from Newman & Banfield, Science, 2002

1. - from Newman & Banfield, Science, 2002

2. Types of models for systems biology

3. From Price & Shmulevich, 2007, Curr Opinion Biotech

4. Biochemical reaction network

5. 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. Modeling Syntrophic growth of Desulfovibrio & Methanococcus

6. From Price & Shmulevich, 2007, Curr Opinion Biotech

8. The GNS framework: a combined approach Genome mining

9. Gene Network Sciences’Network Inference platform Model simulation (hypothesis-generating) Model development (data driven)

10. Iterative experiments to Refine models Perturbation (e.g. drug type and level) Cell response (gene regulation) Modified phenotype (e.g. reduced cancer cell division) GNS framework applied to cancer drug discovery “heterologous” datasets Network inference engine ID’s genes that are biomarkers for cancer and/or targets for drugs

11. Nodes and edges (interactions) in an inference model Key tools: Bayes theorem

12. 0.2 μm (from Maymo-Gatell et al., 1997, Science) Dehalococcoides ethenogenes strain 195: First isolate to dehalorespire PCE 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

13. 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)

14. H H C=C Cl Cl Cl Cl C=C C=C H H Cl Cl Cl Cl ?? HCO2– CO2 ADP + Pi Mod ATP H+ H2 2H+ Ethene Tetrachloroethene H2 S-LAYER 2H++2e- HCl PERIPLASM H2 2H+ H+ H+ H+ RD Hup Fdh Nuo H2 2H+ H2 2H+ NADH? F420? H2? Ech Hym Hyc CYTOPLASM H2 2H+ CODH Vhu CO+H2O CO2+H2 Nif N2+ 8H++ 8e-+16 ATP 2NH3 +H2 +16 ADP+16 Pi Signal? NADNADH FADFADH Ferredoxin 2Fe-Fs Desulforedoxin Glutaredoxin Rubredoxin Flavodoxin Thioredoxin Redox potential PMF e- RD anchoring protein Response regulator RD His kinase sensor = Fe H2ase large subunit = Molybdopterin-containing subunit PAS ATPase Phospho- acceptor = NiFe H2ase large subunit

15. 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

16. DoD project (5/07-12/09): DET mixed cult focused • Overall objectiveis 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.

17. 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) Perturbations and data types