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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 formatetransfer 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
Iterative experiments to
Refine models
Perturbation (e.g. drug type and level)
Cell response (gene regulation)
Modified phenotype (e.g.
reduced cancer cell division)
“heterologous” datasets
Network
inference
engine
ID’s genes that are
biomarkers for cancer
and/or targets for drugs
Nodes and
edges
(interactions)
in an
inference
model
Key tools:
Bayes
theorem
Dehalococcoides ethenogenes strain 195:
First isolate to dehalorespire PCE
Insights gained
0.2 μm
(from Maymo-Gatell et al., 1997, Science)
• 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
Highlights from the genome of D.
ethenogenes
•
•
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
(from Seshadri et al., Science 2005)
Ethene
S-LAYER
H
C=C
H
H
Cl
H
Cl
HCl
PERIPLASM
??
Tetrachloroethene
HCO2– CO2
C =C
H2
Cl
Cl
2H++2e-
2H+
H2
H+
H+
RD
H+
Hup
Mod
Fdh
ATP
Nuo
CYTOPLASM
NADH?
F420?
H2?
CO+H2O
N2+ 8H++ 8e-+16 ATP
e-
Ferredoxin 2Fe-Fs
Desulforedoxin
Glutaredoxin
Rubredoxin
Flavodoxin
Thioredoxin
CODH
Nif
H2
2H+
Hym
H2
2H+
Hyc
2H+
Ech
H2
CO2+H2
Cl
2NH3 +H2 +16 ADP+16 Pi
Cl
C=C
2H+
Vhu
Cl
Cl
Signal?
Redox potential
PMF
Response
regulator
= Fe H2ase large subunit
= Molybdopterin-containing subunit
= NiFe H2ase large subunit
H2
H+
NADNADH
FADFADH
His kinase
sensor
RD
RD anchoring
protein
ADP
+ Pi
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 wellstudied 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/intervention
s (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)