141,739 research outputs found
Logic Integer Programming Models for Signaling Networks
We propose a static and a dynamic approach to model biological signaling
networks, and show how each can be used to answer relevant biological
questions. For this we use the two different mathematical tools of
Propositional Logic and Integer Programming. The power of discrete mathematics
for handling qualitative as well as quantitative data has so far not been
exploited in Molecular Biology, which is mostly driven by experimental
research, relying on first-order or statistical models. The arising logic
statements and integer programs are analyzed and can be solved with standard
software. For a restricted class of problems the logic models reduce to a
polynomial-time solvable satisfiability algorithm. Additionally, a more dynamic
model enables enumeration of possible time resolutions in poly-logarithmic
time. Computational experiments are included
Modeling Genetic Networks: Comparison of Static and Dynamic Models
Biomedical research has been revolutionized by high-throughput
techniques and the enormous amount of biological data they are able to
generate. The interest shown over network models and systems biology is
rapidly raising. Genetic networks arise as an essential task to mine these data
since they explain the function of genes in terms of how they influence other
genes. Many modeling approaches have been proposed for building genetic
networks up. However, it is not clear what the advantages and disadvantages of
each model are. There are several ways to discriminate network building
models, being one of the most important whether the data being mined presents
a static or dynamic fashion. In this work we compare static and dynamic models
over a problem related to the inflammation and the host response to injury. We
show how both models provide complementary information and cross-validate
the obtained results
Exploiting Temporal Complex Network Metrics in Mobile Malware Containment
Malicious mobile phone worms spread between devices via short-range Bluetooth
contacts, similar to the propagation of human and other biological viruses.
Recent work has employed models from epidemiology and complex networks to
analyse the spread of malware and the effect of patching specific nodes. These
approaches have adopted a static view of the mobile networks, i.e., by
aggregating all the edges that appear over time, which leads to an approximate
representation of the real interactions: instead, these networks are inherently
dynamic and the edge appearance and disappearance is highly influenced by the
ordering of the human contacts, something which is not captured at all by
existing complex network measures. In this paper we first study how the
blocking of malware propagation through immunisation of key nodes (even if
carefully chosen through static or temporal betweenness centrality metrics) is
ineffective: this is due to the richness of alternative paths in these
networks. Then we introduce a time-aware containment strategy that spreads a
patch message starting from nodes with high temporal closeness centrality and
show its effectiveness using three real-world datasets. Temporal closeness
allows the identification of nodes able to reach most nodes quickly: we show
that this scheme can reduce the cellular network resource consumption and
associated costs, achieving, at the same time, a complete containment of the
malware in a limited amount of time.Comment: 9 Pages, 13 Figures, In Proceedings of IEEE 12th International
Symposium on a World of Wireless, Mobile and Multimedia Networks (WOWMOM '11
Graph algorithms for predicting subcellular localization at the pathway level
Protein subcellular localization is an important factor in normal cellular
processes and disease. While many protein localization resources treat it as
static, protein localization is dynamic and heavily influenced by biological
context. Biological pathways are graphs that represent a specific biological
context and can be inferred from large-scale data. We develop graph algorithms
to predict the localization of all interactions in a biological pathway as an
edge-labeling task. We compare a variety of models including graph neural
networks, probabilistic graphical models, and discriminative classifiers for
predicting localization annotations from curated pathway databases. We also
perform a case study where we construct biological pathways and predict
localizations of human fibroblasts undergoing viral infection. Pathway
localization prediction is a promising approach for integrating publicly
available localization data into the analysis of large-scale biological data.Comment: 35 pages, 14 figure
Statistical inference of the time-varying structure of gene-regulation networks
<p>Abstract</p> <p>Background</p> <p>Biological networks are highly dynamic in response to environmental and physiological cues. This variability is in contrast to conventional analyses of biological networks, which have overwhelmingly employed static graph models which stay constant over time to describe biological systems and their underlying molecular interactions.</p> <p>Methods</p> <p>To overcome these limitations, we propose here a new statistical modelling framework, the ARTIVA formalism (Auto Regressive TIme VArying models), and an associated inferential procedure that allows us to learn temporally varying gene-regulation networks from biological time-course expression data. ARTIVA simultaneously infers the topology of a regulatory network and how it changes over time. It allows us to recover the chronology of regulatory associations for individual genes involved in a specific biological process (development, stress response, etc.).</p> <p>Results</p> <p>We demonstrate that the ARTIVA approach generates detailed insights into the function and dynamics of complex biological systems and exploits efficiently time-course data in systems biology. In particular, two biological scenarios are analyzed: the developmental stages of <it>Drosophila melanogaster </it>and the response of <it>Saccharomyces cerevisiae </it>to benomyl poisoning.</p> <p>Conclusions</p> <p>ARTIVA does recover essential temporal dependencies in biological systems from transcriptional data, and provide a natural starting point to learn and investigate their dynamics in greater detail.</p
In-silico Models for Capturing the Static and Dynamic Characteristics of Robustness within Complex Networks
Understanding the role of structural patterns within complex networks is essential to establish the governing principles of such networks. Social networks, biological networks, technological networks etc. can be considered as complex networks where information processing and transport plays a central role. Complexity in these net works can be due to abstraction, scale, functionality and structure. Depending on the abstraction each of these can be categorized further. Gene regulatory networks are one such category of biological networks. Gene regulatory networks (GRNs) are assumed to be robust under internal and external perturbations. Network motifs such as feed-forward loop motif and bifan motif are believed to play a central role functionally in retaining GRN behavior under lossy conditions. While the role of static characteristics like average shortest path, density, degree centrality among other topological features is well documented by the research community, the structural role of motifs and their dynamic characteristics are not xiii well understood. Wireless sensor networks in the last decade were intensively studied using network simulators. Can we use in-silico experiments to understand biological network topologies better? Does the structure of these motifs have any role to play in ensuring robust information transport in such networks? How do their static and dynamic roles differ? To understand these questions, we use in-silico network models to capture the dynamic characteristics of complex network topologies. Developing these models involve network mapping, sink selection strategies and identifying metrics to capture robust system behavior. Further, I studied the dynamic aspect of network characteristics using variation in network information flow under perturbations defined by lossy conditions and channel capacity. We use machine learning techniques to identify significant features that contribute to robust network performance. Our work demonstrates that although the structural role of feed-forward loop motif in signal transduction within GRNs is minimal, these motifs stand out under heavy perturbations
A Comparison of Static and Dynamic Functional Connectivities for Identifying Subjects and Biological Sex using Intrinsic Individual Brain Connectivity
Functional magnetic resonance imaging has revealed correlated activities in brain regions even in the absence of a task. Initial studies assumed this resting-state functional connectivity (FC) to be stationary in nature, but recent studies have modeled these activities as a dynamic network. Dynamic spatiotemporal models better model the brain activities, but are computationally more involved. A comparison of static and dynamic FCs was made to quantitatively study their efficacies in identifying intrinsic individual connectivity patterns using data from the Human Connectome project. Results show that the intrinsic individual brain connectivity pattern can be used as a ‘fingerprint’ to distinguish among and identify subjects and is more accurately captured with partial correlation and assuming static FC. It was also seen that the intrinsic individual brain connectivity patterns were invariant over a few months. Additionally, biological sex identification was successfully performed using the intrinsic individual connectivity patterns, and group averages of male and female FC matrices. Edge consistency, edge variability and differential power measures were used to identify the major resting-state networks involved in identifying subjects and their sex
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