-

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)