Bayesian filtering for model predictive control of stochastic gene expression in single cells.

IF 2 4区生物学 Q4 BIOCHEMISTRY & MOLECULAR BIOLOGY Physical biology Pub Date : 2023-07-12 DOI:10.1088/1478-3975/ace094

Zachary Fox, Gregory Batt, Jakob Ruess

引用次数: 0

Abstract

This study describes a method for controlling the production of protein in individual cells using stochastic models of gene expression. By combining modern microscopy platforms with optogenetic gene expression, experimentalists are able to accurately apply light to individual cells, which can induce protein production. Here we use a finite state projection based stochastic model of gene expression, along with Bayesian state estimation to control protein copy numbers within individual cells. We compare this method to previous methods that use population based approaches. We also demonstrate the ability of this control strategy to ameliorate discrepancies between the predictions of a deterministic model and stochastic switching system.

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单细胞随机基因表达的贝叶斯滤波模型预测控制。

本研究描述了一种利用基因表达的随机模型来控制单个细胞中蛋白质产生的方法。通过结合现代显微镜平台和光遗传学基因表达，实验人员能够准确地将光应用于单个细胞，从而诱导蛋白质的产生。在这里，我们使用基于有限状态投影的基因表达随机模型，以及贝叶斯状态估计来控制单个细胞内的蛋白质拷贝数。我们将这种方法与以前使用基于人口的方法进行比较。我们还证明了这种控制策略能够改善确定性模型和随机切换系统预测之间的差异。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Physical biology 生物-生物物理

CiteScore

4.20

自引率

0.00%

发文量

审稿时长

3 months

期刊介绍： Physical Biology publishes articles in the broad interdisciplinary field bridging biology with the physical sciences and engineering. This journal focuses on research in which quantitative approaches – experimental, theoretical and modeling – lead to new insights into biological systems at all scales of space and time, and all levels of organizational complexity. Physical Biology accepts contributions from a wide range of biological sub-fields, including topics such as: molecular biophysics, including single molecule studies, protein-protein and protein-DNA interactions subcellular structures, organelle dynamics, membranes, protein assemblies, chromosome structure intracellular processes, e.g. cytoskeleton dynamics, cellular transport, cell division systems biology, e.g. signaling, gene regulation and metabolic networks cells and their microenvironment, e.g. cell mechanics and motility, chemotaxis, extracellular matrix, biofilms cell-material interactions, e.g. biointerfaces, electrical stimulation and sensing, endocytosis cell-cell interactions, cell aggregates, organoids, tissues and organs developmental dynamics, including pattern formation and morphogenesis physical and evolutionary aspects of disease, e.g. cancer progression, amyloid formation neuronal systems, including information processing by networks, memory and learning population dynamics, ecology, and evolution collective action and emergence of collective phenomena.