37 results match your criteria Plant Genomes and Systems Biology [Journal]

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Transcriptomic and metabolic flux analyses reveal shift of metabolic patterns during rice grain development.

BMC Syst Biol 2018 04 24;12(Suppl 4):47. Epub 2018 Apr 24.

Key Laboratory of Synthetic Biology, CAS Center for Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200032, People's Republic of China.

Background: Rice (Oryza sativa) is one of the most important grain crops, which serves as food source for nearly half of the world population. The study of rice development process as well as related strategies for production has made significant progress. However, the comprehensive study on development of different rice tissues at both transcriptomic and metabolic flux level across different stages was lacked. Read More

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http://dx.doi.org/10.1186/s12918-018-0574-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5998905PMC
April 2018
38 Reads

Identification of regulatory modules in genome scale transcription regulatory networks.

BMC Syst Biol 2017 Dec 15;11(1):140. Epub 2017 Dec 15.

Department of Crop & Soil Environmental Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, 24061, USA.

Background: In gene regulatory networks, transcription factors often function as co-regulators to synergistically induce or inhibit expression of their target genes. However, most existing module-finding algorithms can only identify densely connected genes but not co-regulators in regulatory networks.

Methods: We have developed a new computational method, CoReg, to identify transcription co-regulators in large-scale regulatory networks. Read More

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http://dx.doi.org/10.1186/s12918-017-0493-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5732458PMC
December 2017
6 Reads

Linking physiologically-based pharmacokinetic and genome-scale metabolic networks to understand estradiol biology.

BMC Syst Biol 2017 Dec 15;11(1):141. Epub 2017 Dec 15.

School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, GU2 7XH, UK.

Background: Estrogen is a vital hormone that regulates many biological functions within the body. These include roles in the development of the secondary sexual organs in both sexes, plus uterine angiogenesis and proliferation during the menstrual cycle and pregnancy in women. The varied biological roles of estrogens in human health also make them a therapeutic target for contraception, mitigation of the adverse effects of the menopause, and treatment of estrogen-responsive tumours. Read More

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http://dx.doi.org/10.1186/s12918-017-0520-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5732473PMC
December 2017
39 Reads

Small RNA profiles in soybean primary root tips under water deficit.

BMC Syst Biol 2016 Dec 5;10(Suppl 5):126. Epub 2016 Dec 5.

Department of Biochemistry and Molecular Biology, Oklahoma State University, Stillwater, OK, 74078, USA.

Background: Soybean (Glycine max) production is significantly hampered by frequent droughts in many regions of the world including the United States. Identifying microRNA (miRNA)-controlled posttranscriptional gene regulation under drought will enhance our understanding of molecular basis of drought tolerance in this important cash crop. Indeed, miRNA profiles in soybean exposed to drought were studied but not from the primary root tips, which is not only a main zone of water uptake but also critical for water stress sensing and signaling. Read More

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http://dx.doi.org/10.1186/s12918-016-0374-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5249032PMC
December 2016
42 Reads

The Arabidopsis phytohormone crosstalk network involves a consecutive metabolic route and circular control units of transcription factors that regulate enzyme-encoding genes.

BMC Syst Biol 2016 09 2;10(1):87. Epub 2016 Sep 2.

State Key Laboratory of Crop Biology, College of Life Sciences, Shandong Agricultural University, Tai'an, Shandong, 271018, China.

Background: Phytohormone synergies and signaling interdependency are important topics in plant developmental biology. Physiological and genetic experimental evidence for phytohormone crosstalk has been accumulating and a genome-scale enzyme correlation model representing the Arabidopsis metabolic pathway has been published. However, an integrated molecular characterization of phytohormone crosstalk is still not available. Read More

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http://dx.doi.org/10.1186/s12918-016-0333-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5009710PMC
September 2016
10 Reads

Deciphering the signaling mechanisms of the plant cell wall degradation machinery in Aspergillus oryzae.

BMC Syst Biol 2015 Nov 14;9:77. Epub 2015 Nov 14.

School of Biological Sciences, The University of Hong Kong, Kadoorie Biological Sciences Building, Hong Kong, China.

Background: The gene expression and secretion of fungal lignocellulolytic enzymes are tightly controlled at the transcription level using independent mechanisms to respond to distinct inducers from plant biomass. An advanced systems-level understanding of transcriptional regulatory networks is required to rationally engineer filamentous fungi for more efficient bioconversion of different types of biomass.

Results: In this study we focused on ten chemically defined inducers to drive expression of cellulases, hemicellulases and accessory enzymes in the model filamentous fungus Aspergillus oryzae and shed light on the complex network of transcriptional activators required. Read More

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http://dx.doi.org/10.1186/s12918-015-0224-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4647334PMC
November 2015
6 Reads

A knowledge base for Vitis vinifera functional analysis.

BMC Syst Biol 2015 1;9 Suppl 3:S5. Epub 2015 Jun 1.

Background: Vitis vinifera (Grapevine) is the most important fruit species in the modern world. Wine and table grapes sales contribute significantly to the economy of major wine producing countries. The most relevant goals in wine production concern quality and safety. Read More

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http://dx.doi.org/10.1186/1752-0509-9-S3-S5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4464603PMC
December 2015
13 Reads

Reshaping the epigenetic landscape during early flower development: induction of attractor transitions by relative differences in gene decay rates.

BMC Syst Biol 2015 May 13;9:20. Epub 2015 May 13.

Instituto de Ecología, Universidad Nacional Autónoma de México, Cd. Universitaria, México, 04510, D.F., México.

Background: Gene regulatory network (GRN) dynamical models are standard systems biology tools for the mechanistic understanding of developmental processes and are enabling the formalization of the epigenetic landscape (EL) model.

Methods: In this work we propose a modeling framework which integrates standard mathematical analyses to extend the simple GRN Boolean model in order to address questions regarding the impact of gene specific perturbations in cell-fate decisions during development.

Results: We systematically tested the propensity of individual genes to produce qualitative changes to the EL induced by modification of gene characteristic decay rates reflecting the temporal dynamics of differentiation stimuli. Read More

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http://dx.doi.org/10.1186/s12918-015-0166-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4438470PMC
May 2015
8 Reads

Proteomics-based metabolic modeling and characterization of the cellulolytic bacterium Thermobifida fusca.

BMC Syst Biol 2014 Aug 13;8:86. Epub 2014 Aug 13.

Virginia Commonwealth University, Richmond, USA.

Background: Thermobifida fusca is a cellulolytic bacterium with potential to be used as a platform organism for sustainable industrial production of biofuels, pharmaceutical ingredients and other bioprocesses due to its capability of potential to convert plant biomass to value-added chemicals. To best develop T. fusca as a bioprocess organism, it is important to understand its native cellular processes. Read More

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http://dx.doi.org/10.1186/s12918-014-0086-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4236713PMC
August 2014
9 Reads

Identification of novel motif patterns to decipher the promoter architecture of co-expressed genes in Arabidopsis thaliana.

BMC Syst Biol 2013 Oct 16;7 Suppl 3:S10. Epub 2013 Oct 16.

Background: The understanding of the mechanisms of transcriptional regulation remains a challenge for molecular biologists in the post-genome era. It is hypothesized that the regulatory regions of genes expressed in the same tissue or cell type share a similar structure. Though several studies have analyzed the promoters of genes expressed in specific metazoan tissues or cells, little research has been done in plants. Read More

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http://dx.doi.org/10.1186/1752-0509-7-S3-S10DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3852273PMC
October 2013
4 Reads

Network component analysis provides quantitative insights on an Arabidopsis transcription factor-gene regulatory network.

BMC Syst Biol 2013 Nov 14;7:126. Epub 2013 Nov 14.

Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, MD 20742, USA.

Background: Gene regulatory networks (GRNs) are models of molecule-gene interactions instrumental in the coordination of gene expression. Transcription factor (TF)-GRNs are an important subset of GRNs that characterize gene expression as the effect of TFs acting on their target genes. Although such networks can qualitatively summarize TF-gene interactions, it is highly desirable to quantitatively determine the strengths of the interactions in a TF-GRN as well as the magnitudes of TF activities. Read More

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http://dx.doi.org/10.1186/1752-0509-7-126DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3843564PMC
November 2013
6 Reads

Starch biosynthesis in cassava: a genome-based pathway reconstruction and its exploitation in data integration.

BMC Syst Biol 2013 Aug 10;7:75. Epub 2013 Aug 10.

Bioinfromatics and Systems Biology Program, School of Bioresources and Technology, King Mongkut's University of Technology Thonburi, 10150 Bangkok, Thailand.

Background: Cassava is a well-known starchy root crop utilized for food, feed and biofuel production. However, the comprehension underlying the process of starch production in cassava is not yet available.

Results: In this work, we exploited the recently released genome information and utilized the post-genomic approaches to reconstruct the metabolic pathway of starch biosynthesis in cassava using multiple plant templates. Read More

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http://dx.doi.org/10.1186/1752-0509-7-75DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3847483PMC
August 2013
8 Reads

Maximizing capture of gene co-expression relationships through pre-clustering of input expression samples: an Arabidopsis case study.

BMC Syst Biol 2013 Jun 5;7:44. Epub 2013 Jun 5.

Department of Genetics & Biochemistry, Clemson University, 105 Collings Street, Clemson, SC 29634, USA.

Background: In genomics, highly relevant gene interaction (co-expression) networks have been constructed by finding significant pair-wise correlations between genes in expression datasets. These networks are then mined to elucidate biological function at the polygenic level. In some cases networks may be constructed from input samples that measure gene expression under a variety of different conditions, such as for different genotypes, environments, disease states and tissues. Read More

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http://dx.doi.org/10.1186/1752-0509-7-44DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3679940PMC
June 2013
8 Reads

Integration of time-resolved transcriptomics data with flux-based methods reveals stress-induced metabolic adaptation in Escherichia coli.

BMC Syst Biol 2012 Nov 30;6:148. Epub 2012 Nov 30.

Systems Biology and Mathematical Modeling Group, Max-Planck-Institute of Molecular Plant Physiology, 14476 Potsdam, Germany.

Background: Changes in environmental conditions require temporal effectuation of different metabolic pathways in order to maintain the organisms' viability but also to enable the settling into newly arising conditions. While analyses of robustness in biological systems have resulted in the characterization of reactions that facilitate homeostasis, temporal adaptation-related processes and the role of cellular pathways in the metabolic response to changing conditions remain elusive.

Results: Here we develop a flux-based approach that allows the integration of time-resolved transcriptomics data with genome-scale metabolic networks. Read More

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http://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0
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http://dx.doi.org/10.1186/1752-0509-6-148DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3576321PMC
November 2012
3 Reads

Inferring transcriptional gene regulation network of starch metabolism in Arabidopsis thaliana leaves using graphical Gaussian model.

BMC Syst Biol 2012 Aug 16;6:100. Epub 2012 Aug 16.

School of Bioresources and Technology, King Mongkut's University of Technology Thonburi, Bangkok, 10140, Thailand.

Background: Starch serves as a temporal storage of carbohydrates in plant leaves during day/night cycles. To study transcriptional regulatory modules of this dynamic metabolic process, we conducted gene regulation network analysis based on small-sample inference of graphical Gaussian model (GGM).

Results: Time-series significant analysis was applied for Arabidopsis leaf transcriptome data to obtain a set of genes that are highly regulated under a diurnal cycle. Read More

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http://dx.doi.org/10.1186/1752-0509-6-100DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3490714PMC
August 2012
5 Reads

A transcriptional dynamic network during Arabidopsis thaliana pollen development.

BMC Syst Biol 2011 23;5 Suppl 3:S8. Epub 2011 Dec 23.

College of Life Sciences, Northeast Forestry University, Heilongjiang, Harbin 150040, China.

Background: To understand transcriptional regulatory networks (TRNs), especially the coordinated dynamic regulation between transcription factors (TFs) and their corresponding target genes during development, computational approaches would represent significant advances in the genome-wide expression analysis. The major challenges for the experiments include monitoring the time-specific TFs' activities and identifying the dynamic regulatory relationships between TFs and their target genes, both of which are currently not yet available at the large scale. However, various methods have been proposed to computationally estimate those activities and regulations. Read More

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http://dx.doi.org/10.1186/1752-0509-5-S3-S8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3287576PMC
November 2012
12 Reads
8 Citations
2.440 Impact Factor

iAK692: a genome-scale metabolic model of Spirulina platensis C1.

BMC Syst Biol 2012 Jun 15;6:71. Epub 2012 Jun 15.

Systems Biology and Bioinformatics Research Group, Biochemical and Pilot Plant Research and Development Unit, King Mongkut's University of Technology Thonburi, National Center for Genetic Engineering and Biotechnology, Bangkok, Thailand.

Background: Spirulina (Arthrospira) platensis is a well-known filamentous cyanobacterium used in the production of many industrial products, including high value compounds, healthy food supplements, animal feeds, pharmaceuticals and cosmetics, for example. It has been increasingly studied around the world for scientific purposes, especially for its genome, biology, physiology, and also for the analysis of its small-scale metabolic network. However, the overall description of the metabolic and biotechnological capabilities of S. Read More

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http://dx.doi.org/10.1186/1752-0509-6-71DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3430566PMC
June 2012
6 Reads

Systems biology of bacterial nitrogen fixation: high-throughput technology and its integrative description with constraint-based modeling.

BMC Syst Biol 2011 Jul 29;5:120. Epub 2011 Jul 29.

Programa de Genomica Funcional de Procariotes, Centro de Ciencias Genómicas-UNAM, Av, Universidad s/n, Col, Chamilpa, Cuernavaca Morelos, C,P, 62210, Mexico.

Background: Bacterial nitrogen fixation is the biological process by which atmospheric nitrogen is uptaken by bacteroids located in plant root nodules and converted into ammonium through the enzymatic activity of nitrogenase. In practice, this biological process serves as a natural form of fertilization and its optimization has significant implications in sustainable agricultural programs. Currently, the advent of high-throughput technology supplies with valuable data that contribute to understanding the metabolic activity during bacterial nitrogen fixation. Read More

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http://dx.doi.org/10.1186/1752-0509-5-120DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3164627PMC
July 2011
5 Reads

Regulatory link mapping between organisms.

BMC Syst Biol 2011 May 4;5 Suppl 1:S4. Epub 2011 May 4.

Faculty of Computer Science, University of New Brunswick, Fredericton, NB, Canada.

Background: Identification of gene regulatory networks is useful in understanding gene regulation in any organism. Some regulatory network information has already been determined experimentally for model organisms, but much less has been identified for non-model organisms, and the limited amount of gene expression data available for non-model organisms makes inference of regulatory networks difficult.

Results: This paper proposes a method to determine the regulatory links that can be mapped from a model to a non-model organism. Read More

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http://dx.doi.org/10.1186/1752-0509-5-S1-S4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3121120PMC
May 2011
16 Reads

TF-Cluster: a pipeline for identifying functionally coordinated transcription factors via network decomposition of the shared coexpression connectivity matrix (SCCM).

BMC Syst Biol 2011 Apr 15;5:53. Epub 2011 Apr 15.

Morgridge Institute for Research, Madison, WI 53715, USA.

Background: Identifying the key transcription factors (TFs) controlling a biological process is the first step toward a better understanding of underpinning regulatory mechanisms. However, due to the involvement of a large number of genes and complex interactions in gene regulatory networks, identifying TFs involved in a biological process remains particularly difficult. The challenges include: (1) Most eukaryotic genomes encode thousands of TFs, which are organized in gene families of various sizes and in many cases with poor sequence conservation, making it difficult to recognize TFs for a biological process; (2) Transcription usually involves several hundred genes that generate a combination of intrinsic noise from upstream signaling networks and lead to fluctuations in transcription; (3) A TF can function in different cell types or developmental stages. Read More

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http://dx.doi.org/10.1186/1752-0509-5-53DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3101171PMC
April 2011
8 Reads

A new computational method to split large biochemical networks into coherent subnets.

BMC Syst Biol 2011 Feb 7;5:25. Epub 2011 Feb 7.

Centre for Advanced Computational Solutions, Dept WF & Molecular Bioscience, Lincoln University, Ellesmere Junction Road, Christchurch, New Zealand.

Background: Compared to more general networks, biochemical networks have some special features: while generally sparse, there are a small number of highly connected metabolite nodes; and metabolite nodes can also be divided into two classes: internal nodes with associated mass balance constraints and external ones without. Based on these features, reclassifying selected internal nodes (separators) to external ones can be used to divide a large complex metabolic network into simpler subnetworks. Selection of separators based on node connectivity is commonly used but affords little detailed control and tends to produce excessive fragmentation. Read More

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http://dx.doi.org/10.1186/1752-0509-5-25DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3045323PMC
February 2011
2 Reads

Integrated functional networks of process, tissue, and developmental stage specific interactions in Arabidopsis thaliana.

BMC Syst Biol 2010 Dec 31;4:180. Epub 2010 Dec 31.

Computer Science Department, Princeton University, Princeton, NJ, USA.

Background: Recent years have seen an explosion in plant genomics, as the difficulties inherent in sequencing and functionally analyzing these biologically and economically significant organisms have been overcome. Arabidopsis thaliana, a versatile model organism, represents an opportunity to evaluate the predictive power of biological network inference for plant functional genomics.

Results: Here, we provide a compendium of functional relationship networks for Arabidopsis thaliana leveraging data integration based on over 60 microarray, physical and genetic interaction, and literature curation datasets. Read More

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http://dx.doi.org/10.1186/1752-0509-4-180DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3023688PMC
December 2010
4 Reads

Integration of metabolic databases for the reconstruction of genome-scale metabolic networks.

BMC Syst Biol 2010 Aug 16;4:114. Epub 2010 Aug 16.

Faculty of Life Sciences, University of Manchester, Manchester M13 9PT, UK.

Background: Genome-scale metabolic reconstructions have been recognised as a valuable tool for a variety of applications ranging from metabolic engineering to evolutionary studies. However, the reconstruction of such networks remains an arduous process requiring a high level of human intervention. This process is further complicated by occurrences of missing or conflicting information and the absence of common annotation standards between different data sources. Read More

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http://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0
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http://dx.doi.org/10.1186/1752-0509-4-114DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2930596PMC
August 2010
7 Reads

Modeling the global effect of the basic-leucine zipper transcription factor 1 (bZIP1) on nitrogen and light regulation in Arabidopsis.

BMC Syst Biol 2010 Aug 12;4:111. Epub 2010 Aug 12.

Center for Genomics and Systems Biology, Department of Biology, New York University, 1009 Main Building, New York, NY 10003, USA.

Background: Nitrogen and light are two major regulators of plant metabolism and development. While genes involved in the control of each of these signals have begun to be identified, regulators that integrate gene responses to nitrogen and light signals have yet to be determined. Here, we evaluate the role of bZIP1, a transcription factor involved in light and nitrogen sensing, by exposing wild-type (WT) and bZIP1 T-DNA null mutant plants to a combinatorial space of nitrogen (N) and light (L) treatment conditions and performing transcriptome analysis. Read More

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http://dx.doi.org/10.1186/1752-0509-4-111DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2933594PMC
August 2010
6 Reads

Continuous-time modeling of cell fate determination in Arabidopsis flowers.

BMC Syst Biol 2010 Jul 22;4:101. Epub 2010 Jul 22.

Biometris, Plant Sciences Group, Wageningen University and Research Center, Wageningen, The Netherlands.

Background: The genetic control of floral organ specification is currently being investigated by various approaches, both experimentally and through modeling. Models and simulations have mostly involved boolean or related methods, and so far a quantitative, continuous-time approach has not been explored.

Results: We propose an ordinary differential equation (ODE) model that describes the gene expression dynamics of a gene regulatory network that controls floral organ formation in the model plant Arabidopsis thaliana. Read More

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http://dx.doi.org/10.1186/1752-0509-4-101DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2922098PMC
July 2010
11 Reads

Genome-scale metabolic analysis of Clostridium thermocellum for bioethanol production.

BMC Syst Biol 2010 Mar 22;4:31. Epub 2010 Mar 22.

Department of Chemical and Life Science Engineering, Virginia Commonwealth University, Richmond, VA 23284, USA.

Background: Microorganisms possess diverse metabolic capabilities that can potentially be leveraged for efficient production of biofuels. Clostridium thermocellum (ATCC 27405) is a thermophilic anaerobe that is both cellulolytic and ethanologenic, meaning that it can directly use the plant sugar, cellulose, and biochemically convert it to ethanol. A major challenge in using microorganisms for chemical production is the need to modify the organism to increase production efficiency. Read More

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http://dx.doi.org/10.1186/1752-0509-4-31DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2852388PMC
March 2010
2 Reads

Large-scale analysis of Arabidopsis transcription reveals a basal co-regulation network.

BMC Syst Biol 2009 Sep 3;3:86. Epub 2009 Sep 3.

Department of Plant Sciences, The George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel.

Background: Analyses of gene expression data from microarray experiments has become a central tool for identifying co-regulated, functional gene modules. A crucial aspect of such analysis is the integration of data from different experiments and different laboratories. How to weigh the contribution of different experiments is an important point influencing the final outcomes. Read More

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http://dx.doi.org/10.1186/1752-0509-3-86DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2944327PMC
September 2009
4 Reads

From gene expression to gene regulatory networks in Arabidopsis thaliana.

BMC Syst Biol 2009 Sep 3;3:85. Epub 2009 Sep 3.

School of Computing, University of Leeds, Leeds, UK.

Background: The elucidation of networks from a compendium of gene expression data is one of the goals of systems biology and can be a valuable source of new hypotheses for experimental researchers. For Arabidopsis, there exist several thousand microarrays which form a valuable resource from which to learn.

Results: A novel Bayesian network-based algorithm to infer gene regulatory networks from gene expression data is introduced and applied to learn parts of the transcriptomic network in Arabidopsis thaliana from a large number (thousands) of separate microarray experiments. Read More

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http://dx.doi.org/10.1186/1752-0509-3-85DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2760521PMC
September 2009
2 Reads

The Symbiosis Interactome: a computational approach reveals novel components, functional interactions and modules in Sinorhizobium meliloti.

BMC Syst Biol 2009 Jun 16;3:63. Epub 2009 Jun 16.

Dpt, of Molecular Biology and Biochemistry, University of Malaga, Malaga, Spain.

Background: Rhizobium-Legume symbiosis is an attractive biological process that has been studied for decades because of its importance in agriculture. However, this system has undergone extensive study and although many of the major factors underpinning the process have been discovered using traditional methods, much remains to be discovered.

Results: Here we present an analysis of the 'Symbiosis Interactome' using novel computational methods in order to address the complex dynamic interactions between proteins involved in the symbiosis of the model bacteria Sinorhizobium meliloti with its plant hosts. Read More

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http://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0
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http://dx.doi.org/10.1186/1752-0509-3-63DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2701930PMC
June 2009
2 Reads

A system biology approach highlights a hormonal enhancer effect on regulation of genes in a nitrate responsive "biomodule".

BMC Syst Biol 2009 Jun 6;3:59. Epub 2009 Jun 6.

Department of Biology, New York University, Center for Genomics and Systems Biology, New York 10003, USA.

Background: Nitrate-induced reprogramming of the transcriptome has recently been shown to be highly context dependent. Herein, a systems biology approach was developed to identify the components and role of cross-talk between nitrate and hormone signals, likely to be involved in the conditional response of NO3- signaling.

Results: Biclustering was used to identify a set of genes that are N-responsive across a range of Nitrogen (N)-treatment backgrounds (i. Read More

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http://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0
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http://dx.doi.org/10.1186/1752-0509-3-59DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2702358PMC
June 2009
3 Reads

Information flow during gene activation by signaling molecules: ethylene transduction in Arabidopsis cells as a study system.

BMC Syst Biol 2009 May 5;3:48. Epub 2009 May 5.

Facultad de Ciencias Universidad Autónoma del Estado de Morelos Cuernavaca, Morelos 62209, México.

Background: We study root cells from the model plant Arabidopsis thaliana and the communication channel conformed by the ethylene signal transduction pathway. A basic equation taken from our previous work relates the probability of expression of the gene ERF1 to the concentration of ethylene.

Results: The above equation is used to compute the Shannon entropy (H) or degree of uncertainty that the genetic machinery has during the decoding of the message encoded by the ethylene specific receptors embedded in the endoplasmic reticulum membrane and transmitted into the nucleus by the ethylene signaling pathway. Read More

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http://dx.doi.org/10.1186/1752-0509-3-48DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2688479PMC
May 2009
2 Reads

A combinatorial approach to determine the context-dependent role in transcriptional and posttranscriptional regulation in Arabidopsis thaliana.

Authors:
Le Lu Jinming Li

BMC Syst Biol 2009 Apr 28;3:43. Epub 2009 Apr 28.

Division of Structural and Computational Biology, School of Biological Sciences, Nanyang Technological University, Singapore 637551, Singapore.

Background: While progresses have been made in mapping transcriptional regulatory networks, posttranscriptional regulatory roles just begin to be uncovered, which has arrested much attention due to the discovery of miRNAs. Here we demonstrated a combinatorial approach to incorporate transcriptional and posttranscriptional regulatory sequences with gene expression profiles to determine their probabilistic dependencies.

Results: We applied the proposed method to microarray time course gene expression profiles and could correctly predict expression patterns for more than 50% of 1,132 genes, based on the sequence motifs adopted in the network models, which was statistically significant. Read More

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http://dx.doi.org/10.1186/1752-0509-3-43DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2694151PMC
April 2009
6 Reads

Reconstruction of metabolic pathways for the cattle genome.

BMC Syst Biol 2009 Mar 12;3:33. Epub 2009 Mar 12.

Institute for Genomic Biology, University of Illinois at Urbana-Champaign, IL 61801, USA.

Background: Metabolic reconstruction of microbial, plant and animal genomes is a necessary step toward understanding the evolutionary origins of metabolism and species-specific adaptive traits. The aims of this study were to reconstruct conserved metabolic pathways in the cattle genome and to identify metabolic pathways with missing genes and proteins. The MetaCyc database and PathwayTools software suite were chosen for this work because they are widely used and easy to implement. Read More

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http://dx.doi.org/10.1186/1752-0509-3-33DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2669051PMC
March 2009
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Coordinations between gene modules control the operation of plant amino acid metabolic networks.

BMC Syst Biol 2009 Jan 26;3:14. Epub 2009 Jan 26.

Department of Plant Sciences, the Weizmann Institute of Science, Rehovot 76100, Israel.

Background: Being sessile organisms, plants should adjust their metabolism to dynamic changes in their environment. Such adjustments need particular coordination in branched metabolic networks in which a given metabolite can be converted into multiple other metabolites via different enzymatic chains. In the present report, we developed a novel "Gene Coordination" bioinformatics approach and use it to elucidate adjustable transcriptional interactions of two branched amino acid metabolic networks in plants in response to environmental stresses, using publicly available microarray results. Read More

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http://dx.doi.org/10.1186/1752-0509-3-14DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2646696PMC
January 2009
4 Reads
2.440 Impact Factor

Connecting genes, coexpression modules, and molecular signatures to environmental stress phenotypes in plants.

BMC Syst Biol 2008 Feb 4;2:16. Epub 2008 Feb 4.

Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831-6422, USA.

Background: One of the eminent opportunities afforded by modern genomic technologies is the potential to provide a mechanistic understanding of the processes by which genetic change translates to phenotypic variation and the resultant appearance of distinct physiological traits. Indeed much progress has been made in this area, particularly in biomedicine where functional genomic information can be used to determine the physiological state (e.g. Read More

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http://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0
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http://dx.doi.org/10.1186/1752-0509-2-16DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2277374PMC
February 2008
2 Reads

Unbiased characterization of genotype-dependent metabolic regulations by metabolomic approach in Arabidopsis thaliana.

BMC Syst Biol 2007 Nov 21;1:53. Epub 2007 Nov 21.

RIKEN Plant Science Center, 1-7-22, Yokohama, Kanagawa 230-0045, Japan.

Background: Metabolites are not only the catalytic products of enzymatic reactions but also the active regulators or the ultimate phenotype of metabolic homeostasis in highly complex cellular processes. The modes of regulation at the metabolome level can be revealed by metabolic networks. We investigated the metabolic network between wild-type and 2 mutant (methionine-over accumulation 1 [mto1] and transparent testa4 [tt4]) plants regarding the alteration of metabolite accumulation in Arabidopsis thaliana. Read More

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http://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0
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http://dx.doi.org/10.1186/1752-0509-1-53DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2233643PMC
November 2007
6 Reads

From correlation to causation networks: a simple approximate learning algorithm and its application to high-dimensional plant gene expression data.

BMC Syst Biol 2007 Aug 6;1:37. Epub 2007 Aug 6.

Department of Statistics, Ludwig-Maximilians-Universität München, LudwigstraSSe 33, D-80539 München, Germany.

Background: The use of correlation networks is widespread in the analysis of gene expression and proteomics data, even though it is known that correlations not only confound direct and indirect associations but also provide no means to distinguish between cause and effect. For "causal" analysis typically the inference of a directed graphical model is required. However, this is rather difficult due to the curse of dimensionality. Read More

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http://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0
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http://dx.doi.org/10.1186/1752-0509-1-37DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1995222PMC
August 2007
7 Reads
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