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Title: MinGenome: An In Silico Top-Down Approach for the Synthesis of Minimized Genomes

Abstract

Genome minimized strains offer advantages as production chassis by reducing transcriptional cost, eliminating competing functions and limiting unwanted regulatory interactions. Existing approaches for identifying stretches of DNA to remove are largely ad hoc based on information on presumably dispensable regions through experimentally determined nonessential genes and comparative genomics. Here we introduce a versatile genome reduction algorithm MinGenome that implements a mixed-integer linear programming (MILP) algorithm to identify in size descending order all dispensable contiguous sequences without affecting the organism’s growth or other desirable traits. Known essential genes or genes that cause significant fitness or performance loss can be flagged and their deletion can be prohibited. MinGenome also preserves needed transcription factors and promoter regions ensuring that retained genes will be properly transcribed while also avoiding the simultaneous deletion of synthetic lethal pairs. The potential benefit of removing even larger contiguous stretches of DNA if only one or two essential genes (to be reinserted elsewhere) are within the deleted sequence is explored. We applied the algorithm to design a minimized E. coli strain and found that we were able to recapitulate the long deletions identified in previous experimental studies and discover alternative combinations of deletions that have not yet been exploredmore » in vivo.« less

Authors:
ORCiD logo [1];  [1]
  1. Department of Chemical Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
Publication Date:
Research Org.:
Pennsylvania State Univ., University Park, PA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1417673
Alternate Identifier(s):
OSTI ID: 1508305
Grant/Contract Number:  
SC0012722
Resource Type:
Published Article
Journal Name:
ACS Synthetic Biology
Additional Journal Information:
Journal Name: ACS Synthetic Biology Journal Volume: 7 Journal Issue: 2; Journal ID: ISSN 2161-5063
Publisher:
American Chemical Society
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; E. coli; genome-scale model; MILP; minimal genome; top-down

Citation Formats

Wang, Lin, and Maranas, Costas D. MinGenome: An In Silico Top-Down Approach for the Synthesis of Minimized Genomes. United States: N. p., 2018. Web. doi:10.1021/acssynbio.7b00296.
Wang, Lin, & Maranas, Costas D. MinGenome: An In Silico Top-Down Approach for the Synthesis of Minimized Genomes. United States. doi:10.1021/acssynbio.7b00296.
Wang, Lin, and Maranas, Costas D. Wed . "MinGenome: An In Silico Top-Down Approach for the Synthesis of Minimized Genomes". United States. doi:10.1021/acssynbio.7b00296.
@article{osti_1417673,
title = {MinGenome: An In Silico Top-Down Approach for the Synthesis of Minimized Genomes},
author = {Wang, Lin and Maranas, Costas D.},
abstractNote = {Genome minimized strains offer advantages as production chassis by reducing transcriptional cost, eliminating competing functions and limiting unwanted regulatory interactions. Existing approaches for identifying stretches of DNA to remove are largely ad hoc based on information on presumably dispensable regions through experimentally determined nonessential genes and comparative genomics. Here we introduce a versatile genome reduction algorithm MinGenome that implements a mixed-integer linear programming (MILP) algorithm to identify in size descending order all dispensable contiguous sequences without affecting the organism’s growth or other desirable traits. Known essential genes or genes that cause significant fitness or performance loss can be flagged and their deletion can be prohibited. MinGenome also preserves needed transcription factors and promoter regions ensuring that retained genes will be properly transcribed while also avoiding the simultaneous deletion of synthetic lethal pairs. The potential benefit of removing even larger contiguous stretches of DNA if only one or two essential genes (to be reinserted elsewhere) are within the deleted sequence is explored. We applied the algorithm to design a minimized E. coli strain and found that we were able to recapitulate the long deletions identified in previous experimental studies and discover alternative combinations of deletions that have not yet been explored in vivo.},
doi = {10.1021/acssynbio.7b00296},
journal = {ACS Synthetic Biology},
number = 2,
volume = 7,
place = {United States},
year = {2018},
month = {1}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: 10.1021/acssynbio.7b00296

Citation Metrics:
Cited by: 1 work
Citation information provided by
Web of Science

Figures / Tables:

Figure 1 Figure 1: Schematic representation of the MinGenome algorithm. The MinGenome algorithm builds on GSMs with information on essential genes, gene and promoter positions. The logic constraints are imposed on promoters, genes, and reactions. MinGenome allows three additional options that allow (i) gene reinsertion, (ii) retention of transcriptional factor genes, andmore » (iii) user-supplied expansion of the list of essential genes. The MinGenome identifies the sequence of deletions starting with the largest dispensable region and proceeding monotonically to shorter ones.« less

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Works referencing / citing this record:

Genome-driven cell engineering review: in vivo and in silico metabolic and genome engineering
journal, June 2019

  • Landon, Sophie; Rees-Garbutt, Joshua; Marucci, Lucia
  • Essays in Biochemistry, Vol. 63, Issue 2
  • DOI: 10.1042/ebc20180045

Genome-driven cell engineering review: in vivo and in silico metabolic and genome engineering
journal, June 2019

  • Landon, Sophie; Rees-Garbutt, Joshua; Marucci, Lucia
  • Essays in Biochemistry, Vol. 63, Issue 2
  • DOI: 10.1042/ebc20180045