pathways, networks and systems biology bmi 730

Pathways, Networks and Systems Biology

BMI 730 Kun Huang

Department of Biomedical InformaticsOhio State University

Pathways and Networks

Databases and Resources

Challenges in system biology• Large data• New computation and modeling methods• Kinetics vs. dynamics

Scale-Free Network

Network Motifs

Genes Functions, pathways and networks

Pathway – What’s out there?

240

Pathway software• Pathway Miner (Free, need to compile)• GenMapp (Free)• CytoScape (Free)• GESA (Free)• DAVID (Free)• Pathway Architect (Commercial)• Pathway Studio (Commercial)• Ingenuity Pathway Analysis (Commercial)

• Manually curated• On-demand computation

BiologyDomain knowledge

• Hypothesis testingExperimental work

• Genetic manipulation• Quantitative measurement• Validation

System SciencesTheoryAnalysisModeling

• Synthesis/prediction• Simulation• Hypothesis generation

InformaticsData management

• DatabaseComputational infrastructure

• Modeling tools• High performance computing

Visualization

System Biology

Understanding! Prediction!

“A key element of the GTL program is an integrated computing and technology infrastructure, which is essential for timely and affordable progress in research and in the development of biotechnological solutions. In fact, the new era of biology is as much about computing as it is about biology. Because of this synergism, GTL is a partnership between our two offices within DOE’s Office of Science—the Offices of Biological and Environmental Research and Advanced Scientific Computing Research.

Only with sophisticated computational power and information management can we apply new technologies and the wealth of emerging data to a comprehensive analysis of the intricacies and interactions that underlie biology. Genome sequences furnish the blueprints, technologies can produce the data, and computing can relate enormous data sets to models linking genome sequence to biological processes and function.”

Feedback is ubiquitous; it is essential for the stabilization of any system (biological, engineering, social …)

Control SystemInput Output

Open Loop

Control SystemInput Output

Feedback

+±

Closed Loop

Taniguchi et al. Nature Reviews Molecular Cell Biology 7, 85–96 (February 2006) | doi:10.1038/nrm1837

Challenges in system biology• Large data• Kinetics vs. dynamics• Multiple (temporal) scale• New computation and modeling methods• New mathematics or new physics laws

A B

Oscillation

Maeda et al., Science, 304(5672):875-878, 2004

Simple Two Nodes Pattern

Bistable dynamics in a two-gene system with cross-regulation. A. Gene regulatory circuit diagram. Blunt arrows indicate mutual inhibition of genes X and Y. Dashed arrows indicate a basal synthesis (affected by the inhibition) and an independent first-order degradation of the factors. B. Two-dimensional XY phase plane representing the typical dynamics of the circuit. Every point (X, Y) represents a momentary state defined by the values of the pair X, Y. Red arrows are gradient vectors indicating the direction and extent that the system will move to within a unit time at each of the (X, Y) positions. Collectively, the vector field gives rise to a "potential landscape", visualized by the colored contour lines (numerical approximation). In this "epigenetic landscape", the stable states (attractors) are in the lowest points in the valleys: a (X>>Y) and b (Y>>X) (gray dots). C. Schematic representation of the epigenetic landscape as a section through a and b in which every red dot represents a cell. Experimentally, this bistability is manifested as a bimodal distribution in flow cytometry histograms in which the stable states a and b appear as peaks at the respective level of marker expression (e.g., Y).

Chang et al., Multistable and multistep dynamics in neutrophil differentiation, BMC Cell Biology 2006, 7:11

Marlovits et.al., Biophysical Chemistry, Vol:72, p.169-184

Pomerening et.al., Cell, Vol:122(4), p.565-578

New system biology

Kinetics vs. Dynamics

Compartmentalization (Spatial and Temporal)

Hybrid Systems and System Abstraction• Hierarchical/multiscale description• Discrete Event System • New System Theory

Graph Theory and Network Theory / New Mathematics and New Physics

A Tale of Two Groups

A.-L. BarabasiTen Most Cited Publications:

Albert-László Barabási and Réka Albert, Emergence of scaling in random networks , Science 286, 509-512 (1999). [ PDF ] [ cond-mat/9910332 ]

Réka Albert and Albert-László Barabási, Statistical mechanics of complex networks Review of Modern Physics 74, 47-97 (2002). [ PDF ] [cond-mat/0106096 ]

H. Jeong, B. Tombor, R. Albert, Z.N. Oltvai, and A.-L. Barabási, The large-scale organization of metabolic networks, Nature 407, 651-654 (2000). [ PDF ] [ cond-mat/0010278 ]

R. Albert, H. Jeong, and A.-L. Barabási, Error and attack tolerance in complex networksNature 406 , 378 (2000). [ PDF ] [ cond-mat/0008064 ]

R. Albert, H. Jeong, and A.-L. Barabási, Diameter of the World Wide Web Nature 401, 130-131 (1999). [ PDF ] [ cond-mat/9907038 ]

H. Jeong, S. Mason, A.-L. Barabási and Zoltan N. Oltvai, Lethality and centrality in protein networksNature 411, 41-42 (2001). [ PDF ] [ Supplementary Materials  1,   2  ]

E. Ravasz, A. L. Somera, D. A. Mongru, Z. N. Oltvai, and A.-L. Barabási, Hierarchical organization of modularity in metabolic networks, Science 297, 1551-1555 (2002). [ PDF ] [ cond-mat/0209244 ] [ Supplementary Material ]

A.-L. Barabási, R. Albert, and H. Jeong, Mean-field theory for scale-free random networks Physica A 272, 173-187 (1999). [ PDF ] [ cond-mat/9907068 ]

Réka Albert and Albert-László Barabási, Topology of evolving networks: Local events and universality Physical Review Letters 85, 5234 (2000). [ PDF ] [ cond-mat/0005085 ]

Albert-László Barabási and Zoltán N. Oltvai, Network Biology: Understanding the cells's functional organization, Nature Reviews Genetics 5, 101-113 (2004). [ PDF ]

A Tale of Two Groups

Uri Alon at Weissman InstituteSelected Publications:

R Milo, S Itzkovitz, N Kashtan, R Levitt, S Shen-Orr, I Ayzenshtat, M Sheffer & U Alon , Superfamilies of designed and evolved networks, Science, 303:1538-42 (2004). Pdf. R Milo, S Shen-Orr, S Itzkovitz, N Kashtan, D Chklovskii & U Alon, Network Motifs: Simple Building Blocks of Complex Networks, Science, 298:824-827 (2002). Pdf. S Shen-Orr, R Milo, S Mangan & U Alon, Network motifs in the transcriptional regulation network of Escherichia coli. Nature Genetics, 31:64-68 (2002). Pdf. S. Mangan, S. Itzkovitz, A. Zaslaver and U. Alon, The Incoherent Feed-forward Loop Accelerates the Response-time of the gal System of Escherichia coli. JMB, Vol 356 pp 1073-81 (2006). Pdf. S Mangan & U Alon, Structure and function of the feed-forward loop network motif. PNAS, 100:11980-11985 (2003). Pdf.

S. Mangan, A. Zaslaver and U. Alon, The Coherent Feedforward Loop Serves as a Sign-sensitive Delay Element in Transcription Networks. JMB, Vol 334/2 pp 197-204 (2003). Pdf. Guy Shinar, Erez Dekel, Tsvi Tlusty & Uri Alon, Rules for biological regulation based on error minimization, PNSA. 103(11), 3999-4004 (2006). Pdf. Alon Zaslaver, Avi E Mayo, Revital Rosenberg, Pnina Bashkin, Hila Sberro, Miri Tsalyuk, Michael G Surette & Uri Alon, Just-in-time transcription program in metabolic pathways, Nature Genetics 36, 486 - 491 (2004). Pdf. U. Alon, M.G. Surette, N. Barkai, S. Leibler, Robustness in Bacterial Chemotaxis, Nature 397,168-171 (1999). Pdf M Ronen, R Rosenberg, B Shraiman & U Alon, Assigning numbers to the arrows: Parameterizing a gene regulation network by using accurate expression kinetics. PNAS, 99:10555–10560 (2002). Pdf. N Rosenfeld, M Elowitz & U Alon, Negative Autoregulation Speeds the Response Times of Transcription Networks, JMB, 323:785-793 (2002). Pdf. N Rosenfeld & U Alon, Response Delays and the Structure of Transcription Networks, JMB, 329:645–654 (2003). Pdf. S. Kalir, J. McClure, K. Pabbaraju, C. Southward, M. Ronen, S. Leibler, M.G. Surette, U. Alon , Ordering genes in a flagella pathway by analysis of expression kinetics from living bacteria. Science, 292:2080-2083 (2001). Pdf Y. Setty, A. E. Mayo, M. G. Surette, and U. Alon, Detailed map of a cis-regulatory input function, PNAS, 100:7702-7707 (2003). Pdf. Shiraz Kalir and Uri Alon, Using a Quantitative Blueprint to Reprogram the Dynamics of the Flagella Gene Network, Cell, 117:713–720, (2004). Pdf.

Small world phenomena (http://smallworld.columbia.edu)

P(k) ~ k-

Found

R. Albert, H. Jeong, A-L Barabasi, Nature, 401 130 (1999).

Expected

Other Observations:

• Scientific citations• Paper coauthorship/collaboration• Organization structure• Social structure• Actor joint casting in movies• Online communities• Websites linkage• …• Protein networks• Gene networks• Cell function networks• …

Scale-Free Networks

Metabolic network

Organisms from all three domains of life are scale-free networks!

H. Jeong, B. Tombor, R. Albert, Z.N. Oltvai, and A.L. Barabasi, Nature, 407 651 (2000)

Archaea Bacteria Eukaryotes

Power Law Small World

Rich Get Richer(preferential attachment) Self-similarity

HUBS!

Preferential attachment in protein Interaction networks

k vs. k : increase in the No. of links in a unit time

No PA: k is independent of k

PA: k ~k

Eisenberg E, Levanon EY, Phys. Rev. Lett. 2003

Jeong, Neda, A.-L.B, Europhys. Lett. 2003

jj

ii k

kk

)(

Nature Biotechnology  18, 1257 - 1261 (2000) doi:10.1038/82360 A network of protein−protein interactions in yeastBenno Schwikowski, Peter Uetz & Stanley Fields

Nature Biotechnology  18, 1257 - 1261 (2000) doi:10.1038/82360 A network of protein−protein interactions in yeast

Benno Schwikowski, Peter Uetz & Stanley Fields

C. Elegans

Li et al. Science 2004

Drosophila M.

Giot et al. Science 2003

Nature 408 307 (2000)

…

“One way to understand the p53 network is to compare it to the Internet. The cell, like the Internet, appears to be a ‘scale-free network’.”

Consequence 1 : Hubs and Robustness

Hubs and RobustnessComplex systems maintain their basic functions even under errors and failures

(cell mutations; Internet router breakdowns)

node failure

fc

0 1Fraction of removed nodes, f

1

S

Consequence 1 : Hubs and RobustnessComplex systems maintain their basic functions even under errors and failures

(cell mutations; Internet router breakdowns)

R. Albert, H. Jeong, A.L. Barabasi, Nature 406 378 (2000)

Achilles’ Heel of complex networks

Internet

failureattack

R. Albert, H. Jeong, A.L. Barabasi, Nature 406 378 (2000)

Yeast protein network- lethality and topological position

Highly connected proteins are more essential (lethal)...

H. Jeong, S.P. Mason, A.-L. Barabasi, Z.N. Oltvai, Nature 411, 41-42 (2001)

System biology

• Integration

• Computation

• Theory

• Prediction!!!