PHENSIM: Phenotype Simulator.

Salvatore Alaimo,Gioacchino P Marceca,Rosaria Valentina Rapicavoli,Philip N Tsichlis,Alfredo Ferro,Alessandro La Ferlita,Bud Mishra,Alfredo Pulvirenti,Oksana B Serebrennikova,Christos A Ouzounis

doi:10.1371/journal.pcbi.1009069

Abstract

Despite the unprecedented growth in our understanding of cell biology, it still remains challenging to connect it to experimental data obtained with cells and tissues' physiopathological status under precise circumstances. This knowledge gap often results in difficulties in designing validation experiments, which are usually labor-intensive, expensive to perform, and hard to interpret. Here we propose PHENSIM, a computational tool using a systems biology approach to simulate how cell phenotypes are affected by the activation/inhibition of one or multiple biomolecules, and it does so by exploiting signaling pathways. Our tool's applications include predicting the outcome of drug administration, knockdown experiments, gene transduction, and exposure to exosomal cargo. Importantly, PHENSIM enables the user to make inferences on well-defined cell lines and includes pathway maps from three different model organisms. To assess our approach's reliability, we built a benchmark from transcriptomics data gathered from NCBI GEO and performed four case studies on known biological experiments. Our results show high prediction accuracy, thus highlighting the capabilities of this methodology. PHENSIM standalone Java application is available at https://github.com/alaimos/phensim, along with all data and source codes for benchmarking. A web-based user interface is accessible at https://phensim.tech/.

Highlights

Cells of living organisms are continuously exposed to signals originating in both the extracellular and the intracellular microenvironments
The sets were categorized based on the genes present in KEGG pathways (DS1 contains all sample sets where the up- or downregulated gene was in KEGG; DS2 all the other samples)
PHENSIM and BioNSi simulations were evaluated in terms of Accuracy, Positive Predictive Value (PPV), Sensitivity and Specificity for genes showing altered expression, and PPV and False Negative Rate (FNR) for the others

Summary

Introduction

Cells of living organisms are continuously exposed to signals originating in both the extracellular and the intracellular microenvironments. The proper response to signals depends on the expression, activation, or inhibition of sets of interrelated genes/proteins, acting in a well-defined order within the framework of vector-driven biological processes, aiming to reach specific endpoints. Such subcellular processes are referred to as biological pathways [1]. Despite the improvements in our understanding of cell biology, it is challenging to link omics data to the physiopathological status of cells, tissues, or organs under specific conditions Studies addressing these issues are often labor-intensive, expensive to perform, and produce big datasets for analysis

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