genetic circuits from time-series data with incomplete measurements

Data-Driven Network Models for Genetic Circuits From Time-Series Data with Incomplete Measurements

Stochastic reaction networks with input processes: Analysis and application to gene expression inference

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