Conference Dates: May 22 - 25, 2007

The Conference is devoted to speakers presenting their latest research in Network Science. The level of presentation is accessible to audiences outside of the speaker's respective field. In addition to keynote and invited talks we will have a number of contributed talks. An extended poster session will be held along with a "best poster" competition. Summarizing discussion sessions and panels will be also directed to facilitate common ground between diverse areas.

The talks may be downloaded in either ppt (if available) or pdf format. Click on the name of a speaker in the main table or just scroll down to find the speaker in the list. Download links are next to their schedule information. They are being posted, so please check back often for new postings.

Conference Program

 
TUESDAY 22
WEDNESDAY 23
THURSDAY 24
FRIDAY 25
7:30 - 8:15
BREAKFAST

8:00-8:15
OPENING
8:15-8:50
8:50-9:25
9:25-10:00
10:00-10:15
COFFEE BREAK

10:15-10:50
10:50-11:25
11:25-11:40
11:40-11:55
CLOSING
11:55-1:25
LUNCH
1:25-2:00

2:00-2:35

2:35-3:10

3:10-3:45
POSTERS

3:45-4:00
COFFEE BREAK
4:00-4:15
POSTERS

4:15-4:30
POSTERS

4:30-4:45

4:45-5:00

5:00-5:15 Competition Award Ceremony
DiBONA-5:15
Panel: Networking Networks
5:15-6:00 Competition Award Ceremony Open laptop session Panel: Networking Networks
6:15-7:15

Keynote Talk:
SAGAN


7:15-9:00

Reception


Legend:   Invited talks are 35 min = 30 min + 5 min questions
  Contributed talks are 15 minutes = 12 min + 3 min questions


Adamic, Lada (University of Michigan) Thu., 8:15 - 8:50 ppt pdf m4a m4v

Title: 
Expertise Networks in Online Communities: Structure and Algorithms
Abstract: 
Web-based communities have become an important place for people to seek and share expertise. We find that networks in these communities typically differ in their topology from other online networks such as the World Wide Web. Systems targeted to augment web-based communities by automatically identifying users with expertise, for example, need to adapt to the underlying interaction dynamics. In this study, we analyze the Java Forum, a large online help-seeking community, using social network analysis methods. We test a set of network-based ranking algorithms, including PageRank and HITS, on this large size social network in order to identify users with high expertise. We then use simulations to identify a small number of simple rules governing the question-answer dynamic in the network. These simple rules not only replicate the structural characteristics and algorithm performance on the empirically observed Java Forum, but also allow us to evaluate how other algorithms may perform in communities with different characteristics. We believe this approach will be fruitful for practical algorithm design and implementation for online expertise-sharing communities.

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Albert, Réka (Penn State University) Tue., 9:25 - 10:00 m4a m4v

Title: 
Topology-based logical modeling of biological network dynamics
Abstract: 
Interaction networks between gene products form the basis of essential processes like signal transduction or embryonic development; cell-to-cell interactions determine organ function and pathogen-immune system interactions. Recent experimental advances helped uncover the structure of many molecular and cellular networks, creating a surge of interest in the dynamical description of cellular or systemic regulation. This presentation will explore the connections between network topology and dynamics by introducing qualitative (logical) models of the signal transduction network underlying plant responses to drought and of mammalian immune responses to respiratory pathogens.
The first model uses a compilation of indirect experimental evidence to reconstruct and simulate the signal transduction process leading to closure of the microscopic pores on plant leaves. We find that the network is robust against a significant fraction of possible perturbations (gene disruptions or pharmacological interventions). The model offers a roadmap for the identification of candidate manipulations that have the best chance of conferring increased drought tolerance.
The second model synthesizes experimental and clinical information on the interactions between host immune components and two closely related pathogenic bacteria in the genus bordetellae. Our results indicate that the infection time course of both bordetellae can be separated into three distinct phases based on the most active immune processes. The model offers predictions regarding cytokine regulation, key immune components and clearance of secondary infections; we experimentally validated two of these predictions. Although the biological systems and the technical details of the models differ, the models' success suggests a strong link between the topology and dynamics of biological networks.

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Almaas, Eivind (Lawrence Livermore National Lab) Tue., 2:00 - 2:35 ppt pdf m4a

Title: 
Cellular metabolic network modeling
Abstract: 
Network approaches have provided non-trivial insight on the organization of many biological systems. In the case of cellular metabolism, where nodes correspond to metabolites and links indicate chemical reactions, it is frequently possible to facilitate close interactions between experiments and computational modeling. Using a "flux-balance" modeling approach, it is possible to predict metabolic flux (or link weight) patterns on the organism-level, and study the effect of network topology on cellular function and robustness. In this presentation, I will discuss basics of metabolic network modeling and flux-balance calculations, using examples from the whole-cell network reconstructions of metabolism in the bacterium Escherichia coli and the yeast Saccharomyces cerevisiae. Our metabolic analysis has uncovered that, since the connectivity distribution for all known metabolic networks is scale-free, the possible flux distributions are characterized by a heavy-tail. Also, when subject to varying environmental conditions, persistent metabolic network activity is centered on a core reaction set with special properties. Most recently, we have used this modeling framework to study epistasis, the non-linear interaction between genes as mediated by the metabolic network.

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Bettencourt, Luis (Los Alamos National Laboratory) Fri. 8:50 - 9:25 pdf m4a

Title: 
Quantifying Scientific Discovery: temporal evolution and network structure of six emerging fields.
Abstract: 
It has long been argues that scientific discoveries generate new dynamics and reorganizations of scientific communities. We create temporal series and networks of co-authorship of six emerging fields. The temporal dynamics of number of authors is shown to be well described in terms of population models, which also permit forecasting the future size of a field. Measures of scientific productivity are built by comparing the increase in publication output of fields given their increase in numbers of authors and shown to be well fit by scaling laws. The networks of co-authorship are built and analyzed as the field develops.

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Bonabeau, Eric (Icosystems, Cambridge, MA) Thu., 10:50 - 11:25 m4a m4v

Title: 
A wealth of networks
Abstract: 
Now that we have a nice emerging theoretical framework for understanding the structure, dynamics and evolution of networks, we see networks everywhere. As wonderful as this sounds, it begs the question: is it useful to see networks everywhere? I will try to address this question from the perspective of one of the possible usefulness currencies: money. Can the emerging network science framework provide competitive advantage to companies that know how to use it? I will focus in particular on social networks as I believe this is where the question is most controversial. Indeed, while it is clear that a better understanding of infrastructure and physical networks can provide competitive insights into how to operate them better, there is no such clarity when it comes to social networks. With the help of several real commercial-world examples, I will try to shed some light on this topic. Some of the conclusions may be surprising.

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Börner, Katy (Indiana University) Tue., 2:35 - 3:10 pdf m4a m4v

Title: 
Mapping the Evolving Interface of Mainstream Chemistry and the Fields of Biochemistry, Biology, and Bioengineering
Abstract: 
How does our collective scholarly knowledge grow over time? What major areas of science exist and how are they interlinked? Which areas are major knowledge producers; which ones are consumers? Computational scientometrics - the application of bibliometric/ scientometric methods to large-scale scholarly datasets - and the communication of results via maps of science might help us answer these questions. This talk represents the results of a study that aims to map the structure and evolution of chemistry research over a 30 year time frame. Information from the combined Science (SCIE) and Social Science (SSCI) Citations Indexes from 2002 was used to generate a disciplinary map of 7,227 journals and 671 journal clusters. Clusters relevant to study the structure and evolution of chemistry were identified using JCR categories and were further clustered into 14 disciplines. The changing scientific composition of these 14 disciplines and their knowledge exchange via citation linkages was computed. Major changes on the dominance, influence, and role of Chemistry, Biology, Biochemistry, and Bioengineering over these 30 years are discussed. (This is collaborative work with Kevin W. Boyack and Richard Klavans.)

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Bornholdt, Stefan (University of Bremen) Tue., 8:50 - 9:25 m4a m4v

Title: 
Computation in molecular networks: Reliability despite biochemical stochasticity
Abstract: 
How is living matter regulated so reliably, despite the molecular and fluctuating nature of their central information processing circuits? I will discuss this problem from two perspectives, in a toy model of stochastic dynamical networks, and subsequently in the context of biological examples. Mathematical toy models can teach us about the conditions of when and how stochastic networks can self-synchronize into a stable and reproducable dynamical pattern. The central question is how a network of unreliable elements can function reliably, for example to perform computations. This points us to possible construction principles of real biochemical regulatory networks which we will discuss for some known biological molecular networks.

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Califano, Andrea (Columbia University) Mon., 8:15 - 8:50 ppt pdf m4a m4v

Title: 
A human B lymphocyte interactome for the dissection of dysregulated pathways in lymphoid malignancies.
Abstract: 
The identification of genes that are causally related to the presentation of a specific malignant phenotype is still an open problem in cancer research. The availability of a complete map of molecular interactions - including transcriptional, complex-formation, metabolic, and signaling interaction - would provide a rational basis for this research. Unfortunately, such a map remains quite elusive. We have developed information theoretic methods to predict both transcriptional (ARACNe) and post-translational (MINDY) interactions in human B cells. Predictions from these methods have been biochemically validated in vivo and shown to have very low false positive rates. By combining these methods with other reverse-engineering algorithms and high-throughput experimental data, using a standard Bayesian evidence integration scheme, we have produced the first comprehensive draft of a human B lymphocyte cellular network. We will discuss how such a draft can be used to produce a map of interactions that are dysregulated in specific pathologic or physiologic phenotypes. We also show how the dysregulation maps for three specific B cell malignancies - including Mantle Cell, Burkitt, and Follicular Lymphoma - can be used to pinpoint the causal lesions with high accuracy.

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Cheswick, William R. (Lumeta Corporation) Tue., 1:25 - 2:00 pdf m4a

Title: 
Mapping Internets and Intranets
Abstract: 
The Internet Mapping Project started at Bell Labs in 1997. Hal Burch and Bill Cheswick it explored the Internet daily using traceroute-style probes from a single host, and accumulated some 200 GB of trace data over eight years. The project included attempts to visualize these large graphs using custom brute force layout algorithms.
In 2000, Lumeta was spun off from the Labs to advance and apply this technology to intranets. Corporate and government networks are based on TCP/IP which, by design, resist central control and management. The maps and mapping tools allow improved control over large networks.
But the examination of large graphs, and visualization of the differences and evolution of large graphs is an open research problem. A map of the full Internet is still very much a ball of yarn.

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Collins, James J. (Boston University) Wed., 8:50 - 9:25 m4a m4v

Title: 
Engineering Gene Networks: Integrating Synthetic Biology & Systems Biology
Abstract: 
Many fundamental cellular processes are governed by genetic programs which employ protein-DNA interactions in regulating function. Owing to recent technological advances, it is now possible to design synthetic gene regulatory networks, and the stage is set for the notion of engineered cellular control at the DNA level. Theoretically, the biochemistry of the feedback loops associated with protein-DNA interactions often leads to nonlinear equations, and the tools of nonlinear analysis become invaluable. In this talk, we describe how techniques from nonlinear dynamics and molecular biology can be utilized to model, design and construct synthetic gene regulatory networks. We present examples in which we integrate the development of a theoretical model with the construction of an experimental system. We also discuss the implications of synthetic gene networks for biotechnology, biomedicine and biocomputing. In addition, we present integrated computational-experimental approaches that enable construction of first-order quantitative models of gene-protein regulatory networks using only steady-state expression measurements and no prior information on the network structure or function. We discuss how the reverse-engineered network models, coupled to experiments, can be used: (1) to gain insight into the regulatory role of individual genes and proteins in the network, (2) to identify the pathways and gene products targeted by pharmaceutical compounds, and (3) to identify the genetic mediators of different diseases.

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D'Souza, Raissa (University of California, Davis) Fri., 8:15 - 8:50 pdf m4a

Title: 
Optimization, Preferential Attachment, Viability and Network Growth
Abstract: 
We show how the mechanism of preferential attachment can emerge from an underlying network optimization framework. The preferential attachment (PA) model so obtained has two novel features, saturation and viability, which have natural interpretations in the underlying network. Like PA, saturation has previously been assumed at an axiomatic level. The combination of PA and saturation leads to power-law degree distributions with exponential cutoff which give excellent fits to a broad range of empirical observations of networks. Here we show how a simple underlying optimization framework can give rise to both known mechanisms and likewise to a new concept of viability, and suggest that such models form a good starting point for the analysis of many networks. In addition we discuss the fit provided to a broad range of data, including previously unexplained data on the Internet obtained from "whois" tables.

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Dunne, Jennifer A. (Santa Fe Institute) Wed., 8:15 - 8:50 ppt pdf m4a m4v

Title: 
Ecological network structure, robustness, and uncertainty
Abstract: 
It is increasingly apparent that an ecological network perspective, which encompasses direct and indirect effects among interacting taxa, is critical for understanding, forecasting, and managing the impacts of species loss and invasion, habitat conversion, and climate change. At a basic research level, this suggests that we need to develop a more general framework for understanding ecological network robustness at whole-system and component levels. Using examples of food webs from the present, ~50 million years ago, and ~500 million years ago, I will discuss how research at the interface of ecology and network theory can be fruitfully extended across deep time, increasing our understanding of different aspects of ecological robustness. I will also highlight the importance of explicitly addressing uncertainty in data.

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Gerstein, Mark (Yale University) Thu., 9:25 - 10:00 ppt pdf m4a m4v

Title: 
Understanding Protein Function on a Genome-scale using Networks
Abstract: 
My talk will be concerned with topics in proteomics, in particular predicting protein function on a genomic scale. We approach this through the prediction and analysis of biological networks, focusing on protein-protein interaction and transcription-factor-target ones. I will describe how these networks can be determined through integration of many genomic features and how they can be analyzed in terms of various simple topological statistics. In particular, I will discuss a number of specific analyses: (1) Integrating gene expression data with the regulatory network illuminates transient hubs; (2) Integration of the protein interaction network with 3D molecular structures reveals different types of hubs, depending on the number of interfaces involved in interactions (one or many); (3) Analysis of betweenness in biological networks reveals that this quantity is more strongly correlated with essentially than degree; (4) Analysis of structure of the regulatory network shows that it has a hierarchiel layout with the "middle-managers" acting as information bottlenecks. (5) Development of a useful web-based tools for the analysis of networks, TopNet and tYNA.

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Hausmann, Ricardo (Harvard University) Wed., 1:25 - 2:00 m4a

Title: 
Macro consequences of a discontinuous product space
Abstract: 
Much of economic theory has assumed that the product space is continuous and smooth in the sense that there is always a product through which countries can use their endowments and capabilities. This paper summarizes recent work that has looked empirically at the patterns of relatedness between products using network techniques and standard economic tools. The main findings are that the product space is highly heterogeneous with some dense sections surrounded by a sparse periphery. The capacity of countries to move to higher end products depends strongly on the existence of potential products that are near the areas of current production. Since the product space is heterogeneous, this explains several phenomena such as the differential growth rates of countries, the lack of convergence of incomes at the global level and the prolonged income collapses in developing countries.

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Kleinberg, Jon (Cornell University) Wed., 2:00 - 2:35 pdf m4a

Title: 
Anonymized Social Networks, Hidden Patterns, and Privacy Breaches
Abstract: 
An increasing amount of social network research focuses on large datasets obtained by measuring the interactions among individuals who have strong expectations of privacy. To preserve privacy in such instances, the datasets are typically anonymized -- the names are replaced with meaningless unique identifiers, so that the network structure is maintained while private information has been suppressed. I will discuss recent joint work with Lars Backstrom and Cynthia Dwork in which we identify some fundamental limitations on the power of network anonymization to ensure privacy. In particular, we describe a family of attacks such that even from a single anonymized copy of a social network, it is possible for an adversary to learn whether edges exist or not between specific targeted pairs of nodes.

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Lazer, David M. J. (Harvard University) Thu., 1:25 - 2:00 ppt pdf m4a m4v

Title: 
Life in the Network: The Coming Era of Computational Social Science
Abstract: 
An increasing fraction of human behavior (especially relational behavior) leaves substantial digital traces-- whether in the form of phone logs, e-mail, instant messaging, etc. Further, increased computational power allows the analysis of these digital traces-- e.g., through natural language processing, statistical analysis of massive (millions of individuals) longitudinal data, etc. These two points suggest that we are on the precipice of dramatic new insights into collective human behavior. I will discuss the potential future of a "computational social science", with reference to four ongoing research projects.

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Loscalzo, Joseph (Harvard University) Wed., 10:50 - 11:25 m4a m4v

Title: 
Human Disease Classification in the Postgenomic Era:
A Complex Systems Approach to Human Pathobiology
Abstract: 
Contemporary classification of human disease dates to the late 19th century, and derives from observational correlation between pathological analysis and clinical syndromes. Characterizing disease in this way established a schema that has served clinicians well to the current time, relying on observational skills to define syndromic phenotypes. Throughout the last century, this approach became more objective as the molecular underpinnings of many disorders were identified and definitive laboratory tests became an essential part of the overall diagnostic paradigm. Yet, this classic diagnostic strategy has widely recognized shortcomings that reflect both a lack of sensitivity in identifying preclinical disease, and a lack of specificity in defining disease unequivocally. In this presentation, I will focus on the latter shortcoming, arguing that it is a reflection both of the different clinical presentations of many diseases (variable phenotypic expression), and of the excessive reliance on Cartesian reductionism in establishing diagnoses. With advances in routine sequencing of the human genome, evolving proteomic and metabolomic methodologies, and growing molecular data sets from healthy and phenotypically well-characterized diseased individuals, we are now in the unique position to consider all human disease using rigorous, systems-based approaches. The purpose of this presentation is to provide a logical argument for a new approach to classifying human disease that both appreciates the uses and limits of reductionism and incorporates the tenets of a non-reductionist complex systems analysis. This approach offers the promise of diagnostic accuracy, prognostic utility, and therapeutic efficacy that can serve as the objective basis for the growing field of personalized medicine.

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Makse, Hernán (Benjamin Levich Inst. and CCNY) Fri.,9:25 - 10:00 ppt m4a

Title: 
Scaling, renormalization and self-similarity in complex networks
Abstract: 
Our recent finding of scaling and topological self-similarity in complex networks provides a new perspective on our view of biological complexity. When we observe the networks of protein-protein interactions and cellular metabolism with varying resolution, they consistently show the self-replicating pattern of fractal with finite fractal dimensions, which has a direct implication on the structural stability and growth mechanism of the network. In particular, we show the relevance of the scale transformation to the evolutionary process, where the evolution of the cellular network follows the inverse of the renormalization scheme. We investigate how the emerging topological properties of biological networks were achieved in the long history of evolution and how it is related with the error-tolerance level of the network. We find that the scale-invariant topology implies a significant increase in the robustness of the network, in accordance with the established rules of natural selection. We also characterize the large-scale modular organization in terms of scale-invariant laws of modularity and develop a mathematical framework to understand the emergence of the modular properties of the network.

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Maslov, Sergei (Brookhaven National Lab) Thu., 8:50 - 9:25 ppt pdf m4a m4v

Title: 
Propagation of large concentration changes in reversible protein binding networks
Abstract: 
We study how the dynamic equilibrium of the reversible protein - protein binding network in yeast S. cerevisiae responds to large changes in abundances of individual proteins. The magnitude of shifts between free and bound concentrations of their immediate and more distant neighbors in the network is influenced by such factors as the network topology, the distribution of protein concentrations among its nodes, and the average binding strength. Our primary conclusion is that on average the effects of a perturbation are strongly localized and exponentially decay with the network distance away from the perturbed node. This explains why, despite globally connected topology, individual functional modules in such networks are able to operate fairly independently. We also found that under specific favorable conditions, realized fin a significant number of paths in the yeast network, concentration perturbations can selectively propagate over considerable network distances (up to four steps). Such ”action-at-a-distance” requires high concentrations of heterodimers along the path as well as low free (unbound) concentration of intermediate proteins.

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Masuda, Naoki (University of Tokyo) Thu., 2:35 - 3:10 pdf m4a m4v

Title: 
Evolutionary games with participation costs on networks

Abstract: 
Networks with heterogeneous degree distributions such as scale-free networks are known to promote evolution of cooperation in social dilemma games. With standard examples of payoff matrices, cooperation is facilitated because hubs are more advantageous than others by playing more often. I show that even a relatively small cost of participation imposed on players neutralizes the constructive role of heterogeneous networks in altruism. With participation costs, hubs are charged more so that they do not spread cooperation. The participation cost is irrelevant in many evolutionary dynamics on networks without degree dispersion and in well-mixed populations, but it controls the level of cooperation on heterogeneous networks.

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Newman, Mark E. J. (University of Michigan) Wed., 9:25 - 10:00 pdf m4a m4v

Title: 
Maximum likelihood methods for discovering structure in networks
Abstract: 
Likelihood maximization is one of the fundamental tools of data analysis in statistics but has yet to find widespread use in the study of networks. This talk will describe two new methods for revealing structure in networks using maximum likelihood methods. The first probes for hierarchical structure in networks using a Markov Chain Monte Carlo technique while the second attempts to classify network nodes into classes according to their connection patterns using an expectation-maximization algorithm. A variety of applications to real-world network data will be described, including social, biological, and information networks.

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North, Stephen (AT&T) Tue., 10:50 - 11:25 m4a

Title: 
Scaling up network visualization
Abstract: 
The past two decades of research have yielded surprisingly effective methods for visualizing networks of hundreds of nodes, and sometimes far larger ones. In this talk we will review progress to date and discuss some recent work in the Graphviz project on visualizing large networks. This includes better optimization for drawing real-world networks, interactive adjustment of the level of detail within a network viewer, and a method of extracting and displaying the subgraph "between" two or more nodes in a quasi-random network (such as a social network, possibly large enough to require external database storage).

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Ravasz-Regan, Erzsébet (Harvard University) Thu., 2:00 - 2:35 pdf m4a m4v

Title: 
Network Structure of Protein Folding Pathways
Abstract: 
The classical approach to protein folding inspired by statistical mechanics avoids the high dimensional structure of the conformation space by using effective coordinates. Here we introduce a network approach to capture the statistical properties of the structure of conformation spaces, and reveal the correlations induced in the energy landscape by the self-avoidance property of a polypeptide chain. We show that the folding pathways along energy gradients organize themselves into scale free networks, thus explaining previous observations made via molecular dynamics simulations. We also show that these energy landscape correlations are essential for recovering the observed connectivity exponent, which belongs to a different universality class than that of random energy models.

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Roth, Fritz (Harvard University) Thu., 3:10 - 3:45 ppt pdf m4a m4v

Title: 
MouseFunc 1: A Critical Assessment of Mammalian Gene Function Prediction Based on a Rich Evidence Network.
Abstract: 
Several years after sequencing the human genome and the mouse genome, much remains to be discovered about the functions of most human and mouse genes. Computational prediction of gene function promises to help focus limited experimental resources on the most likely hypotheses. Several algorithms using diverse genomic data have been applied to this task, but these have been primarily tested on the unicellular yeast S. cerevisiae. We assembled a rich network of mouse functional genomic evidence, such that each node is associated with a vector of gene/protein properties, and each node pair is associated with a vector of evidence for various biological relationships. Nine bioinformatics teams used this rich network to independently train classifiers and generate predictions of function for 21,603 mouse genes. We identified strengths and weaknesses of current functional genomic datasets and compared the performance of function prediction algorithms. This analysis inferred functions for 76% of mouse genes, including five thousand currently uncharacterized genes, with an average precision value of 35% at a recall value of 20%.

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Sagan, Dorion (Sciencewriters) Wed., 3:10-3:45

Title: 
Complex Systems in Energetic Context: How Real-World Complex Systems and Networks Accomplish Natural Goals
Abstract: 
Although one of the simplest imaginable systems, the heated air in Abe Lincoln's log cabin demonstrates properties we associate with purpose, consciousness, and planning. Wherever there is a crack or crevice in the cabin, the heat acts as if it "wants" to escape. Such behavior in simple close-to-thermal equilibrium systems may lie at the root of the behavior of complex living systems. In this lecture I provide a brief survey of real--as opposed to algorithmic or computer-generated--systems that appear in regions of energy flow. The second law of thermodynamics can be restated to say simply that energy spreads. As it delocalizes gradients are reduced. The reduction of ambient gradients in complex open systems appears to be their raison d'etre. Arrogant humanity, and indeed all evolving life, appear to belong to a general class of naturally appearing complex systems. These systems may lose parts of themselves and assemble into larger systems that together more effectively accomplish the mandate of energy dispersal than individuals. The global economy, with its focus on energy acquisition, and its disregard for human suffering and individual freedoms, exemplify the inexorable second law no less than heat escaping Lincoln's cabin.

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Talas, Annamaria (Real Pictures) Fri., 10:50 - 11:25

Title: 
CONNECTING THE DOTS - The role of documentary films in public understanding of science
Abstract: 
Communicating the new science of networks to a broad television audience is a challenge. The author is an experienced documentary filmmaker working for public broadcasters around the world. She has been preparing a film for years on the origins and impact of network science and will relate her experiences in communicating the scientific concepts to broadcast commissioning editors, film funding agencies and the general public in trying to raise interest in the project.
It has not been smooth sailing. Reactions range from disbelief to disinterest with occasional enthusiasm. While on the surface connectedness and networks seems all too obvious to the general public, on a deeper level they carry surprises that are not easy to comprehend. The science is still in its infancy and thus its implications are not yet widely understood. Without overwhelming practical applications it remains difficult to ‘sell’ a film based on concepts. Nevertheless, as the broad potential of network theory evolves, there is at last a growing acceptance of its potential public importance.

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Tang, Chao (University of California, San Francisco) Fri., 10:15 - 10:50 ppt pdf m4a m4v

Title: 
Connecting function and topology of small biological circuits through dynamics
Abstract: 
There is an intimate relationship between function and form (structure) on both the macroscopic/organismic and the microscopic/molecular scales of the biological world. On the "mesoscopic" scales, e.g. the cellular networks, this relationship is far less clear and may have been masked by physical, biochemical, and evolutionary constraints. I will demonstrate with a few examples that at the scale of small cellular circuits the requirement of a robust function drastically limits the choice of network topologies for that function. Such knowledge can help us to understand natural biological circuits as well as to synthesize the artificial ones.

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Uzzo, Stephen M. (The New York Hall of Science) Tue., 3:10 - 3:45 m4a m4v

Title: 
Connections: The Nature of Networks A New Science Exhibition
Abstract: 
The understanding of how networks function and evolve is important to the lives of global citizens. It may provide a breakthrough in the ability of policy-makers, teachers, voters, and public planners to grasp the otherwise elusive complex relationships and behaviors in biological, ecological, social, and economic systems. "Connections: The Nature of Networks," (NSF Award No. 0229268) is a unique public exhibition about networks with an emphasis on characteristics of complex networks (social, biological, communications networks, and others). It premiered in November, 2004 as part of a museum expansion project sponsored by the City of New York. "Connections . . ." explores the fundamental structures of networks (such as how nodes, links, and hubs form) and how they manifest themselves in what visitors see around them in their daily lives, providing them with tools to understand similarities and differences among various kinds of networks. Because complex networks have the attribute of emergent behavior, the theme of emergence is an important aspect of the exhibition. The talk will include an annotated tour of the exhibition and explore how it was planned, executed, and evaluated; as well as some of the problems inherent in communicating network science concepts to lay audiences. At the end of the tour there will be time for questions and answer, as well as your comments on the exhibition.

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Vespignani, Alessandro (Indiana University) Thu., 10:15 - 10:50 ppt pdf m4a m4v

Title: 
The impact of mobility networks on the worldwide spread of epidemics
Abstract: 
Networks which trace the activities and interactions of individuals, transportation fluxes and population movements on a local and global scale have been analyzed and found to exhibit large scale heterogeneity, self-organization and other properties typical of complex systems. Here we analyze the impact of mobility networks on the global spreading of emerging infectious diseases. We define a computational model for the large scale spread if infectious diseases that integrates the air transportation network with demographic data. The model is used to study the specific case of the SARS epidemic and to provide scenario forecasts for pandemic influenza. The effect of the network complexity on the predictability of the global spreading pattern of emerging diseases is analyzed.

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Vicsek, Tamás (Eötvös University, Budapest) Wed., 10:15 - 10:50 ppt pdf m4a

Title: 
Statistics and evolution of the community structure of large social networks
Abstract: 
Complex societal systems can be described in terms of networks capturing the intricate web of connections among the units they are made of. A fundamental question of great current interest is how to interpret the global organisation of such networks as the coexistence of their structural sub-units (called modules, communities, clusters, etc) associated with more highly interconnected parts. Identifying these unknown building blocks (e.g., industrial sectors, groups of people) is crucial to the understanding of the structural and functional properties of networks.
Here we first present an approach to analyse the main statistical features of the interwoven sets of overlapping communities, where two communities overlap if they have common members. Our studies of a variety of networks, including mobile phone call, collaboration, and school friendship graphs demonstrate that the web of modules has highly non-trivial correlations and specific scaling properties. Due to the versatility of our method we are able to follow the time development of individual communities and investigate quantitative aspects of social dynamics on a large scale.

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Vidal, Marc (Dana-Farber Cancer Institute) Wed., 6:15 - 7:15

Title: 
Interactome networks
Abstract: 
For over half a century it has been conjectured that macromolecules form complex networks of functionally interacting components, and that the molecular mechanisms underlying most biological processes correspond to particular steady states adopted by such cellular networks. However, until recently, systems-level theoretical conjectures remained largely unappreciated, mainly because of lack of supporting experimental data.
To generate the information necessary to eventually address how complex cellular networks relate to biology, we initiated, at the scale of the whole proteome, an integrated approach for modeling protein-protein interaction or "interactome" networks. Our main questions are: How are interactome networks organized at the scale of the whole cell? How can we uncover local and global features underlying this organization, and how are interactome networks modified in human disease, such as cancer?

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Wiggins, Chris Wed., 2:35 - 3:10 pdf m4a

Title: 
Predicting evolution: a machine-learning approach to validating models for biological networks
Abstract: 
The past decade has witnessed an explosion of interest and research activity in statistical descriptions of real-world networks. Early on, it was observed that certain select statistical attributes of real-world networks could be reproduced by simple stochastic models. This observation fueled the proliferation of numerous such models -- particularly those purporting to describe growing and evolving networks -- based on simple design principles. However, as it became clear that multiple models could reproduce varying attributes within a fidelity comparable to the certainty with which they could be measured, it also became clear that reproducibility did not imply predictability. We present a resolution to this problem of over-universality -- the problem of multiple design principles reproducing features observed in real-world networks -- by an explicitly predictive approach. That is, by exploiting machine learning approaches more commonly used in high-dimensional statistical problems, we learn a prediction function, which takes as input potentially hundreds or thousands of network attributes and takes as output the `best' design principle: which of the several competing design principles is the most consistent with the observed network. Our method is accurate (on held-out data not seen while learning the prediction function) and interpretable (revealing {\it which} topological attributes best distinguish the putative models). We apply the method to several biological networks, using several machine learning algorithms, with several choices of input attributes, and reveal a consistent trend: that duplication mutation mechanisms are distinguishable from and more consistent with real biological networks than preferential attachment mechanisms, and that even similar duplication mutation design principles are topologically distinguishable.

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