CS/BIOL 8802

Networks in Systems Biology

 

Mondays 12-2 EST L1125

 

Instructors:

Constantine Dovrolis, KACB 3346: dovrolis@cc.gatech.edu

Todd Streelman, EST 2244: todd.streelman@biology.gatech.edu

 

Course URL:

http://www.cc.gatech.edu/~dovrolis/BioNets

 

This course development effort was supported by the NSF award 0831848 ("Towards a Theory of Network Evolution")

 

The Principles of Biological and Engineered Networks

Understanding complexity is a central goal of science. Complex networks govern connection speed on the Internet, air traffic and highway control patterns, the flow of energy and species composition in ecological communities, and the relationships of proteins in human hearts and brains. To get a handle on complexity, engineers have begun to collaborate with biologists to study how evolution has "engineered" complex biological designs (i.e., organisms). The upshot of this research includes a catalogue of design principles for complex systems (e.g., diversity, robustness, modularity, evolvability) and unexpected consequences for the relationship between form and function. It has been suggested that complexity is an emergent property of these design principles and that highly complex systems are qualitatively different from simple ones. Are there in fact general rules that biological systems (genes, cells, communities) follow? If so, what are they? These are the sorts of questions we will attempt to answer in this course.

 

Syllabus

 

First two weeks:

(classes on August 18 and August 25)

Slides from Network Science overview on August 25

The instructors will cover important concepts in systems biology and network theory. These lectures will be designed to familiarize everyone with the basics. It is important that students read as much of the following literature as possible.

 

Literature for first two weeks:

 

Biological Networks: The Tinkerer as an Engineer

U. Alon

Science 26 September 2003: Vol. 301. no. 5641, pp. 1866 - 1867.

 

Reverse Engineering of Biological Complexity

Marie E. Csete, John C. Doyle

Science 295 (5560): 1664-1669

 

Network biology: understanding the cell's functional organization

Barabasi AL, Oltvai ZN.

Nat Rev Genet. 2004 Feb;5(2):101-13.

 

Network thinking in ecology and evolution

Stephen R. Proulx, Daniel E.L. Promislow and Patrick C. Phillips

Trends in Ecology & Evolution, Volume 20, Issue 6, June 2005, Pages 345-353

 

Exploring Complex Networks

Steven Strogatz

NATURE | VOL 410 | 8 MARCH 2001

 

Statistical mechanics of complex networks

Reka Albert and Albert-Laszlo Barabasi

Rev. Mod. Phys. 74, 47 - 97 (2002)

 

Complex networks: Structure and dynamics

S. Boccalettia, V. Latorab, Y. Morenod, M. Chavezf and D.-U. Hwanga

Physics Reports, Volume 424, Issues 4-5, February 2006, Pages 175-308

 

The Structure and Function of Complex Networks

M. E. J. Newman

SIREV Volume 45 Issue 2, Pages 167-256.

 

Revisiting scale-free networks

Evelyn Fox Keller

BioEssays, Volume 27 Issue 10, Pages 1060 - 1068.

 

 

Next 10 classes

(Sep 8,15,22,29, Oct 6,20,27,Nov 3,10,17. Note: Sep 1 & Oct 13 are holidays)

 

Students will form 10 teams of 2-4 students by the end of the second class. These teams will work together in two tasks: First, to prepare and present in class one of the syllabus topics, and second, to define and execute a research project. Each student team should be as interdisciplinary as possible. Ideally, each team should include at least one student with Biology background and one student with CS (or Engineering) background.

 

Each of the 10 teams will present a syllabus topic. We will adopt the following process:

 

 

SECTION-I: NETWORK TYPOLOGY

 

September 8: Networks of Gene Expression

 

Network motifs in the transcriptional regulation network of Escherichia coli.

Shai S. Shen-Orr, Ron Milo, Shmoolik Mangan, Uri Alon

Nature Genetics 31, 64 - 68 (2002).

 

A Gene-Coexpression Network for Global Discovery of Conserved Genetic Modules.

Joshua M. Stuart, Eran Segal, Daphne Koller, Stuart K. Kim

Science 10 October 2003: Vol. 302. no. 5643, pp. 249 - 255

 

Core Transcriptional Regulatory Circuitry in Human Embryonic Stem Cells.

L . Boyer et al.

Cell, Volume 122, Issue 6, Pages 947 - 956.

 

Transcriptional Regulatory Networks in Saccharomyces cerevisiae

Tong Ihn Lee, et al.

Science 25 October 2002: Vol. 298. no. 5594, pp. 799 - 804.

 

Global similarity and local divergence in human and mouse gene co-expression networks.

Tsaparas P, Marino-Ramirez L, Bodenreider O, Koonin EV, Jordan IK.

BMC Evol Biol 2006, 6:70.

 

A gene expression map for Caenorhabditis elegans.

Kim SK, Lund J, Kiraly M, Duke K, Jiang M, Stuart J, Eizinger A, Wylie BN, Davidson GS

Science 2001, 293:2087-2092.

 

Natural selection governs local, but not global, evolutionary gene coexpression networks in Caenorahbditis elegans.

Jordan, I.K., L.S. Katz, D.R. Denver and J.T. Streelman. 2008.

Manuscript.

 

 

September 15: Protein Interaction Networks

 

Lethality and centrality in protein networks

H. Jeong, S. P. Mason, A.-L. Barabasi, Z. N. Oltvai

Nature 411, 41 - 42 (2001)

 

The protein-protein interaction map of Helicobacter pylori

Jean-Christophe Rain et al.

Nature 409, 211 - 215 (2001)

 

Global protein function prediction from protein-protein interaction networks

Alexei Vazquez, Alessandro Flammini, Amos Maritan, Alessandro Vespignani

Nature Biotechnology 21, 697 - 700 (2003)

 

Modeling of Protein Interaction Networks

Alexei Vzqueza, Alessandro Flamminia, Amos Maritana,b, Alessandro Vespignani

Complexus 2003;1:38-44 (DOI: 10.1159/000067642)

 

Specificity and Stability in Topology of Protein Networks

Sergei Maslov, Kim Sneppen

Science 3 May 2002: Vol. 296. no. 5569, pp. 910 - 913

 

Functional and topological characterization of protein interaction networks

S-H Yook, Z-N Oltvai, A-L Barabasi

PROTEOMICS - Clinical Applications, Vol 4 Issue 4, Pages 928 - 942.

 

A model of large-scale proteome evolution

Ricard V. Sole, Romualdo Pastor-Satorras, Eric Smith, Thomas B. Kepler

Advances in Complex Systems 5, 43 (2002).

 

Herpesviral protein networks and their interaction with the human proteome.

Peter Uetz et al.

Science 311: 239 - 242.

 

 

September 22: Metabolic networks

 

The large-scale organization of metabolic networks

H. Jeong, B. Tombor, R. Albert, Z. N. Oltvai, A.-L. Barabasi

Nature 407, 651 - 654 (2000).

 

Global organization of metabolic fluxes in the bacterium Escherichia coli

E. Almaas, B. Kovcs, T. Vicsek, Z. N. Oltvai, A.-L. Barabasi

Nature 427, 839 - 843 (2004).

 

The Small World inside Large Metabolic Networks

A. Wagner and D. A. Fell

Proc Biological Sciences, Vol. 268, No. 1478 (Sep. 7, 2001), pp. 1803-1810.

 

The connectivity structure, giant strong component and centrality of metabolic networks

Hong-Wu Ma and An-Ping Zeng

Bioinformatics Vol. 19 no. 11 2003 Pages 1423-1430.

 

The metabolic world of Escherichia coli is NOT small

Arita, Masanori

Proceedings of the National Academy of Science, vol. 101, Issue 6, p.1543-1547.

 

 

September 29: Developmental Networks

 

Systematic mapping of genetic interactions in C. elegans identifies common modifiers of diverse signaling pathways

Lehner B et al.

Nature Genetics 38:896-903, 2006.

 

A genomic regulatory network for development

Davidson EH et al.

Science 295:1669-1678, 2002.

 

Gene regulatory networks and the evolution of animal body plans

Davidson EH and DH Erwin

Science 311:796-800, 2006.

 

The segment polarity network is a robust developmental module

von Dassow G, Meir E, Munro EM and GM Odell

406:188-192, 2000.

 

Global regulatory logic for specification of an embryonic cell lineage

Oliveri et al.

PNAS 105: 5955-5962, 2008.

 

 

October 6: Ecological Networks

 

Interaction strength combinations and the overfishing of a marine food web

Bascompte J, Melian CJ, Sala E

PNAS 102:5443-5447, 2005.

 

Foraging adaptation and the relationship between food-web complexity and stability

Kondoh M

Science 299:1388-1391, 2003

 

Ecological networks and their fragility

Montoya JM, Pimm SL and RV Sole

Nature 442:259-264, 2006

 

Two degrees of separation in complex food webs

R. J. Williams, E. L. Berlowdagger, J. A. Dunne, A-L. Barabasi, N.D. Martinez

PNAS | October 1, 2002 | vol. 99 | no. 20 | 12913-12916

 

Food-web structure and network theory: The role of connectance and size

Jennifer A. Dunne, Richard J. Williams, and Neo D. Martinez

PNAS | October 1, 2002 | vol. 99 | no. 20 | 12917-12922

 

Universal scaling relations in food webs

Garlaschelli D, Caldarelli G, Pietronero L.

Nature. 2003 May 8;423(6936):165-8.

 

 

October 20: Networks from other disciplines

 

Superfamilies of Evolved and Designed Networks

Ron Milo, Shalev Itzkovitz, Nadav Kashtan, et al.

Science 5 March 2004, Vol. 303. no. 5663, pp. 1538 - 1542.

 

Organization, development and function of complex brain networks

Olaf Spornsa, Dante R. Chialvob, Marcus Kaiserc and Claus C. Hilgetagc

Trends in Cognitive Sciences, Vol 8, Issue 9, September 2004, Pages 418-425.

 

Scaling phenomena in the Internet: Critically examining criticality

W. Willinger, R.Govindan, S.Jamin , V.Paxson, S. Shenker

PNAS | February 19, 2002 | vol. 99 | Suppl. 1 | 2573-2580.

 

Graph structure in the Web

Andrei Broder, Ravi Kumar, et al.

Computer Networks, Vol. 33, Issues 1-6, June 2000, Pages 309-320.

 

The web of human sexual contacts

F. Liljeros, C. R. Edling, L.A. Nunes Amaral, H. E. Stanley & Y. Berg

Nature 411, 907-908 (21 June 2001).

 

 

 

SECTION-II: NETWORK PROPERTIES

 

October 27: Robustness

 

Biological robustness

Hiroaki Kitano

Nature Reviews Genetics, 2004

 

Robustness in bacterial chemotaxis

Alon, U | Surette, MG | Barkai, N | Leibler, S*

Nature. Vol. 397, no. 6715, pp. 168-171. 14 Jan 1999.

 

Robustness of the BMP morphogen gradient in Drosophila embryonic patterning.

Eldar A, Dorfman R, Weiss D, Ashe H, Shilo BZ, Barkai N.

Nature. 2002 Sep 19;419(6904):261-2.

 

The yeast cell-cycle network is robustly designed

Fangting Li, Tao Long, Ying Lu, Qi Ouyang, and Chao Tang

PNAS | April 6, 2004 | vol. 101 | no. 14 | 4781-4786.

 

Genetic complexity, robustness and genetic interactions in digital organisms

Lenski RE et al.

Nature 400:661-664, 1999.

 

 

November 3: Modularity

 

From molecular to modular cell biology

Hartwell, Leland H.; Hopfield, John J.; Leibler, Stanislas; Murray, Andrew W.

Nature, Volume 402, Issue 6761, pp. (1999).

 

Hierarchical Organization of Modularity in Metabolic Networks

E. Ravasz, A. L. Somera, D. A. Mongru, Z. N. Oltvai, A.-L. Barabasi

Science 30 August 2002, Vol. 297. no. 5586, pp. 1551 - 1555.

 

Protein complexes and functional modules in molecular networks

Victor Spirin, and Leonid A. Mirny

PNAS | October 14, 2003 | vol. 100 | no. 21 | 12123-12128.

 

Modular organization of cellular networks

Rives AW, Galitski T.

Proc Natl Acad Sci U S A. 2003 Feb 4;100(3):1128-33.

 

 

 

November 10: Evolvability

 

Evolvability is a selectable trait

David J. Earl, and Michael W. Deem

PNAS | August 10, 2004 | vol. 101 | no. 32 | 11531-11536.

 

The evolutionary origin of complex features

Richard E. Lenski, Charles Ofria, Robert T. Pennock and Christoph Adami

Nature 423, 139-144 (8 May 2003).

 

Balancing Robustness and Evolvability

Richard E. Lenski, Jeffrey E. Barrick, Charles Ofria

PLoS Biol 4(12): e428

 

Evolvability and hierarchy in rewired bacterial gene networks

Mark Isalan et al.

Nature 452, 840-845 (17 April 2008).

 

 

 

November 17: Dynamical properties

 

Just-in-time transcription program in metabolic pathways

A.Zaslaver et al.

Nature Genetics 36, 486 - 491 (2004)

 

Large extinctions in an evolutionary model: The role of innovation and keystone species

S.Jain and S.Krishna

Proc Natl Acad Sci USA. 2002 February 19; 99(4): 2055 - 2060.

 

The role of computation in complex regulatory networks

P.Fernandez and R.V.Sole

Book chapter in Power Laws, Scale-Free Networks and Genome Biology, Springer 2006.

 

Function constrains network architecture and dynamics: A case study on the yeast cell cycle Boolean network

K-Y.Lau, S.Ganguli and C.Tang

Physical Review E 75, 051907 (2007)

 

Random Boolean network models and the yeast transcriptional network

S.Kauffman, C.Peterson, B.Samuelsson and C.Troein

Proc Natl Acad Sci USA, 2003 December 1, 100(25), 14796-14799

 

The regulatory network of E.coli metabolism as a Boolean dynamical system exhibits both homeostasis and flexibility of response

A.Samal and S.Jain

BMC Systems Biology, 2(21), 2008

 

 

 

SECTION-III: Student research projects

 

During the semester, each student team will carry out a research project. During the last two classes (Nov 24 and Dec 1), groups will report on their findings/conclusions. Each presentation will last approximately 25-30 minutes. The presentations will probably take place in the early evening hours (5-8pm?).

 

Group projects might entail (i) reproduction/simulation of some computational results from a paper discussed during class, (ii) synthesis of the literature in a very specific area and a written "research agenda" for that area, (iii) discovery and "proof of concept" of new ways to use existing biological datasets, and/or (iv) conceptual or theoretical design of new experiments with existing datasets.

 

Project milestones:

  1. Late September: Determine a narrow area/problem where your research will focus on. Read several papers in that area to understand the state of the art. Submit a short (1-2 pages) report to the instructors that explains why you selected that area, the state of the art, and what you want to investigate.
  2. Late October: Define in a very specific manner what your project is going to be about. For instance, if you want to reproduce some computational results, you should have access to the required data by this point and you should be familiar with the algorithms you need to implement. Submit a short (1-2 pages) report to the instructors that describes in specific terms what you plan to do and how.
  3. Nov 24 and Dec 1: Project presentations.
  4. Promising projects can be extended to publishable research papers after the end of the semester.

 

 

PREREQUISITES:

Because this is a highly cross-disciplinary course, it is unlikely that any student will have all the required background. It is thus imperative that students from different disciplines work together in mixed reading and discussion groups. It is also important that students have a strong research aptitude, not being afraid to study well beyond the "comfort zone" of their academic background.

 

 

GRADING:

Topic presentation: 35%

Research project: 35%

Class participation and lecture reviews: 30%

 

In each of the previous three categories, you will receive one of the following grades: Outstanding (125%), Excellent (100%), Very Good (75%), Good (50%) or Poor (0%). To get an A in the course, you will need to have a weighted average of more than 80%.