scholarly journals TiFoSi: an Efficient Tool for Mechanobiology Simulations of Epithelia

2020 ◽  
Author(s):  
Oriol Canela-Xandri ◽  
Samira Anbari ◽  
Javier Buceta
Keyword(s):  
Gene Expression ◽  
In Silico ◽  
Cell Communication ◽  
Efficient Tool ◽  
Cellular Mechanics ◽  
Paracrine Signalling ◽  
Link Type ◽  
Custom Design ◽  
User Friendly ◽  
Protein Number

AbstractAboutThis document is an extended version of the main text where some details and results are fleshed out. Further details can be also found in the manual of the code and at TiFoSi’s website: http://tifosi.thesimbiosys.com.MotivationEmerging phenomena in developmental biology and tissue engineering are the result of feedbacks between gene expression and cell biomechanics. In that context, in silico experiments are a powerful tool to understand fundamental mechanisms and to formulate and test hypotheses.ResultsHere we present TiFoSi, a computational tool to simulate the cellular dynamics of planar epithelia. TiFoSi allows to model feedbacks between cellular mechanics and gene expression (either in a deterministic or a stochastic way), the interaction between different cell populations, the custom design of the cell cycle and cleavage properties, the protein number partitioning upon cell division, and the modeling of cell communication (juxtacrine and paracrine signalling). TiFoSi fills a niche in the field of software solutions to simulate the mechanobiology of epithelia because of its functionalities, computational efficiency, and its user-friendly approach to design in silico experiments using XML configuration files.Availabilityhttp://[email protected]

scholarly journals TiFoSi: an efficient tool for mechanobiology simulations of epithelia

2020 ◽  
Vol 36 (16) ◽  
pp. 4525-4526
Author(s):  
Oriol Canela-Xandri ◽  
Samira Anbari ◽  
Javier Buceta
Keyword(s):  
Gene Expression ◽  
Cell Communication ◽  
Supplementary Information ◽  
Cell Populations ◽  
Paracrine Signaling ◽  
Computational Tool ◽  
Efficient Tool ◽  
Cellular Mechanics ◽  
Custom Design ◽  
Protein Number

Abstract Motivation Emerging phenomena in developmental biology and tissue engineering are the result of feedbacks between gene expression and cell biomechanics. In that context, in silico experiments are a powerful tool to understand fundamental mechanisms and to formulate and test hypotheses. Results Here, we present TiFoSi, a computational tool to simulate the cellular dynamics of planar epithelia. TiFoSi allows to model feedbacks between cellular mechanics and gene expression (either in a deterministic or a stochastic way), the interaction between different cell populations, the custom design of the cell cycle and cleavage properties, the protein number partitioning upon cell division, and the modeling of cell communication (juxtacrine and paracrine signaling). Availability and implementation http://tifosi.thesimbiosys.com. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals THU0008 DEVELOPMENT OF A NOVEL TRANSLATIONAL IN SILICO INDICATION DISCOVERY FRAMEWORK: EXEMPLIFIED BY THE CLINICAL COMPOUND CENERIMOD

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 216.2-217
Author(s):  
D. Hartl ◽  
M. Keller ◽  
A. Klenk ◽  
M. Murphy ◽  
M. Martinic ◽  
...  
Keyword(s):  
Gene Expression ◽  
Animal Models ◽  
Clinical Response ◽  
Gene Expression Data ◽  
In Silico ◽  
Drug Candidate ◽  
Expression Data ◽  
Computer Algorithms ◽  
Clinical Patient ◽  
Link Type

Background:To explore the full therapeutic spectrum of a drug it is crucial to consider its potential effectiveness in all diseases. Serendipitous clinical observations have often shown that approved drugs and those in development to be efficacious in indications different to those originally tested for. Traditional approaches to match a drug candidate with possible indications are mostly based on matching drug mechanistic knowledge with disease pathophysiology. Proof-of-concept trials or elaborate pre-clinical studies in animal models do not allow for a broad assessment due to high costs and slow progress. Gene expression changes in patients or animal models represent a good proxy to comprehensively assess both disease and drug effects. Furthermore, this data type can be integrated with a plethora of publicly available data.Objectives:Generation of a novel in silico framework to support the selection and expansion of potential indications which associate with a compound or approved drug. The framework was exemplified by the clinical compound cenerimod, a potent, selective, and orally active sphingosine-1-phosphate receptor 1 modulator (Piali et al., 2017).Methods:A total of ~13’000 public patient gene expression datasets from ~140 diseases were evaluated against cenerimod gene expression data generated in mouse disease models. To improve comparability of studies across platforms and species, computer algorithms (neural networks) were trained and employed to reduce noise within the data sets and improve signal. The predicted response to cenerimod for individual patients was contrasted against clinical patient characteristics.Results:The neural network algorithm efficiently reduced experimental noise and improved sensitivity in the gene expression data. The results predicted cenerimod to be efficacious in several auto-immune diseases foremost SLE. Additionally, focused analysis on individual patients rather than disease cohorts revealed potential determinants predictive of maximal clinical response, with the highest predicted clinical response for cenerimod in patients with severe inflammatory endotype and/or high SLE Disease Activity Index (SLEDAI).Conclusion:Combining preclinical compound data with the wealth of public disease gene expression data, provides great potential to support indication selection. The novel in silico framework identified SLE as a prime potential indication for cenerimod and supported the cenerimod phase 2b clinical trial in patients with SLE (CARE study,NCT03742037).References:[1]Piali, L., Birker-Robaczewska, M., Lescop, C., Froidevaux, S., Schmitz, N., Morrison, K., … Nayler, O. (2017). Cenerimod, a novel selective S1P1 receptor modulator with unique signaling properties. Pharmacology Research & Perspectives, 5(6), 1–12.https://doi.org/10.1002/prp2.370Disclosure of Interests:Dominik Hartl Shareholder of: Idorsia shares, Employee of: Idorsia employee, Marcel Keller Shareholder of: Idorsia options/shares, Employee of: Idorsia employee, Axel Klenk Shareholder of: Idorsia option/shares, Employee of: Idorsia employee, Mark Murphy Shareholder of: Idorsia shares and stock options, Employee of: Idorsia employee, Marianne Martinic Shareholder of: Idorsia options/shares, Employee of: Idorsia employee, Gabin Pierlot Shareholder of: Idorsia options/shares, Employee of: Idorsia employee, Peter Groenen Shareholder of: Idorsia options/shares, Employee of: Idorsia employee, Daniel Strasser Shareholder of: Idorsia options/shares, Employee of: Idorsia employee

scholarly journals CoNekT: an open-source framework for comparative genomic and transcriptomic network analyses

2018 ◽  
Author(s):  
Sebastian Proost ◽  
Marek Mutwil
Keyword(s):  
Gene Expression ◽  
Open Source ◽  
Gene Expression Data ◽  
Expression Profiles ◽  
Specific Gene ◽  
Expression Data ◽  
Comparative Analyses ◽  
Network Analyses ◽  
Link Type ◽  
User Friendly

ABSTRACTThe recent accumulation of gene expression data in the form of RNA sequencing creates unprecedented opportunities to study gene regulation and function. Furthermore, comparative analysis of the expression data from multiple species can elucidate which functional gene modules are conserved across species, allowing the study of the evolution of these modules. However, performing such comparative analyses on raw data is not feasible for many biologists. Here, we present CoNekT (Co-expression Network Toolkit), an open source, user-friendly web server, that contains user-friendly tools and interactive visualizations for comparative analyses of gene expression data and co-expression networks. These tools allow analysis and cross-species comparison of (i) gene expression profiles; (ii) co-expression networks; (iii) co-expressed clusters involved in specific biological processes; (iv) tissue-specific gene expression; and (v) expression profiles of gene families. To demonstrate these features, we constructed CoNekT-Plants for green alga, seed plants and flowering plants (Picea abies, Chlamydomonas reinhardtii, Vitis vinifera, Arabidopsis thaliana, Oryza sativa, Zea mays and Solanum lycopersicum) and thus provide a web-tool with the broadest available collection of plant phyla. CoNekT-Plants is freely available from http://conekt.plant.tools, while the CoNekT source code and documentation can be found at https://github.molgen.mpg.de/proost/CoNekT/.

scholarly journals GREIN: An Interactive Web Platform for Reanalyzing GEO RNA-seq Data

2018 ◽  
Author(s):  
Naim Al Mahi ◽  
Mehdi Fazel Najafabadi ◽  
Marcin Pilarczyk ◽  
Michal Kouril ◽  
Mario Medvedovic
Keyword(s):  
Gene Expression ◽  
User Interfaces ◽  
Web Application ◽  
Statistical Power ◽  
Functional Characterization ◽  
Gene Expression Omnibus ◽  
Rna Seq ◽  
Link Type ◽  
Front End ◽  
User Friendly

ABSTRACTThe vast amount of RNA-seq data deposited in Gene Expression Omnibus (GEO) and Sequence Read Archive (SRA) is still a grossly underutilized resource for biomedical research. To remove technical roadblocks for reusing these data, we have developed a web-application GREIN (GEO RNA-seq Experiments Interactive Navigator) which provides user-friendly interfaces to manipulate and analyze GEO RNA-seq data. GREIN is powered by the back-end computational pipeline for uniform processing of RNA-seq data and the large number (>6,500) of already processed datasets. The front-end user interfaces provide a wealth of user-analytics options including sub-setting and downloading processed data, interactive visualization, statistical power analyses, construction of differential gene expression signatures and their comprehensive functional characterization, and connectivity analysis with LINCS L1000 data. The combination of the massive amount of back-end data and front-end analytics options driven by user-friendly interfaces makes GREIN a unique open-source resource for re-using GEO RNA-seq data. GREIN is accessible at: https://shiny.ilincs.org/grein, the source code at: https://github.com/uc-bd2k/grein, and the Docker container at: https://hub.docker.com/r/ucbd2k/grein.

scholarly journals ProkSeq for complete analysis of RNA-seq data from prokaryotes

2020 ◽  
Author(s):  
A K M Firoj Mahmud ◽  
Soumyadeep Nandi ◽  
Maria Fällman
Keyword(s):  
Gene Expression ◽  
Pathogenic Bacteria ◽  
Complete Analysis ◽  
Rna Seq ◽  
Link Type ◽  
Eukaryotic Genes ◽  
Differential Gene ◽  
User Friendly ◽  
Multiple Samples

AbstractSummarySince its introduction, RNA-seq technology has been used extensively in studies of pathogenic bacteria to identify and quantify differences in gene expression across multiple samples from bacteria exposed to different conditions. With some exceptions, the current tools for assessing gene expression have been designed around the structures of eukaryotic genes. There are a few stand-alone tools designed for prokaryotes, and they require improvement. A well-defined pipeline for prokaryotes that includes all the necessary tools for quality control, determination of differential gene expression, downstream pathway analysis, and normalization of data collected in extreme biological conditions is still lacking. Here we describe ProkSeq, a user-friendly, fully automated RNA-seq data analysis pipeline designed for prokaryotes. ProkSeq provides a wide variety of options for analysing differential expression, normalizing expression data, and visualizing data and results, and it produces publication-quality figures.Availability and implementationProkSeq is implemented in Python and is published under the ISC open source license. The tool and a detailed user manual are hosted at Docker: https://hub.docker.com/repository/docker/snandids/prokseq-v2.1, Anaconda: https://anaconda.org/snandiDS/prokseq; Github: https://github.com/snandiDS/prokseq.

scholarly journals Biochat: a database for natural language processing of Gene Expression Omnibus data

2018 ◽  
Author(s):  
Bohdan B. Khomtchouk ◽  
Vsevolod Dyomkin ◽  
Kasra A. Vand ◽  
Themistocles Assimes ◽  
Or Gozani
Keyword(s):  
Gene Expression ◽  
Language Processing ◽  
Web Application ◽  
Gene Expression Omnibus ◽  
Omics Data ◽  
Textual Information ◽  
Link Type ◽  
Different Types ◽  
User Friendly ◽  
Omics Data Integration

AbstractA biological dataset’s metadata profile (e.g., study description, organism name, sequencing type, etc.) typically contains terse but descriptive textual information that can be used to link it with other similar biological datasets for the purpose of integrating omics data of different types to inform hypotheses and biological questions. Here we present Biochat, a database containing a multi-omics data integration support system to aid in cross-linking Gene Expression Omnibus (GEO) records to each other by metadata similarity through a user-friendly web application. Biochat is publicly available at: http://www.biochat.ai. Biochat source code is hosted at: https://github.com/Bohdan-Khomtchouk/Bio-chat.Database URLhttps://github.com/Bohdan-Khomtchouk/Bio-chat

Pedigree and Pedigree Import Wizard

HortScience ◽  
1998 ◽  
Vol 33 (3) ◽  
pp. 552g-553
Author(s):  
Shahrokh Khandizadeh
Keyword(s):  
Additional Data ◽  
File Format ◽  
Fruit Crops ◽  
Operating Environment ◽  
Agronomic Characteristics ◽  
Link Type ◽  
Plant Characteristics ◽  
User Friendly

Pedigree for Windows is a user-friendly program that allows the user to trace agronomic characteristics, draw pedigrees, and view images of several fruit crops, including more than 1400 apple, 800 strawberry, 800 almond, 100 blackberry, 80 blueberry, 790 pear, 200 raspberry examples. Pedigree Import Wizard®© for Windows is an add-on software for users who are interested in importing their research or breeding data records of fruit, flower, and plant characteristics and any related images into Pedigree for Windows. Pedigree for Windows and Pedigree Import Wizard have been designed so that a user familiar with the Windows operating environment should have little need to refer to the documentation provided with the program. Pedigree Import Wizard uses a comma-separated value (csv) file format under the MS Excel environment. This option allows the user to add or import additional data to the existing database that are already stored in other software such as Lotus, Excel, Access, QuattroPro, WordPerfect, and MS Word tables, etc., as long as they work under the Windows environment. A free demo version of Pedigree and Pedigree Import Wizard for Windows is available from http://www.pgris.com.

Reverse Screening Bioinformatics Approach to Identify Potential Anti Breast Cancer Targets Using Thymoquinone from Neutraceuticals Black Cumin Oil

2019 ◽  
Vol 19 (5) ◽  
pp. 599-609 ◽  
Author(s):  
Sumathi Sundaravadivelu ◽  
Sonia K. Raj ◽  
Banupriya S. Kumar ◽  
Poornima Arumugamand ◽  
Padma P. Ragunathan
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cell Cycle ◽  
Cell Death ◽  
In Silico ◽  
Chemotherapeutic Agents ◽  
Normal Cells ◽  
Black Cumin ◽  
In Silico Docking ◽  
Wide Range

Background: Functional foods, neutraceuticals and natural antioxidants have established their potential roles in the protection of human health and diseases. Thymoquinone (TQ), the main bioactive component of Nigella sativa seeds (black cumin seeds), a plant derived neutraceutical was used by ancient Egyptians because of their ability to cure a variety of health conditions and used as a dietary food supplement. Owing to its multi targeting nature, TQ interferes with a wide range of tumorigenic processes and counteracts carcinogenesis, malignant growth, invasion, migration, and angiogenesis. Additionally, TQ can specifically sensitize tumor cells towards conventional cancer treatments (e.g., radiotherapy, chemotherapy, and immunotherapy) and simultaneously minimize therapy-associated toxic effects in normal cells besides being cost effective and safe. TQ was found to play a protective role when given along with chemotherapeutic agents to normal cells. Methods: In the present study, reverse in silico docking approach was used to search for potential molecular targets for cancer therapy. Various metastatic and apoptotic targets were docked with the target ligand. TQ was also tested for its anticancer activities for its ability to cause cell death, arrest cell cycle and ability to inhibit PARP gene expression. Results: In silico docking studies showed that TQ effectively docked metastatic targets MMPs and other apoptotic and cell proliferation targets EGFR. They were able to bring about cell death mediated by apoptosis, cell cycle arrest in the late apoptotic stage and induce DNA damage too. TQ effectively down regulated PARP gene expression which can lead to enhanced cancer cell death. Conclusion: Thymoquinone a neutraceutical can be employed as a new therapeutic agent to target triple negative breast cancer which is otherwise difficult to treat as there are no receptors on them. Can be employed along with standard chemotherapeutic drugs to treat breast cancer as a combinatorial therapy.

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