51,815 research outputs found
Graph Theory and Networks in Biology
In this paper, we present a survey of the use of graph theoretical techniques
in Biology. In particular, we discuss recent work on identifying and modelling
the structure of bio-molecular networks, as well as the application of
centrality measures to interaction networks and research on the hierarchical
structure of such networks and network motifs. Work on the link between
structural network properties and dynamics is also described, with emphasis on
synchronization and disease propagation.Comment: 52 pages, 5 figures, Survey Pape
A dynamic network approach for the study of human phenotypes
The use of networks to integrate different genetic, proteomic, and metabolic
datasets has been proposed as a viable path toward elucidating the origins of
specific diseases. Here we introduce a new phenotypic database summarizing
correlations obtained from the disease history of more than 30 million patients
in a Phenotypic Disease Network (PDN). We present evidence that the structure
of the PDN is relevant to the understanding of illness progression by showing
that (1) patients develop diseases close in the network to those they already
have; (2) the progression of disease along the links of the network is
different for patients of different genders and ethnicities; (3) patients
diagnosed with diseases which are more highly connected in the PDN tend to die
sooner than those affected by less connected diseases; and (4) diseases that
tend to be preceded by others in the PDN tend to be more connected than
diseases that precede other illnesses, and are associated with higher degrees
of mortality. Our findings show that disease progression can be represented and
studied using network methods, offering the potential to enhance our
understanding of the origin and evolution of human diseases. The dataset
introduced here, released concurrently with this publication, represents the
largest relational phenotypic resource publicly available to the research
community.Comment: 28 pages (double space), 6 figure
Spatially-constrained clustering of ecological networks
Spatial ecological networks are widely used to model interactions between
georeferenced biological entities (e.g., populations or communities). The
analysis of such data often leads to a two-step approach where groups
containing similar biological entities are firstly identified and the spatial
information is used afterwards to improve the ecological interpretation. We
develop an integrative approach to retrieve groups of nodes that are
geographically close and ecologically similar. Our model-based
spatially-constrained method embeds the geographical information within a
regularization framework by adding some constraints to the maximum likelihood
estimation of parameters. A simulation study and the analysis of real data
demonstrate that our approach is able to detect complex spatial patterns that
are ecologically meaningful. The model-based framework allows us to consider
external information (e.g., geographic proximities, covariates) in the analysis
of ecological networks and appears to be an appealing alternative to consider
such data
From data towards knowledge: Revealing the architecture of signaling systems by unifying knowledge mining and data mining of systematic perturbation data
Genetic and pharmacological perturbation experiments, such as deleting a gene
and monitoring gene expression responses, are powerful tools for studying
cellular signal transduction pathways. However, it remains a challenge to
automatically derive knowledge of a cellular signaling system at a conceptual
level from systematic perturbation-response data. In this study, we explored a
framework that unifies knowledge mining and data mining approaches towards the
goal. The framework consists of the following automated processes: 1) applying
an ontology-driven knowledge mining approach to identify functional modules
among the genes responding to a perturbation in order to reveal potential
signals affected by the perturbation; 2) applying a graph-based data mining
approach to search for perturbations that affect a common signal with respect
to a functional module, and 3) revealing the architecture of a signaling system
organize signaling units into a hierarchy based on their relationships.
Applying this framework to a compendium of yeast perturbation-response data, we
have successfully recovered many well-known signal transduction pathways; in
addition, our analysis have led to many hypotheses regarding the yeast signal
transduction system; finally, our analysis automatically organized perturbed
genes as a graph reflecting the architect of the yeast signaling system.
Importantly, this framework transformed molecular findings from a gene level to
a conceptual level, which readily can be translated into computable knowledge
in the form of rules regarding the yeast signaling system, such as "if genes
involved in MAPK signaling are perturbed, genes involved in pheromone responses
will be differentially expressed"
Estimating sample-specific regulatory networks
Biological systems are driven by intricate interactions among the complex
array of molecules that comprise the cell. Many methods have been developed to
reconstruct network models of those interactions. These methods often draw on
large numbers of samples with measured gene expression profiles to infer
connections between genes (or gene products). The result is an aggregate
network model representing a single estimate for the likelihood of each
interaction, or "edge," in the network. While informative, aggregate models
fail to capture the heterogeneity that is represented in any population. Here
we propose a method to reverse engineer sample-specific networks from aggregate
network models. We demonstrate the accuracy and applicability of our approach
in several data sets, including simulated data, microarray expression data from
synchronized yeast cells, and RNA-seq data collected from human lymphoblastoid
cell lines. We show that these sample-specific networks can be used to study
changes in network topology across time and to characterize shifts in gene
regulation that may not be apparent in expression data. We believe the ability
to generate sample-specific networks will greatly facilitate the application of
network methods to the increasingly large, complex, and heterogeneous
multi-omic data sets that are currently being generated, and ultimately support
the emerging field of precision network medicine
Degeneracy: a link between evolvability, robustness and complexity in biological systems
A full accounting of biological robustness remains elusive; both in terms of the mechanisms by which robustness is achieved and the forces that have caused robustness to grow over evolutionary time. Although its importance to topics such as ecosystem services and resilience is well recognized, the broader relationship between robustness and evolution is only starting to be fully appreciated. A renewed interest in this relationship has been prompted by evidence that mutational robustness can play a positive role in the discovery of adaptive innovations (evolvability) and evidence of an intimate relationship between robustness and complexity in biology.
This paper offers a new perspective on the mechanics of evolution and the origins of complexity, robustness, and evolvability. Here we explore the hypothesis that degeneracy, a partial overlap in the functioning of multi-functional components, plays a central role in the evolution and robustness of complex forms. In support of this hypothesis, we present evidence that degeneracy is a fundamental source of robustness, it is intimately tied to multi-scaled complexity, and it establishes conditions that are necessary for system evolvability
Mixed membership stochastic blockmodels
Observations consisting of measurements on relationships for pairs of objects
arise in many settings, such as protein interaction and gene regulatory
networks, collections of author-recipient email, and social networks. Analyzing
such data with probabilisic models can be delicate because the simple
exchangeability assumptions underlying many boilerplate models no longer hold.
In this paper, we describe a latent variable model of such data called the
mixed membership stochastic blockmodel. This model extends blockmodels for
relational data to ones which capture mixed membership latent relational
structure, thus providing an object-specific low-dimensional representation. We
develop a general variational inference algorithm for fast approximate
posterior inference. We explore applications to social and protein interaction
networks.Comment: 46 pages, 14 figures, 3 table
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