Modular and hierarchically modular organization of brain. A biological object can be investigated with different experimental methods each biological process can be described with different mathematical model the choice of a mathematical model or an algorithm to describe a biological object depends on problem. In this paper, we propose a new model for generating networks that have the modular structures and clustering characteristic of real biological networks. We show that both algorithms find structure in biological data that is consistent with known biological processes, protein complexes, genetic diseases, and operational taxonomic units. Hierarchical modularity in human brain functional networks. Modularity has been considered to be one of the main organization principles of biological networks in the past decade years. Biological networks have high clustering coefficients gene coexpression network proteinprotein interaction network source gnf hprd nodes genes proteins edges coexpression physical interaction number of nodes 6,342 5,881 number of edges 74,830 23,333 clustering coefficient actual 0. Proteinprotein interaction networks are also highly modular in structure wang and zhang 2007. Modular neural networks, as combined structures, have also a biological background. A number of biological metaphors are incorporated in the method. Computational approaches to biological network inference.
Algorithms to explore the structure and evolution of. Thus, it appears that the structural modules in the ppi network may have. A network is any system with subunits that are linked into a whole, such as species units linked into a whole food web. Biological background structure of a prototypical biological neuron simpli. The figure is a general outline that visualizes the spatiotemporal information within the modules, and clearly reveals the relationship between protein complexes and functional modules by integrating the proteinprotein. One such property is the an small world effect, which is the name given to the finding that ne. Several methods have also been discovered to analyze the pathway structure of metabolic networks 4448. However, we find little evidence that the structural modules correspond to functional units. For example, clustermaker 2 implemented several clustering algorithms such as kmeans, kmedoid, scps, and autosome to visualize a structure of modules within biological networks.
Evolution of complex modular biological networks arxiv. Community structure in social and biological networks. Finding an adequate network model that generates networks that closely replicate the structure of real data is one of the. Giant 3 was proposed to investigate topological or functional rela. It can be clearly seen that a single layer of dors connects between most of the transcription factors and the effector operons. A structural approach for finding functional modules from. Detecting modules in biological networks by edge weight. Transcriptional networks promoter sequence analysis lecturer. While many models capture key aspects of networks, such as the distribution of connectivity, the density of clustering, or modular structure, it has proven difficult to devise simple models that accurately recapitulate all of the features of experimental networks albert and barabasi, 2002. In the previous lecture we saw several local methods of finding functions.
Aug 12, 2008 biological systems can be modeled as complex network systems with many interactions between the components. A differential network approach to exploring differences between biological states. The problem of nding modules in networks or \community detection has received much attention in the physics literature, wherein many approaches 4, 5 focus on optimizing an energybased cost function with xed parameters over possible assignments of nodes into modules. In general, combined networks are more powerful than. Several approaches have therefore been proposed in order to allow for more. It will be argued that modular artificial neural networks have a better performance than their non modular counterparts. Reconstructing evolutionary modular networks from time. Determining modular organization of protein interaction. Nov 18, 2011 the analysis of complex biological networks has traditionally relied on decomposition into smaller, semiautonomous units such as individual signaling pathways. Biological processes can be described in more than one way as follows. Request pdf integrative approaches for finding modular structure in biological networks a central goal of systems biology is to elucidate the structural and functional architecture of the cell.
Recently, the statistical mechanical formalism of complex network. In network approaches to psychopathology, disorders result from the causal interplay between symptoms e. Maximizing modularity density for exploring modular organization of protein interaction networks shihua zhang 1, xuemei ning 2 chris ding 3 1 academy of mathematics and systems science, cas, beijing 100190, china 2 college of science, beijing forestry university, beijing 83, china 3 department of computer science and engineering, university of texas at arlington arlington. Despite some promising research directions, several important theoretical challenges remain open on the way to formal, functioncentered modular decompositions for dynamic biological networks. Biological systems can be modeled as complex network systems with many interactions between the components. Biological networks provide a mathematical representation of connections found in ecological, evolutionary, and physiological studies, such as neural networks. Many complex systems in nature and society can be described in terms of. For example, a pioneering perspective 1 on modular cell biology described a module as a distinct group of interacting. Finding an adequate network model that generates networks that closely replicate the. Network module community structure has been a hot research topic in recent years. Mining the modular structure of protein interaction networks. Integrative approaches for finding modular structure in. Identifying of such modules in protein interaction networks.
Spatial analysis of functional enrichment safe is a systematic, quantitative method for mapping local enrichment for functional attributes in biological networks. Abstract a central goal of systems biology is to elucidate the structural and functional architecture of the cell. A new multiscale method to reveal hierarchical modular structures in biological networks qingju jiao, yan huang and hongbin shen reporter. Few such networks are known in anything approaching their complete structure, even in the simplest bacteria. A biological network is any network that applies to biological systems. These interactions give rise to the function and behavior of that system. Computational methods to explore hierarchical and modular. Identifying of such modules in protein interaction. Integrative approaches for finding modular structure in biological networks. Using graph theory to analyze biological networks biodata. Modular neural networking is the learning of networks of flexible nodes interacting with other nodes neurons which are developed for generalization of neural biology. Modular neural networking is the learning of networks of flexible nodes interacting with other nodes neurons which are. At the molecular level, modules have been variously described as groups of genes, gene products, or metabolites that are functionally coordinated, physically interacting andor coregulated 17. However, carving a disease network module from the whole interactome is a difficult.
Therefore, a number of socalled differential network analyses figure 2 have adopted an experimental approach whereby biological networks are measured and compared across conditions to identify interactions and modules that are differentially present. Integrated analysis of multiple data sources reveals modular. Maximizing modularity density for exploring modular. Cellular organization is thought to be fundamentally modular 1,2.
Searching for modular structure in complex phenotypes. Many types of biological networks exist, including transcriptional, signalling and metabolic. Modeling and analysis of modular structure in diverse. Biological metaphors and the design of modular artificial. Modular and hierarchically modular organization of brain networks. This lecture focuses on computational approaches to decipher transcriptional regulation.
Dendrograms displaying significant modular and sub modular structure for a a very largescale integrated circuit, b caenorhabditis elegans, c the human anatomical network estimated using mri data on 259 normal volunteers, and d the human cortical network estimated using. Progress in our understanding of the modular nature of biological networks must come from new functional data that allow us to study different groups of genes both together and apart, and compare this data to our topological. Natural neural systems are composed of a hierarchy of networks built of elements specialized for di. Hierarchical modularity in human brain functional networks david meunier1,2, renaud lambiotte3, alex fornito1,2,4. A read is counted each time someone views a publication summary such as the title, abstract, and list of authors, clicks on a figure, or views or downloads the fulltext. The human brain can also be seen as a modular neural network, and the proposed search method is.
To this end, large and complex networks of molecular. Structuring and modularizing the network, chapter 3 of designing for cisco internetwork solutions desgn, second edition, introduces a modular hierarchical approach to network design, the cisco enterprise architecture. Many computer readable formats are available to describe biological networks. Brain networks are increasingly understood as one of a large class of information processing systems that share important organizational principles in common, including the property of a modular community structure. With the increased scope of systems biology models, rational approaches to modularization have become an important topic. Structuring and modularizing the network from designing. Integrative approach to the structure of psychopathology.
In the experiments we describe below, we evolve large metabolic networks of many hundreds of nodes with over a thousand edges for up to 5,000 generations from simple networks with only five genes. A new multiscale method to reveal hierarchical modular structures in biological networks. A network is a network be it between words those associated with bright in this case or protein structures. Detection of the modular structure of biological networks is of interest to. A profound consequence of the modular structure of complex networks is the enhanced robustness to various internal and external perturbations and disturbances. Therefore, network modules showing conservation over large evolutionary distances are likely to reflect well preserved core functions maintained by natural selection. The next section introduces network modularization and discusses the details of the cisco. It will be argued that modular artificial neural networks have a better performance than their nonmodular counterparts. We find that for our evolved complex networks as well as for the yeast protein. For example, the proteinprotein interaction network is the physical basis of multiple cellular functions.
The modules of cellular networks were thought to be formed in a way that reflects the relative independence and coherence of the various functional. Some authors have attributed the robustness of biological networks to their high modularity. Many information processing networks have a fractal community structure of moduleswithinmodules. Mathematical models of cell functions can be accomplished by one of the model of ann i. However, global methods use different strategies for the prediction of protein. Feedforward loops and sims are frequent at the output of this layer.
Increasing evidence shows that biological networks are essential, albeit not. In search of the biological significance of modular. Cellular functions and biochemical events are coordinately carried out by groups of proteins interacting each other in biological modules. Pdf detecting hierarchical modularity in biological networks. The second, vicut, uses the variation of information to nonparametrically find groups of topologically cohesive and similarly annotated nodes in the network. Uncovering the overlapping community structure of complex. Using the yeast genetic interaction network as a test case, safe proved to be accurate, robust, and predictive of new biological mechanisms, such as resistance to the anticancer drug bortezomib.
With ever increasing amount of available data on biological networks, modeling and understanding the structure of these large networks is an important problem with profound biological implications. Modular analysis of biological networks springerlink. In search of the biological significance of modular structures in. A module is topologically defined as a subset of highly interconnected nodes which are relatively sparsely connected to nodes in other modules. Clusterbased descriptions of biological networks have received much attention in recent years fostered by accumulated evidence of the existence of meaningful correlations between topological network clusters and biological functional modules. Specifically, our approach incorporates and propagates the functional impact of. The human brain can also be seen as a modular neural network, and the proposed search method is based on the natural process that. In search of the biological significance of modular structures in protein networks zhi wang, jianzhi zhang department of ecology and evolutionary biology, university of michigan, ann arbor, michigan, united states of america many complex networks such as computer and social networks exhibit modular structures, where links between nodes.
Systematic functional annotation and visualization of. An integrative approach to the structure of psychopathology denny borsboom and angelique o. The analysis of biological networks with respect to human diseases has led to the field of network medicine. By providing rapid decomposition, the algorithm enabled us to study the modular structure of whole brain networks on a larger scale thousands of. Integrated analysis reveals modular structure of the ppi network with the spatial and temporal information included. Find materials for this course in the pages linked along the left. The systems biology markup language sbml is an xmllike machinereadable language, that is able to represent models to be analyzed by a computer. Imagine seeing a reasonable explanation of all these capabilities through the combinatorial interactions of myriad biological neuronal network modules. Hierarchical organization of modularity in complex networks. Many methods have been proposed for module detection and identification. Modular structure modularity the fraction of the edges that fall within the given groups minus the expected fraction if edges were distributed at random. To understand complex biological network data, one must be able to successfully reproduce them. Hierarchical modular structure identification with its. An integrative approach to modeling biological networks.
Several wellperforming clustering algorithms exist to infer topological network partitions. Compared to the partitional module identification methods, less research is done on the inference of hierarchical modular. Our model is appealing because it begins with a simple structure, namely a stochastic block model sbm holland et al. First, if a ppi already has a native structure, it is extracted from a protein data. In this thesis, a method is proposed with which good modular artificial neural network structures can be found automatically using a computer program. A new multiscale method to reveal hierarchical modular. Computational approaches to biological network inference and modeling in systems biology hungxuanta departmentofbiosciences divisionofgenetics p. Computational approaches to biological network inference and. In biological systems like the brain, selfsimilarity is statistical rather than exact so the modular community structure brain networks is approximately not perfectly invariant over a finite number of hierarchical levels.
One goal of emerging systems biology is to analyze very large complex biological networks such as protein. Integrated analysis of multiple data sources reveals. Hierarchical structure of modules is shown to exist in many networks such as biological networks and social networks. One popular class of methods for dissecting modular structure in the eld of general complex networks is based on optimizing a global quality function called modularity 2, 11 to partition the network into modules. Determining modular organization of protein interaction networks by. Hierarchical organization of modularity 3 functional properties are ultimately encoded into a complex intracellular web of molecular interactions 1823. Department of psychology, university of amsterdam, amsterdam 1018 xa, the netherlands. Biological networks undergo significant rewiring through evolutionary time, concomitant with gains, losses, or modifications in gene functions 108111. In essence, one of the main approaches currently used to reconstruct a sequence of networks is to apply a changepoint based algorithm that would segment the time series and the variables, with subsequent. When the tf binds the dna, the chromatin structure in the promoter region changes and the binding area of the rna polymerase becomes more accessible. A network is a set of nodes and a set of directed or undirected edges between the nodes. Robustness is considered to be one of the key factors that shaped biological systems through evolution. Modular neural networks chronicles in biological aspects. First we sought to extend the static methods to dynamic clustering problems, and observed general patterns of dynamics of network modules.
Integrative approaches for finding modular structure in biological. Lecture 11, january 4, 2007 1 introduction each cell of an organism contains an identical copy of the whole genome. Types of biological networks interaction data gathered through both individual studies and largescale screens can be assembled into a network format whose topological structure contains significant biological properties. Genes free fulltext enriching human interactome with. The chapter begins with a discussion of the hierarchical network structure. A highly modular structure is a common feature of biological networks 45,81, 192, 193,196. Therefore, the structure of the ppi networks is the most consistent with the structure of a noisy geo. Evolution of complex modular biological networks arend hintze, christoph adami keck graduate institute of applied life sciences, claremont, california, united states of america biological networks have evolved to be highly functional within uncertain environments while remaining extremely adaptable.
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