PHENSIM: Phenotype Simulator

Abstract

Motivation:Despite the unprecedented increase of our understanding of cell biology, connecting experimental data to the physiopathological status of cells and tissues under precise circumstances is still a challenge. This often results in difficulties during the design of validation experiments, which are usually labor-intensive, expensive to perform and hard to interpret.Results: Here we propose PHENSIM, a systems biology approach, which can simulate the effects of activation/inhibition of one or multiple biomolecules on cell phenotypes by exploiting signaling pathways. Possible applications of our tool include prediction of the outcome of drug administration, knockdown experiments, gene transduction and exposure to exosomal cargo. Importantly, this method allows the user to make inferences on well-defined cell lines and includes pathway maps from three different model organisms. The basic assumption is that phenotypes can be described through changes in pathway activity. Results from our study show discrete prediction accuracy, highlighting the capabilities of this methodology.Availability and Implementation: PHENSIM has been developed in Java and it is available, along with all data and source codes, at https://github.com/alaimos/phensim. A web-based user interface, developed in PHP is accessible at https://phensim.atlas.dmi.unict.it/. IntroductionCells of living organisms are continuously exposed to signals originating in both the extracellular and the intracellular microenvironment. These signals regulate multiple cellular functions, including gene expression, chromatin remodeling, DNA replication and repair, protein synthesis and cellular metabolism. The proper response to signals depends on expression, activation or inhibition of sets of interrelated genes/proteins, acting in a well-defined order within the context of well-defined vectordriven biological processes, aiming to reach specific endpoints. Such sub-cellular processes are referred to as biological pathways. 1

NETME: on-the-fly knowledge network construction from biomedical literature

“…Many tools (Alaimo et al. 2020 ) and computational models rely on existing network databases, such as KEGG (Kanehisa and Goto 2000 ; Kanehisa 2019 , 2000 ) and Reactome (Croft et al. 2010 ; Joshi-Tope 2004 ; Croft et al.…”

Rapid Identification of Druggable Targets and the Power of the Phenotype SIMulator for Effective Drug Repurposing in COVID-19

“…Using DEGs as input for our cell PHENotype SIMulator PHENSIM [ 15,19 we define a signature of pathogen predicted effects on human pathways (pathogen alterations profile; here termed the "viral signature"; see Fig. 1).…”

Rapid Identification of Druggable Targets and the Power of the PHENotype SIMulator for Effective Drug Repurposing in COVID-19

“…PHENSIM is a systems biology approach, simulating the effect of the alteration of one or more biomolecules (genes, proteins, microRNAs, or metabolites) in a specific cellular context using KEGG (Kyoto Encyclopedia of Genes and Genomes) meta-pathways 15,20 . The meta-pathway concept, introduced by us previously 61 , has been devised to account for pathway cross-talk in analysis.…”

“…The algorithm uses these details to compute synthetic Log Fold Changes (LogFC). Synthetic LogFCs are computed by sampling the normal distribution fitted to the actual LogFCs of a particular gene, as described previously 15 . Such values are then propagated within the meta-pathway, using the MITHrIL (Mirna enrIched paTHway Impact anaLysis) pathway perturbation analysis 20 .…”

“…Here we aim to utilize our systems biology tool, the PHENotype SIMulator (PHENSIM), to leverage the power of pathway analysis by simulating tissue-specific infection of host cells of SARS-CoV-2 and subsequently perform in silico drug selection for potential repurposing. PHENSIM is a web-based user-friendly computational tool that allows phenotype prediction on selected cells, cell-lines and tissues, using a probabilistic algorithm 15 via "message passing" 16 across a network of meta-pathways 17 . These metapathways are obtained by joining all validated biological pathways, enriched with gene regulatory elements 18 .…”