A Predictive Model for Transcriptional Control of Physiology in a Free Living Cell

Richard Bonneau; Marc T. Facciotti; David J. Reiss; Amy K. Schmid; Min Pan; Amardeep Kaur; Vesteinn Thorsson; Paul Shannon; Michael H. Johnson; J. Christopher Bare; William Longabaugh; Madhavi Vuthoori; Kenia Whitehead; Aviv Madar; Lena Suzuki; Tetsuya Mori; Dong Eun Chang; Jocelyne DiRuggiero; Carl H. Johnson; Leroy Hood; Nitin S. Baliga

doi:10.1016/j.cell.2007.10.053

A Predictive Model for Transcriptional Control of Physiology in a Free Living Cell

Richard Bonneau, Marc T. Facciotti, David J. Reiss, Amy K. Schmid, Min Pan, Amardeep Kaur, Vesteinn Thorsson, Paul Shannon, Michael H. Johnson, J. Christopher Bare, William Longabaugh, Madhavi Vuthoori, Kenia Whitehead, Aviv Madar, Lena Suzuki, Tetsuya Mori, Dong Eun Chang, Jocelyne DiRuggiero, Carl H. Johnson, Leroy HoodNitin S. Baliga

Research output: Contribution to journal › Article › peer-review

252 Scopus citations

Abstract

The environment significantly influences the dynamic expression and assembly of all components encoded in the genome of an organism into functional biological networks. We have constructed a model for this process in Halobacterium salinarum NRC-1 through the data-driven discovery of regulatory and functional interrelationships among ∼80% of its genes and key abiotic factors in its hypersaline environment. Using relative changes in 72 transcription factors and 9 environmental factors (EFs) this model accurately predicts dynamic transcriptional responses of all these genes in 147 newly collected experiments representing completely novel genetic backgrounds and environments-suggesting a remarkable degree of network completeness. Using this model we have constructed and tested hypotheses critical to this organism's interaction with its changing hypersaline environment. This study supports the claim that the high degree of connectivity within biological and EF networks will enable the construction of similar models for any organism from relatively modest numbers of experiments.

Original language	English
Pages (from-to)	1354-1365
Number of pages	12
Journal	Cell
Volume	131
Issue number	7
DOIs	https://doi.org/10.1016/j.cell.2007.10.053
State	Published - Dec 28 2007

Keywords

DNA
MICROBIO
SYSBIO

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10.1016/j.cell.2007.10.053

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Bonneau, R., Facciotti, M. T., Reiss, D. J., Schmid, A. K., Pan, M., Kaur, A., Thorsson, V., Shannon, P., Johnson, M. H., Bare, J. C., Longabaugh, W., Vuthoori, M., Whitehead, K., Madar, A., Suzuki, L., Mori, T., Chang, D. E., DiRuggiero, J., Johnson, C. H., ... Baliga, N. S. (2007). A Predictive Model for Transcriptional Control of Physiology in a Free Living Cell. Cell, 131(7), 1354-1365. https://doi.org/10.1016/j.cell.2007.10.053

@article{1f720416d9c24196ba2c170b7199c109,

title = "A Predictive Model for Transcriptional Control of Physiology in a Free Living Cell",

abstract = "The environment significantly influences the dynamic expression and assembly of all components encoded in the genome of an organism into functional biological networks. We have constructed a model for this process in Halobacterium salinarum NRC-1 through the data-driven discovery of regulatory and functional interrelationships among ∼80% of its genes and key abiotic factors in its hypersaline environment. Using relative changes in 72 transcription factors and 9 environmental factors (EFs) this model accurately predicts dynamic transcriptional responses of all these genes in 147 newly collected experiments representing completely novel genetic backgrounds and environments-suggesting a remarkable degree of network completeness. Using this model we have constructed and tested hypotheses critical to this organism's interaction with its changing hypersaline environment. This study supports the claim that the high degree of connectivity within biological and EF networks will enable the construction of similar models for any organism from relatively modest numbers of experiments.",

keywords = "DNA, MICROBIO, SYSBIO",

author = "Richard Bonneau and Facciotti, {Marc T.} and Reiss, {David J.} and Schmid, {Amy K.} and Min Pan and Amardeep Kaur and Vesteinn Thorsson and Paul Shannon and Johnson, {Michael H.} and Bare, {J. Christopher} and William Longabaugh and Madhavi Vuthoori and Kenia Whitehead and Aviv Madar and Lena Suzuki and Tetsuya Mori and Chang, {Dong Eun} and Jocelyne DiRuggiero and Johnson, {Carl H.} and Leroy Hood and Baliga, {Nitin S.}",

note = "Funding Information: This work was supported by grants from NIH (P50GM076547), DoE (MAGGIE: DE-FG02-07ER64327), and Institute for Systems Biology institutional support to N.S.B.; NSF (EF-0313754 and EIA-0220153), DoD (DAAD13-03-O-0057), and DoE (DE-FG02-04ER63807) to N.S.B. and L.H.; USAMRAA (US Army Medical Research Acquisition Activity) W81XWH-04-01-0307 to R.B.; NSF (MCB-0425825) and NASA (NNG05GN58G) to J.D.R. and N.S.B.; and NIH (R01GM067152) to C.H.J. Postdoctoral fellowships are acknowledged from NSF to M.T.F. (0400598) and K.W. and from NIH to A.K.S. We also thank Nathan Price and David Galas for critical reading of the manuscript and their helpful comments. ",

year = "2007",

month = dec,

day = "28",

doi = "10.1016/j.cell.2007.10.053",

language = "English",

volume = "131",

pages = "1354--1365",

journal = "Cell",

issn = "0092-8674",

number = "7",

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Bonneau, R, Facciotti, MT, Reiss, DJ, Schmid, AK, Pan, M, Kaur, A, Thorsson, V, Shannon, P, Johnson, MH, Bare, JC, Longabaugh, W, Vuthoori, M, Whitehead, K, Madar, A, Suzuki, L, Mori, T, Chang, DE, DiRuggiero, J, Johnson, CH, Hood, L & Baliga, NS 2007, 'A Predictive Model for Transcriptional Control of Physiology in a Free Living Cell', Cell, vol. 131, no. 7, pp. 1354-1365. https://doi.org/10.1016/j.cell.2007.10.053

TY - JOUR

T1 - A Predictive Model for Transcriptional Control of Physiology in a Free Living Cell

AU - Bonneau, Richard

AU - Facciotti, Marc T.

AU - Reiss, David J.

AU - Schmid, Amy K.

AU - Pan, Min

AU - Kaur, Amardeep

AU - Thorsson, Vesteinn

AU - Shannon, Paul

AU - Johnson, Michael H.

AU - Bare, J. Christopher

AU - Longabaugh, William

AU - Vuthoori, Madhavi

AU - Whitehead, Kenia

AU - Madar, Aviv

AU - Suzuki, Lena

AU - Mori, Tetsuya

AU - Chang, Dong Eun

AU - DiRuggiero, Jocelyne

AU - Johnson, Carl H.

AU - Hood, Leroy

AU - Baliga, Nitin S.

N1 - Funding Information: This work was supported by grants from NIH (P50GM076547), DoE (MAGGIE: DE-FG02-07ER64327), and Institute for Systems Biology institutional support to N.S.B.; NSF (EF-0313754 and EIA-0220153), DoD (DAAD13-03-O-0057), and DoE (DE-FG02-04ER63807) to N.S.B. and L.H.; USAMRAA (US Army Medical Research Acquisition Activity) W81XWH-04-01-0307 to R.B.; NSF (MCB-0425825) and NASA (NNG05GN58G) to J.D.R. and N.S.B.; and NIH (R01GM067152) to C.H.J. Postdoctoral fellowships are acknowledged from NSF to M.T.F. (0400598) and K.W. and from NIH to A.K.S. We also thank Nathan Price and David Galas for critical reading of the manuscript and their helpful comments.

PY - 2007/12/28

Y1 - 2007/12/28

N2 - The environment significantly influences the dynamic expression and assembly of all components encoded in the genome of an organism into functional biological networks. We have constructed a model for this process in Halobacterium salinarum NRC-1 through the data-driven discovery of regulatory and functional interrelationships among ∼80% of its genes and key abiotic factors in its hypersaline environment. Using relative changes in 72 transcription factors and 9 environmental factors (EFs) this model accurately predicts dynamic transcriptional responses of all these genes in 147 newly collected experiments representing completely novel genetic backgrounds and environments-suggesting a remarkable degree of network completeness. Using this model we have constructed and tested hypotheses critical to this organism's interaction with its changing hypersaline environment. This study supports the claim that the high degree of connectivity within biological and EF networks will enable the construction of similar models for any organism from relatively modest numbers of experiments.

AB - The environment significantly influences the dynamic expression and assembly of all components encoded in the genome of an organism into functional biological networks. We have constructed a model for this process in Halobacterium salinarum NRC-1 through the data-driven discovery of regulatory and functional interrelationships among ∼80% of its genes and key abiotic factors in its hypersaline environment. Using relative changes in 72 transcription factors and 9 environmental factors (EFs) this model accurately predicts dynamic transcriptional responses of all these genes in 147 newly collected experiments representing completely novel genetic backgrounds and environments-suggesting a remarkable degree of network completeness. Using this model we have constructed and tested hypotheses critical to this organism's interaction with its changing hypersaline environment. This study supports the claim that the high degree of connectivity within biological and EF networks will enable the construction of similar models for any organism from relatively modest numbers of experiments.

KW - DNA

KW - MICROBIO

KW - SYSBIO

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U2 - 10.1016/j.cell.2007.10.053

DO - 10.1016/j.cell.2007.10.053

M3 - Article

C2 - 18160043

AN - SCOPUS:37349099750

SN - 0092-8674

VL - 131

SP - 1354

EP - 1365

JO - Cell

JF - Cell

IS - 7

ER -

A Predictive Model for Transcriptional Control of Physiology in a Free Living Cell

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