# Core structure development and note building Key project of the wiki - - YAML Frontmatter Schema - Page Templates - Homepage - Maps of content - Widgets and apps for the content - Badges and icons - create and define NOTE DEVELOPMENT: ## A. Note types (atomic vs overview) - **gene**, **protein**, - **complex**, **ligand**, **receptor**, - **pathway**, - **process** (e.g., EMT, ferroptosis), - **phenotype** (e.g., desmoplasia), - **disease** (DSRCT, Ewing), - **drug/therapy**, - **method** (e.g., ChIP-seq), - **dataset**, - **model** (cell line/PDX/xenograft), - **study** (paper/preprint), - **concept** (frameworks like survivability onion), - **overview/hub** (theme pages), - **figure/visual**. ## B. Cross-cutting facets (apply to _every_ note) - **disease_context**: dsrct | ewing | pan-cancer | other - **model_system**: cell_line(JN-DSRCT-1, …) | xenograft | PDX | clinical - **evidence_strength**: Strong | Moderate | Weak | Unknown - **evidence_type**: in_vitro | in_vivo | clinical | computational - **data_type**: RNA-seq | ChIP-seq | ATAC-seq | WES | IHC | flow | CRISPR | imaging - **compartment**: membrane | cytosol | nucleus | ECM | secreted - **hallmarks**: proliferative_signaling | metabolism | angiogenesis | apoptosis_evasion | immune_evasion | invasion_metastasis | genome_instability | TME_remodelling - **activity_state**: upregulated | downregulated | activated | inhibited (optional) - **confidence (1–5)**, - **curation_status**: draft | reviewed ## C. Relations (keep the verb set small and precise) `activates`, `inhibits`, `upregulates`, `downregulates`, `binds`, `phosphorylates`, `demethylates`, `recruits`, `interacts_with`, `part_of`, `involved_in`, `expressed_in`, `essential_for`, `synthetic_lethal_with`, `implicated_in` (disease/process). Each relation gets **{target, evidence[], confidence}**. ## D. Theme clusters (overview pages to curate) - **Epigenetics**: PRC2/EZH2, LSD1, SWI/SNF, DNMT, HDAC, super-enhancers - **TME & Immune**: desmoplasia/CAF, angiogenesis/VEGF, hypoxia/HIF1A, macrophages/TAMs, checkpoints (PD-1/PD-L1, B7-H3), cytokines (IL-6, IL-2/IL-15Rβ) - **Metabolism**: glycolysis/Warburg, glutamine anaplerosis, arginine/ASS1, lactate/MCT4, AMPK/mTOR coupling - **Cell death & DDR**: apoptosis (BCL2 family, DR4/DR5), CHK1, PARP/HRD, senescence, ferroptosis - **Growth signalling**: IGF1R–PI3K–AKT–mTOR, RAS–RAF–MEK–ERK, PDGF/PDGFR-A, NTRK3, INSR cross-talk - **Metastasis & Plasticity**: EMT/mesenchymal programs, adhesion/ECM, MMPs, EPCAM - **Oncogenesis**: cell-of-origin hypotheses, fusion isoforms (±KTS), transcriptional rewiring - **Models & Methods**: cell lines, xenografts/PDX, key assays (RNA-seq, ChIP-seq, flow) This covers and organizes the items in your draft TOC while giving each hub a clear remit. ## 3) Front-matter “field pack” (drop into templates) One schema, optional sub-blocks activated by `type:`. Add/omit without breaking parsers. ```yaml --- schema: version: 0.3 profile: "onco-entity+learning" curator: "sam" type: gene # gene | protein | complex | ligand | receptor | pathway | process | phenotype # | disease | drug | method | dataset | model | study | concept | overview id: "GENE:IGF1R" # CURIE/ID; quote values with colons title: "IGF1R" aliases: ["IGF-1R", "CD221"] summary: "One-sentence, ≤200 chars." # Cross-cutting facets disease_context: [dsrct, ewing, pan-cancer] model_system: [cell_line, clinical] evidence_strength: Strong # Strong | Moderate | Weak | Unknown evidence_type: [in_vitro, in_vivo] data_type: [RNA-seq, IHC] compartment: membrane hallmarks: [proliferative_signaling, metabolism] confidence: 4 # 1–5 curation_status: draft # Links/IDs links: uniprot: "P08069" ncbi_gene: "3480" pmid: ["20332245"] doi: [] # Relations (edges in your graph) relations: - predicate: activates target: "PATHWAY:PI3K_AKT" evidence: [{ pmid: "20332245" }] confidence: 4 - predicate: interacts_with target: "PROT:IRS1" evidence: [] confidence: 3 # Type-specific packs (fill only what applies) pack: gene: gene_family: "RTK" isoforms: [] pathway: members: ["PROT:IRS1", "KINASE:AKT1"] upstream: ["RTK:IGF1R"] downstream: ["COMPLEX:mTORC1"] drug: moa: "anti-IGF1R mAb" targets: ["IGF1R"] trial_ids: [] model: species: human name: "JN-DSRCT-1" provider: "" method: category: "omics" subcategory: "RNA-seq" # Learning (optional: for card generation) learning: prompt: "Trace IGF1R→PI3K in 2 steps." answer: "IGF1 binds IGF1R → IRS1 recruits PI3K → PIP3." difficulty: 2 target_latency_sec: 20 objective_metric: "≤20s, ≥85% accuracy (last 5)" _notes: | Use this for free-form rationale or TODOs. --- ``` YAML & Templates Think in “entities + evidence + relations” so everything can be graphed later. NEED TO DEFINE- 'Entity' Relations **Knowledge graph (interactive):** nodes = entity notes; edges = your `relations`. Add size/colour by `confidence`, `evidence_count`, or `type` so patterns pop. **Evidence matrix:** rows = interventions, columns = targets/pathways; cells = strength/replication and model type (in vitro/PDX/clinical). Great for “where are the holes?”. **Microenvironment lens:** filters for “immune”, “stroma”, “vasculature”, “metabolism”; same content, different facet. **Uncertainty dial:** every page has `confidence:` and `replication:` → render badges; let users filter to “high-confidence only.” **Core entity pages (one note per entity):** - **Gene / protein** (e.g., _ASS1_, _EZH2_) - **Pathway / process** (e.g., IL-6→STAT3, arginine metabolism) - **Phenotype / hallmark** (e.g., immune evasion, desmoplasia) - **Microenvironment cell** (e.g., CAF, T cell, endothelium) - **Model** (cell line, PDX, organoid) - **Intervention** (drug/class/combo), **Trial**, **Case report** - **Data resource / dataset** # 5) A couple of high-leverage UX flourishes - **Evidence badges & tooltips:** Render `confidence:` and `replication:` as little tokens next to headings; hover shows “why?” with links to the underlying papers. - **Facet filters on big tables:** Add small JS in `publish.js` to filter by `model:` (cell line/PDX/clinical) and `axis:` (immune/stroma/metabolism). - **Canonical IDs everywhere:** Put HGNC/UniProt/PMID in YAML for disambiguation and future data pulls. - **Callouts for “Open Questions” and “Contradictions”.** Make them first-class citizens so holes become visible targets for reading. --- **Gene** page template (`Targets/ASS1.md`) ```yaml title: ASS1 type: gene ids: hgnc: 756 entrez: 445 aliases: [Argininosuccinate synthase 1] functions: [urea-cycle, arginine-biosynthesis] status: role: potential vulnerability confidence: medium relations: - type: auxotrophy_for target: Arginine evidence: [PMID:12345678, PMID:23456789] - type: pathway_link target: Urea-cycle direction: loss_of_function notes: > Frequent dysfunction in DSRCT suggested → arginine dependence hypothesis. ``` Pathway page template (`Pathways/IL6-STAT3.md`) ```yaml title: IL-6 / STAT3 type: pathway axes: [immune, stromal] effects: [immunosuppression, fibrosis] relations: - type: activates source: IL-6 target: STAT3 evidence: [PMID:...] therapies: candidates: [Tocilizumab, Ruxolitinib] rationale: > Dampens suppressive loop; may improve T-cell function and stroma. ``` Paper page template (`Papers/Smith2024-ASS1-loss.md`) ```yaml title: ASS1 loss sensitizes DSRCT to arginine deprivation type: paper pmid: 12345678 year: 2024 model: [PDX, cell-line] species: human cohort: n=12 claims: - statement: ASS1 frequently downregulated in DSRCT evidence_level: moderate supports: [{type: auxotrophy_for, target: Arginine}] - statement: Arginine depletion reduces growth in vitro evidence_level: preliminary links: [doi:..., pdf:...] ``` ## Map Project Current status: Idea Cellular mapping Ribbonfarm style territory map --- How to solve it book - Lens application --- Tally up studies good bad neutral/mixed Icons +/- Evidence levels Can be like, in this cancer target was an inhibitor, conversely it can be a promoter in xyz situation Cell surface proteins - generally only accept water soluble ligands - for receptor targets, intervention has to be water soluble - many phytochems are not For DSRCT studies table, make and host some icons of the journal ie nature etc --- # Widgets and apps - js dashboard ## Your interactive **p5.js** cell console (two solid ways to embed) ### Easiest & robust: host as a tiny site and iframe it I bundled your uploaded sketch into a minimal static site: - `index.html` (loads p5 from CDN) - `sketch.js` (your `DSRCT_dashboard_idea.js`) - `config.js` (mapping from nodes → your wiki URLs) - `styles.css` Download → deploy anywhere static (Cloudflare Pages/GitHub Pages/Vercel) → embed via iframe. **Download:** dsrct_dashboard_site.zip **Quick deploy (Cloudflare Pages):** 1. Create a new Pages project, drag-drop the zip contents (or push a repo). 2. Set the subdomain, e.g. `https://lab.yourdomain.com/`. 3. In your Obsidian note, add: ```html <div class="viz-wrap"> <iframe src="https://lab.yourdomain.com/" title="DSRCT Systems Lab Console" loading="lazy"></iframe> </div> ``` …and in `publish.css`: ```css .viz-wrap{position:relative; width:100%; aspect-ratio:16/10; max-height:80vh} .viz-wrap iframe{position:absolute; inset:0; width:100%; height:100%; border:0} ``` **Deep-linking your wiki from the sketch:** edit `config.js` in the bundle. ```js window.DSRCT_LINK_MAP = { "EWS–WT1": "/Biology/EWS-WT1", "ASS1": "/Targets/ASS1", "EZH2": "/Targets/EZH2", "STAT3": "/Pathways/IL6-STAT3", "VEGF-A": "/Microenvironment/Angiogenesis", }; // Call this from clicks inside your sketch: window.DSRCT_OPEN("ASS1") ``` Inside your sketch, wherever a node/chip is clicked, call: ```js if (window.DSRCT_OPEN) window.DSRCT_OPEN(nodeIdOrUrl); ``` Why iframe? It cleanly bypasses Publish’s HTML sanitization and keeps your sketch code separate and easy to iterate. Obsidian officially documents iframe embeds, and some external sites restrict embedding—hosting your own subdomain avoids that. --- ## Badges and icons - create and define In Vitro - Petri dish or test tube In Vivo - Mice In Vivo - PDX Mice In Vivo - Zebrafish? In Silico Clinical Theory Paper Gene DSRCT Evidence Gedanken experiment Game theory - logically optimise sequential set of of insults The immortals... usually it's a mistake to send your best army first Keep them primed for the final blow We just need to cause population collapse from an ecological perspective If the environment and the conditions are not hospitable, mathematically there can be a tipping point of population collapse but everything up to that point will make it stronger in the long run. Disasters, rapid environmental changes, civil disorder. How do you make a species extinct?