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/.

Construct and Compare Gene Coexpression Networks with DAPfinder and DAPview

Background: DAPfinder and DAPview are novel BRB-ArrayTools plug-ins to construct gene coexpression networks and identify significant differences in pairwise gene-gene coexpression between two phenotypes. Results: Each significant difference in gene-gene association represents a Differentially Associated Pair (DAP). Our tools include several choices of filtering methods, gene-gene association metrics, statistical testing methods and multiple comparison adjustments. Network results are easily displayed in Cytoscape. Analyses of glioma experiments and microarray simulations demonstrate the utility of these tools. Conclusions: DAPfinder is a new friendly-user tool for reconstruction and comparison of biological networks.

The impact of measurement errors in the identification of regulatory networks

Background: There are several studies in the literature depicting measurement error in gene expression data and also, several others about regulatory network models. However, only a little fraction describes a combination of measurement error in mathematical regulatory networks and shows how to identify these networks under different rates of noise. Results: This article investigates the effects of measurement error on the estimation of the parameters in regulatory networks. Simulation studies indicate that, in both time series (dependent) and non-time series (independent) data, the measurement error strongly affects the estimated parameters of the regulatory network models, biasing them as predicted by the theory. Moreover, when testing the parameters of the regulatory network models, p-values computed by ignoring the measurement error are not reliable, since the rate of false positives are not controlled under the null hypothesis. In order to overcome these problems, we present an improved version of the Ordinary Least Square estimator in independent (regression models) and dependent (autoregressive models) data when the variables are subject to noises. Moreover, measurement error estimation procedures for microarrays are also described. Simulation results also show that both corrected methods perform better than the standard ones (i.e., ignoring measurement error). The proposed methodologies are illustrated using microarray data from lung cancer patients and mouse liver time series data. Conclusions: Measurement error dangerously affects the identification of regulatory network models, thus, they must be reduced or taken into account in order to avoid erroneous conclusions. This could be one of the reasons for high biological false positive rates identified in actual regulatory network models.

On the template synthesis of nanostructured inorganic/organic hybrid films

Prussian Blue has been introduced as a mediator to achieve stable, sensitive, reproducible, and interference-free biosensors. However, Na(+), Li(+), H(+), and all group II cations are capable to block the activity of Prussian Blue and, because Na(+) can be found in most human fluids, Prussian Blue analogs have already been developed to overcome this problem. These analogs, such as copper hexacyanoferrate, have also been introduced in a conducting polypyrrole matrix to create hybrid materials (copper hexacyanoferrate/polypyrrole, CuHCNFe/Ppy) with improved mechanical and electrochemical characteristics. Nowadays, the challenges in amperometric enzymatic biosensors consist of improving the enzyme immobilization and in making the chemical signal transduction more efficient. The incorporation of nanostructured materials in biosensors can optimize both steps and a nanostructured hybrid CuHCNFe/Ppy mediator has been developed using a template of colloidal polystyrene particles. The nanostructured material has achieved sensitivities 7.6 times higher than the bulk film during H(2)O(2) detection and it has also presented better results in other analytical parameters such as time response and detection limit. Besides, the nanostructured mediator was successfully applied at glucose biosensing in electrolytes containing Prussian Blue blocking cations. (C) 2008 The Electrochemical Society.

The role of exon shuffling in shaping protein-protein interaction networks

Background: Physical protein-protein interaction (PPI) is a critical phenomenon for the function of most proteins in living organisms and a significant fraction of PPIs are the result of domain-domain interactions. Exon shuffling, intron-mediated recombination of exons from existing genes, is known to have been a major mechanism of domain shuffling in metazoans. Thus, we hypothesized that exon shuffling could have a significant influence in shaping the topology of PPI networks. Results: We tested our hypothesis by compiling exon shuffling and PPI data from six eukaryotic species: Homo sapiens, Mus musculus, Drosophila melanogaster, Caenorhabditis elegans, Cryptococcus neoformans and Arabidopsis thaliana. For all four metazoan species, genes enriched in exon shuffling events presented on average higher vertex degree (number of interacting partners) in PPI networks. Furthermore, we verified that a set of protein domains that are simultaneously promiscuous (known to interact to multiple types of other domains), self-interacting (able to interact with another copy of themselves) and abundant in the genomes presents a stronger signal for exon shuffling. Conclusions: Exon shuffling appears to have been a recurrent mechanism for the emergence of new PPIs along metazoan evolution. In metazoan genomes, exon shuffling also promoted the expansion of some protein domains. We speculate that their promiscuous and self-interacting properties may have been decisive for that expansion.

Lignin phenols used to infer organic matter sources to Sepetiba Bay - RJ, Brasil

Lignin phenols were measured in the sediments of Sepitiba Bay, Rio de Janeiro, Brazil and in bedload sediments and suspended sediments of the four major fluvial inputs to the bay: Sao Francisco and Guandu Channels and the Guarda and Cacao Rivers. Fluvial suspended lignin yields (Sigma 8 3.5-14.6 mgC 10 g dw(-1)) vary little between the wet and dry seasons and are poorly correlated with fluvial chlorophyll concentrations (0.8-50.2 mu gC L(-1)). Despite current land use practices that favor grassland agriculture or industrial uses, fluvial lignin compositions are dominated by a degraded leaf-sourced material. The exception is the Guarda River, which has a slight influence from grasses. The Lignin Phenol Vegetation Index, coupled with acid/aldehyde and 3.5 Db/V ratios, indicate that degraded leaf-derived phenols are also the primary preserved lignin component in the bay. The presence of fringe Typha sp. and Spartina sp. grass beds surrounding portions of the Bay are not reflected in the lignin signature. Instead, lignin entering the bay appears to reflect the erosion of soils containing a degraded signature from the former Atlantic rain forest that once dominated the watershed, instead of containing a significant signature derived from current agricultural uses. A three-component mixing model using the LPVI, atomic N:C ratios, and stable carbon isotopes (which range between -26.8 and -21.8 parts per thousand) supports the hypothesis that fluvial inputs to the bay are dominated by planktonic matter (78% of the input), with lignin dominated by leaf (14% of the input) over grass (6%). Sediments are composed of a roughly 50-50 mixture of autochthonous material and terrigenous material, with lignin being primarily sourced from leaf. (C) 2010 Elsevier Ltd. All rights reserved.

Stable isotopes of bulk organic matter to trace carbon and nitrogen dynamics in an estuarine ecosystem in Babitonga Bay (Santa Catarina, Brazil)

The biogeochemical processes affecting the transport and cycling of terrestrial organic carbon in coastal and transition areas are still not fully understood One means of distinguishing between the sources of organic materials contributing to particulate organic matter (POM) in Babitonga Bay waters and sediments is by the direct measurement of delta(13)C of dissolved inorganic carbon (DIC) and delta(13)C and delta(15)N in the organic constituents. An isotopic survey was taken from samples collected in the Bay in late spring of 2004. The results indicate that the delta(13)C and delta(15)N compositions of OM varied from -21.7 parts per thousand to -26 2 parts per thousand. and from + 9 2 parts per thousand. to -0 1 parts per thousand, respectively. delta(13)C from DIC ranges from +0.04 parts per thousand to -12.7 parts per thousand The difference in the isotope compositions enables the determination of three distinct end-members terrestrial, marine and urban Moreover, the evaluation of source contribution to the particulate organic matter (POM) in the Bay, enables assessment of the anthropogenic impact. Comparing the depleted values of delta(13)C(DIC) and delta(13)C(POC) it is possible to further understand the carbon dynamic within Babitonga Bay (C) 2010 Elsevier BV All rights reserved

Changes in soil organic carbon storage under different agricultural management systems in the Southwest Amazon Region of Brazil

The Cerrado and Amazon regions of Brazil are probably the largest agricultural frontier of the world, and Could be a sink or source for C depending on the net effect of land use change and subsequent management on soil organic C pools. We evaluated the effects of agricultural management systems on soil organic C (SOC) stocks in the Brazilian states of Rondonia and Mato Grosso, and derived regional specific factors for soil C stock change associated with different management systems. We used 50 observations (data points) in this study, including 42 dealing with annual cropping practices and 8 dealing with perennial cropping, and analyzed the data in linear mixed-effect models. No tillage (NT) systems in Cerrado areas increased SOC Storage by 1.08 +/- 0.06 relative to SOC stocks under native conditions, while SOC storage increased by a modest factor of 1.01 +/- 0.17 in Cerradao and Amazon Forest conditions. Full tillage (FT) had negative effect on SOC storage relative to NT, decreasing SOC stocks by a factor of 0.94 +/- 0.04. but did not significantly reduce SOC stocks relative to native levels when adopted in the Cerrado region. Perennial cropping had a minimal impact on SOC stocks, estimated at a factor Value of 0.98 +/- 0.14, suggesting these systems maintain about 98% of the SOC stock found under native vegetation. The results Suggest that NT adoption may be increasing SOC with land use change from native vegetation to cropland management in the Cerrado region of Brazil. (C) 2009 Elsevier B.V. All rights reserved.

Soil organic carbon and fertility interactions affected by a tillage chronosequence in a Brazilian Oxisol

No-till (NT) adoption is an essential tool for development of sustainable agricultural systems, and how NT affects the soil organic C (SOC) dynamics is a key component of these systems. The effect of a plow tillage (PT) and NT age chronosequence on SOC concentration and interactions with soil fertility were assessed in a variable charge Oxisol, located in the South Center quadrant of Parana State, Brazil (50 degrees 23`W and 24 degrees 36`S). The chronosequence consisted of the following six sites: (i) native field (NF); (ii) PT of the native field (PNF-1) involving conversion of natural vegetation to cropland; (iii) NT for 10 years (NT-10); (iv) NT for 20 years (NT-20); (v) NT for 22 years (NT-22); and (vi) conventional tillage for 22 years (CT-22) involving PT with one disking after summer harvest and one after winter harvest to 20 cm depth plus two harrow disking. Soil samples were collected from five depths (0-2.5; 2.5-5; 5-10; 10-20; and 20-40 cm) and SOC, pH (in H(2)O and KCl), Delta pH, potential acidity, exchangeable bases, and cation exchangeable capacity (CEC) were measured. An increase in SOC concentration positively affected the pH, the negative charge and the CEC and negatively impacted potential acidity. Regression analyses indicated a close relationship between the SOC concentration and other parameters measured in this study. The regression fitted between SOC concentration and CEC showed a close relationship. There was an increase in negative charge and CEC with increase in SOC concentration: CEC increased by 0.37 cmol(c) kg(-1) for every g of C kg(-1) soil. The ratio of ECEC:SOC was 0.23 cmol(c) kg(-1) for NF and increased to 0.49 cmol(c) kg(-1) for NT-22. The rates of P and K for 0-10 cm depth increased by 9.66 kg ha(-1) yr(-1) and 17.93 kg ha(-1) yr(-1), respectively, with NF as a base line. The data presented support the conclusion that long-term NT is a useful strategy for improving fertility of soils with variable charge. (C) 2008 Elsevier B.V. All rights reserved.

Sedimentary Organic Matter in Cores of the Cananeia-Iguape Lagoonal-Estuarine System, Sao Paulo State, Brazil

Sedimentary organic matter is a good tool for environmental evaluation where the sediments are deposited. We determined the elemental and C- and N-isotopic compositions of 211 sub-surface sediment samples from 13 cores (ranging from 18 to 46cm), collected in the Cananeia-Iguape estuarine-lagoonal system. The aim of this research is to evaluate the environmental variations of this tropical coastal micro-tidal system over the last decades, through SOM distribution. The studied parameters show differences between the cores located in the northern (sandy-silt sediments) and southern (sand and silty-sand) portions. The whole area presents a mixed organic matter origin signature (local mangrove plants: < -25.6 parts per thousand PDB/ phytoplancton delta(13)C values: -19.4 parts per thousand PDB). The northern cores, which submitted higher sedimentation deposition (1.46cm year(-1)), are more homogenous, presenting lower delta(13)C (< -25.2 parts per thousand PDB) and higher C/N values (in general >14), directly related to the terrestrial input from Ribeira de Iguape River (24,000 km(2) basin). The southern portion presents lower sedimentation rates (0.38cm year(-1)) and is associated to a small river basin (1,340 km(2)), presenting values Of delta(13)C: -25.0 to 23.0 parts per thousand PDB and of C/N ratio: 11 to 15. In general, the elemental contents in the 15 cores may be considered from low to medium (< 2.0% C - < 0.1% N), compared to similar environments. Although a greater marine influence is observed in the southern system portion, the majority of the cores present an elevated increase of continental deposition, most likely related to the strong silting process that the area has been subjected to since the 1850s, when an artificial channel was built linking, directly, the Ribeira River to the estuarine-lagoonal system.

Fertilizers applied to certified organic tomato culture

The tomato culture demands large quantities of mineral nutrients, which are supplied by synthetic fertilizers in the conventional cultivation system. In the organic cultivation system only alternative fertilizers are allowed by the certifiers and accepted as safe for humans and environment. The chemical composition of rice bran, oyster flour, cattle manure and ground charcoal, as well as soils and tomato fruits were evaluated by instrumental neutron activation analysis (INAA). The potential contribution of organic fertilizers to the enrichment of chemical elements in soil and their transfer to fruits was investigated using concentration ratios for fertilizer and soil samples, and also for soil and tomato. Results evidenced that these alternative fertilizers could be taken as important sources of Br, Ca, Ce, K, Na and Zn for the organic tomato culture.

Chemical characterization of agricultural supplies applied to organic tomato cultivation

The agricultural supplies used in the organic system to control pests and diseases as well as to fertilize soil are claimed to be beneficial to plants and innocuous to human health and to the environment. The chemical composition of six agricultural supplies commonly used in the organic tomato culture, was evaluated by instrumental neutron activation analysis (INAA). Results were compared to the maximum limits established by the Environment Control Agency of the Sao Paulo State (CETESB) and the Guidelines for Organic Quality Standard of Instituto Biodinamico (IBD). Concentrations above reference values were found for Co, Cr and Zn in compost, Cr and Zn in cattle manure and Zn in rice bran.

Chemical composition of tomato seeds affected by conventional and organic production systems

Tomato is amongst the most consumed vegetables in the world, not only for its culinary versatility but also for its high nutritional value. In the last years, consumers have shown an increased concern regarding food origin and safety. The organic tomato production has been a promising alternative for the consumer offering a safer food in relation to environmental, social and nutritional aspects. This study assessed the chemical composition of tomato seeds produced in both conventional and organic systems by INAA. The results showed significant differences (P <= 0.05) in the mass fractions of Br, Cs, Eu, Fe, K, Mo, Na, Rb and Sm between both systems, indicating influence of the crop management adopted in the different tomato production systems.

Correlation of Modified Natural Rubber Properties by Artificial Neural Networks

Natural rubber (NR) is a raw material largely used by the modern industry; however, it is common that chemical modifications must be made to NR in order to improve properties such as hydrophobicity or mechanical resistance. This work deals with the correlation of properties of NR modified with dimethylaminoethylmethacrylate or methylmethacrylate as grafting agents. Dynamic-mechanical behavior and stress/strain relations are very important properties because they furnish essential characteristics of the material such as glass transition temperature and rupture point. These properties are concerned with different physical principles; for this reason, normally they are not related to each other. This work showed that they can be correlated by artificial neural networks (ANN). So, from one type of assay, the properties that as a rule only could be obtained from the other can be extracted by ANN correlation. POLYM. ENG. SCI., 49:499-505, 2009. (c) 2009 Society of Plastics Engineers