PHENSIM: Phenotype Simulator.

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/.

Computational Methods for Drug Repurposing.

The wealth of knowledge and multi-omics data available in drug research has allowed the rise of several computational methods in the drug discovery field, resulting in a novel and exciting strategy called drug repurposing. Drug repurposing consists in finding new applications for existing drugs. Numerous computational methods perform a high-level integration of different knowledge sources to facilitate the discovery of unknown mechanisms. In this chapter, we present a survey of data resources and computational tools available for drug repositioning.

IGF2: A Role in Metastasis and Tumor Evasion from Immune Surveillance?

Insulin-like growth factor 2 (IGF2) is upregulated in both childhood and adult malignancies. Its overexpression is associated with resistance to chemotherapy and worse prognosis. However, our understanding of its physiological and pathological role is lagging behind what we know about IGF1. Dysregulation of the expression and function of IGF2 receptors, insulin receptor isoform A (IR-A), insulin growth factor receptor 1 (IGF1R), and their downstream signaling effectors drive cancer initiation and progression. The involvement of IGF2 in carcinogenesis depends on its ability to link high energy intake, increase cell proliferation, and suppress apoptosis to cancer risk, and this is likely the key mechanism bridging insulin resistance to cancer. New aspects are emerging regarding the role of IGF2 in promoting cancer metastasis by promoting evasion from immune destruction. This review provides a perspective on IGF2 and an update on recent research findings. Specifically, we focus on studies providing compelling evidence that IGF2 is not only a major factor in primary tumor development, but it also plays a crucial role in cancer spread, immune evasion, and resistance to therapies. Further studies are needed in order to find new therapeutic approaches to target IGF2 action.

Application of the PHENotype SIMulator for rapid identification of potential candidates in effective COVID-19 drug repurposing.

The current, rapidly diversifying pandemic has accelerated the need for efficient and effective identification of potential drug candidates for COVID-19. Knowledge on host-immune response to SARS-CoV-2 infection, however, remains limited with few drugs approved to date. Viable strategies and tools are rapidly arising to address this, especially with repurposing of existing drugs offering significant promise. Here we introduce a systems biology tool, the PHENotype SIMulator, which -by leveraging available transcriptomic and proteomic databases-allows modeling of SARS-CoV-2 infection in host cells in silico to i) determine with high sensitivity and specificity (both>96%) the viral effects on cellular host-immune response, resulting in specific cellular SARS-CoV-2 signatures and ii) utilize these cell-specific signatures to identify promising repurposable therapeutics. Powered by this tool, coupled with domain expertise, we identify several potential COVID-19 drugs including methylprednisolone and metformin, and further discern key cellular SARS-CoV-2-affected pathways as potential druggable targets in COVID-19 pathogenesis.

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

BACKGROUND: The rapidly increasing biological literature is a key resource to automatically extract and gain knowledge concerning biological elements and their relations. Knowledge Networks are helpful tools in the context of biological knowledge discovery and modeling. RESULTS: We introduce a novel system called NETME, which, starting from a set of full-texts obtained from PubMed, through an easy-to-use web interface, interactively extracts biological elements from ontological databases and then synthesizes a network inferring relations among such elements. The results clearly show that our tool is capable of inferring comprehensive and reliable biological networks. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s41109-021-00435-x.

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

The current, rapidly diversifying pandemic has accelerated the need for efficient and effective identification of potential drug candidates for COVID-19. Knowledge on host-immune response to SARS-CoV-2 infection, however, remains limited with very few drugs approved to date. Viable strategies and tools are rapidly arising to address this, especially with repurposing of existing drugs offering significant promise. Here we introduce a systems biology tool, the PHENotype SIMulator, which - by leveraging available transcriptomic and proteomic databases - allows modeling of SARS-CoV-2 infection in host cells in silico to i) determine with high sensitivity and specificity (both > 96%) the viral effects on cellular host-immune response, resulting in a specific cellular SARS-CoV-2 signature and ii) utilize this specific signature to narrow down promising repurposable therapeutic strategies. Powered by this tool, coupled with domain expertise, we have identified several potential COVID-19 drugs including methylprednisolone and metformin, and further discern key cellular SARS-CoV-2-affected pathways as potential new druggable targets in COVID-19 pathogenesis.