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1.
Background
Genome-scale metabolic models provide an opportunity for rational approaches to studies of the different reactions taking place inside the cell. The integration of these models with gene regulatory networks is a hot topic in systems biology. The methods developed to date focus mostly on resolving the metabolic elements and use fairly straightforward approaches to assess the impact of genome expression on the metabolic phenotype.Results
We present here a method for integrating the reverse engineering of gene regulatory networks into these metabolic models. We applied our method to a high-dimensional gene expression data set to infer a background gene regulatory network. We then compared the resulting phenotype simulations with those obtained by other relevant methods.Conclusions
Our method outperformed the other approaches tested and was more robust to noise. We also illustrate the utility of this method for studies of a complex biological phenomenon, the diauxic shift in yeast.2.
Background
During the HIV infection several quasispecies of the virus arise, which are able to use different coreceptors, in particular the CCR5 and CXCR4 coreceptors (R5 and X4 phenotypes, respectively). The switch in coreceptor usage has been correlated with a faster progression of the disease to the AIDS phase. As several pharmaceutical companies are starting large phase III trials for R5 and X4 drugs, models are needed to predict the co-evolutionary and competitive dynamics of virus strains.Results
We present a model of HIV early infection which describes the dynamics of R5 quasispecies and a model of HIV late infection which describes the R5 to X4 switch. We report the following findings: after superinfection (multiple infections at different times) or coinfection (simultaneous infection by different strains), quasispecies dynamics has time scales of several months and becomes even slower at low number of CD4+ T cells. Phylogenetic inference of chemokine receptors suggests that viral mutational pathway may generate a large variety of R5 variants able to interact with chemokine receptors different from CXCR4. The decrease of CD4+ T cells, during AIDS late stage, can be described taking into account the X4-related Tumor Necrosis Factor dynamics.Conclusion
The results of this study bridge the gap between the within-patient and the inter-patients (i.e. world-wide) evolutionary processes during HIV infection and may represent a framework relevant for modeling vaccination and therapy.3.
Pathomwat Wongrattanakamon Vannajan Sanghiran Lee Piyarat Nimmanpipug Supat Jiranusornkul 《生物学前沿》2016,11(5):391-395
Background
P-glycoprotein (P-gp) is a 170-kDa membrane protein. It provides a barrier function and help to excrete toxins from the body as a transporter. Some bioflavonoids have been shown to block P-gp activity.Objective
To evaluate the important amino acid residues within nucleotide binding domain 1 (NBD1) of P-gp that play a key role in molecular interactions with flavonoids using structure-based pharmacophore model.Methods
In the molecular docking with NBD1 models, a putative binding site of flavonoids was proposed and compared with the site for ATP. The binding modes for ligands were achieved using LigandScout to generate the P-gp–flavonoid pharmacophore models.Results
The binding pocket for flavonoids was investigated and found these inhibitors compete with the ATP for binding site in NBD1 including the NBD1 amino acid residues identified by the in silico techniques to be involved in the hydrogen bonding and van der Waals (hydrophobic) interactions with flavonoids.Conclusion
These flavonoids occupy with the same binding site of ATP in NBD1 proffering that they may act as an ATP competitive inhibitor.4.
Wesley W. Ingwersen Ezra Kahn Joyce Cooper 《The International Journal of Life Cycle Assessment》2018,23(11):2266-2270
Introduction
New platforms are emerging that enable more data providers to publish life cycle inventory data.Background
Providing datasets that are not complete LCA models results in fragments that are difficult for practitioners to integrate and use for LCA modeling. Additionally, when proxies are used to provide a technosphere input to a process that was not originally intended by the process authors, in most LCA software, this requires modifying the original process.Results
The use of a bridge process, which is a process created to link two existing processes, is proposed as a solution.Discussion
Benefits to bridge processes include increasing model transparency, facilitating dataset sharing and integration without compromising original dataset integrity and independence, providing a structure with which to make the data quality associated with process linkages explicit, and increasing model flexibility in the case that multiple bridges are provided. A drawback is that they add additional processes to existing LCA models which will increase their size.Conclusions
Bridge processes can be an enabler in allowing users to integrate new datasets without modifying them to link to background databases or other processes they have available. They may not be the ideal long-term solution but provide a solution that works within the existing LCA data model.5.
N. Cesbron A.-L. Royer Y. Guitton A. Sydor B. Le Bizec G. Dervilly-Pinel 《Metabolomics : Official journal of the Metabolomic Society》2017,13(8):99
Introduction
Collecting feces is easy. It offers direct outcome to endogenous and microbial metabolites.Objectives
In a context of lack of consensus about fecal sample preparation, especially in animal species, we developed a robust protocol allowing untargeted LC-HRMS fingerprinting.Methods
The conditions of extraction (quantity, preparation, solvents, dilutions) were investigated in bovine feces.Results
A rapid and simple protocol involving feces extraction with methanol (1/3, M/V) followed by centrifugation and a step filtration (10 kDa) was developed.Conclusion
The workflow generated repeatable and informative fingerprints for robust metabolome characterization.6.
7.
Background
Adverse drug reactions (ADRs) are unintended and harmful reactions caused by normal uses of drugs. Predicting and preventing ADRs in the early stage of the drug development pipeline can help to enhance drug safety and reduce financial costs.Methods
In this paper, we developed machine learning models including a deep learning framework which can simultaneously predict ADRs and identify the molecular substructures associated with those ADRs without defining the substructures a-priori.Results
We evaluated the performance of our model with ten different state-of-the-art fingerprint models and found that neural fingerprints from the deep learning model outperformed all other methods in predicting ADRs. Via feature analysis on drug structures, we identified important molecular substructures that are associated with specific ADRs and assessed their associations via statistical analysis.Conclusions
The deep learning model with feature analysis, substructure identification, and statistical assessment provides a promising solution for identifying risky components within molecular structures and can potentially help to improve drug safety evaluation.8.
Background
Gene expression is a key intermediate level that genotypes lead to a particular trait. Gene expression is affected by various factors including genotypes of genetic variants. With an aim of delineating the genetic impact on gene expression, we build a deep auto-encoder model to assess how good genetic variants will contribute to gene expression changes. This new deep learning model is a regression-based predictive model based on the MultiLayer Perceptron and Stacked Denoising Auto-encoder (MLP-SAE). The model is trained using a stacked denoising auto-encoder for feature selection and a multilayer perceptron framework for backpropagation. We further improve the model by introducing dropout to prevent overfitting and improve performance.Results
To demonstrate the usage of this model, we apply MLP-SAE to a real genomic datasets with genotypes and gene expression profiles measured in yeast. Our results show that the MLP-SAE model with dropout outperforms other models including Lasso, Random Forests and the MLP-SAE model without dropout. Using the MLP-SAE model with dropout, we show that gene expression quantifications predicted by the model solely based on genotypes, align well with true gene expression patterns.Conclusion
We provide a deep auto-encoder model for predicting gene expression from SNP genotypes. This study demonstrates that deep learning is appropriate for tackling another genomic problem, i.e., building predictive models to understand genotypes’ contribution to gene expression. With the emerging availability of richer genomic data, we anticipate that deep learning models play a bigger role in modeling and interpreting genomics.9.
Mengjuan Liu Jiaqi Wang Hongping Tang Li Fan Liang Zhao Hai-Bin Wang Yan Zhou Wen-Song Tan 《Biotechnology letters》2018,40(11-12):1487-1493
Objective
To explore the impact of taurine on monoclonal antibody (mAb) basic charge variants in Chinese hamster ovary (CHO) cell culture.Results
In fed-batch culture, adding taurine in the feed medium slightly increased the maximum viable cell density and mAb titers in CHO cells. What’s more, taurine significantly decreased the lysine variant and oxidized variant levels, which further decreased basic variant contents from 32 to 27%. The lysine variant content in the taurine culture was approximately 4% lower than that in control condition, which was the main reason for the decrease in basic variants. Real-time PCR and cell-free assay revealed that taurine played a critical role in the upregulation of relative basic carboxypeptidase and stimulating extracellular basic carboxypeptidase activities.Conclusion
Taurine exhibits noticeable impact on lower basic charge variants, which are mainly due to the decrease of lysine variant and oxidized protein variants.10.
Background
Accurately predicting pathogenic human genes has been challenging in recent research. Considering extensive gene–disease data verified by biological experiments, we can apply computational methods to perform accurate predictions with reduced time and expenses.Methods
We propose a probability-based collaborative filtering model (PCFM) to predict pathogenic human genes. Several kinds of data sets, containing data of humans and data of other nonhuman species, are integrated in our model. Firstly, on the basis of a typical latent factorization model, we propose model I with an average heterogeneous regularization. Secondly, we develop modified model II with personal heterogeneous regularization to enhance the accuracy of aforementioned models. In this model, vector space similarity or Pearson correlation coefficient metrics and data on related species are also used.Results
We compared the results of PCFM with the results of four state-of-arts approaches. The results show that PCFM performs better than other advanced approaches.Conclusions
PCFM model can be leveraged for predictions of disease genes, especially for new human genes or diseases with no known relationships.11.
Objective
To isolate and characterize the kinetics of variants of E. coli β-glucuronidase (GUS) having altered substrate specificity.Results
Two small combinatorial libraries of E. coli GUS variants were constructed and screened for improved activities towards the substrate p-nitrophenyl-β-d-galactoside (pNP-gal). Nine of the most active variants were purified and their kinetic parameters were determined. These variants show up to 134-fold improved kcat/KM value towards pNP-gal compared to wild-type GUS, up to 9 × 108-fold shift in specificity from p-nitrophenyl-β-d-glucuronide (pNP-glu) to pNP-gal compared to wild-type, and 103-fold increase in specificity shift compared to a previously evolved GUS variant.Conclusions
The kinetic data collected for nine new GUS variants is invaluable for training computational protein design models that better predict amino acid substitutions which improve activity of enzyme variants having altered substrate specificity.12.
Edoardo Saccenti Age K. Smilde José Camacho 《Metabolomics : Official journal of the Metabolomic Society》2018,14(6):73
Introduction
Modern omics experiments pertain not only to the measurement of many variables but also follow complex experimental designs where many factors are manipulated at the same time. This data can be conveniently analyzed using multivariate tools like ANOVA-simultaneous component analysis (ASCA) which allows interpretation of the variation induced by the different factors in a principal component analysis fashion. However, while in general only a subset of the measured variables may be related to the problem studied, all variables contribute to the final model and this may hamper interpretation.Objectives
We introduce here a sparse implementation of ASCA termed group-wise ANOVA-simultaneous component analysis (GASCA) with the aim of obtaining models that are easier to interpret.Methods
GASCA is based on the concept of group-wise sparsity introduced in group-wise principal components analysis where structure to impose sparsity is defined in terms of groups of correlated variables found in the correlation matrices calculated from the effect matrices.Results
The GASCA model, containing only selected subsets of the original variables, is easier to interpret and describes relevant biological processes.Conclusions
GASCA is applicable to any kind of omics data obtained through designed experiments such as, but not limited to, metabolomic, proteomic and gene expression data.13.
Background
Few finite element models (FEM) have been developed to describe the electric field, specific absorption rate (SAR), and the temperature distribution surrounding hepatic radiofrequency ablation probes. To date, a coupled finite element model that accounts for the temperature-dependent electrical conductivity changes has not been developed for ablation type devices. While it is widely acknowledged that accounting for temperature dependent phenomena may affect the outcome of these models, the effect has not been assessed.Methods
The results of four finite element models are compared: constant electrical conductivity without tissue perfusion, temperature-dependent conductivity without tissue perfusion, constant electrical conductivity with tissue perfusion, and temperature-dependent conductivity with tissue perfusion.Results
The data demonstrate that significant errors are generated when constant electrical conductivity is assumed in coupled electrical-heat transfer problems that operate at high temperatures. These errors appear to be closely related to the temperature at which the ablation device operates and not to the amount of power applied by the device or the state of tissue perfusion.Conclusion
Accounting for temperature-dependent phenomena may be critically important in the safe operation of radiofrequency ablation device that operate near 100°C.14.
Rachel A. Spicer Christoph Steinbeck 《Metabolomics : Official journal of the Metabolomic Society》2018,14(1):16
Introduction
Data sharing is being increasingly required by journals and has been heralded as a solution to the ‘replication crisis’.Objectives
(i) Review data sharing policies of journals publishing the most metabolomics papers associated with open data and (ii) compare these journals’ policies to those that publish the most metabolomics papers.Methods
A PubMed search was used to identify metabolomics papers. Metabolomics data repositories were manually searched for linked publications.Results
Journals that support data sharing are not necessarily those with the most papers associated to open metabolomics data.Conclusion
Further efforts are required to improve data sharing in metabolomics.15.
Marta R. Hidalgo Alicia Amadoz Cankut Çubuk José Carbonell-Caballero Joaquín Dopazo 《Biology direct》2018,13(1):16
Background
Despite the progress in neuroblastoma therapies the mortality of high-risk patients is still high (40–50%) and the molecular basis of the disease remains poorly known. Recently, a mathematical model was used to demonstrate that the network regulating stress signaling by the c-Jun N-terminal kinase pathway played a crucial role in survival of patients with neuroblastoma irrespective of their MYCN amplification status. This demonstrates the enormous potential of computational models of biological modules for the discovery of underlying molecular mechanisms of diseases.Results
Since signaling is known to be highly relevant in cancer, we have used a computational model of the whole cell signaling network to understand the molecular determinants of bad prognostic in neuroblastoma. Our model produced a comprehensive view of the molecular mechanisms of neuroblastoma tumorigenesis and progression.Conclusion
We have also shown how the activity of signaling circuits can be considered a reliable model-based prognostic biomarker.Reviewers
This article was reviewed by Tim Beissbarth, Wenzhong Xiao and Joanna Polanska. For the full reviews, please go to the Reviewers’ comments section.16.
Background
The thermophilic anaerobe Thermoanaerobacterium saccharolyticum is capable of directly fermenting xylan and the biomass-derived sugars glucose, cellobiose, xylose, mannose, galactose and arabinose. It has been metabolically engineered and developed as a biocatalyst for the production of ethanol.Results
We report the initial characterization of the carbon catabolite repression system in this organism. We find that sugar metabolism in T. saccharolyticum is regulated by histidine-containing protein HPr. We describe a mutation in HPr, His15Asp, that leads to derepression of less-favored carbon source utilization.Conclusion
Co-utilization of sugars can be achieved by mutation of HPr in T. saccharolyticum. Further manipulation of CCR in this organism will be instrumental in achieving complete and rapid conversion of all available sugars to ethanol.17.
Beatriz?Somovilla-Crespo Manuel?Alfonso-Pérez Carlos?Cuesta-Mateos Cristina?Carballo-de Dios Amada?E?Beltrán Fernando?Terrón Juan?J?Pérez-Villar Carlos?Gamallo-Amat Gema?Pérez-Chacón Elena?Fernández-Ruiz Juan?M?Zapata Cecilia?Mu?oz-Calleja
Background
The chemokine receptor CCR7 mediates lymphoid dissemination of many cancers, including lymphomas and epithelial carcinomas, thus representing an attractive therapeutic target. Previous results have highlighted the potential of the anti-CCR7 monoclonal antibodies to inhibit migration in transwell assays. The present study aimed to evaluate the in vivo therapeutic efficacy of an anti-CCR7 antibody in a xenografted human mantle cell lymphoma model.Methods
NOD/SCID mice were either subcutaneously or intravenously inoculated with Granta-519 cells, a human cell line derived from a leukemic mantle cell lymphoma. The anti-CCR7 mAb treatment (3 × 200 μg) was started on day 2 or 7 to target lymphoma cells in either a peri-implantation or a post-implantation stage, respectively.Results
The anti-CCR7 therapy significantly delayed the tumor appearance and also reduced the volumes of tumors in the subcutaneous model. Moreover, an increased number of apoptotic tumor cells was detected in mice treated with the anti-CCR7 mAb compared to the untreated animals. In addition, significantly reduced number of Granta-519 cells migrated from subcutaneous tumors to distant lymphoid organs, such as bone marrow and spleen in the anti-CCR7 treated mice. In the intravenous models, the anti-CCR7 mAb drastically increased survival of the mice. Accordingly, dissemination and infiltration of tumor cells in lymphoid and non-lymphoid organs, including lungs and central nervous system, was almost abrogated.Conclusions
The anti-CCR7 mAb exerts a potent anti-tumor activity and might represent an interesting therapeutic alternative to conventional therapies.18.
Zhanglong Ji Xiaoqian Jiang Shuang Wang Li Xiong Lucila Ohno-Machado 《BMC medical genomics》2014,7(Z1):S14
Background
Privacy protecting is an important issue in medical informatics and differential privacy is a state-of-the-art framework for data privacy research. Differential privacy offers provable privacy against attackers who have auxiliary information, and can be applied to data mining models (for example, logistic regression). However, differentially private methods sometimes introduce too much noise and make outputs less useful. Given available public data in medical research (e.g. from patients who sign open-consent agreements), we can design algorithms that use both public and private data sets to decrease the amount of noise that is introduced.Methodology
In this paper, we modify the update step in Newton-Raphson method to propose a differentially private distributed logistic regression model based on both public and private data.Experiments and results
We try our algorithm on three different data sets, and show its advantage over: (1) a logistic regression model based solely on public data, and (2) a differentially private distributed logistic regression model based on private data under various scenarios.Conclusion
Logistic regression models built with our new algorithm based on both private and public datasets demonstrate better utility than models that trained on private or public datasets alone without sacrificing the rigorous privacy guarantee.19.
Background
Pandemic is a typical spreading phenomenon that can be observed in the human society and is dependent on the structure of the social network. The Susceptible-Infective-Recovered (SIR) model describes spreading phenomena using two spreading factors; contagiousness (β) and recovery rate (γ). Some network models are trying to reflect the social network, but the real structure is difficult to uncover.Methods
We have developed a spreading phenomenon simulator that can input the epidemic parameters and network parameters and performed the experiment of disease propagation. The simulation result was analyzed to construct a new marker VRTP distribution. We also induced the VRTP formula for three of the network mathematical models.Results
We suggest new marker VRTP (value of recovered on turning point) to describe the coupling between the SIR spreading and the Scale-free (SF) network and observe the aspects of the coupling effects with the various of spreading and network parameters. We also derive the analytic formulation of VRTP in the fully mixed model, the configuration model, and the degree-based model respectively in the mathematical function form for the insights on the relationship between experimental simulation and theoretical consideration.Conclusions
We discover the coupling effect between SIR spreading and SF network through devising novel marker VRTP which reflects the shifting effect and relates to entropy.20.