Página 1 dos resultados de 7434 itens digitais encontrados em 0.012 segundos

## Measures of degeneracy and redundancy in biological networks

Fonte: The National Academy of Sciences
Publicador: The National Academy of Sciences

Tipo: Artigo de Revista Científica

Publicado em 16/03/1999
EN

Relevância na Pesquisa

563.49984%

Degeneracy, the ability of elements that are structurally different to perform the same function, is a prominent property of many biological systems ranging from genes to neural networks to evolution itself. Because structurally different elements may produce different outputs in different contexts, degeneracy should be distinguished from redundancy, which occurs when the same function is performed by identical elements. However, because of ambiguities in the distinction between structure and function and because of the lack of a theoretical treatment, these two notions often are conflated. By using information theoretical concepts, we develop here functional measures of the degeneracy and redundancy of a system with respect to a set of outputs. These measures help to distinguish the concept of degeneracy from that of redundancy and make it operationally useful. Through computer simulations of neural systems differing in connectivity, we show that degeneracy is low both for systems in which each element affects the output independently and for redundant systems in which many elements can affect the output in a similar way but do not have independent effects. By contrast, degeneracy is high for systems in which many different elements can affect the output in a similar way and at the same time can have independent effects. We demonstrate that networks that have been selected for degeneracy have high values of complexity...

Link permanente para citações:

## Topology of biological networks and reliability of information processing

Fonte: National Academy of Sciences
Publicador: National Academy of Sciences

Tipo: Artigo de Revista Científica

EN

Relevância na Pesquisa

565.85477%

Survival of living cells and organisms is largely based on highly reliable function of their regulatory networks. However, the elements of biological networks, e.g., regulatory genes in genetic networks or neurons in the nervous system, are far from being reliable dynamical elements. How can networks of unreliable elements perform reliably? We here address this question in networks of autonomous noisy elements with fluctuating timing and study the conditions for an overall system behavior being reproducible in the presence of such noise. We find a clear distinction between reliable and unreliable dynamical attractors. In the reliable case, synchrony is sustained in the network, whereas in the unreliable scenario, fluctuating timing of single elements can gradually desynchronize the system, leading to nonreproducible behavior. The likelihood of reliable dynamical attractors strongly depends on the underlying topology of a network. Comparing with the observed architectures of gene regulation networks, we find that those 3-node subgraphs that allow for reliable dynamics are also those that are more abundant in nature, suggesting that specific topologies of regulatory networks may provide a selective advantage in evolution through their resistance against noise.

Link permanente para citações:

## BE.440 Analysis of Biological Networks, Fall 2004; Analysis of Biological Networks

Fonte: MIT - Massachusetts Institute of Technology
Publicador: MIT - Massachusetts Institute of Technology

EN-US

Relevância na Pesquisa

567.5986%

#systems#networks#biochemistry#biology#chemistry#chemotaxis#lactation#interferon#response#DNA#replication

This class analyzes complex biological processes from the molecular, cellular, extracellular, and organ levels of hierarchy. Emphasis is placed on the basic biochemical and biophysical principles that govern these processes. Examples of processes to be studied include chemotaxis, the fixation of nitrogen into organic biological molecules, growth factor and hormone mediated signaling cascades, and signaling cascades leading to cell death in response to DNA damage. In each case, the availability of a resource, or the presence of a stimulus, results in some biochemical pathways being turned on while others are turned off. The course examines the dynamic aspects of these processes and details how biochemical mechanistic themes impinge on molecular/cellular/tissue/organ-level functions. Chemical and quantitative views of the interplay of multiple pathways as biological networks are emphasized. Student work will culminate in the preparation of a unique grant application in an area of biological networks.

Link permanente para citações:

## Simulation and optimization tools to study design principles of biological networks

Fonte: Massachusetts Institute of Technology
Publicador: Massachusetts Institute of Technology

Tipo: Tese de Doutorado
Formato: 146 leaves

ENG

Relevância na Pesquisa

668.6673%

Recent studies have developed preliminary wiring diagrams for a number of important biological networks. However, the design principles governing the construction and operation of these networks remain mostly unknown. To discover design principles in these networks, we investigated and developed a set of computational tools described below. First, we looked into the application of optimization techniques to explore network topology, parameterization, or both, and to evaluate relative fitness of networks operational strategies. In particular, we studied the ability of an enzymatic cycle to produce dynamic properties such as responsiveness and transient noise filtering. We discovered that non-linearity of the enzymatic cycle allows more effective filtering of transient noise. Furthermore, we found that networks with multiple activation steps, despite being less responsive, are better in filtering transient noise. Second, we explored a method to construct compact models of signal transduction networks based on a protein-domain network representation. This method generates models whose number of species, in the worst case, scales quadratically to the number of protein-domain sites and modification states, a tremendous saving over the combinatorial scaling in the more standard mass-action model was estimated to consist of more that 10⁷ species and was too large to simulate; however...

Link permanente para citações:

## Design principles of mammalian signaling networks : emergent properties at modular and global scales

Fonte: Massachusetts Institute of Technology
Publicador: Massachusetts Institute of Technology

Tipo: Tese de Doutorado
Formato: 249 leaves

ENG

Relevância na Pesquisa

569.87086%

This thesis utilizes modeling approaches rooted in statistical physics and physical chemistry to investigate several aspects of cellular signal transduction at both the modular and global levels. Design principles of biological networks and cell signaling processes pertinent to disease progression emerge from these studies. It is my hope that knowledge of these principles may provide new mechanistic insights and conceptual frameworks for thinking about therapeutic intervention into diseases such as cancer and diabetes that arise from aberrant signaling. Areas of interest have emphasized the role of scaffold proteins in protein kinase cascades, modeling relevant biophysical processes related to T cell activation, design principles of signal transduction focusing on multisite phosphorylation, quantifying the notion of signal duration and the time scale dependence of signal detection, and entropy based models of network architecture inferred from proteomics data. These problems are detailed below. The assembly of multiple signaling proteins into a complex by a scaffold protein guides many cellular decisions. Despite recent advances, the overarching principles that govern scaffold function are not well understood. We carried out a computational study using kinetic Monte Carlo simulations to understand how spatial localization of kinases on a scaffold may regulate signaling under different physiological condition. Our studies identify regulatory properties of scaffold proteins that allow them to both amplify and attenuate incoming signals in different biological contexts. In a further...

Link permanente para citações:

## Communication on structure of biological networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Relevância na Pesquisa

569.37555%

#Quantitative Biology - Quantitative Methods#Physics - Physics and Society#Quantitative Biology - Molecular Networks

Networks are widely used to represent interaction pattern among the
components in complex systems. Structures of real networks from differ- ent
domains may vary quite significantly. Since there is an interplay be- tween
network architecture and dynamics, structure plays an important role in
communication and information spreading on a network. Here we investigate the
underlying undirected topology of different biological networks which support
faster spreading of information and are better in communication. We analyze the
good expansion property by using the spectral gap and communicability between
nodes. Different epidemic models are also used to study the transmission of
information in terms of disease spreading through individuals (nodes) in those
networks. More- over, we explore the structural conformation and properties
which may be responsible for better communication. Among all biological
networks studied here, the undirected structure of neuronal networks not only
pos- sesses the small-world property but the same is expressed remarkably to a
higher degree than any randomly generated network which possesses the same
degree sequence. A relatively high percentage of nodes, in neuronal networks,
form a higher core in their structure. Our study shows that the underlying
undirected topology in neuronal networks is significantly qualitatively
different than the same from other biological networks and that they may have
evolved in such a way that they inherit a (undirected) structure which is
excellent and robust in communication.

Link permanente para citações:

## On the sufficiency of pairwise interactions in maximum entropy models of biological networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 11/05/2015

Relevância na Pesquisa

566.5724%

#Quantitative Biology - Quantitative Methods#Condensed Matter - Statistical Mechanics#Physics - Biological Physics

Biological information processing networks consist of many components, which
are coupled by an even larger number of complex multivariate interactions.
However, analyses of data sets from fields as diverse as neuroscience,
molecular biology, and behavior have reported that observed statistics of
states of some biological networks can be approximated well by maximum entropy
models with only pairwise interactions among the components. Based on
simulations of random Ising spin networks with $p$-spin ($p>2$) interactions,
here we argue that this reduction in complexity can be thought of as a natural
property of densely interacting networks in certain regimes, and not
necessarily as a special property of living systems. By connecting our analysis
to the theory of random constraint satisfaction problems, we suggest a reason
for why some biological systems may operate in this regime.

Link permanente para citações:

## Knowledge-fused differential dependency network models for detecting significant rewiring in biological networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Relevância na Pesquisa

565.15152%

Modeling biological networks serves as both a major goal and an effective
tool of systems biology in studying mechanisms that orchestrate the activities
of gene products in cells. Biological networks are context specific and dynamic
in nature. To systematically characterize the selectively activated regulatory
components and mechanisms, the modeling tools must be able to effectively
distinguish significant rewiring from random background fluctuations. We
formulated the inference of differential dependency networks that incorporates
both conditional data and prior knowledge as a convex optimization problem, and
developed an efficient learning algorithm to jointly infer the conserved
biological network and the significant rewiring across different conditions. We
used a novel sampling scheme to estimate the expected error rate due to random
knowledge and based on which, developed a strategy that fully exploits the
benefit of this data-knowledge integrated approach. We demonstrated and
validated the principle and performance of our method using synthetic datasets.
We then applied our method to yeast cell line and breast cancer microarray data
and obtained biologically plausible results.; Comment: 7 pages, 7 figures

Link permanente para citações:

## What you see is not what you get: how sampling affects macroscopic features of biological networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 01/06/2011

Relevância na Pesquisa

564.24695%

#Quantitative Biology - Quantitative Methods#Condensed Matter - Disordered Systems and Neural Networks#82B44, 05Cxx

We use mathematical methods from the theory of tailored random graphs to
study systematically the effects of sampling on topological features of large
biological signalling networks. Our aim in doing so is to increase our
quantitative understanding of the relation between true biological networks and
the imperfect and often biased samples of these networks that are reported in
public data repositories and used by biomedical scientists. We derive exact
explicit formulae for degree distributions and degree correlation kernels of
sampled networks, in terms of the degree distributions and degree correlation
kernels of the underlying true network, for a broad family of sampling
protocols that include (un-)biased node and/or link undersampling as well as
(un-)biased link oversampling. Our predictions are in excellent agreement with
numerical simulations.; Comment: 26 pages, 8 figures

Link permanente para citações:

## Application of Random Matrix Theory to Biological Networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 22/03/2005

Relevância na Pesquisa

563.05406%

We show that spectral fluctuation of interaction matrices of yeast a core
protein interaction network and a metabolic network follows the description of
the Gaussian orthogonal ensemble (GOE) of random matrix theory (RMT).
Furthermore, we demonstrate that while the global biological networks evaluated
belong to GOE, removal of interactions between constituents transitions the
networks to systems of isolated modules described by the Poisson statistics of
RMT. Our results indicate that although biological networks are very different
from other complex systems at the molecular level, they display the same
statistical properties at large scale. The transition point provides a new
objective approach for the identification of functional modules.; Comment: 3 pages, 2 figures

Link permanente para citações:

## An integrative approach to modeling biological networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Relevância na Pesquisa

567.95223%

Since proteins carry out biological processes by interacting with other
proteins, analyzing the structure of protein-protein interaction (PPI) networks
could explain complex biological mechanisms, evolution, and disease. Similarly,
studying protein structure networks, residue interaction graphs (RIGs), might
provide insights into protein folding, stability, and function. The first step
towards understanding these networks is finding an adequate network model that
closely replicates their structure. Evaluating the fit of a model to the data
requires comparing the model with real-world networks. Since network
comparisons are computationally infeasible, they rely on heuristics, or
"network properties." We show that it is difficult to assess the reliability of
the fit of a model with any individual network property. Thus, our approach
integrates a variety of network properties and further combines these with a
series of probabilistic methods to predict an appropriate network model for
biological networks. We find geometric random graphs, that model spatial
relationships between objects, to be the best-fitting model for RIGs. This
validates the correctness of our method, since RIGs have previously been shown
to be geometric. We apply our approach to noisy PPI networks and demonstrate
that their structure is also consistent with geometric random graphs.; Comment: 10 pages...

Link permanente para citações:

## Theory of Interface: Category Theory, Directed Networks and Evolution of Biological Networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Relevância na Pesquisa

568.98582%

#Mathematics - Category Theory#Condensed Matter - Disordered Systems and Neural Networks#Quantitative Biology - Molecular Networks

Biological networks have two modes. The first mode is static: a network is a
passage on which something flows. The second mode is dynamic: a network is a
pattern constructed by gluing functions of entities constituting the network.
In this paper, first we discuss that these two modes can be associated with the
category theoretic duality (adjunction) and derive a natural network structure
(a path notion) for each mode by appealing to the category theoretic
universality. The path notion corresponding to the static mode is just the
usual directed path. The path notion for the dynamic mode is called lateral
path which is the alternating path considered on the set of arcs. Their general
functionalities in a network are transport and coherence, respectively. Second,
we introduce a betweenness centrality of arcs for each mode and see how the two
modes are embedded in various real biological network data. We find that there
is a trade-off relationship between the two centralities: if the value of one
is large then the value of the other is small. This can be seen as a kind of
division of labor in a network into transport on the network and coherence of
the network. Finally, we propose an optimization model of networks based on a
quality function involving intensities of the two modes in order to see how
networks with the above trade-off relationship can emerge through evolution. We
show that the trade-off relationship can be observed in the evolved networks
only when the dynamic mode is dominant in the quality function by numerical
simulations. We also show that the evolved networks have features qualitatively
similar to real biological networks by standard complex network analysis.; Comment: 59 pages...

Link permanente para citações:

## Algorithmic Perspectives of Network Transitive Reduction Problems and their Applications to Synthesis and Analysis of Biological Networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 27/12/2013

Relevância na Pesquisa

563.05406%

#Computer Science - Computational Complexity#Computer Science - Data Structures and Algorithms#Quantitative Biology - Molecular Networks#68Q25, 68Q17, 68R10, 05C85, 68W25, 92C42, 92C40#E.1#F.2.2#G.2.1#J.3

In this survey paper, we will present a number of core algorithmic questions
concerning several transitive reduction problems on network that have
applications in network synthesis and analysis involving cellular processes.
Our starting point will be the so-called minimum equivalent digraph problem, a
classic computational problem in combinatorial algorithms. We will subsequently
consider a few non-trivial extensions or generalizations of this problem
motivated by applications in systems biology. We will then discuss the
applications of these algorithmic methodologies in the context of three major
biological research questions: synthesizing and simplifying signal transduction
networks, analyzing disease networks, and measuring redundancy of biological
networks.

Link permanente para citações:

## Biological Networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 12/02/2002

Relevância na Pesquisa

567.95223%

In this review, we give an introduction to the structural and functional
properties of the biological networks. We focus on three major themes: topology
of complex biological networks like the metabolic and protein-protein
interaction networks, nonlinear dynamics in gene regulatory networks and in
particular the design of synthetic genetic networks using the concepts and
techniques of nonlinear physics and lastly the effect of stochasticity on the
dynamics. The examples chosen illustrate the usefulness of interdisciplinary
approaches in the study of biological networks.; Comment: 24 pages, latex, 6 pages

Link permanente para citações:

## Potential unsatisfiability of cyclic constraints on stochastic biological networks biases selection toward hierarchical architectures

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 08/06/2015

Relevância na Pesquisa

563.05406%

#Quantitative Biology - Molecular Networks#Quantitative Biology - Populations and Evolution#Quantitative Biology - Quantitative Methods

Constraints placed upon the phenotypes of organisms result from their
interactions with the environment. Over evolutionary timescales, these
constraints feed back onto smaller molecular subnetworks comprising the
organism. The evolution of biological networks is studied by considering a
network of a few nodes embedded in a larger context. Taking into account this
fact that any network under study is actually embedded in a larger context, we
define network architecture, not on the basis of physical interactions alone,
but rather as a specification of the manner in which constraints are placed
upon the states of its nodes. We show that such network architectures
possessing cycles in their topology, in contrast to those that do not, may be
subjected to unsatisfiable constraints. This may be a significant factor
leading to selection biased against those network architectures where such
inconsistent constraints are more likely to arise. We proceed to quantify the
likelihood of inconsistency arising as a function of network architecture
finding that, in the absence of sampling bias over the space of possible
constraints and for a given network size, networks with a larger number of
cycles are more likely to have unsatisfiable constraints placed upon them. Our
results identify a constraint that...

Link permanente para citações:

## Study of the structure and dynamics of complex biological networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 30/12/2008

Relevância na Pesquisa

568.98582%

In this thesis, we have studied the large scale structure and system level
dynamics of certain biological networks using tools from graph theory,
computational biology and dynamical systems. We study the structure and
dynamics of large scale metabolic networks inside three organisms, Escherichia
coli, Saccharomyces cerevisiae and Staphylococcus aureus. We also study the
dynamics of the large scale genetic network controlling E. coli metabolism. We
have tried to explain the observed system level dynamical properties of these
networks in terms of their underlying structure. Our studies of the system
level dynamics of these large scale biological networks provide a different
perspective on their functioning compared to that obtained from purely
structural studies. Our study also leads to some new insights on features such
as robustness, fragility and modularity of these large scale biological
networks. We also shed light on how different networks inside the cell such as
metabolic networks and genetic networks are interrelated to each other.; Comment: 165 Pages, PhD Thesis, University of Delhi, India

Link permanente para citações:

## Bayesian analysis of biological networks: clusters, motifs, cross-species correlations

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 28/09/2006

Relevância na Pesquisa

564.9207%

An important part of the analysis of bio-molecular networks is to detect
different functional units. Different functions are reflected in a different
evolutionary dynamics, and hence in different statistical characteristics of
network parts. In this sense, the {\em global statistics} of a biological
network, e.g., its connectivity distribution, provides a background, and {\em
local deviations} from this background signal functional units. In the
computational analysis of biological networks, we thus typically have to
discriminate between different statistical models governing different parts of
the dataset. The nature of these models depends on the biological question
asked. We illustrate this rationale here with three examples: identification of
functional parts as highly connected \textit{network clusters}, finding
\textit{network motifs}, which occur in a similar form at different places in
the network, and the analysis of \textit{cross-species network correlations},
which reflect evolutionary dynamics between species.; Comment: 12 pages, to appear in Statistical and Evolutionary Analysis of
Biological Network Data, M. Stumpf and C. Wiuf (Eds.)

Link permanente para citações:

## Modeling for evolving biological networks with scale-free connectivity, hierarchical modularity, and disassortativity

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 04/11/2006

Relevância na Pesquisa

567.95223%

#Quantitative Biology - Molecular Networks#Condensed Matter - Disordered Systems and Neural Networks

We propose a growing network model that consists of two tunable mechanisms:
growth by merging modules which are represented as complete graphs and a
fitness-driven preferential attachment. Our model exhibits the three prominent
statistical properties are widely shared in real biological networks, for
example gene regulatory, protein-protein interaction, and metabolic networks.
They retain three power law relationships, such as the power laws of degree
distribution, clustering spectrum, and degree-degree correlation corresponding
to scale-free connectivity, hierarchical modularity, and disassortativity,
respectively. After making comparisons of these properties between model
networks and biological networks, we confirmed that our model has inference
potential for evolutionary processes of biological networks.; Comment: 19 pages, 8 figures

Link permanente para citações:

## Evolution of complex modular biological networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Relevância na Pesquisa

569.94055%

Biological networks have evolved to be highly functional within uncertain
environments while remaining extremely adaptable. One of the main contributors
to the robustness and evolvability of biological networks is believed to be
their modularity of function, with modules defined as sets of genes that are
strongly interconnected but whose function is separable from those of other
modules. Here, we investigate the in silico evolution of modularity and
robustness in complex artificial metabolic networks that encode an increasing
amount of information about their environment while acquiring ubiquitous
features of biological, social, and engineering networks, such as scale-free
edge distribution, small-world property, and fault-tolerance. These networks
evolve in environments that differ in their predictability, and allow us to
study modularity from topological, information-theoretic, and gene-epistatic
points of view using new tools that do not depend on any preconceived notion of
modularity. We find that for our evolved complex networks as well as for the
yeast protein-protein interaction network, synthetic lethal pairs consist
mostly of redundant genes that lie close to each other and therefore within
modules, while knockdown suppressor pairs are farther apart and often straddle
modules...

Link permanente para citações:

## New Scaling Relation for Information Transfer in Biological Networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 17/08/2015

Relevância na Pesquisa

573.5364%

Living systems are often described utilizing informational analogies. An
important open question is whether information is merely a useful conceptual
metaphor, or intrinsic to the operation of biological systems. To address this
question, we provide a rigorous case study of the informational architecture of
two representative biological networks: the Boolean network model for the
cell-cycle regulatory network of the fission yeast S. pombe and that of the
budding yeast S. cerevisiae. We compare our results for these biological
networks to the same analysis performed on ensembles of two different types of
random networks. We show that both biological networks share features in common
that are not shared by either ensemble. In particular, the biological networks
in our study, on average, process more information than the random networks.
They also exhibit a scaling relation in information transferred between nodes
that distinguishes them from either ensemble: even when compared to the
ensemble of random networks that shares important topological properties, such
as a scale-free structure. We show that the most biologically distinct regime
of this scaling relation is associated with the dynamics and function of the
biological networks. Information processing in biological networks is therefore
interpreted as an emergent property of topology (causal structure) and dynamics
(function). These results demonstrate quantitatively how the informational
architecture of biologically evolved networks can distinguish them from other
classes of network architecture that do not share the same informational
properties.

Link permanente para citações: