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Systems Biology The search for the syntax of biological information, that is, the study of the dynamic networks of interacting biological elements. The aim is to obtain, integrate and analyze complex data from multiple experimental sources using interdisciplinary tools. Some typical technology platforms are: Transcriptomics :whole cell or tissue gene expression measurements. Proteomics :complete identification of proteins and protein expression patterns of a cell or tissue Metabolomics :identification and measurement of all small-molecules metabolites within a cell or tissue Glycomics :identification of the entirety of all carbohydrates in a cell or tissue. Interactomics :encompasses interactions between all molecules within a cell Fluxomics :which deals with the dynamic changes of molecules within a cell over time

Systems Biology

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Systems Biology. The search for the syntax of biological information, that is, the study of the dynamic networks of interacting biological elements. The aim is to obtain, integrate and analyze complex data from multiple experimental sources using interdisciplinary tools. - PowerPoint PPT Presentation

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Page 1: Systems Biology

Systems Biology

• The search for the syntax of biological information, that is, the study of the dynamic networks of interacting biological elements.

• The aim is to obtain, integrate and analyze complex data from multiple experimental sources using interdisciplinary tools.

• Some typical technology platforms are: • Transcriptomics:whole cell or tissue gene expression measurements.

• Proteomics:complete identification of proteins and protein expression patterns of a cell or

tissue • Metabolomics :identification and measurement of all small-molecules metabolites within a

cell or tissue

• Glycomics:identification of the entirety of all carbohydrates in a cell or tissue. • Interactomics:encompasses interactions between all molecules within a cell • Fluxomics:which deals with the dynamic changes of molecules within a cell over

time

Page 2: Systems Biology

Illustrative example of the interaction and contrast between a traditional approach and a Systems Biology approach

Systems Biology is characterized by:Integration of the levels of biological organization and complexity addressed and

Interaction between experimentation and predictive modeling

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Integrated Genomic and Proteomic Analyses of a Systematically Perturbed

Metabolic Network

Trey Ideker, Vesteinn Thorsson, Jeffrey A. Ranish,Rowan Christmas, Jeremy Buhler, Jimmy K. Eng,

Roger Bumgarner, David R. Goodlett, Ruedi Aebersold, Leroy Hood

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The Integrated Approach

Microarrays

Two Hybrid System

Global Quantification and Measurement Of Protein

Integration and Assimilation Into BIOLOGICAL MODELS

DATA

Page 5: Systems Biology

The Strategy

A) Define all of the genes in the genome and the subset of

genes, proteins, and other small molecules constituting the

pathway of interest.

B) Perturb each pathway component through a series of

genetic or environmental manipulations

If possible, define an initial model of the molecular interactions governing

pathway function.

C) Integrate the observed mRNA and protein responses

with the current, pathway-specific model and with the global network of protein-

protein, protein-DNA, and other known physical interactions.

D) Formulate new hypotheses to explain observations not

predicted by the model.

Design additional perturbation experiments

Detect and quantify the corresponding global cellular

response

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A) Define all of the genes in the genome and the subset of genes, proteins, and other small molecules

constituting the pathway of interest.

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A) BASICS of Galactose Utilization(GAL) pathway in Saccharomyces cerevisiae

• Controlled by an operon-like system.• It converts Galactose to Glocose-6-phosphate.• It produces the enzymes essential for galactose

breakdown to glucose.• In the absence of galactose ENZYMES ARE NOT

PRODUCED.• In the presence of galactose STRUCTURAL

GENES(GAL 1, GAL 7, GAL 10) ARE ACTIVATED.• The above enzymes are regulated by Gal 4 and Gal 80

regulatory genes.

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REGULATION of Galactose Utilization GAL) pathway in Saccharomyces cerevisiae

Galactose

Gal3p

Gal 80p

Gal 4p

GAL7

GAL3

GAL10 GAL1

GAL80

GAL2

GALACTOSE INDUCTION LOOP

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B) Perturb each pathway component through a series of genetic or environmental manipulations

Page 10: Systems Biology

B) Application of 20 perturbations to the GAL pathway

• Wild type and nine genetically altered strains were examined. These were:

Transport(gal∆2) Enzymatic(gal∆1, gal∆5, gal∆7, gal∆10) Regulatory(gal∆3, gal∆4, gal∆6, gal∆80)• The strains were perturbed environmentally by growth in presence

(+gal) or absence(-gal) of galactose.• Global changes in mRNA of 6200 nuclear yeast genes were seen.• Identified 997 genes whose mRNA level significantly differed from

control.• These were then divided into 16 clusters in which each cluster

represented genes with similar expression responses over all perturbations.

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RESULTS

PERTURBATION MATRIX

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Comparison of Northern vs. microarray analysis

Medium-gray representing no change, darker or lighter shades representing increasing or decreasing amounts of expression respectively,

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Are the observed changes in mRNA expressionalso reflected at the level of protein abundance?

Wt+gal

Protein extracts

Wt-gal

Protein extracts

Labeled with isotopically heavy and normal ICAT reagents

Combined and digested with trypsin

Fractionated by Multidimensional chromatography

Analyzed by MS/MS

RESULTS:

As a whole, protein-abundance ratios were moderately correlated with their mRNA counterparts.

30 proteins showed clear changes in abundance between wt+gal and wt-gal conditions.

mRNA of 15 did not change significantly in response to perturbation.

Many ribosomal-protein genes increased three-to five fold in mRNA but not in protein abundance.

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C) Integrate the observed mRNA and protein responses with the current, pathway-specific model and with the global network

of protein-protein, protein-DNA, and other known physical interactions.

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C) Can we attribute the observed mRNA and protein changes to underlying regulatory interactions in the cell?

• They assembled a catalogue of previously observed physical interaction in yeast by COMBINING:

a) 2709 protein-protein interactions

b) 317 protein-DNA interactions

• Out of the total genes above they observed 348 genes that were affected in mRNA or protein expression by at least one perturbation and also involved in two or more interactions with the affected genes….

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348 Genes along with their 362 associated interactions as a PHYSICAL INTERACTION NETWORK

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POSSIBILITIES

• A protein DNA interaction may be responsible for directly transmitting an expression change from a transcription factor to a highly co-related target gene.

e.g. Mcm1 Far1and Mig1 Fbp1

• Two genes may be under control of a common transcription factor which is the 3rd gene. C (A,B)

e.g. GAL enzymes regulated by Gnc4 Class of gluconeogenic genes controlled by Sip4 (Fbp1, Pck1, Ic11)

• Scanning of network for indirect effects, such as a change in one gene transmitted to the other through a protein-protein interaction with a signaling protein.

e.g. Gcr2-Gcr1 Tpi1

• Gal4p directly regulates genes in several processes of other networks through novel protein DNA interaction.

e.g. Cluster 1,2,3 contained genes with Gal4 binding sites

• The above genes were involved in glycogen accumulation, protein metabolism and others with unknown function

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Tree comparing gene-expression changes resultingfrom different perturbations to the GAL pathway.

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D) How do the observed responses Of GAL genes compare to their predicted behavior?

• In general the results were same as predicted.

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D) Formulate new hypotheses to explain observations not predicted by the model.

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New Observations, Hypotheses, and possible tests OBSERVATIONS EXAMPLE

HYPOTHESES SYSTEMS-LEVEL TESTS

1) Decrease in GAL-gene expression in

response to gal7D+gal or gal10D+gal Dependent on levels of Gal-1-P or a

derivative metabolite

Examine EP of a gal1Dgal10D+gal double deletion strain, in which Gal-1-

P levels are reduced

2) Effect of gal80D-gal: slow growth and widespread changes in metabolic-gene expression

Stress-related, caused by de-repression of either the GAL enzymes or

transporter

Examine EP of a gal4Dgal80D-gal double deletion, in which the GAL enzymes and

transporter are not expressed

3) GAL5 and GAL6 mRNA levels are unaffected by galactose addition or by deletion of GAL3, 4, or 80

Caused by differences in strains and/or media between this and

previous studies

Obtain EPs for identical strains and media as in previous

studies

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Contd….

OBSERVATIONS EXAMPLE HYPOTHESES SYSTEMS-LEVEL TESTS

5) Expression levels of genes in many other metabolic pathways respond to perturbations of the GAL pathway

Each affected pathway depends on galactose, specific GAL genes, or on the total amount of available energy

Examine EPs of yeast growing in carbon sources other than galactose, e.g. 2% glucose

6) In wt+gal vs. wt-gal, approx. 15 genes change in protein but not mRNA abundance.

These genes are regulated at the level of protein translation or degradation

Compare global translation state of proteins between + vs. - gal, using method of [Q. Zong et al. Proc Natl Acad Sci U.S.A. 96, 10632-6. (1999)]

7) Nine genes w/ predicted Gal4p-binding sites have EPs that are similar to those of known GAL genes

Gal4p regulates transcription of these genes via protein-DNA interactions

Verify predicted interactions by global chromatin immuno-precipitation experiments [B. Ren et al., Science 290, 2306-9. (2000)]

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Tree comparing gene-expression changes resultingfrom different perturbations to the GAL pathway.

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Refinements in the GAL Pathway

Model of galactose utilization. Yeast metabolize galactose through a series of steps involving the GAL2 transporter and enzymes produced by GAL1, GAL7, GAL10, and GAL5. These genes are transcriptionally regulated by a mechanism consisting primarily of GAL4, GAL80, and GAL3. GAL6 produces another regulatory factor thought to repress the GAL enzymes in a manner similar to GAL80. Dotted interactions denote model refinements supported by this study.

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References

• http://www.bbsrc.ac.uk/science/spotlight/systems_biology.html

• 1. Supplementary material is available at www.sciencemag. org/cgi/content/full/292/5518/929/DC1 E. S. Lander, Nature Genet. 21, 3 (1999).

• 2. S. P. Gygi et al., Nature Biotechnol. 17, 994 (1999).• 3. B. Schwikowski, P. Uetz, S. Fields, Nature Biotechnol.• 18, 1257 (2000). [www.nature.com/nbt/web_extras/• supp_info/nbt1200_1257/]• 4. D. Lohr, P. Venkov, J. Zlatanova, FASEB J. 9, 777• (1995).• 5. H. C. Douglas, D. C. Hawthorne, Genetics 49, 837• (1964).• 6. R. J. Reece, Cell Mol. Life Sci. 57, 1161 (2000).• 7. R. Wieczorke et al., FEBS Lett. 464, 123 (1999).• 8. M. Johnston, J. S. Flick, T. Pexton, Mol. Cell. Biol. 14,• 3834 (1994).• 9. I. H. Greger, N. J. Proudfoot, EMBO J. 17, 4771 (1998).

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• 36. M. Ashburner et al., Nature Genet. 25, 25 (2000).• [www.geneontology.org/]• 37. K. Mehlhorn, S. Naeher, The LEDA Platform of Combinatorial• and Geometric Computing (Cambridge Univ. Press,• Cambridge, 1999). [www.algorithmic-solutions.com/]• 38. J. Felsenstein, Cladistics 5, 164 (1989).• 39. M. B. Eisen, P. T. Spellman, P. O. Brown, D. Botstein, Proc.• Natl. Acad. Sci. U.S.A. 95, 14863 (1998).