Figure 4
Hierarchical organization of biomolecular systems. Molecules interact in hierarchical organizational levels of increasing complexity. Data can be collected at the molecular and phenotype levels. The organizational hierarchy can be used as a framework to discover quantitative relationships between interacting molecules and the complex properties of cells.
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Model organisms, especially the budding yeast, are leading systems in the transformation of biology into an information science. With the availability of genome sequences and genome-scale data generation technologies, the extraction of biological insight from complex integrated molecular networks has become a major area of research. Here I examine...
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... inactive. By assigning Boolean functions, or other types of more complex functions, to network nodes, one can generate explicit predictions of the cascading consequences of molecular perturbations. Probabilistic Boolean Networks (PBNs) (31) are examples of models with dynamical properties. PBNs incorporate the appealing determinism of Boolean logic and practical uncertainties, both in the data and in the selection of Boolean functions. Note that graph edges can represent molecular relationships more complex than the presence of an interaction. In Bayesian genetic-network models, edges represent probabilistic dependencies of gene-expression patterns (14). Key advantages of these models are the ability to rigorously score the fit of models to data, the accommodation of hidden variables, and the ability to describe arbitrarily complex (more than pair-wise) relationships. Approaches such as those cited above can drive the exploration of poorly characterized complex networks to reach a level of detail allowing biochemical simulations. Gilman & Arkin (13) reviewed biochemical modeling in detail. Naturally, together with computational methods and algorithms, software is proliferating. Continuing needs are the development of general-purpose network analysis and modeling platforms and standards for network model communication. Candidates are emerging. Two prominent general-purpose software platforms for biological network integration, visualization, and analysis are Osprey (7) and Cytoscape (30). Osprey simplifies data access and visualization (. on.ca/osprey/). Cytoscape has the advantages of a highly flexible and generalized approach to data integration, network visualization, and analysis, as well as open- source development and distribution (). To enable the exploitation of an increasing diversity of computational methods and implemen- tations, and to facilitate collaboration and the communication of results, standards are needed for the transmission of network models. Cell Markup Language (CellML) (16) and Systems Biology Markup Language (SBML) (17) are evolving machine-readable eXtensible Markup Language (XML)-based standards for the communication of network models. The analysis of molecular network structures has been a valuable approach to the extraction of biological insight. This approach aims to identify highly nonrandom network structural patterns that reflect function and the processes that created complex networks. These processes may include the action of selection driving either structural convergence or conservation. In both protein-DNA and protein-protein interaction networks, one can find repeated local structural units, motifs, that occur much more often than expected by random chance. Lee et al. (21) collected a global data set of transcription– factor–binding interactions with target genes in yeast. This transcription network contained several repeating local structures (Figure 3). These transcriptional regulatory motifs can be associated with regulatory functions or behaviors, for example, the positive autoregulation of Ste12. Conant & Wagner (8) investigated the evolution of transcriptional regulatory motifs in yeast and E. coli . They show that the instances of these motifs evolved independently, rather than by duplication and divergence of one or a small number of ancestral circuits. In other words, the repeated instances of each motif are the result of evolutionary convergence on the motif structure. These results support the suggestion that the collective function of a transcriptional regulatory motif can be found in the decision-making behavior encoded in the structure, per se. This evolutionary convergence contrasts the results suggesting the evolutionary conservation of motif constituents in the yeast protein- protein interaction network (38). Furthermore, specific protein-protein interaction motifs show overrepresentation of specific functional classes of yeast proteins. Apparently, in the protein-protein interaction network, the collective functions of network motifs are more closely associated with specific cellular tasks, for example, transport facilitation. Network motifs may be common core components of molecular modules. Modules are groups of preferred molecular partners that interact to perform some collective function (15). Evidence for the existence of modules comes from various groups (4, 20, 28, 29, 33, 34). The modular organization of molecular systems may be a consequence of the advantages of modules in the evolutionary process (2). Engineered modules provide the advantage of reuse of well-tested units perform- ing complex functions. Rather than designing completely new systems to solve new problems, the engineer can use pre-existing modules. Similarly, in changing environments, biological systems that are able to readily reconfigure themselves to adapt will be afforded an evolutionary advantage. This logic may explain the hierarchical organization of molecular systems (Figure 4). In this hierarchy, molecules interact to form modules. Modules interact to form the complex networks of a cell. We collect data at the levels of cell properties (phenotypes) and molecular measurements (for example, expression and interaction). A central challenge is to quantitatively understand cell properties in terms of the activities of many molecules. The built-in organizational hierarchy of cells may provide a framework to enable this. Module identification and abstraction can be applied to specific biological responses to gain insight into the control of complex cell properties. Prinz et al. (27) developed and applied this strategy to the molecular network controlling yeast cell differentiation to filamentous-form growth. They assembled an integrated response network from genes implicated by either significant filamentous- form/yeast-form expression change or a filamentous-growth phenotype. Then they integrated protein-protein interactions if they connected a pair of implicated proteins. Prinz et al. added metabolic interactions, and the associated metabolites, if they involved an implicated protein. Figure 5 shows the results of automated abstraction of the filamentation network. Modules were identified by clustering network nodes based on network topology. Modular units, representing clusters of interacting molecules, are circular nodes with an area proportional to the number of molecular members. Molecules falling outside modules are shown as rectangles (genes/proteins) or triangles (metabolites). Blue edges indicate protein-protein interactions. Green edges are protein-metabolite interactions, which indicates that the metabolite is a substrate or product of the protein enzyme. Expression ratios are mapped as node colors. Red nodes are induced in the filamentous form; blue nodes are repressed. Module-node color reflects the average among member genes. Module-node names are from a member node of greatest intramodule connectiv- ity, the “module organizer” (29). Significant expression change coordination of cluster comembers and automated annotation of clusters with significantly over- represented gene annotations in the clusters support the modular nature of these network clusters. The annotations, expression changes, and connections among modules in this network enable formulating and testing hypotheses on the control of yeast filamentous growth (27). From an engineering perspective (9), the nature of modules facilitates hypothesis generation and testing. Modules have unique independent identities. For example, a mitogen-activated protein–kinase cascade is a signal amplifier no matter what its context. This property of modules imparts several advantages. ( a ) Be- cause one can consider modules as units of biological organization, they allow network abstraction or simplification (29). Instead of relating the activities and interactions of hundreds of molecules to complex cell properties, one can study the determination of cell properties by a much smaller number of interacting modular units. ( b ) The principle of molecular guilt-by-association (25) applies to modules as well. In a specific biological response network, the modules to which a given module is connected provide information about the role of its collective function in determining cell properties. ( c ) If cell properties are associated with specific modules, those properties become associated with their component molecules. This forms a basis for molecular hypotheses. ( d ) Module functions can be mod- ified somewhat independently to test hypotheses and potentially to engineer new networks. The identification and abstraction of network motifs and modules with collective properties is advantageous in two important ways. The first is that these network units have collective functions and behaviors that isolated components do not have. Such collective properties emerge from the interactions of the components. Second, cell properties arise from the action of hundreds or thousands of molecules and interactions. Network abstraction will make the quantitative understanding of cell properties more accessible. Identifying and abstracting motifs and modules is a prelude to the analysis and modeling of highly complex networks of abstracted network units. A quantitative understanding of the hierarchical organization of cells requires col- lecting data at different levels of the hierarchy. This includes the high-throughput collection of quantitative phenotype data, direct measurements of complex cell properties. In addition to adding phenotypes as quantitative variables, systematic or strategic network perturbation and phenotyping links cell properties to molecular network elements. The design and execution of screens and selections to identify genetic perturbations (and their respective genes) affecting a complex biological response is a classical genetic approach. Recently, systematic methods for gene perturbation and phenotype ...
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... single genes or proteins) never work in isolation, but rather in integrated networks, reductionist methodologies will never give us a complete picture of how those complex systems work. Because health and disease are the result of the dynamic changes in these integrated networks, a more holistic Ôsystems biologyÕ approach will be required to understand them fully (105). There is no consensus regarding the definition of systems biology, but it can be described as methods aimed at integrating quantitative data from different outputs with the objective to describe and predict the function of biological systems. ...
Periodontal diseases are initiated by bacterial species living in polymicrobial biofilms at or below the gingival margin and progress largely as a result of the inflammation elicited by specific subgingival species. In the past few decades, efforts to understand the periodontal microbiota have led to an exponential increase in information about biofilms associated with periodontal health and disease. In fact, the oral microbiota is one of the best-characterized microbiomes that colonize the human body. Despite this increased knowledge, one has to ask if our fundamental concepts of the etiology and pathogenesis of periodontal diseases have really changed. In this article we will review how our comprehension of the structure and function of the subgingival microbiota has evolved over the years in search of lessons learned and unlearned in periodontal microbiology. More specifically, this review focuses on: (i) how the data obtained through molecular techniques have impacted our knowledge of the etiology of periodontal infections; (ii) the potential role of viruses in the etiopathogenesis of periodontal diseases; (iii) how concepts of microbial ecology have expanded our understanding of host-microbe interactions that might lead to periodontal diseases; (iv) the role of inflammation in the pathogenesis of periodontal diseases; and (v) the impact of these evolving concepts on therapeutic and preventive strategies to periodontal infections. We will conclude by reviewing how novel systems-biology approaches promise to unravel new details of the pathogenesis of periodontal diseases and hopefully lead to a better understanding of their mechanisms.
... In a current approach variously known as system-or systems-genetics it has been argued that the incorporation of a systems biology perspective can greatly aid efforts to delineate the genetic architecture underlying transcriptional regulatory networks (Galitski, 2004;Schadt et al. 2005;Drake et al. 2006;Kadarmideen et al. 2006;Sieberts and Schadt 2007;Werner 2007;Ayroles et al. 2009;Mackay et al. 2009). Here we employ these methods to study the genetic regulation of the NF-κB STN. ...
A theory of aging holds that senescence is caused by a dysregulated nuclear factor kappa B (NF-κB) signal transduction network (STN). We adopted a systems genetics approach in our study of the NF-κB STN. Ingenuity Pathways Analysis (IPA) was used to identify gene/gene product interactions between NF-κB and the genes in our transcriptional profiling array. Principal components factor analysis (PCFA) was performed on a sub-network of 19 genes, including two initiators of the toll-like receptor (TLR) pathway, myeloid differentiation primary response gene (88) (MyD88) and TIR (Toll/interleukin-1 receptor)-domain-containing adapter-inducing interferon-β (TRIF). TLR pathways are either MyD88-dependent or TRIF-dependent. Therefore, we also performed PCFA on a subset excluding the MyD88 transcript, and on another subset excluding two TRIF transcripts. Using linkage analysis we found that each set gave rise to at least one factor with a logarithm of the odds (LOD) score greater than 3, two on chromosome 15 at 15q12 and 15q22.2, and another two on chromosome 17 at 17p13.3 and 17q25.3. We also found several suggestive signals (2<LOD score<3) at 1q32.1, 1q41, 2q34, 3q23, and 7p15.3. We are currently examining potential associations with single nucleotide polymorphisms within the 1-LOD intervals of our linkage signals.
... Recently, high-throughput methods have been developed to acquire a global description of the complete network of protein interactions for a given organism (interactome). Constructing a comprehensive sets of PPIs or interactomes, then validating, and integrating them with other functional genomic and proteomic data sets will significantly improve biological models in terms of reliability and robustness [28], [29]. Moreover, graphs that contain the set of nodes connected by edges are used frequently as a framework for network analysis and genome-scale data integration. ...
... In this case, for the PPI networks, the nodes represent proteins and the edges represent the interactions. Graph-based methods can provide a general solution for data integration, visualization, and computational analysis of disparate data [28], [30], [31]. Clusters in protein-protein interaction networks represent the two types of modules, protein complexes and functional modules. ...
Markov clustering (MCL) is becoming a key algorithm within bioinformatics for determining clusters in networks. However,with increasing vast amount of data on biological networks, performance and scalability issues are becoming a critical limiting factor in applications. Meanwhile, GPU computing, which uses CUDA tool for implementing a massively parallel computing environment in the GPU card, is becoming a very powerful, efficient, and low-cost option to achieve substantial performance gains over CPU approaches. The use of on-chip memory on the GPU is efficiently lowering the latency time, thus, circumventing a major issue in other parallel computing environments, such as MPI. We introduce a very fast Markov clustering algorithm using CUDA (CUDA-MCL) to perform parallel sparse matrix-matrix computations and parallel sparse Markov matrix normalizations, which are at the heart of MCL. We utilized ELLPACK-R sparse format to allow the effective and fine-grain massively parallel processing to cope with the sparse nature of interaction networks data sets in bioinformatics applications. As the results show, CUDA-MCL is significantly faster than the original MCL running on CPU. Thus, large-scale parallel computation on off-the-shelf desktop-machines, that were previously only possible on supercomputing architectures, can significantly change the way bioinformaticians and biologists deal with their data.
... Measuring nutritionally responsive genome activity involves (1) model object(s), (2) type of nutrition or diet, (3) methodological platforms, and (4) data evaluation/compilation, involving the analysis of hundreds of samples. This constitutes the advantages of exploring high throughput technologies (10) . This review article attempts to summarise the available high throughput technological platforms used in nutrigenomics studies. ...
Human health is affected by many factors. Diet and inherited genes play an important role. Food constituents, including secondary metabolites of fruits and vegetables, may interact directly with DNA via methylation and changes in expression profiles (mRNA, proteins) which results in metabolite content changes. Many studies have shown that food constituents may affect human health and the exact knowledge of genotypes and food constituent interactions with both genes and proteins may delay or prevent the onset of diseases. Many high throughput methods have been employed to get some insight into the whole process and several examples of successful research, namely in the field of genomics and transcriptomics, exist. Studies on epigenetics and RNome significance have been launched. Proteomics and metabolomics need to encompass large numbers of experiments and linked data. Due to the nature of the proteins, as well as due to the properties of various metabolites, experimental approaches require the use of comprehensive high throughput methods and a sufficiency of analysed tissue or body fluids. In this contribution, we describe the basic tools currently used in nutrigenomics studies and indicate the general requirements for future technology methodological routings.
... The key to analysis of interaction networks is the assumption of information transitivity: information can flow through or can be exchanged via paths of interactions. Biology in post-genomic era also contains numerous examples of molecular networks (Galitski, 2004). Metabolic networks have been modeled by representing metabolites as nodes and chemical reactions as links: two metabolites are linked if they participate in the same reaction (Ma and Zeng, 2003). ...
Interaction networks, consisting of agents linked by their interactions, are ubiquitous across many disciplines of modern science. Many methods of analysis of interaction networks have been proposed, mainly concentrating on node degree distribution or aiming to discover clusters of agents that are very strongly connected between themselves. These methods are principally based on graph-theory or machine learning. We present a mathematically simple formalism for modelling context-specific information propagation in interaction networks based on random walks. The context is provided by selection of sources and destinations of information and by use of potential functions that direct the flow towards the destinations. We also use the concept of dissipation to model the aging of information as it diffuses from its source. Using examples from yeast protein-protein interaction networks and some of the histone acetyltransferases involved in control of transcription, we demonstrate the utility of the concepts and the mathematical constructs introduced in this paper.
... The resulting network models will have the capacity to predict, systematically and explicitly, the effects of multiple interacting genetic perturbations. This capacity will enable testing of genetically complex hypotheses, prioritization of candidate genes for targeted intervention, and the personalization of prognoses and therapies (Ideker et al, 2001; Galitski, 2004). The identification of functionally relevant interactions in databases of diverse high-throughput data types is a substantial challenge for the construction of predictive network models. ...
... However, because mutant phenotypes result from the activities of complex molecular pathways, the biochemical interpretation of a genetic interaction is often ambiguous and frequently involves multiple alternative molecular models and both direct and indirect mechanisms (Kelley and Ideker, 2005; Zhang et al, 2005). Conversely, molecular interactions, plentifully generated through high-throughput methods, often lack in functional interpretation or are of uncertain relevance to specific genetic observations (Galitski, 2004). Therefore, & 2007 our approach is to exploit this complementarity of genetic and molecular interactions. ...
Molecular interactions provide paths for information flows. Genetic interactions reveal active information flows and reflect their functional consequences. We integrated these complementary data types to model the transcription network controlling cell differentiation in yeast. Genetic interactions were inferred from linear decomposition of gene expression data and were used to direct the construction of a molecular interaction network mediating these genetic effects. This network included both known and novel regulatory influences, and predicted genetic interactions. For corresponding combinations of mutations, the network model predicted quantitative gene expression profiles and precise phenotypic effects. Multiple predictions were tested and verified.
... Future investigations will show how far this will extend to metabolic flux data (Kromer et al. 2004; Oh and Liao 2000). The integration of transcription analysis with comparative genome sequencing , proteomics, metabolic flux analysis, and computer modeling of the cell physiology will be an important data source for system biology, which represents a new approach for a global quantitative picture of cell physiology (Galitski 2004). The system biology approach requires a fundamental framework involving several distinct steps: (1) definition of all components of a system; (2) systematic perturbation and monitoring of the components either genetically or by modification of the environment; (3) reconcile the experimentally observed responses with those predicted by a quantitative model; and (4) design and perform new perturbations to distinguish between multiple or competing model hypothesis (Ideker et al. 2001). ...
DNA microarrays have found widespread use as a flexible tool to investigate bacterial metabolism. Their main advantage is the comprehensive data they produce on the transcriptional response of the whole genome to an environmental or genetic stimulus. This allows the microbiologist to monitor metabolism and to define stimulons and regulons. Other fields of application are the identification of microorganisms or the comparison of genomes. The importance of this technology increases with the number of sequenced genomes and the falling prices for equipment and oligonucleotides. Knowledge of DNA microarrays is of rising relevance for many areas in microbiological research. Much literature has been published on various specific aspects of this technique that can be daunting to the casual user and beginner. This article offers a comprehensive outline of microarray technology for transcription analysis in microbiology. It shortly discusses the types of DNA microarrays available, the printing of custom arrays, common labeling strategies for targets, hybridization, scanning, normalization, and clustering of expression data.
... In general, cells are highly robust to uncertainty in their environments and the failure of component parts, yet can be disabled catastrophically by even small perturbations to certain genes (mutation, dosage change), trace amounts of toxins (drugs) that disrupt the structural elements or regulatory control networks or inactivation of essential network components. Cancer cells reconfigure normal cellular networks to establish a pathological kind of robustness, including evasion of apoptosis and treatment resistance in response to selection pressure through anti-cancer drugs or radiation therapy (Albert, Jeong and Barabási, 2000; Barabási and Oltvai, 2004; Cork and Purugganan, 2004; Galitski, 2004; Kitano, 2004; Papin and Subramaniam, 2004; Papin et al., 2005). ...
Book description: Cancer bioinformatics is now emerging as a new interdisciplinary field, which is facilitating an unprecedented synthesis of knowledge arising from the life and clinical sciences. This groundbreaking title provides a comprehensive and up-to-date account of the enormous range of bioinformatics for cancer therapy development from the laboratory to clinical trials. It functions as a guide to integrated data exploitation and synergistic knowledge discovery, and support the consolidation of the interdisciplinary research community involved.
... The extraction of biological information from high throughput genomic expression data is a fundamentally network-based systems biology problem [1]. Complex cell properties such as pathogenicity, growth control, and metabolic capabilities arise from networks of molecular interactions. ...
The biological information in genomic expression data can be understood, and computationally extracted, in the context of systems of interacting molecules. The automation of this information extraction requires high throughput management and analysis of genomic expression data, and integration of these data with other data types.
SBEAMS-Microarray, a module of the open-source Systems Biology Experiment Analysis Management System (SBEAMS), enables MIAME-compliant storage, management, analysis, and integration of high-throughput genomic expression data. It is interoperable with the Cytoscape network integration, visualization, analysis, and modeling software platform.
SBEAMS-Microarray provides end-to-end support for genomic expression analyses for network-based systems biology research.
... Nearly all the signals in Table 3 have established roles in maintaining physiological homeostasis in normal muscle, so it would seem that most factors that trigger muscle wasting do not do so except when circumstances push past physiological limits. One of the principal insights from recent studies of signal-transduction is that we increasingly understand signaling in terms of networks rather than pathways with adventitious "crosstalk" (Galitski, 2004;Xia et al., 2004). That is, physiological homeostasis is rarely controlled by a single independent sequence of linear steps from signal to response, but rather through the interplay of multiple signals and their downstream effectors, linked together so that the state of the output is determined by integration of several or many input signals. ...
Protein degradation in muscle functions in maintaining normal physiological homeostasis and adapting to new homeostatic states, and is required for muscle wasting or atrophy in various pathological states. The interplay between protein synthesis and degradation to maintain homeostasis is complex and responds to a variety of autocrine and intercellular signals from neuronal inputs, hormones, cytokines, growth factors and other regulatory molecules. The intracellular events that connect extracellular signals to the molecular control of protein degradation are incompletely understood, but likely involve interacting signal-transduction networks rather than isolated pathways. We review some examples of signal-transduction systems that regulate protein degradation, including effectors of proteolysis inducing factor (PIF), insulin and insulin-like growth factor (IGF) and their receptors, and fibroblast growth factor (FGF) and its receptors.
