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## Dinâmica da Fermentação Alcóolica: Aplicação de Redes Booleanas na Dinâmica de Expressão Gênica em Linhagens de Saccharomyces Cerevisiae durante o Processo Fermentativo; Dynamics of alcoholic fermentation: application of Boolean networks in the dynamics of gene expression in Saccharomyces cerevisiae strains during fermentation process

Noronha, Melline Fontes
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
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## Shape and efficiency of wood ant foraging networks

Buhl, J.; Hicks, K.; Miller, E.R.; Persey, S.; Alinvi, O.; Sumpter, D.J.T.
Fonte: Springer Verlag Publicador: Springer Verlag
Tipo: Artigo de Revista Científica
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45.77%
We measured the shape of the foraging trail networks of 11 colonies of the wood ant Formica aquilonia (Formica rufa group). We characterized these networks in terms of their degree of branching and the angles between branches, as well as in terms of their efficiency. The measured networks were compared with idealized model networks built to optimize one of two components of efficiency, total length (i.e., total amount of trail) and route factor (i.e., average distance between nest and foraging site). The analysis shows that the networks built by the ants obtain a compromise between the two modes of efficiency. These results are largely independent of the size of the network or colony size. The ants’ efficiency is comparable to that of networks built by humans but achieved without the benefit of centralized control.; Jerome Buhl, Kerri Hicks, Esther R. Miller, Sophie Persey, Ola Alinvi, David J. T. Sumpter

## Exact Controllability of Complex Networks

Yuan, Zhengzhong; Zhao, Chen; Di, Zengru; Wang, Wen-Xu; Lai, Ying-Cheng
Tipo: Artigo de Revista Científica
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45.79%
Controlling complex networks is of paramount importance in science and engineering. Despite the recent development of structural-controllability theory, we continue to lack a framework to control undirected complex networks, especially given link weights. Here we introduce an exact-controllability paradigm based on the maximum multiplicity to identify the minimum set of driver nodes required to achieve full control of networks with arbitrary structures and link-weight distributions. The framework reproduces the structural controllability of directed networks characterized by structural matrices. We explore the controllability of a large number of real and model networks, finding that dense networks with identical weights are difficult to be controlled. An efficient and accurate tool is offered to assess the controllability of large sparse and dense networks. The exact-controllability framework enables a comprehensive understanding of the impact of network properties on controllability, a fundamental problem towards our ultimate control of complex systems.; Comment: 19 pages, 3 figures, 3 tables

## Synchronization in model networks of class I neurons

Katriel, Guy
Tipo: Artigo de Revista Científica
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45.77%
We study a modification of the canonical model for networks of class I neurons, presented by Hoppensteadt and Izhikevich, in which the 'pulse' emitted by a neuron is smooth rather than a delta-function. We prove two types of results about synchronization and desynchronization of such networks, the first type pertaining to 'pulse' functions which are symmetric, and the other type in the regime in which each neuron is connected to many other neurons.; Comment: 14 pages. An error has been found in the asymptotic analysis in section 5 of the first version, due to which some of the claims made in this section were untrue. In this version the correct results on synchronization/desynchronization in the 'many connections' regime are given

## Exploring self-similarity of complex cellular networks: The edge-covering method with simulated annealing and log-periodic sampling

Zhou, Wei-Xing; Jiang, Zhi-Qiang; Sornette, Didier
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
45.77%
Song, Havlin and Makse (2005) have recently used a version of the box-counting method, called the node-covering method, to quantify the self-similar properties of 43 cellular networks: the minimal number $N_V$ of boxes of size $\ell$ needed to cover all the nodes of a cellular network was found to scale as the power law $N_V \sim (\ell+1)^{-D_V}$ with a fractal dimension $D_V=3.53\pm0.26$. We propose a new box-counting method based on edge-covering, which outperforms the node-covering approach when applied to strictly self-similar model networks, such as the Sierpinski network. The minimal number $N_E$ of boxes of size $\ell$ in the edge-covering method is obtained with the simulated annealing algorithm. We take into account the possible discrete scale symmetry of networks (artifactual and/or real), which is visualized in terms of log-periodic oscillations in the dependence of the logarithm of $N_E$ as a function of the logarithm of $\ell$. In this way, we are able to remove the bias of the estimator of the fractal dimension, existing for finite networks. With this new methodology, we find that $N_E$ scales with respect to $\ell$ as a power law $N_E \sim \ell^{-D_E}$ with $D_E=2.67\pm0.15$ for the 43 cellular networks previously analyzed by Song...

## Branching process approach for Boolean bipartite networks of metabolic reactions

Lee, Deokjae; Goh, K. -I.; Kahng, B.
Tipo: Artigo de Revista Científica
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45.76%
The branching process (BP) approach has been successful in explaining the avalanche dynamics in complex networks. However, its applications are mainly focused on unipartite networks, in which all nodes are of the same type. Here, motivated by a need to understand avalanche dynamics in metabolic networks, we extend the BP approach to a particular bipartite network composed of Boolean AND and OR logic gates. We reduce the bipartite network into a unipartite network by integrating out OR gates, and obtain the effective branching ratio for the remaining AND gates. Then the standard BP approach is applied to the reduced network, and the avalanche size distribution is obtained. We test the BP results with simulations on the model networks and two microbial metabolic networks, demonstrating the usefulness of the BP approach.

## Range-limited Centrality Measures in Complex Networks

Ercsey-Ravasz, Maria; Lichtenwalter, Ryan; Chawla, Nitesh V.; Toroczkai, Zoltan
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
45.77%
Here we present a range-limited approach to centrality measures in both non-weighted and weighted directed complex networks. We introduce an efficient method that generates for every node and every edge its betweenness centrality based on shortest paths of lengths not longer than $\ell = 1,...,L$ in case of non-weighted networks, and for weighted networks the corresponding quantities based on minimum weight paths with path weights not larger than $w_{\ell}=\ell \Delta$, $\ell=1,2...,L=R/\Delta$. These measures provide a systematic description on the positioning importance of a node (edge) with respect to its network neighborhoods 1-step out, 2-steps out, etc. up to including the whole network. We show that range-limited centralities obey universal scaling laws for large non-weighted networks. As the computation of traditional centrality measures is costly, this scaling behavior can be exploited to efficiently estimate centralities of nodes and edges for all ranges, including the traditional ones. The scaling behavior can also be exploited to show that the ranking top-list of nodes (edges) based on their range-limited centralities quickly freezes as function of the range, and hence the diameter-range top-list can be efficiently predicted. We also show how to estimate the typical largest node-to-node distance for a network of $N$ nodes...

## Finding communities in sparse networks

Singh, Abhinav; Humphries, Mark
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
45.8%
Spectral algorithms based on matrix representations of networks are often used to detect communities but classic spectral methods based on the adjacency matrix and its variants fail to detect communities in sparse networks. New spectral methods based on non-backtracking random walks have recently been introduced that successfully detect communities in many sparse networks. However, the spectrum of non-backtracking random walks ignores hanging trees in networks that can contain information about the community structure of networks. We introduce the reluctant backtracking operators that explicitly account for hanging trees as they admit a small probability of returning to the immediately previous node unlike the non-backtracking operators that forbid an immediate return. We show that the reluctant backtracking operators can detect communities in certain sparse networks where the non-backtracking operators cannot while performing comparably on benchmark stochastic block model networks and real world networks. We also show that the spectrum of the reluctant backtracking operator approximately optimises the standard modularity function similar to the flow matrix. Interestingly, for this family of non- and reluctant-backtracking operators the main determinant of performance on real-world networks is whether or not they are normalised to conserve probability at each node.; Comment: 11 pages...

## Robustness of Transcriptional Regulation in Yeast-like Model Boolean Networks

Tugrul, Murat; Kabakcioglu, Alkan
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
45.88%
We investigate the dynamical properties of the transcriptional regulation of gene expression in the yeast Saccharomyces Cerevisiae within the framework of a synchronously and deterministically updated Boolean network model. By means of a dynamically determinant subnetwork, we explore the robustness of transcriptional regulation as a function of the type of Boolean functions used in the model that mimic the influence of regulating agents on the transcription level of a gene. We compare the results obtained for the actual yeast network with those from two different model networks, one with similar in-degree distribution as the yeast and random otherwise, and another due to Balcan et al., where the global topology of the yeast network is reproduced faithfully. We, surprisingly, find that the first set of model networks better reproduce the results found with the actual yeast network, even though the Balcan et al. model networks are structurally more similar to that of yeast.; Comment: 7 pages, 4 figures, To appear in Int. J. Bifurcation and Chaos, typos were corrected and 2 references were added

## Spectral Properties of Directed Random Networks with Modular Structure

Jalan, Sarika; Zhu, Guimei; Li, Baowen
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
45.85%
We study spectra of directed networks with inhibitory and excitatory couplings. We investigate in particular eigenvector localization properties of various model networks for different value of correlation among their entries. Spectra of random networks, with completely uncorrelated entries show a circular distribution with delocalized eigenvectors, where as networks with correlated entries have localized eigenvectors. In order to understand the origin of localization we track the spectra as a function of connection probability and directionality. As connections are made directed, eigenstates start occurring in complex conjugate pairs and the eigenvalue distribution combined with the localization measure shows a rich pattern. Moreover, for a very well distinguished community structure, the whole spectrum is localized except few eigenstates at boundary of the circular distribution. As the network deviates from the community structure there is a sudden change in the localization property for a very small value of deformation from the perfect community structure. We search for this effect for the whole range of correlation strengths and for different community configurations. Furthermore, we investigate spectral properties of a metabolic network of zebrafish...

## Deciphering the global organization of clustering in real complex networks

Colomer-de-Simon, Pol; Serrano, M. Angeles; Beiro, Mariano G.; Alvarez-Hamelin, J. Ignacio; Boguna, Marian
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
45.88%
We uncover the global organization of clustering in real complex networks. As it happens with other fundamental properties of networks such as the degree distribution, we find that real networks are neither completely random nor ordered with respect to clustering, although they tend to be closer to maximally random architectures. We reach this conclusion by comparing the global structure of clustering in real networks with that in maximally random and in maximally ordered clustered graphs. The former are produced with an exponential random graph model that maintains correlations among adjacent edges at the minimum needed to conform with the expected clustering spectrum; the later with a random model that arranges triangles in cliques inducing highly ordered structures. To compare the global organization of clustering in real and model networks, we compute $m$-core landscapes, where the $m$-core is defined, akin to the $k$-core, as the maximal subgraph with edges participating at least in $m$ triangles. This property defines a set of nested subgraphs that, contrarily to $k$-cores, is able to distinguish between hierarchical and modular architectures. To visualize the $m$-core decomposition we developed the LaNet-vi 3.0 tool.

## Classification of weighted networks through mesoscale homological features

Sizemore, Ann; Giusti, Chad; Bassett, Danielle
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
45.85%
As complex networks find applications in a growing range of disciplines, the diversity of naturally occurring and model networks being studied is exploding. The adoption of a well-developed collection of network taxonomies is a natural method for both organizing this data and understanding deeper relationships between networks. Most existing metrics for network structure rely on classical graph-theoretic measures, extracting characteristics primarily related to individual vertices or paths between them, and thus classify networks from the perspective of local features. Here, we describe an alternative approach to studying structure in networks that relies on an algebraic-topological metric called persistent homology, which studies intrinsically mesoscale structures called cycles, constructed from cliques in the network. We present a classification of 14 commonly studied weighted network models into four groups or classes, and discuss the structural themes arising in each class. Finally, we compute the persistent homology of two real-world networks and one network constructed by a common dynamical systems model, and we compare the results with the three classes to obtain a better understanding of those networks; Comment: 32 pages, 16 figures

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

Takemoto, Kazuhiro; Oosawa, Chikoo
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
45.9%
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

## The Structure and Dynamics of Gene Regulation Networks

Tuğrul, Murat
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
45.89%
The structure and dynamics of a typical biological system are complex due to strong and inhomogeneous interactions between its constituents. The investigation of such systems with classical mathematical tools, such as differential equations for their dynamics, is not always suitable. The graph theoretical models may serve as a rough but powerful tool in such cases. In this thesis, I first consider the network modeling for the representation of the biological systems. Both the topological and dynamical investigation tools are developed and applied to the various model networks. In particular, the attractor features' scaling with system size and distributions are explored for model networks. Moreover, the theoretical robustness expressions are discussed and computational studies are done for confirmation. The main biological research in this thesis is to investigate the transcriptional regulation of gene expression with synchronously and deterministically updated Boolean network models. I explore the attractor structure and the robustness of the known interaction network of the yeast, Saccharomyces Cerevisiae and compare with the model networks. Furthermore, I discuss a recent model claiming a possible root to the topology of the yeast's gene regulation network and investigate this model dynamically. The thesis also included another study which investigates a relation between folding kinetics with a new network representation...

## Origin and implications of zero degeneracy in networks spectra

Tipo: Artigo de Revista Científica
Relevância na Pesquisa
45.77%
Spectra of real world networks exhibit properties which are different from the random networks. One such property is the existence of a very high degeneracy at zero eigenvalues. In this work, we provide possible reasons behind occurrence of the zero degeneracy in various networks spectra. Comparison of zero degeneracy in protein-protein interaction networks of six different species and in their corresponding model networks sheds light in understanding the evolution of complex biological systems.; Comment: 5 pages, 3 figures

## Chaotic Gene Regulatory Networks Can Be Robust Against Mutations and Noise

Sevim, Volkan; Rikvold, Per Arne
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
45.88%
Robustness to mutations and noise has been shown to evolve through stabilizing selection for optimal phenotypes in model gene regulatory networks. The ability to evolve robust mutants is known to depend on the network architecture. How do the dynamical properties and state-space structures of networks with high and low robustness differ? Does selection operate on the global dynamical behavior of the networks? What kind of state-space structures are favored by selection? We provide damage propagation analysis and an extensive statistical analysis of state spaces of these model networks to show that the change in their dynamical properties due to stabilizing selection for optimal phenotypes is minor. Most notably, the networks that are most robust to both mutations and noise are highly chaotic. Certain properties of chaotic networks, such as being able to produce large attractor basins, can be useful for maintaining a stable gene-expression pattern. Our findings indicate that conventional measures of stability, such as the damage-propagation rate, do not provide much information about robustness to mutations or noise in model gene regulatory networks.; Comment: JTB accepted

## Learning multifractal structure in large networks

Benson, Austin R.; Riquelme, Carlos; Schmit, Sven
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
45.85%
Generating random graphs to model networks has a rich history. In this paper, we analyze and improve upon the multifractal network generator (MFNG) introduced by Palla et al. We provide a new result on the probability of subgraphs existing in graphs generated with MFNG. From this result it follows that we can quickly compute moments of an important set of graph properties, such as the expected number of edges, stars, and cliques. Specifically, we show how to compute these moments in time complexity independent of the size of the graph and the number of recursive levels in the generative model. We leverage this theory to a new method of moments algorithm for fitting large networks to MFNG. Empirically, this new approach effectively simulates properties of several social and information networks. In terms of matching subgraph counts, our method outperforms similar algorithms used with the Stochastic Kronecker Graph model. Furthermore, we present a fast approximation algorithm to generate graph instances following the multi- fractal structure. The approximation scheme is an improvement over previous methods, which ran in time complexity quadratic in the number of vertices. Combined, our method of moments and fast sampling scheme provide the first scalable framework for effectively modeling large networks with MFNG.

## Modeling the topology of protein interaction networks

Schneider, Christian M.; de Arcangelis, Lucilla; Herrmann, Hans J.
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
45.83%
A major issue in biology is the understanding of the interactions between proteins. These interactions can be described by a network, where the proteins are modeled by nodes and the interactions by edges. The origin of these protein networks is not well understood yet. Here we present a two-step model, which generates clusters with the same topological properties as networks for protein-protein interactions, namely, the same degree distribution, cluster size distribution, clustering coefficient and shortest path length. The biological and model networks are not scale free but exhibit small world features. The model allows the fitting of different biological systems by tuning a single parameter.; Comment: 5 pages, 5 figures

## Sparse essential interactions in model networks of gene regulation

Burda, Z.; Krzywicki, A.; Martin, O. C.; Zagorski, M.
Tipo: Artigo de Revista Científica
We study the transport properties of model networks such as scale-free and Erd\H{o}s-R\'{e}nyi networks as well as a real network. We consider the conductance $G$ between two arbitrarily chosen nodes where each link has the same unit resistance. Our theoretical analysis for scale-free networks predicts a broad range of values of $G$, with a power-law tail distribution $\Phi_{\rm SF}(G)\sim G^{-g_G}$, where $g_G=2\lambda -1$, and $\lambda$ is the decay exponent for the scale-free network degree distribution. We confirm our predictions by large scale simulations. The power-law tail in $\Phi_{\rm SF}(G)$ leads to large values of $G$, thereby significantly improving the transport in scale-free networks, compared to Erd\H{o}s-R\'{e}nyi networks where the tail of the conductivity distribution decays exponentially. We develop a simple physical picture of the transport to account for the results. We study another model for transport, the \emph{max-flow} model, where conductance is defined as the number of link-independent paths between the two nodes, and find that a similar picture holds. The effects of distance on the value of conductance are considered for both models, and some differences emerge. We then extend our study to the case of multiple sources...