Functional Genomics and Systems Biology Group and at IBM Gus - - PowerPoint PPT Presentation

IBM Computational Biology Center IBM Research

Functional Genomics and Systems Biology Group and at IBM

Computational Biology Center Thomas J. Watson Research Center gustavo@us.ibm.com

www.research.ibm.com/FunGen

Gus Stolovitzky Jorge Lepre Rich Mushlin Jeremy Rice Keith Duggar Lan Ma John Wagner Aaron Kershenbaum Accomplices Gyan Bhanot Yuhai Tu: Phys. Sci Glenn Held: Phys. Sci.

IBM Computational Biology Center

Start from a known Network Topology

518 actual connections - 423 nodes

Original E-coli Network

IBM Computational Biology Center

Simulate a dynamic behavior

simulated dynamics using known topology

Produce a simulated gene expression Dataset:

Gene 423

….

Gene 2 Gene 1

Exp N …. Exp 2

Exp 1

u 11 u12

u1N u2N

u22 u21

518 actual connections - 423 nodes

Original E-coli Network

IBM Computational Biology Center

Reconstructed Network

Using your favorite algorithm, reconstructed original network from gene expression data

Gene 423

….

Gene 2 Gene 1

Exp N …. Exp 2

Exp 1

u 11 u12

u1N u2N

u22 u21

Reverse Engineer this

IBM Computational Biology Center

Use some metrics to compare inferred to original

518 actual connections - 423 nodes 495 connections correctly predicted 85 connections wrongly predicted

Original E-coli Network

Rice, Tu and Stolovitzky, “Reconstructing synthetic biological network”, Bioinformatics, 21(6):765-73 (2005)

Reconstructed Network

IBM Computational Biology Center

Inference of Biological Networks

Reconstructed Network

518 actual connections - 423 nodes 495 connections correctly predicted 85 connections wrongly predicted

Original E-coli Network

+ synthetic dynamics + protocols representing actual experimental assays + conditional correlation algorithms (blind to original network) = Reconstructed network Network topology

Rice, Tu and Stolovitzky, “Reconstructing synthetic biological network”, Bioinformatics, 21(6):765-73 (2005) Rice and Stolovitzky, Making the most of it: Pathway reconstruction and integrative simulation using the data at hand, Biosilico 2(2):70-7 (2004). Basso, Margolin, Nemenman, Klein, Wiggins, Stolovitzky, Dalla Favera, and Califano, Reverse engineering

• f regulatory networks in human B cells, 37(4):382-90 (2005).

IBM Computational Biology Center

Standardized Datasets for Tool Development

DREAM: A Dialogue on Reverse Engineering Assessment and Methods Critical Assessment of Techniques for Protein Structure Prediction (CASP)

GSMLISHSDMNQQLKSAGIGFNATELHGFLSGLLCGGLKDQSWLPLLYQFSNDNHA YPTGLVQPVTELYEQISQTLSDVEGFTFELGLTEDENVFTQADSLSDWANQFLLGIG LAQPELAKEKGEIGEAVDDLQDICQLGYDEDDNEEELAEALEEIIEYVRTIAMLFYS HFNEGEIESKPVLH

http://www.nyas.org/dream2 Columbia University (Andrea Califano) & IBM Computational Biology Center (G. Stolovitzky)

IBM Computational Biology Center In E. coli, some functional modules are composed out of smaller motifs, such as in the flagella formation pathway.

• E. coli regulatory network
• Biological Networks have an architecture yet to be understood…

External source - 37 nodes External sink - 208 nodes Internal source - 29 nodes Internal sink – 94 nodes Intermediary - 21 nodes

• …and functional modules. We designed algorithms for discovery of

network motifs using sub-graph isomorphism algorithms.

…

Squares Triangles

tar flgKL flgNM moaA-E flgAMN motABcheAW tsr fliDST fliC hns fliL-R flhBAE fliE flgB-K fliG-K flhDS 4 other target genes

Motifs Combination of Motifs

Motif Discovery in Biological Networks

Rice, Kershenbaum and Stolovitzky. Analyzing and reconstructing gene regulatory networks. “Specialist review”, The Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics, John Wiley & Sons, Ltd:Chichester (2005). Rice, Kershenbaum and Stolovitzky, Lasting impressions: Motifs in protein-protein maps may provide footprints of evolutionary events, Proc. Natl. Acad. Sci. USA, 102, 3173-4 (2005).

IBM Computational Biology Center

p53 protein

Digital response of tumor suppressor p53 to IR

DNA damage initiation & repair ATM: DNA damage detection P53- MDM2

• scillator

Irradiation Cell Cycle Arrest and DNA Repair

?

DNA damage initiation & repair ATM: DNA damage detection P53- MDM2

• scillator

Irradiation Cell Cycle Arrest and DNA Repair

?

Irradiation

Lahav, Rosenfeld, Sigal, Geva-Zatorsky, Levine, Elowitz, & Alon: Dynamics of the p53-Mdm2 feedback loop in individual cells. Nat Genet. 36: 147-50 (2004)

IBM Computational Biology Center

m R N A m R N A p 53 Protein T P 5 3 D N A p53

Delay=?

Protein TP53* Basal m d m 2 Protein M D M 2 D N A

Delay=?

Basal (P1) m d m 2 (Fast) (Slow) Induced (P2) A T M * m R N A m R N A p 53 Protein T P 5 3 D N A p53

Delay=?

Protein TP53* Basal m d m 2 Protein M D M 2 D N A

Delay=?

Basal (P1) m d m 2 (Fast) (Slow) Induced (P2) A T M *

Lahav et al., Nature Genetics 2004

500 1000 1500 1 2 3 4 5

TP53 mdm2 MDM2

Molecular intensity (fold)

Time (min)

Response to 5Gy

DNA damage

Digital response of tumor suppressor p53 to IR

Ma, Wagner, Rice, Hu, Levine and Stolovitzky, A plausible model for the digital response of p53 to DNA damage, Proc. Natl. Acad. Sci. U S A. 102, 14266 (2005). Wagner; Ma; Rice; Hu; Levine; Stolovitzky, p53-Mdm2 loop controlled by a balance of its feedback strength and effective dampening using ATM and delayed feedback, IEE PROCEEDINGS SYSTEMS BIOLOGY, 152, 3, 109-118 (2005).

IBM Computational Biology Center

Predictions

Basal Protein (Fast) (Slow) ATM* mRNA p53 P53-induced transcription 53 p DNA Mdm2 DNA Mdm2 Protein Protein p53 p53*

Delay??

Basal mRNA Mdm2

Delay??

Figure 4 (From Ma, Wagner et.al.) - Diagram of the p53-Mdm2 oscillator. p53 is translated from p53 mRNA and inactive for induction of its targets. Phosphorylated by ATM*, p53 becomes active (p53*), and able to transcribe (after a time delay) Mdm2 which also has a basal transcription rate. Mdm2 protein promotes a fast degradation of p53 and a slow degradation of p53*. In addition to a basal self-degradation, Mdm2 is degraded by a mechanism stimulated by ATM*.

Figures 4 and 8 from Ma, Wagner et al., A B

Figure 8 (From Ma, Wagner, et. al.) - One- dimensional bifurcation diagrams of steady-state p53 versus single parameter variation of Mdm2 basal transcription rate (A) or p53 basal transcription rate (B). The stable equilibrium is represented by solid line. The lower and upper bounds of stable oscillation are represented by paired dotted lines.

2 4 6 8 5 10 15 20 25 30

p53 concentration (fold)

equilibrium

• scillation

p53 basal transcription (fold)

1 2 3 2 4 6 8

mdm2 basal transcription (fold) p53 concentration (fold)

equilibrium

• scillation

IBM Computational Biology Center

Validation of Predictions

2 4 6 8 10 12 1 2 3 4 5

2 4 6 8 10 12 0.5 1 1.5 2 2.5 3

Relative induction fold of Mdm2 Relative induction fold of p53

Hu, Feng, Ma, Wagner, Rice, Stolovitzky, Levine. “A single nucleotide polymorphism in the MDM2 gene disrupts the oscillation

• f p53 and MDM2 levels in cells.” Cancer Res. 2007 Mar

15;67(6):2757-65.