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Automated design of bacterial genome sequences.

Carrera J, Jaramillo A - BMC Syst Biol (2013)

Bottom Line: We found that it is theoretically possible to reorganize E. coli genome into 86% fewer regulated operons.Such refactored genomes are constituted by operons that contain sets of genes sharing around the 60% of their biological functions and, if evolved under highly variable environmental conditions, have regulatory networks, which turn out to respond more than 20% faster to multiple external perturbations.This work provides the first algorithm for producing a genome sequence encoding a rewired transcriptional regulation with wild-type behavior under alternative environments.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Life Sciences, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, UK. Alfonso.Jaramillo@warwick.ac.uk.

ABSTRACT

Background: Organisms have evolved ways of regulating transcription to better adapt to varying environments. Could the current functional genomics data and models support the possibility of engineering a genome with completely rearranged gene organization while the cell maintains its behavior under environmental challenges? How would we proceed to design a full nucleotide sequence for such genomes?

Results: As a first step towards answering such questions, recent work showed that it is possible to design alternative transcriptomic models showing the same behavior under environmental variations than the wild-type model. A second step would require providing evidence that it is possible to provide a nucleotide sequence for a genome encoding such transcriptional model. We used computational design techniques to design a rewired global transcriptional regulation of Escherichia coli, yet showing a similar transcriptomic response than the wild-type. Afterwards, we "compiled" the transcriptional networks into nucleotide sequences to obtain the final genome sequence. Our computational evolution procedure ensures that we can maintain the genotype-phenotype mapping during the rewiring of the regulatory network. We found that it is theoretically possible to reorganize E. coli genome into 86% fewer regulated operons. Such refactored genomes are constituted by operons that contain sets of genes sharing around the 60% of their biological functions and, if evolved under highly variable environmental conditions, have regulatory networks, which turn out to respond more than 20% faster to multiple external perturbations.

Conclusions: This work provides the first algorithm for producing a genome sequence encoding a rewired transcriptional regulation with wild-type behavior under alternative environments.

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Related in: MedlinePlus

Computational approach for the automated design of synthetic genome sequences. (A) Steps designed to construct the regulatory network of E. coli required to sense environmental changes. (B) A scheme of the algorithm used to re-design the E. coli TRN [17]. The wild-type genome was used as the starting point for an optimization process based on Monte Carlo Simulated Annealing. During the in silico evolution, we modified gene regulation (Figure 2) and computed the resulting genome fitness as a function combining the genome modularity and the distance between the gene expression levels of the re-engineered and wild-type genomes.
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Figure 1: Computational approach for the automated design of synthetic genome sequences. (A) Steps designed to construct the regulatory network of E. coli required to sense environmental changes. (B) A scheme of the algorithm used to re-design the E. coli TRN [17]. The wild-type genome was used as the starting point for an optimization process based on Monte Carlo Simulated Annealing. During the in silico evolution, we modified gene regulation (Figure 2) and computed the resulting genome fitness as a function combining the genome modularity and the distance between the gene expression levels of the re-engineered and wild-type genomes.

Mentions: We used a recent genome-wide model of E. coli gene transcription in response to selected external signals to predict changes in cell growth after genome modification [17]. Such model was inferred from experimental data and the, InferGene inference methodology [15], which is used to obtain kinetic parameters from experimental steady-state data. The model contains 4,298 non-redundant genes, 330 of which are putative TFs. As detailed in the Methods, this model is described by ordinary differential equations for the transcription level of each gene and its transcription regulation. This model allows the assignment of mathematical parameters to promoters and TF sequences, which we have assumed to be independent of genomic context (Figure 1A). In our previous work [17], we showed that we could predict experimental growth rates by assigning transcriptional parameters to genome regulatory sequences. Such assignment allows us to predict the TRN model after reshuffling genetic elements (Figure 2; see Methods).


Automated design of bacterial genome sequences.

Carrera J, Jaramillo A - BMC Syst Biol (2013)

Computational approach for the automated design of synthetic genome sequences. (A) Steps designed to construct the regulatory network of E. coli required to sense environmental changes. (B) A scheme of the algorithm used to re-design the E. coli TRN [17]. The wild-type genome was used as the starting point for an optimization process based on Monte Carlo Simulated Annealing. During the in silico evolution, we modified gene regulation (Figure 2) and computed the resulting genome fitness as a function combining the genome modularity and the distance between the gene expression levels of the re-engineered and wild-type genomes.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4016488&req=5

Figure 1: Computational approach for the automated design of synthetic genome sequences. (A) Steps designed to construct the regulatory network of E. coli required to sense environmental changes. (B) A scheme of the algorithm used to re-design the E. coli TRN [17]. The wild-type genome was used as the starting point for an optimization process based on Monte Carlo Simulated Annealing. During the in silico evolution, we modified gene regulation (Figure 2) and computed the resulting genome fitness as a function combining the genome modularity and the distance between the gene expression levels of the re-engineered and wild-type genomes.
Mentions: We used a recent genome-wide model of E. coli gene transcription in response to selected external signals to predict changes in cell growth after genome modification [17]. Such model was inferred from experimental data and the, InferGene inference methodology [15], which is used to obtain kinetic parameters from experimental steady-state data. The model contains 4,298 non-redundant genes, 330 of which are putative TFs. As detailed in the Methods, this model is described by ordinary differential equations for the transcription level of each gene and its transcription regulation. This model allows the assignment of mathematical parameters to promoters and TF sequences, which we have assumed to be independent of genomic context (Figure 1A). In our previous work [17], we showed that we could predict experimental growth rates by assigning transcriptional parameters to genome regulatory sequences. Such assignment allows us to predict the TRN model after reshuffling genetic elements (Figure 2; see Methods).

Bottom Line: We found that it is theoretically possible to reorganize E. coli genome into 86% fewer regulated operons.Such refactored genomes are constituted by operons that contain sets of genes sharing around the 60% of their biological functions and, if evolved under highly variable environmental conditions, have regulatory networks, which turn out to respond more than 20% faster to multiple external perturbations.This work provides the first algorithm for producing a genome sequence encoding a rewired transcriptional regulation with wild-type behavior under alternative environments.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Life Sciences, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, UK. Alfonso.Jaramillo@warwick.ac.uk.

ABSTRACT

Background: Organisms have evolved ways of regulating transcription to better adapt to varying environments. Could the current functional genomics data and models support the possibility of engineering a genome with completely rearranged gene organization while the cell maintains its behavior under environmental challenges? How would we proceed to design a full nucleotide sequence for such genomes?

Results: As a first step towards answering such questions, recent work showed that it is possible to design alternative transcriptomic models showing the same behavior under environmental variations than the wild-type model. A second step would require providing evidence that it is possible to provide a nucleotide sequence for a genome encoding such transcriptional model. We used computational design techniques to design a rewired global transcriptional regulation of Escherichia coli, yet showing a similar transcriptomic response than the wild-type. Afterwards, we "compiled" the transcriptional networks into nucleotide sequences to obtain the final genome sequence. Our computational evolution procedure ensures that we can maintain the genotype-phenotype mapping during the rewiring of the regulatory network. We found that it is theoretically possible to reorganize E. coli genome into 86% fewer regulated operons. Such refactored genomes are constituted by operons that contain sets of genes sharing around the 60% of their biological functions and, if evolved under highly variable environmental conditions, have regulatory networks, which turn out to respond more than 20% faster to multiple external perturbations.

Conclusions: This work provides the first algorithm for producing a genome sequence encoding a rewired transcriptional regulation with wild-type behavior under alternative environments.

Show MeSH
Related in: MedlinePlus