from newman & banfield, science, 2002
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-. from Newman & Banfield, Science, 2002. Types of models for systems biology. From Price & Shmulevich, 2007, Curr Opinion Biotech. Biochemical reaction network. Selected known pathways and generated two-organism model 170 reactions; 147 compounds in stoich matrix - PowerPoint PPT PresentationTRANSCRIPT
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from Newman & Banfield, Science, 2002
Types of models for systems biology
From Price & Shmulevich, 2007, Curr Opinion Biotech
• Biochemical reaction network
Modeling Syntrophic growth of Desulfovibrio & Methanococcus
• Selected known pathways and generated two-organism model
• 170 reactions; 147 compounds in stoich matrix
• Fluxanalyzer program (run via MatLab)
• Good predictions of behavior of pure cultures and relative growth rates of orgs in co-culture
• In silico & real knockout mutants suggested that interspecies H2-transfer essential and formate-transfer not.
From Price & Shmulevich, 2007, Curr Opinion Biotech
The GNS framework: a combined approach
Genome mining
Gene Network Sciences’Network Inference platform
Aksenov et al., 2005
Model development (data driven) Model simulation (hypothesis-generating)
GNS framework applied to cancer drug discovery
Perturbation (e.g. drug type and level)
Cell response (gene regulation)
Modified phenotype (e.g. reduced cancer cell division)
“heterologous” datasets
ID’s genes that are biomarkers for cancer and/or targets for drugs
Network inference engine
Iterative experiments toRefine models
Nodes and edges (interactions) in an inference model
Key tools:Bayes theorem
Insights gained• H2 as only electron-donor• obligately uses halogenated
compounds as e- acceptors• TceA protein: TCE-dehalogenase
enzyme discovered • complex media requirements
(mixed culture extract added)• Still no genetic system or
successful heterologous expression of RDase genes
(from Maymo-Gatell et al., 1997, Science)
0.2 μm
Dehalococcoides ethenogenes strain 195: First isolate to dehalorespire PCE
Highlights from the genome of D. ethenogenes
(from Seshadri et al., Science 2005)
• 1.5 Mb in size (streamlined)• Annotation suggests:
– Up to 19 Reductive Dehalogenases (RDases)
– 5 Hydrogenases– Vitamin B12 salvage pathways– Other oxidoreductases (including
“formate dehydrogenase”) that might be directly involved in dehalorespiration
– Evidence of extensive horizontal gene transfer
– Lesions in key intermediary pathways (TCA cycle; amino acid biosynth)
– Lots of unknown topology (even around RDases
His kinase sensor
Responseregulator
Signal?NADNADHFADFADH
C=C
Cl
Cl Cl
Cl
PERIPLASM
S-LAYER
CYTOPLASM
C=C
Cl
Cl Cl
Cl
Ethene
C=C
H
H H
H
RD RD anchoring protein
e-
Ferredoxin 2Fe-FsDesulforedoxinGlutaredoxinRubredoxin Flavodoxin Thioredoxin
Tetrachloroethene
Redox potentialPMF
PA
S
Phospho-
acceptor
AT
Pase
H2
HCl2H++2e-
H+
ADP+ Pi
ATP
RD
HCO2– CO2
NADH?F420?H2?
H+
2H+H2 2H+H2
H+
2H+H2
H+
2H+H2
Hup
EchHyc
Nuo
Hym
Fdh
??
Mod
= NiFe H2ase large subunit
= Fe H2ase large subunit
= Molybdopterin-containing subunit
CO+H2O CO2+H2
CODH
N2+ 8H++ 8e-+16 ATP 2NH3 +H2 +16 ADP+16 Pi
Nif
Vhu
2H+H2
Some key questions the gene network modeling will address:
• What networks of RDases emerge in cultures grown on different substrates? Are there specific transcriptional regulators with expression tied to individual or groups of RDases?
• Are individual RDases co-regulated with other elements of the proposed electron transport chain (e.g Hup)?
• Which genes are co-regulated with highly-expressed genes of unknown function: “Fdh” and DET00754/755 – each of which were found in all DHC cultures in high abundance.
• Which gene networks correlate with the presence of other community members? Does this provide any insight regarding the nutritional benefit to DHC of mixed culture growth?
• Which, if any, networks are sensitive to hydrogen concentrations?• How do candidate bioindicators (highlighted in Preliminary Results)
correlate with respiration rate over a wider range of growth conditions?
• Which biomarkers are indicative of DHC stress
DoD project (5/07-12/09): DET mixed cult focused
• Overall objective is to develop a whole-cell model of gene networks in DHC that relates growth conditions to gene expression levels and, in turn, relates these levels to dehalorespiration rates.
• Approach framework will be to quantitatively monitor genome-wide RNA and protein levels in a model DHC strain (D. ethenogenes strain 195 - DET) growing in mixed-culture conditions in pseudo-steady-state reactors and to utilize systems biology algorithms of network inference to compile the data into a model
NSF (9/07 – 8/10):KB-1 focused• The overall objective of the proposed work is to understand how two well-
studied DHC cultures respond to environmental conditions and how DHC gene expression can be monitored to inform enhanced bioremediation and forecast modeling efforts at contaminated field sites. The three main objectives (Phases) are:– Objective/Phase 1: Develop in-depth models of gene networks for two well-
studied DHC growing in mixed culture conditions. Here, we aim to determine key gene networks in the DHC that correlate with the type and rate of dechlorination and that indicate how these organisms respond to stressors.
– Objective/Phase 2. Test model predictions for one of the DHC models (the bioaugmentation culture KB1) under various field conditions.
– Objective/Phase 3. Determine robust quantitative chloroethene dehalorespiration bioindicators and develop qRTPCR and RNA-biosensor assays for them.
Perturbations and data types
• Perturbations/interventions (n=30-50 initially)
• Variations in– Type and loading rate of
chlorinated compounds– Type and loading rate of
electron donor– Culture density– Stressors (Oxygen, pH,
chloroform).
• Datasets to be collected– Omics (microarray;
proteomics)– Metabolites (organic acids;
H2)– Populations of DHC &
other orgs (qPCR)– General activity of pop’ns
(qRTPCR) – Chlorinated substrates &
products– Dechlorination rates
(phenotype of interest)