10/2 - Harvard ARDD Aging Research and Drug Development conference 10/1/26-10/3/26 in the TreeHouse in Allston, MA
How best to develop a #RealisticVirtualEarthFor, a #RVEforHypothesisTesting - eg think a forked #GoogleStreetView with #TimeSlider & with #Gpegman becoming our #AvatarAgentElectronicMedicalRecords or #DigitalHealthTwins ? https://t.co/riX0twp9wz & see: https://t.co/vNHkatL2Zc ~
— WorldUnivandSch (@WorldUnivAndSch) October 3, 2026
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Thursday, October 22, 2026
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To build a Realistic Virtual Earth (RVE) for Hypothesis Testing—combining spatial geospatial rendering, time-slider environmental contexts, and personal digital health twins—you can leverage Google Cloud’s geospatial stack, Google DeepMind’s biological AI tools, and medical language models.
Part 1: System Architecture for the RVE Platform
+----------------------------- ------------------------------ ------------------------+
| RVE UI LAYER |
| CesiumJS / Unreal Engine 5 <---> Google Photorealistic 3D Tiles API |
| Sidebar UI: 3Dmol.js / NGL Viewer (Molecular) + FHIR EMR Dashboard (Avatar Agent)|
+----------------------------- ------------------------------ ------------------------+
|
v
+----------------------------- ------------------------------ ------------------------+
| RVE CORE PLATFORM |
| 1. Geospatial Engine: Google Earth Engine (Historical Time-Slider & Climate) |
| 2. Agent Framework: Avatar Pegman (Digital Twin / OMOP CDM / FHIR EMR) |
| 3. Orchestration: Google Cloud Vertex AI Pipelines |
+----------------------------- ------------------------------ ------------------------+
|
v
+----------------------------- ------------------------------ ------------------------+
| GOOGLE AI / ML SUITE |
| * Gemini 1.5 Pro / MedLM: Literature Synthesis & Experimental Design |
| * AlphaFold 3 / AlphaProteo: Molecular Docking & Binding Affinity Predictions |
| * Differential Equation / Neural ODE Models: Cellular Pathways & Dynamics |
+----------------------------- ------------------------------ ------------------------+
1. Spatial Geospatial & Temporal Rendering Engine
Base Viewer: Fork an open-source 3D geospatial engine such as CesiumJS or Unreal Engine 5 (via the Cesium plugin) integrated with Google Photorealistic 3D Tiles API.
Temporal Layer (Time Slider): Connect the temporal slider to Google Earth Engine API and historical street-level imagery archives. This supplies time-series environmental data (e.g., air quality indices, temperature variations, UV index, urban greenspace density) mapped to specific geographic coordinates.
2. Avatar Pegman as an EMR-Linked Digital Health Twin
Data Standard: Map the avatar agent to standard HL7 FHIR resources or an OMOP Common Data Model (CDM) schema.
Spatial Agent Logic: Pegman acts as an autonomous agent representing a cohort or individual. As Pegman moves through space and time on the RVE, environmental exposures dynamically update the agent's physiological baseline parameters.
3. Google AI / ML Middleware Integration
Vertex AI Pipelines: Orchestrate workflows between spatial queries, biological modeling, and inference tasks.
Gemini / MedLM: Drive automated literature retrieval, hypothesis synthesis, and natural-language query interfaces in the sidebar.
AlphaFold 3 & AlphaProteo: Simulate 3D protein structures, ligand-receptor binding interactions, and custom biological agent modifications.
Part 2: Testing the Longevity Hormone Hypothesis
Here is a step-by-step workflow using Klotho (a well-established lifespan-extending hormone) targeting FGF21 / FGFR1c-Klotho receptor complexes in the brain’s choroid plexus and hippocampus.
[Phase 1: Lit Review] ---> [Phase 2: UI Context Integration] ---> [Phase 3: Multi-Scale Brain Mapping]
|
[Phase 6: In-Vivo Protocol] <--- [Phase 5: In-Silico Experiment] <----------------+
Step 1: AI Scientific Literature Review (Gemini / MedLM Agent)
The agent executes an automated pipeline querying PubMed, PMC, and bioRxiv to extract molecular pathways, target receptors, and dosage-response metrics.
Key Extracted Synthesis:
Hormone Target: Recombinant $\alpha$-Klotho ($\text{sKL}$).
Primary Brain Target: Hippocampal dentate gyrus (neural stem cell niche) and choroid plexus.
Receptor Mechanism: Forms a binary complex with FGFR1c, triggering the extracellular signal-regulated kinase ($\text{ERK1/2}$) and $\text{Akt}$ phosphorylation cascades, upregulating $\text{NMDAR}$ subunit GluN2B, and decreasing neuroinflammation ($\text{TNF-}\alpha$, $\text{IL-6}$).
Step 2: Sidebar UI Integration (Spatial & Biological Mapping)
When selecting Pegman or clicking a target brain region in the RVE interface:
Left Main Window: RVE environment (e.g., Street View at a specific geographic location with environmental stress sliders like particulate matter $\text{PM}_{2.5}$).
Right Sidebar:
Molecular View: Embedded 3Dmol.js or NGL Viewer showing the interactive 3D co-crystal structure of Klotho bound to FGFR1c (PDB ID derived via AlphaFold 3).
Spatial Health Panel: Real-time physiological telemetry of the Pegman avatar (e.g., baseline plasma Klotho concentration $c_0 = 500 \text{ pg/mL}$, biological age vs. chronological age).
Step 3: Multi-Scale Brain Mapping (Molecular to Cellular Levels)
| Scale | Biological Domain | Mathematical / Machine Learning Representation |
| Molecular | Hormone-Receptor Binding Kinetics | Rate equations: $\frac{d[RL]}{dt} = k_{\text{on}} [R][L] - k_{\text{off}} [RL]$ with $K_d = \frac{k_{\text{off}}}{k_{\ |
| Cellular | Neural Stem Cell Proliferation & Microglial Activation | Systems biology differential equations (ODE/SDE) predicting $\text{p16}^{\text{INK4a}}$ senescence reduction |
| Tissue / Organ | Hippocampal Long-Term Potentiation (LTP) & Synaptic Density | Neural network surrogate modeling synaptic transmission efficiency ($V_m$) |
| Organism / Population | Survival Function & Cox Proportional Hazard | Survival model: $h(t \mid X) = h_0(t) \exp(\beta_1 X_{\text{Klotho}} + \beta_2 X_{\text{environment}})$ |
Step 4: Designing and Executing the Virtual (In-Silico) Experiment
A. Virtual Cohort & Experimental Groups
Define two avatar agent populations within the RVE across environmental conditions (e.g., low vs. high urban pollution exposure):
Control Group ($N=1,000$): Vehicle administration ($c_{\text{Klotho}} = \text{baseline}$).
Treatment Group ($N=1,000$): Systemic or central administration of elevated $\alpha$-Klotho ($c_{\text{Klotho}} = 2.5 \times \text{baseline}$).
B. Simulation Execution
Molecular Dynamics (AlphaFold 3 / GROMACS): Calculate binding free energy ($\Delta G_{\text{bind}}$) of Klotho variants to brain FGFR1c receptors.
Cellular Pathway Simulation: Integrate drug delivery kinetics across the Blood-Brain Barrier (BBB) into a differential kinetic solver:
$$\frac{d[\text{p-ERK}]}{dt} = \frac{V_{\max} \cdot [RL]}{K_m + [RL]} - k_{\text{dephos}} [\text{p-ERK}]$$Lifespan Acceleration Simulation: Project survival curves using a Monte Carlo Markov Chain (MCMC) engine over a virtual 30-month rodent equivalent timeframe.
100% |-------------\
| \=== Treatment (Klotho Boosted)
Survival | \--- Control
Rate | \
0% +----------------------------- ----->
0 15 30 Months
Step 5: Translating Virtual Results into an In-Vivo Protocol
Based on the virtual experiment yielding a statistically significant hazard ratio reduction ($\text{HR} = 0.74, p < 0.001$), the RVE exports a laboratory-ready in vivo translation guide:
============================== ============================== =======================
IN-VIVO TRANSLATIONAL PROTOCOL OUTPUT
============================== ============================== =======================
1. ANIMAL MODEL & SAMPLE SIZE
* Species: C57BL/6J Mice (Aged 18 months, human equivalent ~60 years).
* Sample Size: N = 40 per arm (20 Male, 20 Female) powered for 80% beta at alpha=0.05.
2. DOSAGE & ADMINISTRATION ROUTE
* Compound: Recombinant α-Klotho protein.
* Route: Intranasal administration (to bypass BBB and target olfactory bulb / hippocampus).
* Dose: 10 µg/kg body weight, administered twice weekly for 4 months.
3. PRIMARY & SECONDARY ENDPOINTS
* Primary Endpoint: Median lifespan extension and Kaplan-Meier survival curves.
* Secondary Biological Biomarkers:
- Western blot / ELISA: Brain p-ERK1/2 to ERK ratio and GluN2B expression in hippocampus.
- Immunohistochemistry: Ki-67+ and DCX+ neuroblasts in dentate gyrus (neurogenesis).
- Single-cell RNA-seq: Reduction of senescent cell markers (p16INK4a, p21) in microglia.
4. BEHAVIORAL & COGNITIVE ASSAYS
* Morris Water Maze & Novel Object Recognition at Months 1, 2, and 4 post-treatment.
5. SAFETY BOUNDS & TOXICOLOGY ALERTS
* Monitor serum calcium and phosphate levels bi-weekly (to detect potential hyperphosphatemia).
============================== ============================== =======================Kejun Ying — Postdoctoral Scholar, Stanford University School of Medicine
14:40–15:00 · "An open world model for human health and mortality"
Key takeaway: World models borrowed from video generation and protein language models can forecast individual disease trajectories and link protein structure to species lifespan.
• Treats health as a "movie," not a sentence: 11 data domains (EHR, labs, MRI, proteomics, genetics and more) merge into a joint health state evolving in continuous time.
• State-of-the-art prediction for more than 1,000 diseases up to 20 years out; predicts unseen diseases from a text description; simulated drug effects (e.g., statins) were checked against 286 real trials.
• Structural model of 2M proteins from 287 species found 170 proteins whose structural features track maximum lifespan, independent of expression; it predicts lifespan about as well as methylation clocks (Cape golden mole predicted at ~30 years).
• Starting his own lab in January 2027.
Virtual Cell in Time Forum — Session II
15:15–16:45 · Cedar Grove · Parallel track · Chair: Max Unfried, The Thalion Initiative
Le Song — Professor, Mohamed bin Zayed University of Artificial Intelligence
15:15–15:35 · "A World Model of the Virtual Cell"
Key takeaway: A true virtual cell should be multiscale, multimodal and stateful, and should drive generative design toward desired cell states.
• Current models handle one perturbation and one response in one cell type.
• Needed: multiscale simulation (DNA to cell), a persistent state for sequential perturbations, and "inverse design" of molecules for a target cell state rather than a binding target.
• Hybrid architecture: graph neural networks on pathway knowledge (Reactome, KEGG) plus transformers to learn what is missing.
• Multi-stage training on single modalities, then paired data; temporal live imaging and perturbation data are crucial.
• The field is at the "GPT-1 or AlphaFold 1" stage; GenBio AI released a model (transcribed "iCell") pitched as the first world model of a human cell.
Petrina Kamya — Global Head of AI Platforms, Insilico Medicine
15:35–15:55 · "Introducing Insilico Medicine's Virtual Aging Cell"
Key takeaway: The Virtual Aging Cell is a multi-agent system that simulates interventions across five biological scales, now undergoing prospective lab validation.
• Insilico has 10 clinical-stage programs (one in Phase 3), 18 IND-enabling studies and more than 47 internal programs.
• Frontier LLMs do well at single-step retrosynthesis but poorly at ab initio tasks; Insilico's Pharmaceutical Superintelligence framework trains and benchmarks models on 12 years of data.
• Lineage: 2016 embryonic-development AI (COX7A1) and the Precious1GPT–3GPT models.
• ~43 specialized agents per layer across molecular, intracellular, intercellular, tissue/organ and organism scales; a patient-seeded baseline graph is compared with the post-intervention graph.
• Q&A: prospective validation on molecules with unknown mechanism, in Insilico's robotic lab, is the only way to rule out literature leakage.
Multiplex testing of de-aging polypharmacy & targeted delivery
12:10–12:30 · Canopy Hall
George Church — Professor, Harvard Medical School
Key takeaway: Multi-gene therapies, ML-designed delivery vectors and engineered organs aim to address aging across several pathways at once.
• Single-pathway interventions may yield only ~2–20% effects; combinations (e.g., alpha-Klotho, TGF-beta receptor 2, FGF21) showed synergy in mouse diabetes and kidney models.
• OSK delivered in a single AAV to mice late in life (after half the cohort had died) increased longevity.
• Machine learning plus multiplex libraries: Dyno Therapeutics AAV capsids reported with 280× higher brain delivery and 50× lower liver targeting.
• Herpesvirus-based vectors under development to carry dozens of genes.
• iPSC-derived oligodendrocyte precursor cells rescued mice from an MS-like demyelinating disease; heading to clinical trials.
• Xenotransplantation: pig organs with 69 edits (retrovirus knockouts, glycan removal, seven human transgenes) in an FDA-cleared trial; later patients have fared better than the first.
• Allogeneic CAR-T programs from companies co-founded by Luhan Yang (e.g., QT-019B, QN-139b) are in FDA clinical trials for autoimmune disease.
Leveraging Glycans in Precision Longevity: From Individualized Monitoring to Targeted Intervention
18:20–18:40 · Canopy Hall
Gordan Lauc — Professor, University of Zagreb; Co-founder, GlycanAge
Key takeaway: IgG glycans are modifiable biomarkers that track biological age, predict disease years ahead and act as effectors of inflammation.
• His lab has profiled glycans in >300,000 people across major cohorts.
• Glycan profiles become more pro-inflammatory with age: linearly in men, while women are protected until menopause and then age rapidly.
• Each additional year of GlycanAge corresponds to ~1% higher all-cause mortality hazard; largely additive to standard blood biomarkers and uncorrelated with epigenetic clocks.
• Pro-inflammatory glycan changes appear up to a decade before diagnosis of diabetes, cardiovascular and inflammatory disease.
• Weight loss and calorie restriction improve GlycanAge; hormone therapy reverses the perimenopausal rise in most women; metformin, SGLT2 inhibitors and GLP-1s help some but not all patients.
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Retweeting -
How with #YamanakaFactorsOSK to #ReverseAging w emerging timelines pot. to say the 75%, 50% & 25% ages of say 100 mice that are one year old, 100 dogs that are 10 years old, & then 100 humans that are around 100 years of age? https://share.gemini.google/
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Dear Chang Eui Song,
- Scott GK MacLeod
Founder, President, CEO & Professor
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* * *
Stanford [codex_group_meetings] TODAY! CodeX Mtg (10/22@1.30p PT): Project Fungzhao (via Zoom)
Thank you, Yin Huang, so much! Are you focusing on even patents for aging reversal genetic drug therapy emerging, possibly FDA approved first by 2050? - Scott
Life Biosciences :)? - with its ER-100 gene drug therapy for 2 glaucoma conditions, which seems to be reversing aging potentially, & re epigenetic reprogramming, and which is going through Phase 1 clinical trials presently and which seems promising ... is a potentially fascinating company to explore in some of Project Fungzhao's regards.
Thanks for your great #StanfordLawCodeX 2/22 #ProjectFungzhao presentation. How best to interoperate this in an emerging #
AAAB
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- Scott GK MacLeod
Founder, President, CEO & Professor
at / of best STEAM CC licensed OCW, Wiki,
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Law Schools at World University and School (planned in main languages in them)
Nation States
Afghanistan Law School at WUaS: https://wiki.
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Iowa Law School at WUaS: https://wiki.
- Scott GK MacLeod
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*
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https://en.wikipedia.org/wiki/Abeliophyllum
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....

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