18 research outputs found
Signalogs: Orthology-Based Identification of Novel Signaling Pathway Components in Three Metazoans
BACKGROUND: Uncovering novel components of signal transduction pathways and their interactions within species is a central task in current biological research. Orthology alignment and functional genomics approaches allow the effective identification of signaling proteins by cross-species data integration. Recently, functional annotation of orthologs was transferred across organisms to predict novel roles for proteins. Despite the wide use of these methods, annotation of complete signaling pathways has not yet been transferred systematically between species. PRINCIPAL FINDINGS: Here we introduce the concept of 'signalog' to describe potential novel signaling function of a protein on the basis of the known signaling role(s) of its ortholog(s). To identify signalogs on genomic scale, we systematically transferred signaling pathway annotations among three animal species, the nematode Caenorhabditis elegans, the fruit fly Drosophila melanogaster, and humans. Using orthology data from InParanoid and signaling pathway information from the SignaLink database, we predict 88 worm, 92 fly, and 73 human novel signaling components. Furthermore, we developed an on-line tool and an interactive orthology network viewer to allow users to predict and visualize components of orthologous pathways. We verified the novelty of the predicted signalogs by literature search and comparison to known pathway annotations. In C. elegans, 6 out of the predicted novel Notch pathway members were validated experimentally. Our approach predicts signaling roles for 19 human orthodisease proteins and 5 known drug targets, and suggests 14 novel drug target candidates. CONCLUSIONS: Orthology-based pathway membership prediction between species enables the identification of novel signaling pathway components that we referred to as signalogs. Signalogs can be used to build a comprehensive signaling network in a given species. Such networks may increase the biomedical utilization of C. elegans and D. melanogaster. In humans, signalogs may identify novel drug targets and new signaling mechanisms for approved drugs
Water and molecular chaperones act as weak links of protein folding networks: energy landscape and punctuated equilibrium changes point towards a game theory of proteins
Water molecules and molecular chaperones efficiently help the protein folding
process. Here we describe their action in the context of the energy and
topological networks of proteins. In energy terms water and chaperones were
suggested to decrease the activation energy between various local energy minima
smoothing the energy landscape, rescuing misfolded proteins from conformational
traps and stabilizing their native structure. In kinetic terms water and
chaperones may make the punctuated equilibrium of conformational changes less
punctuated and help protein relaxation. Finally, water and chaperones may help
the convergence of multiple energy landscapes during protein-macromolecule
interactions. We also discuss the possibility of the introduction of protein
games to narrow the multitude of the energy landscapes when a protein binds to
another macromolecule. Both water and chaperones provide a diffuse set of
rapidly fluctuating weak links (low affinity and low probability interactions),
which allow the generalization of all these statements to a multitude of
networks.Comment: 9 pages, 1 figur
Uniformly curated signaling pathways reveal tissue-specific cross-talks and support drug target discovery
Motivation: Signaling pathways control a large variety of cellular processes.
However, currently, even within the same database signaling pathways are often
curated at different levels of detail. This makes comparative and cross-talk
analyses difficult. Results: We present SignaLink, a database containing 8
major signaling pathways from Caenorhabditis elegans, Drosophila melanogaster,
and humans. Based on 170 review and approx. 800 research articles, we have
compiled pathways with semi-automatic searches and uniform, well-documented
curation rules. We found that in humans any two of the 8 pathways can
cross-talk. We quantified the possible tissue- and cancer-specific activity of
cross-talks and found pathway-specific expression profiles. In addition, we
identified 327 proteins relevant for drug target discovery. Conclusions: We
provide a novel resource for comparative and cross-talk analyses of signaling
pathways. The identified multi-pathway and tissue-specific cross-talks
contribute to the understanding of the signaling complexity in health and
disease and underscore its importance in network-based drug target selection.
Availability: http://SignaLink.orgComment: 9 pages, 4 figures, 2 tables and a supplementary info with 5 Figures
and 13 Table
Learning and innovative elements of strategy adoption rules expand cooperative network topologies
Cooperation plays a key role in the evolution of complex systems. However,
the level of cooperation extensively varies with the topology of agent networks
in the widely used models of repeated games. Here we show that cooperation
remains rather stable by applying the reinforcement learning strategy adoption
rule, Q-learning on a variety of random, regular, small-word, scale-free and
modular network models in repeated, multi-agent Prisoners Dilemma and Hawk-Dove
games. Furthermore, we found that using the above model systems other long-term
learning strategy adoption rules also promote cooperation, while introducing a
low level of noise (as a model of innovation) to the strategy adoption rules
makes the level of cooperation less dependent on the actual network topology.
Our results demonstrate that long-term learning and random elements in the
strategy adoption rules, when acting together, extend the range of network
topologies enabling the development of cooperation at a wider range of costs
and temptations. These results suggest that a balanced duo of learning and
innovation may help to preserve cooperation during the re-organization of
real-world networks, and may play a prominent role in the evolution of
self-organizing, complex systems.Comment: 14 pages, 3 Figures + a Supplementary Material with 25 pages, 3
Tables, 12 Figures and 116 reference
Stress-induced rearrangements of cellular networks: consequences for protection and drug design
The complexity of the cells can be described and understood by a number of
networks such as protein-protein interaction, cytoskeletal, organelle,
signalling, gene transcription and metabolic networks. All these networks are
highly dynamic producing continuous rearrangements in their links, hubs,
network-skeleton and modules. Here we describe the adaptation of cellular
networks after various forms of stress causing perturbations, congestions and
network damage. Chronic stress decreases link-density, decouples or even
quarantines modules, and induces an increased competition between network hubs
and bridges. Extremely long or strong stress may induce a topological phase
transition in the respective cellular networks, which switches the cell to a
completely different mode of cellular function. We summarize our initial
knowledge on network restoration after stress including the role of molecular
chaperones in this process. Finally, we discuss the implications of
stress-induced network rearrangements in diseases and ageing, and propose
therapeutic approaches both to increase the robustness and help the repair of
cellular networks.Comment: 9 pages, 1 table, 2 figures, invited paper of FEBS Letters Cellular
Stress special issu
Community landscapes: an integrative approach to determine overlapping network module hierarchy, identify key nodes and predict network dynamics
Background: Network communities help the functional organization and
evolution of complex networks. However, the development of a method, which is
both fast and accurate, provides modular overlaps and partitions of a
heterogeneous network, has proven to be rather difficult. Methodology/Principal
Findings: Here we introduce the novel concept of ModuLand, an integrative
method family determining overlapping network modules as hills of an influence
function-based, centrality-type community landscape, and including several
widely used modularization methods as special cases. As various adaptations of
the method family, we developed several algorithms, which provide an efficient
analysis of weighted and directed networks, and (1) determine pervasively
overlapping modules with high resolution; (2) uncover a detailed hierarchical
network structure allowing an efficient, zoom-in analysis of large networks;
(3) allow the determination of key network nodes and (4) help to predict
network dynamics. Conclusions/Significance: The concept opens a wide range of
possibilities to develop new approaches and applications including network
routing, classification, comparison and prediction.Comment: 25 pages with 6 figures and a Glossary + Supporting Information
containing pseudo-codes of all algorithms used, 14 Figures, 5 Tables (with 18
module definitions, 129 different modularization methods, 13 module
comparision methods) and 396 references. All algorithms can be downloaded
from this web-site: http://www.linkgroup.hu/modules.ph