TRND physical chemistry

Slide1: 

Transcriptional Regulatory Network Discovery: the Physical Chemistry of Cell Transformation Center for Cell and Virus Theory Indiana University Bloomington IN 47405 ortoleva@indiana.edu http://sysbio.indiana.edu

Slide2: 

Research Support U.S. Department of Energy U.S. Air Force/DARPA – Bio-SPICE Indiana 21st Century Science and Technology IBM

Cell Transformation: 

Cell Transformation The feedback and nonlinearity of the chemical kinetics implied by transcriptional and other regulatory networks supports multiple distinct cell states that are stable in the same extra-cellular medium. We are investigating the possibility that transitions between these states could correspond to differentiation and/or the onset of disease.

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Schematic Cell Regulatory Flowchart

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TRN-Discovery – Medical Applications Examination of TRN structure and TF profiles will yield a rich set of biomarkers.

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Workflow Taking Gene Expression Microarray Data as Input and Yielding Cell Biological Understanding as Output

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TRN Discovery Workflow

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The GenDat TF/Gene Database: Reaching Critical Mass 2240 genes, 7530 interactions =>average of 3.4 interactions/gene Assuming approximately 25,000x3.4 interactions exist, we have 8% as of August 2005 Human Cell Gene-TF Interactions

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CCVT Website TRN Reconstruction System Options for Making the Preliminary Network Set organism preference list for genes and TFs Edit any interaction Change names of simple monomer TFs to dimers Change names of TF complexes like A/B/B to A/B Delete or merge TFs Delete genes Remove TFs that only regulate a few genes Remove interactions for which the up/down nature is unknown Remove genes with too much missing data Add predicted interactions (e.g. from an earlier run) with scores greater than a user-determined threshold

Automated Generation of Complex TRNs: 

Automated Generation of Complex TRNs Monocyte SEB partial network automatically generated using our transcriptional regulatory network discovery system and Jett/Hammamieh microarray data (degree>9).

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Hypothesis-Driven Informatics Sin = score for proposed gene i/TF n regulatory interaction ftr(S)dS = fraction of scores in the increment dS centered about S for the training set (GenDat) fran(S)dS = same for random gene/TF pair

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Phylogenetic Similarity with Preliminary TRN Ratio of probability distribution of the training set over that of the random set for E. coli K-12

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Chemical Kinetic Model This is a schematized, reduced version of the transcription, translation, post-translation model used in our nonlinear dynamical systems analysis of cell dynamics and transformation.

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Multigene Bifurcation Diagram Bifurcation curve showing total RNA as a function of transcription rate constant for an epithelial cell TRN. This chemical kinetic model is being used to study cell-level responses to an anti-cancer drug (tamoxifen).

Single Gene Bifurcation Diagram: 

Single Gene Bifurcation Diagram Cell state bifurcation diagram showing RNA level of ESR1 and TP53 (left graph), and TBP (right graph) as a function of transcription rate forefactor.

Cell state bifurcation diagram showing RNA level of an oncogene (JUN red), a tumor suppressor gene (BRCA1 blue) and an auxiliary gene (ATF1 green) as a function of transcription rate forefactor. Curves JUN and ATF1 use the left vertical scale, while the curve BRCA1 uses the right vertical scale. This diagram illustrates the behavior of a coupled oncogene, tumor suppressor gene, and auxiliary gene.: 

Cell state bifurcation diagram showing RNA level of an oncogene (JUN red), a tumor suppressor gene (BRCA1 blue) and an auxiliary gene (ATF1 green) as a function of transcription rate forefactor. Curves JUN and ATF1 use the left vertical scale, while the curve BRCA1 uses the right vertical scale. This diagram illustrates the behavior of a coupled oncogene, tumor suppressor gene, and auxiliary gene.

Slide17: 

Number of genes considered to have distinguishable features at each point along the transcription rate forefactor axis showing four areas having multiple states. The criterion of significance for a gene at a given transcription rate forefactor relies on whether there are distinct states (i.e., that the RNA concentration difference is larger than 1% of the variation in RNA level (maximum minus minimum concentration over the full range of transcription rate forefactor for that gene)).

Slide18: 

Most important genes (up to 40) for each of the four zones. The importance factor is defined in the previous slide. Oncogenes (*) and tumor suppressor genes (+) are from http://embryology.med.unsw.edu.au/DNA/DNA10.htm

Slide19: 

In Progress: Multiple Dataset Integration Multiple types of data are automatically integrated into the calibration and model-building procedure via our information theory methodology.