Dataset Information

Kinetic Modeling of the Genetic Information Processes in a Minimal Cell.

ABSTRACT: JCVI-syn3A is a minimal bacterial cell with a 543 kbp genome consisting of 493 genes. For this slow growing minimal cell with a 105 min doubling time, we recently established the essential metabolism including the transport of required nutrients from the environment, the gene map, and genome-wide proteomics. Of the 452 protein-coding genes, 143 are assigned to metabolism and 212 are assigned to genetic information processing. Using genome-wide proteomics and experimentally measured kinetic parameters from the literature we present here kinetic models for the genetic information processes of DNA replication, replication initiation, transcription, and translation which are solved stochastically and averaged over 1,000 replicates/cells. The model predicts the time required for replication initiation and DNA replication to be 8 and 50 min on average respectively and the number of proteins and ribosomal components to be approximately doubled in a cell cycle. The model of genetic information processing when combined with the essential metabolic and cell growth networks will provide a powerful platform for studying the fundamental principles of life.

SUBMITTER: Thornburg ZR

PROVIDER: S-EPMC6892953 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Kinetic Modeling of the Genetic Information Processes in a Minimal Cell.

Thornburg Zane R ZR Melo Marcelo C R MCR Bianchi David D Brier Troy A TA Crotty Cole C Breuer Marian M Smith Hamilton O HO Hutchison Clyde A CA Glass John I JI Luthey-Schulten Zaida Z

Frontiers in molecular biosciences 20191128

JCVI-syn3A is a minimal bacterial cell with a 543 kbp genome consisting of 493 genes. For this slow growing minimal cell with a 105 min doubling time, we recently established the essential metabolism including the transport of required nutrients from the environment, the gene map, and genome-wide proteomics. Of the 452 protein-coding genes, 143 are assigned to metabolism and 212 are assigned to genetic information processing. Using genome-wide proteomics and experimentally measured kinetic param ...[more]

PMID: 31850364

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Project description:Transcription and translation are at the heart of metabolism and signal transduction. In this study, we developed an effective biophysical modeling approach to simulate transcription and translation processes. The model, composed of coupled ordinary differential equations, was tested by comparing simulations of two cell free synthetic circuits with experimental measurements generated in this study. First, we considered a simple circuit in which sigma factor 70 induced the expression of green fluorescent protein. This relatively simple case was then followed by a more complex negative feedback circuit in which two control genes were coupled to the expression of a third reporter gene, green fluorescent protein. Many of the model parameters were estimated from previous biophysical studies in the literature, while the remaining unknown model parameters for each circuit were estimated by minimizing the difference between model simulations and messenger RNA (mRNA) and protein measurements generated in this study. In particular, either parameter estimates from published studies were used directly, or characteristic values found in the literature were used to establish feasible ranges for the parameter estimation problem. In order to perform a detailed analysis of the influence of individual model parameters on the expression dynamics of each circuit, global sensitivity analysis was used. Taken together, the effective biophysical modeling approach captured the expression dynamics, including the transcription dynamics, for the two synthetic cell free circuits. While, we considered only two circuits here, this approach could potentially be extended to simulate other genetic circuits in both cell free and whole cell biomolecular applications as the equations governing the regulatory control functions are modular and easily modifiable. The model code, parameters, and analysis scripts are available for download under an MIT software license from the Varnerlab GitHub repository.

Dataset Information

Kinetic Modeling of the Genetic Information Processes in a Minimal Cell.

Publications

Kinetic Modeling of the Genetic Information Processes in a Minimal Cell.

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