Download Networks Dynamics

DYNAMICS OF BIOLOGICAL NETWORKS:
SESSION INTRODUCTION
TANYA Y. BERGER-WOLF*
Department of Computer Science, University of Illinois at Chicago
Chicago IL 60607, USA
TERESA M. PRZYTYCKA†
National Center of Biotechnology Information, NLM, NIH
Bethesda MD 20814, USA
MONA SINGH ‡
Department of Computer Science, Lewis Sigler Institute for Integrative Genomics
Princeton University, Princeton NJ 08544, USA
In modern physics, all phenomena in the universe are considered to be the results of
interactions between particles. Biologists are coming to the same conclusion. From
genome products working together in a coordinated fashion in order to realize
specific cellular functions to diseases spreading through contacts between
individuals, the living world is a conflation of myriads of networks of interacting
entities. To analyze this added level of complexity, new areas of research have
emerged that focus on understanding the roles of interactions between genes,
proteins, and other cell components, as well as between cells and even entire
organisms.
*
Work partially supported by NSF CAREER grant IIS-0747369 and NSF grants IIS-0705822 and IIS0612044
†
Supported by the Intramural Research Program of the National Institutes of Health and National Library
of Medicine
‡
Work partially supported by NSF grant CCF-0542187, NIH grant GM076275 and NIH Center of
Excellence grant P50 GM071508
Network analysis provides a unifying language to describe relations within complex
systems and has played an increasingly important role in understanding biological
systems. Over the past decade, computational methods have been developed to infer,
analyze, and predict the structure of gene and protein networks. The majority of
these approaches have focused on the topology, rather than the dynamics of these
networks. Yet most biological networks change temporally, spatially and in a
context-dependent manner. Therefore, in addition to a description of these networks
as collections of nodes and edges, researchers have began to elucidate dynamic
properties of biological networks. In molecular networks, this is frequently obtained
by integrating static interactions (such as protein-protein or regulatory interactions)
with time- or environment-dependent expression data, protein localization data, or
other contextual information.
Understanding cellular dynamics will play a key component in efforts to reverse
engineer cellular networks. Gene expression or molecular activity data (such as
phophorylation state) collected in different time points or under different conditions
can be used to infer direct or indirect connections between genes or gene products
and, thus, to infer networks. Such reverse network engineering is increasingly more
successful thanks to the accumulating availability of biological data and the
development of new computational methods. Moreover, computational methods for
inference and analysis of dynamic networks are currently being developed in other
domains, such as social networks, and recently some of these approaches for
topology reconstruction, identification of clusters, and prediction of dynamic
networks are starting to be successfully applied to biological networks.
Uncovering the dynamic nature of cellular networks also has clear impacts in human
health and disease, as defects in signaling and regulatory pathways are associated
with many serious diseases, such as cancer. This necessitates the development of
predictive computational approaches that can be used to model the underlying
dynamics of signaling and regulatory networks, and may have important
ramifications in drug development and discovery.
The session on Dynamics of Biological Networks brings together scientists working
on various aspects of the dynamic nature of biological networks. This year, the
session includes an invited talk, six contributed papers, and a panel discussion. The
invited speaker, Trey Ideker, will provide his perspective on recent important
directions in network and systems biology. The papers selected for presentation
address a broad spectrum of problems related to network dynamics.
The first group of papers addresses questions related to the analyses of protein
interaction networks. Two papers study protein interaction networks from the
perspective of evolutionary dynamics. Gibson and Goldberg present a novel
framework for reverse engineering the evolution of protein interaction networks of
extant species using phylogenetic gene trees and protein interaction data. Colak,
Hormozdiari, Schonhuth, Moser, Holman, Ester and Sahinalp utilize multiple
interaction networks across large evolutionary distances to understand the formation
of certain patterns in protein interaction networks. The paper by Jin, McCallen, Liu,
Xiang, Almaas, and Zhou proposes a method for identifying dynamic modules in
PPI networks. While temporal information is not readily available in these networks,
the authors propose to infer it from the similarity of expression time series data.
They then infer modules from the resulting dynamic networks by identifying
connected components that are contiguous in time. The paper by Mitrofanova,
Farach-Colton, and Mishra gives an efficient and robust algorithm for extracting
from physical interaction networks groups of proteins that work together in the same
context as protein complexes.
Gene expression data has been proven to be useful in untangling other aspects
related to the dynamics of biological networks. Wang and colleagues give an
algorithm for identifying post-translational modulators of transcription factor
activity that they utilize to produce a first genome-wide map of the interface
between signaling and transcriptional regulatory programs in human B cells. They
show that the serine-threonine kinase STK38 emerges as the most pleiotropic
signaling protein in this cellular context and then biochemically validate this finding
by shRNA-mediated silencing of this kinase, followed by gene expression profile
analysis.
Gene expression data have been also utilized in the work of Tamada et al. which
combines protein-protein interaction networks with transcriptional networks
estimated from drug-response time-course expression data to uncover autocrine
pathways that are dynamically regulated by drug response.
In addition to the oral presentations two papers have been selected for presentation
in the proceedings. The paper by Palisano et al. shows how to automatically
translate a system described using ODEs into a stochastic approach featured by the
BlenX language. They then apply their framework to model the yeast cell cycle. The
paper of Nakamura et al. demonstrates the potential power of large-scale particle
filtering for parameter estimations of in silico biological pathways where timecourse measurements of biochemical reactions are available.
The panel will discuss questions related to the opportunities and challenges of
analyzing the dynamics of biological networks gleaned from large-scale
experimental data. In our tutorial, we hope to touch upon the techniques developed
in the field of dynamic network analysis and contemplate how they may be relevant
in biological settings. As more temporal, spatial, and contextual data become
available for proteins and other cellular macromolecules, we are certain that
computational methods for analyses of dynamic networks will become increasingly
important. With this session, we hope to encourage computational biologists and
bioinformaticians to begin to view networks from a dynamic perspective.
Acknowledgements
We are grateful to those who submitted manuscripts for consideration for inclusion
in this session, and we thank the numerous reviewers for their valuable expertise and
time throughout the peer review process.