Viviam Bermudez

Introduction: The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. Methods: Extensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors. Results: Results revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19. Discussion: The key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies.

Abstract The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. Community-driven and highly interdisciplinary, the project is collaborative and supports community standards, open access, and the FAIR data principles. The coordination of community work allowed for an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework links key molecules highlighted from broad omics data analysis and computational modeling to dysregulated pathways in a cell-, tissue- or patient-specific manner. We also employ text mining and AI-assisted analysis to identify potential drugs and drug targets and use topological analysis to reveal interesting structural features of the map. The proposed framework is versatile and expandable, offering a significant upgrade in the arsenal used to understand virus-host interactions and other complex pathologies.

<b>S1. TIDieR checklist and supplementary material guide (SPARK_TIDieR_checklist.pdf):</b><br>Completed TIDieR checklist for the SPARK workshop intervention, together with an overview and description of all supplementary materials provided in this repository.<b>S2. SPARK card-set (SPARK_cards.xlsx):</b> Complete card set used during both workshop sessions, including card number, type (scenario, information, issue), title, full content, and references where applicable.<b>S3. SPARK placemat template (SPARK_placemat_template.pdf):</b> Full A4 workshop template (front and back) used to structure discussion, individual reflection, scenario rating, and group prioritisation.<b>S4. Participant reflections and workshop feedback (SPARK_feedback.xlsx):</b><br>Anonymised participant reflections and feedback collected after the workshop sessions, including responses to prompts such as “What stuck with you from this discussion?” and “Write one impactful takeaway from this workshop”. The prompt “What stuck with you from this discussion?” was administered only in the medical student session.<b>S5. Card uptake analysis (SPARK_card_uptake.xlsx):</b> Documentation of card selection patterns analysed at the level of predefined tension themes, including raw counts, normalised uptake ratios, coding structure, and transcribed placemat selections (where available).<b>S6. Digital results via Mentimeter (SPARK_bioethics_cohort.xlsx; SPARK_medical_cohort.xlsx):</b> Anonymised, aggregated workshop responses from bioethics and medical students, including pre- and post-activity reflections, scenario ratings, and prioritisation outcomes.<b>S7. Comparative methods overview (SPARK_comparative_methods.xlsx):</b><br>Comparative overview situating SPARK in relation to problem-based learning (PBL), team-based learning (TBL), and public deliberation approaches, highlighting similarities, differences, and intended educational role.<br>

Abstract Effective drug combination therapies can improve cancer treatment, yet the mechanistic basis of drug synergy remains poorly understood. Most computational approaches prioritize predictive accuracy over molecular mechanistic interpretability, providing hence limited insights into how synergistic effects emerge across signalling contexts. We developed Trafikk, a molecular-signalling network-based framework that simulates drug perturbations in cell line-specific computational models to mirror functional outcomes in experimental combination screens. Across two independent large-scale datasets, Trafikk identified synergistic combinations with &gt;77% recall. Functional response predictions revealed both conserved and context-dependent mechanisms. While AKT-MEK co-inhibition consistently disrupted coordinated survival and apoptotic signalling in 742 cell lines, PI3K-BCL2 synergy arose through distinct death programs shaped by cell-context-specific network constraints. Trafikk combines predictive performance with mechanistic interpretability, capturing how and why drug synergy emerges across cellular contexts. Source code, installation instructions and usage tutorial are freely available at https://github.com/druglogics/trafikk . Abstract Figure

<b>S2. SPARK card-set (SPARK_cards.xlsx):</b> Complete card set used during both workshop sessions, including card number, type (scenario, information, issue), title, full content, and references where applicable.<b>S3. SPARK placemat template (SPARK_placemat_template.pdf):</b> Full A4 workshop template (front and back) used to structure discussion, individual reflection, scenario rating, and group prioritisation.<b>S4. Card uptake analysis (SPARK_card_uptake.xlsx):</b> Documentation of card selection patterns analysed at the level of predefined tension themes, including raw counts, normalised uptake ratios, coding structure, and transcribed placemat selections (where available).<b>S5. Digital results via Mentimeter (SPARK_menti1.xlsx; SPARK_menti2.xlsx):</b> Anonymised, aggregated workshop responses from bioethics and medical students, including pre- and post-activity reflections, scenario ratings, and prioritisation outcomes.