Complex networks: the key to systems biology

Though introduced recently, complex networks research has grown steadily because of its potential to represent, characterize and model a wide range of intricate natural systems and phenomena. Because of the intrinsic complexity and systemic organization of life, complex networks provide a specially promising framework for systems biology investigation. The current article is an up-to-date review of the major developments related to the application of complex networks in biology, with special attention focused on the more recent literature. The main concepts and models of complex networks are presented and illustrated in an accessible fashion. Three main types of networks are covered: transcriptional regulatory networks, protein-protein interaction networks and metabolic networks. The key role of complex networks for systems biology is extensively illustrated by several of the papers reviewed.

Integration and visualization of systems biology data in context of the genome

Background: High-density tiling arrays and new sequencing technologies are generating rapidly increasing volumes of transcriptome and protein-DNA interaction data. Visualization and exploration of this data is critical to understanding the regulatory logic encoded in the genome by which the cell dynamically affects its physiology and interacts with its environment. Results: The Gaggle Genome Browser is a cross-platform desktop program for interactively visualizing high-throughput data in the context of the genome. Important features include dynamic panning and zooming, keyword search and open interoperability through the Gaggle framework. Users may bookmark locations on the genome with descriptive annotations and share these bookmarks with other users. The program handles large sets of user-generated data using an in-process database and leverages the facilities of SQL and the R environment for importing and manipulating data. A key aspect of the Gaggle Genome Browser is interoperability. By connecting to the Gaggle framework, the genome browser joins a suite of interconnected bioinformatics tools for analysis and visualization with connectivity to major public repositories of sequences, interactions and pathways. To this flexible environment for exploring and combining data, the Gaggle Genome Browser adds the ability to visualize diverse types of data in relation to its coordinates on the genome. Conclusions: Genomic coordinates function as a common key by which disparate biological data types can be related to one another. In the Gaggle Genome Browser, heterogeneous data are joined by their location on the genome to create information-rich visualizations yielding insight into genome organization, transcription and its regulation and, ultimately, a better understanding of the mechanisms that enable the cell to dynamically respond to its environment.

Symmetry in biology: from genetic code to stochastic gene regulation

Mathematical models, as instruments for understanding the workings of nature, are a traditional tool of physics, but they also play an ever increasing role in biology - in the description of fundamental processes as well as that of complex systems. In this review, the authors discuss two examples of the application of group theoretical methods, which constitute the mathematical discipline for a quantitative description of the idea of symmetry, to genetics. The first one appears, in the form of a pseudo-orthogonal (Lorentz like) symmetry, in the stochastic modelling of what may be regarded as the simplest possible example of a genetic network and, hopefully, a building block for more complicated ones: a single self-interacting or externally regulated gene with only two possible states: ` on` and ` off`. The second is the algebraic approach to the evolution of the genetic code, according to which the current code results from a dynamical symmetry breaking process, starting out from an initial state of complete symmetry and ending in the presently observed final state of low symmetry. In both cases, symmetry plays a decisive role: in the first, it is a characteristic feature of the dynamics of the gene switch and its decay to equilibrium, whereas in the second, it provides the guidelines for the evolution of the coding rules.

A reliable measure of similarity based on dependency for short time series: an application to gene expression networks

Background: Microarray techniques have become an important tool to the investigation of genetic relationships and the assignment of different phenotypes. Since microarrays are still very expensive, most of the experiments are performed with small samples. This paper introduces a method to quantify dependency between data series composed of few sample points. The method is used to construct gene co-expression subnetworks of highly significant edges. Results: The results shown here are for an adapted subset of a Saccharomyces cerevisiae gene expression data set with low temporal resolution and poor statistics. The method reveals common transcription factors with a high confidence level and allows the construction of subnetworks with high biological relevance that reveals characteristic features of the processes driving the organism adaptations to specific environmental conditions. Conclusion: Our method allows a reliable and sophisticated analysis of microarray data even under severe constraints. The utilization of systems biology improves the biologists ability to elucidate the mechanisms underlying celular processes and to formulate new hypotheses.

Reciprocal Modulation of Cognitive and Emotional Aspects in Pianistic Performances

Background: High level piano performance requires complex integration of perceptual, motor, cognitive and emotive skills. Observations in psychology and neuroscience studies have suggested reciprocal inhibitory modulation of the cognition by emotion and emotion by cognition. However, it is still unclear how cognitive states may influence the pianistic performance. The aim of the present study is to verify the influence of cognitive and affective attention in the piano performances. Methods and Findings: Nine pianists were instructed to play the same piece of music, firstly focusing only on cognitive aspects of musical structure (cognitive performances), and secondly, paying attention solely on affective aspects (affective performances). Audio files from pianistic performances were examined using a computational model that retrieves nine specific musical features (descriptors) - loudness, articulation, brightness, harmonic complexity, event detection, key clarity, mode detection, pulse clarity and repetition. In addition, the number of volunteers' errors in the recording sessions was counted. Comments from pianists about their thoughts during performances were also evaluated. The analyses of audio files throughout musical descriptors indicated that the affective performances have more: agogics, legatos, pianos phrasing, and less perception of event density when compared to the cognitive ones. Error analysis demonstrated that volunteers misplayed more left hand notes in the cognitive performances than in the affective ones. Volunteers also played more wrong notes in affective than in cognitive performances. These results correspond to the volunteers' comments that in the affective performances, the cognitive aspects of piano execution are inhibited, whereas in the cognitive performances, the expressiveness is inhibited. Conclusions: Therefore, the present results indicate that attention to the emotional aspects of performance enhances expressiveness, but constrains cognitive and motor skills in the piano execution. In contrast, attention to the cognitive aspects may constrain the expressivity and automatism of piano performances.

Gene Expression Complex Networks: Synthesis, Identification, and Analysis

Thanks to recent advances in molecular biology, allied to an ever increasing amount of experimental data, the functional state of thousands of genes can now be extracted simultaneously by using methods such as cDNA microarrays and RNA-Seq. Particularly important related investigations are the modeling and identification of gene regulatory networks from expression data sets. Such a knowledge is fundamental for many applications, such as disease treatment, therapeutic intervention strategies and drugs design, as well as for planning high-throughput new experiments. Methods have been developed for gene networks modeling and identification from expression profiles. However, an important open problem regards how to validate such approaches and its results. This work presents an objective approach for validation of gene network modeling and identification which comprises the following three main aspects: (1) Artificial Gene Networks (AGNs) model generation through theoretical models of complex networks, which is used to simulate temporal expression data; (2) a computational method for gene network identification from the simulated data, which is founded on a feature selection approach where a target gene is fixed and the expression profile is observed for all other genes in order to identify a relevant subset of predictors; and (3) validation of the identified AGN-based network through comparison with the original network. The proposed framework allows several types of AGNs to be generated and used in order to simulate temporal expression data. The results of the network identification method can then be compared to the original network in order to estimate its properties and accuracy. Some of the most important theoretical models of complex networks have been assessed: the uniformly-random Erdos-Renyi (ER), the small-world Watts-Strogatz (WS), the scale-free Barabasi-Albert (BA), and geographical networks (GG). The experimental results indicate that the inference method was sensitive to average degree k variation, decreasing its network recovery rate with the increase of k. The signal size was important for the inference method to get better accuracy in the network identification rate, presenting very good results with small expression profiles. However, the adopted inference method was not sensible to recognize distinct structures of interaction among genes, presenting a similar behavior when applied to different network topologies. In summary, the proposed framework, though simple, was adequate for the validation of the inferred networks by identifying some properties of the evaluated method, which can be extended to other inference methods.

Inference of gene regulatory networks from time series by Tsallis entropy

Background: The inference of gene regulatory networks (GRNs) from large-scale expression profiles is one of the most challenging problems of Systems Biology nowadays. Many techniques and models have been proposed for this task. However, it is not generally possible to recover the original topology with great accuracy, mainly due to the short time series data in face of the high complexity of the networks and the intrinsic noise of the expression measurements. In order to improve the accuracy of GRNs inference methods based on entropy (mutual information), a new criterion function is here proposed. Results: In this paper we introduce the use of generalized entropy proposed by Tsallis, for the inference of GRNs from time series expression profiles. The inference process is based on a feature selection approach and the conditional entropy is applied as criterion function. In order to assess the proposed methodology, the algorithm is applied to recover the network topology from temporal expressions generated by an artificial gene network (AGN) model as well as from the DREAM challenge. The adopted AGN is based on theoretical models of complex networks and its gene transference function is obtained from random drawing on the set of possible Boolean functions, thus creating its dynamics. On the other hand, DREAM time series data presents variation of network size and its topologies are based on real networks. The dynamics are generated by continuous differential equations with noise and perturbation. By adopting both data sources, it is possible to estimate the average quality of the inference with respect to different network topologies, transfer functions and network sizes. Conclusions: A remarkable improvement of accuracy was observed in the experimental results by reducing the number of false connections in the inferred topology by the non-Shannon entropy. The obtained best free parameter of the Tsallis entropy was on average in the range 2.5 <= q <= 3.5 (hence, subextensive entropy), which opens new perspectives for GRNs inference methods based on information theory and for investigation of the nonextensivity of such networks. The inference algorithm and criterion function proposed here were implemented and included in the DimReduction software, which is freely available at http://sourceforge.net/projects/dimreduction and http://code.google.com/p/dimreduction/.

GPIomics: global analysis of glycosylphosphatidylinositol-anchored molecules of Trypanosoma cruzi

Glycosylphosphatidylinositol (GPI) anchoring is a common, relevant posttranslational modification of eukaryotic surface proteins. Here, we developed a fast, simple, and highly sensitive (high attomole-low femtomole range) method that uses liquid chromatography-tandem mass spectrometry (LC-MS(n)) for the first large-scale analysis of GPI-anchored molecules (i.e., the GPIome) of a eukaryote, Trypanosoma cruzi, the etiologic agent of Chagas disease. Our genome-wise prediction analysis revealed that approximately 12% of T. cruzi genes possibly encode GPI-anchored proteins. By analyzing the GPIome of T. cruzi insect-dwelling epimastigote stage using LC-MS(n), we identified 90 GPI species, of which 79 were novel. Moreover, we determined that mucins coded by the T. cruzi small mucin-like gene (TcSMUG S) family are the major GPI-anchored proteins expressed on the epimastigote cell surface. TcSMUG S mucin mature sequences are short (56-85 amino acids) and highly O-glycosylated, and contain few proteolytic sites, therefore, less likely susceptible to proteases of the midgut of the insect vector. We propose that our approach could be used for the high throughput GPIomic analysis of other lower and higher eukaryotes. Molecular Systems Biology 7 April 2009; doi:10.1038/msb.2009.13

Suramin inhibits macrophage activation by human group IIA phospholipase A(2), but does not affect bactericidal activity of the enzyme

Suramin is a polysulphonated napthylurea antiprotozoal and anthelminitic drug, which also presents inhibitory activity against a broad range of enzymes. Here we evaluate the effect of suramin on the hydrolytic and biological activities of secreted human group IIA phospholipase A(2) (hsPLA(2)GIIA). The hsPLA(2)GIIA was expressed in E. coli, and refolded from inclusion bodies. The hydrolytic activity of the recombinant enzyme was measured using mixed dioleoylphosphatidylcholine/dioleoylphosphatidylglycerol (DOPC/DOPG) liposomes. The activation of macrophage cell line RAW 264.7 by hsPLA(2) GIIA was monitored by NO release, and bactericidal activity against Micrococcus luteus was evaluated by colony counting and by flow cytometry using the fluorescent probe Sytox Green. The hydrolytic activity of the hsPLA(2) GIIA was inhibited by a concentration of 100 nM suramin and the activation of macrophages by hsPLA(2) GIIA was abolished at protein/suramin molar ratios where the hydrolytic activity of the enzyme was inhibited. In contrast, both the bactericidal activity of hsPLA(2) GIIA against Micrococcus luteus and permeabilization of the bacterial inner membrane were unaffected by suramin concentrations up to 50 mu M. These results demonstrate that suramin selectively inhibits the activity of the hsPLA(2) GIIA against macrophages, whilst leaving the anti-bacterial function unchanged.

Mitochondria, endoplasmic reticulum and actin filament behavior after PDT with chloroaluminum phthalocyanine liposomal in HeLa cells

Photodynamic therapy (PDT) for cancer is a therapeutic modality in the treatment of tumors in which visible light is used to activate a photosensitizer. Cell membranes have been identified as an important intracellular target for singlet oxygen produced during the photochemical pathway. This study analyzed the cytotoxicity in specific cellular targets of a photosensitizer used in PDT in vitro. The photosensitizing effects of chloroaluminum phthalocyanine liposomal were studied on the mitochondria, cytoskeleton and endoplasmic reticulum of HeLa cells. Cells were irradiated with a diode laser working at 670 nm, energy density of 4.5 J/cm(2) and power density of 45 mW/cm(2). Fluorescence microscopic analysis of the mitochondria showed changes in membrane potential. After PDT treatment, the cytoskeleton and endoplasmic reticulum presented basic alterations in distribution. The combined effect of AlPHCl liposomal and red light in the HeLa cell line induced photodamage to the mitochondria, endoplasmic reticulum and actin filaments in the cytoskeleton. (c) 2008 International Federation for Cell Biology. Published by Elsevier Ltd. All rights reserved.

Mitochondrial DNA depletion and its correlation with TFAM, TFB1M, TFB2M and POLG in human diffusely infiltrating astrocytomas

Mitochondrial DNA (mtDNA) alterations and their clinical and pathological implications have been analyzed in several human malignancies. A marked decrease in mtDNA copy number along with the increase in malignancy was observed in diffusely infiltrating astrocytomas (24 WHO grade II, 18 grade III, and 78 grade IV or GBM) compared to non-neoplastic brain tissues, being mostly depleted in GBM. Although high relative gene expression levels of mtDNA replication regulators (mitochondrial polymerase catalytic subunit (POLG), transcription factors A (TFAM), B1 (TFB1M) and B2 (TFB2M)) were detected, it cannot successfully revert the mtDNA depletion observed in our samples. On the other hand, a strong correlation among the expression levels of mitochondrial transcription factors corroborates with the TFAM role in the direct control of TFB1M and TFB2M during initiation of mtDNA replication. POLG expression was related to decreased mtDNA copy number, and its overexpression associated with TFAM expression levels also have an impact on long-term survival among GBM patients, interpreted as a potential predictive factor for better prognosis. (C) 2010 Elsevier B.V. and Mitochondria Research Society. All rights reserved.

Upregulation of E2F1 in cerebellar neuroprogenitor cells and cell cycle arrest during postnatal brain development

In the developing cerebellum, proliferation of granular neuroprogenitor (GNP) cells lasts until the early postnatal stages when terminal maturation of the cerebellar cortex occurs. GNPs are considered cell targets for neoplastic transformation, and disturbances in cerebellar GNP cell proliferation may contribute to the development of pediatric medulloblastoma. At the molecular level, proliferation of GNPs is regulated through an orchestrated action of the SHH, NOTCH, and WNT pathways, but the underlying mechanisms still need to be dissected. Here, we report that expression of the E2F1 transcription factor in rat GNPs is inversely correlated with cell proliferation rate during postnatal development, as opposed to its traditional SHH-dependent induction of cell cycle. Proliferation of GNPs peaked at postnatal day 3 (P3), with a subsequent continuing decrease in proliferation rates occurring until P12. Such gradual decline in proliferating neuroprogenitors paralleled the extent of cerebellum maturation confirmed by histological analysis with cresyl violet staining and temporal expression profiling of SHH, NOTCH2, and WNT4 genes. A time course analysis of E2F1 expression in GNPs revealed significantly increased levels at P12, correlating with decreased cell proliferation. Expression of the cell cycle inhibitor p18 (Ink4c) , a target of E2F1, was also significantly higher at P12. Conversely, increased E2F1 expression did not correlate with either SMAC/DIABLO and BCL2 expression profiles or apoptosis of cerebellar cells. Altogether, these results suggest that E2F1 may also be involved in the inhibition of GNP proliferation during rat postnatal development despite its conventional mitogenic effects.

Maintenance and differentiation of neural stem cells

The adult mammalian brain contains self-renewable, multipotent neural stem cells (NSCs) that are responsible for neurogenesis and plasticity in specific regions of the adult brain. Extracellular matrix, vasculature, glial cells, and other neurons are components of the niche where NSCs are located. This surrounding environment is the source of extrinsic signals that instruct NSCs to either self-renew or differentiate. Additionally, factors such as the intracellular epigenetics state and retrotransposition events can influence the decision of NSC`s fate into neurons or glia. Extrinsic and intrinsic factors form an intricate signaling network, which is not completely understood. These factors altogether reflect a few of the key players characterized so far in the new field of NSC research and are covered in this review. (C) 2010 John Wiley & Sons, Inc. WIREs Syst Biol Med 2011 3 107-114 DOI:10.1002/wsbm:100

Molecular Characterization of Propolis-Induced Cell Death in Saccharomyces cerevisiae

Propolis, a natural product of plant resins, is used by the bees to seal holes in their honeycombs and protect the hive entrance. However, propolis has also been used in folk medicine for centuries. Here, we apply the power of Saccharomyces cerevisiae as a model organism for studies of genetics, cell biology, and genomics to determine how propolis affects fungi at the cellular level. Propolis is able to induce an apoptosis cell death response. However, increased exposure to propolis provides a corresponding increase in the necrosis response. We showed that cytochrome c but not endonuclease G (Nuc1p) is involved in propolis-mediated cell death in S. cerevisiae. We also observed that the metacaspase YCA1 gene is important for propolis-mediated cell death. To elucidate the gene functions that may be required for propolis sensitivity in eukaryotes, the full collection of about 4,800 haploid S. cerevisiae deletion strains was screened for propolis sensitivity. We were able to identify 138 deletion strains that have different degrees of propolis sensitivity compared to the corresponding wild-type strains. Systems biology revealed enrichment for genes involved in the mitochondrial electron transport chain, vacuolar acidification, negative regulation of transcription from RNA polymerase II promoter, regulation of macroautophagy associated with protein targeting to vacuoles, and cellular response to starvation. Validation studies indicated that propolis sensitivity is dependent on the mitochondrial function and that vacuolar acidification and autophagy are important for yeast cell death caused by propolis.

Comparison of the interactomic networks of different species in terms of accessibility

Protein-protein interaction networks were investigated in terms of outward accessibility, which quantifies the effectiveness of each protein in accessing other proteins and is related to the internality of nodes. By comparing the accessibility between 144 ortholog proteins in yeast and the fruit fly, we found that the accessibility tends to be higher among proteins in the fly than in yeast. In addition, z-scores of the accessibility calculated for different species revealed that the protein networks of less evolved species tend to be more random than those of more evolved species. The accessibility was also used to identify the border of the yeast protein interaction network, which was found to be mainly composed of viable proteins.