OLIVER'S mTOR ATLAS
Evidence PlatformThe first mTOR resource built around the question every other database dodges: is there strong evidence this actually extends human life?
Welcome
Ask Atlas
Pathway/Entities
Studies
Authors
Evidence
Gaps & Hypotheses
Events
Lineage
About
Oliver's mTOR Atlas · Welcome
Everything we know about mTOR — and everything we don’t
mTOR has plenty of maps — interaction networks, pathway diagrams, drug catalogs. None of them tell you how strong the evidence behind a claim actually is, or whether it holds up in humans at all. Oliver's mTOR Atlas starts from a hand-picked corpus of landmark studies — not all of PubMed — and grades every one by evidence tier, human vs. animal, and outcome, including the failures no one else tracks. Ask it anything in plain language and each claim in the answer comes back cited and color-coded by evidence strength. And where the evidence runs thin, it maps the open gaps — and the testable hypotheses hiding inside them.
The evidence pyramid
250 studies, a hundred different kinds of proof. This shows what level of
scientific certainty mTOR research actually stands on today — from test-tube mechanisms
to large human trials. Colors match the Pyramid Level tags used throughout this Atlas.
Systematic Reviewmeta-analyses
Human Clinical TrialRCTs, phase 2/3
Human Observationalnon-randomized
Animal Studymouse, fly, worm…
Mechanistic / In Vitrocells, biochemistry, structure
—
Direct human evidence
—
Animal + mechanistic
A vast knowledge base of HOW mTOR works — but only indirect evidence the same holds true in people.
Outside the evidence hierarchy
These categories describe the form a publication takes, not the strength of its
evidence — which is why they sit outside the pyramid rather than inside it. They account for
the remaining — studies, so the pyramid's
— and these together add up to the full Atlas.
Narrative Review—
Preprint (not peer-reviewed)—
Registered Trial (ongoing)—
All counts on this page are computed live from ATLAS_STUDIES
(— records) — re-run the Airtable sync whenever the base changes
to keep this page and the tabs below in step.
Oliver's mTOR Atlas · Pathway & Entities
The mTOR pathway, mapped end to end.
Entities
Knowledge Graph — shared studies
click any node to open its detail
Evidence Tiers click to filter
Atlas Snapshot
Oliver's mTOR Atlas · Studies
Every study, sortable by tier and year.
All Studies
| Study | Year | Model | Tier | Type | Source |
|---|
Oliver's mTOR Atlas · Authors
The people behind the papers.
All Authors
| Author | # Studies | Studies |
|---|
Oliver's mTOR Atlas · Evidence
How strong is the evidence, and why.
Evidence Tiers click to filter — jumps to Studies
Studies by Tier
Atlas Snapshot
Two Axes of Evidence
A study's evidence tier (how strong the proof is) and its study type (what kind of experiment produced it) are two separate things — a mouse study can be extremely well-designed and still cap out at Tier C, simply because it isn't a human. This matrix cross-references both, so you can see, for example, exactly how many of the Atlas's studies are human trials that also carry Tier B evidence.
Oliver's mTOR Atlas · Gaps & Hypotheses
Where the evidence runs out.
What this section is
A pathway map or a study table can only show what's already known. This section is the opposite: real gaps that show up once you actually connect the Atlas's own entities and studies — questions a landmark paper raised but didn't answer, or two findings that don't yet fit together cleanly. Each one links back to the specific entities and studies behind it, so you can go check the primary literature yourself rather than take the gap on faith.
Q1Recently addressed
"Can mTORC1 be selectively inhibited without disrupting mTORC2?"
Largely yes, as of 2023-2025. Bi-steric molecules (a rapamycin-like arm linked to an active-site inhibitor) hit mTORC1 with >25x selectivity over mTORC2. RMC-6272 overcame drug resistance in breast cancer cell lines and PDX (2023), and its clinical successor RMC-5552 completed a human Phase 1 trial (2025) with only 4% treatment-related hyperglycemia — direct clinical confirmation that sparing mTORC2 avoids the toxicity that plagues chronic rapamycin. What's still open is whether this selectivity translates into a longevity benefit outside oncology.
mTORC1mTORC2RapamycinLAM2012MEN2023SCH2025
Q2Partially addressed
"What is the optimal pulsatile (on/off) dosing schedule for mTOR inhibition?"
One study found that just 3 months of rapamycin in middle-aged mice extended lifespan afterward — the benefit outlasted the drug. That suggests dosing doesn't need to be continuous, but nobody has mapped out the actual optimal on/off schedule. This is more tractable than it used to be: mTORC1 activity has been shown to oscillate naturally across the cell cycle, with the low-activity phase sensitizing cells to autophagy induction, and live single-cell biosensors (e.g. AIMTOR) now exist to measure the on/off pattern directly rather than infer it. What "optimal" actually means — frequency, amplitude, or on/off ratio — is still unmapped.
RapamycinBIT2016JOS2024BOU2020
Q3Open
"Why hasn't rapamycin's mouse lifespan benefit shown up yet in a completed human trial?"
The PEARL trial — the first completed long-term human RCT of rapamycin for aging — found no significant change on its primary endpoint. That could mean the effect doesn't translate to humans, or it could mean the dose, duration, or endpoint chosen weren't the right ones to detect it. The Atlas doesn't have enough data yet to tell which.
RapamycinLongevityMOE2025HAN2025
Q4Open
"Do leucine, isoleucine, and valine act on mTORC1 through genuinely separate mechanisms?"
Restricting isoleucine or valine (but not leucine) improved metabolic health in one Alzheimer's mouse study, even though all three are grouped together as "BCAAs." That result implies they're not interchangeable — but it's one study, and it doesn't yet explain the mechanism behind the difference.
Leucine/BCAA restrictionSestrin2BAB2025
Q5Open
"Does the frequency of mTORC1 pulses -- not just their average level -- decide between growth and autophagy?"
Other major signaling pathways (ERK, p53, NF-κB) are known to encode information in the frequency and amplitude of their activity pulses rather than in the average level. mTORC1 is now known to pulse too (cell-cycle oscillation, feeding cycles), and live biosensors exist to read it out -- but nobody has tested whether the pulse pattern itself is a decision variable for mTORC1.
DynamicsAutophagyJOS2024BOU2020
Q6Open
"Does the timing of an mTOR-inhibitor dose relative to the cell cycle or circadian clock change its efficacy or toxicity?"
Autophagy sensitivity depends on cell-cycle phase, and mTORC1 activity is itself under circadian control. That implies the same drug and dose could land very differently depending on when it's given -- a chronotherapy question nobody has directly tested for rapamycin or the newer selective inhibitors.
ChronotherapyRapamycinJOS2024
Q7Open
"Do pulsed and continuous dosing of a selective mTORC1 inhibitor differ in triggering autophagy or resistance?"
Bi-steric mTORC1-selective inhibitors (RMC-6272/RMC-5552) make this comparison possible for the first time -- earlier tools couldn't isolate mTORC1 cleanly enough to ask it. Nobody has yet compared intermittent versus continuous selective dosing head-to-head for autophagy induction or acquired resistance.
Bi-stericmTORC1-selectiveMEN2023SCH2025
As new relevant papers get added to the Atlas, an open question here should either get resolved or get sharper — that's the point of keeping it visible rather than folding it into a review paragraph somewhere.
Oliver's mTOR Atlas · Lineage
How mTOR biology actually grew
Every node is a landmark study. Every edge is a claim about what one study made possible for the next — not who cited whom. The vertical axis is real time, so the empty stretches are part of the argument. Nodes carry the Atlas's own evidence tiers: blue is direct human evidence. Count the blue.
How we got here
Every entry in this Atlas traces back to a real paper. Here are six moments that built
the field — from a soil sample on a Pacific island to a clinical trial that didn't work.
1975
A molecule from Rapa Nui
Vézina and colleagues isolate a compound from Streptomyces bacteria in a soil
sample from Rapa Nui (Easter Island), with antifungal activity nobody can yet explain.
They name it after the island: rapamycin.
DOI: 10.7164/antibiotics.28.721
1994
The target gets a name
Sabatini and colleagues identify the protein rapamycin actually binds inside human
cells — the Mammalian Target Of Rapamycin. Two decades of "what does this drug even
do?" finally has a molecular answer.
DOI: 10.1016/0092-8674(94)90570-3
2002–2004
One protein, two complexes
Kim et al. discover Raptor, the partner protein that defines mTOR Complex 1. Two
years later, Sarbassov et al. discover Rictor — defining a second, rapamycin-resistant
complex, mTORC2. mTOR turns out to have two faces at the molecular level too.
DOI: 10.1016/s0092-8674(02)00808-5
· DOI: 10.1016/j.cub.2004.06.054
2009
The first drug shown to extend mammalian lifespan
The NIA Interventions Testing Program, led by Harrison and colleagues, shows rapamycin
extends lifespan in genetically heterogeneous mice — even when treatment starts late
in life. First pharmacological intervention ever shown to do this in a mammal.
DOI: 10.1038/nature08221
2018
From mice to people
Mannick and colleagues show low-dose mTOR inhibition reduces infection rates and
improves vaccine response in elderly volunteers — the first hint the mouse result
might translate to humans.
DOI: 10.1126/scitranslmed.aaq1564
2021
The reality check
The follow-up phase 3 trial — 1,024 participants — misses its primary endpoint. The
drug still switched on the right antiviral genes; it just didn't reduce clinically
symptomatic illness. This Atlas keeps that result next to the success that came before
it, because a database that only remembers the wins isn't science.
DOI: 10.1016/S2666-7568(21)00062-3
Oliver's mTOR Atlas · About
The person and the project.
Author
Oliver Barton
Creator & Curator — Prague, Czech Republic — Age 15
Oliver is a high school student in Prague with a self-directed research interest in mTOR signaling, longevity biology, and evidence-based science curation. He started Oliver's mTOR Atlas to build the kind of resource he wished existed when he began reading primary literature on the pathway: one structured database that holds studies, mechanisms, interventions, and honest evidence grades side by side, rather than scattered across dozens of review articles and pathway maps.
His current focus is mTOR regulation under dynamic rather than steady-state conditions — most of the literature treats mTORC1/mTORC2 activity as fixed rather than something that shifts over time in a real cell. Oliver is interested in whether the pattern of activity over time — not just its average level — is what actually shapes outcomes like autophagy or growth, one of the open questions raised in this Atlas's Gaps & Hypotheses tab.
Outside of mTOR, Oliver is a lifelong football fan and follows Real Madrid closely, plays electric guitar, and has a strong interest in AI and programming, fashion, and entrepreneurship.
Contact — oliver.barton1113(at)gmail.com
What this project is
Oliver's mTOR Atlas is an evidence-graded, literature-grounded platform for the mTOR signaling pathway — not just a record of what's known, but a tool for asking how strongly it's known. You can query it in plain language — Ask Atlas returns the relevant studies ranked, tiered, and cited, with each claim color-coded by the strength of the evidence behind it — and it actively surfaces the field's open gaps and turns them into testable hypotheses.
It currently holds 66 cross-linked entities and 255 studies, spanning:
- Upstream regulators — PI3K, Akt, TSC1/TSC2, Rheb, the Rag GTPases, Sestrin2, AMPK, and the growth hormone/IGF-1 axis further upstream still
- Two core complexes — mTORC1 and mTORC2 — and their direct downstream effectors: S6K1, ULK1, Atg5
- Drugs, supplements, and interventions — rapamycin, everolimus, temsirolimus, metformin, acarbose, caloric restriction, macronutrient ratio, leucine/BCAA restriction — alongside supplements like resveratrol and curcumin that were tested and found not to work
- Outcomes and disease contexts — longevity, autophagy, muscle growth, immune function, kidney transplant rejection, renal cell carcinoma, Huntington's disease, Alzheimer's disease — plus side effects, like the insulin resistance that comes with chronic rapamycin dosing
Every entry traces back to a specific paper, indexed with its evidence tier, model organism, and DOI.
It deliberately includes negative results — studies where a popular longevity compound did not extend lifespan — because a database that only shows positive findings misrepresents the actual state of the science. The Interventions Testing Program's published null results for resveratrol, curcumin, and simvastatin sit in the Atlas with the same visibility as the positive rapamycin findings.
What makes it different
Pathway databases like Reactome or KEGG map the biochemistry. PubMed lets you search papers one at a time. General AI research tools read all of PubMed but treat a mouse study and a human trial as equal evidence. The Atlas does three things none of them combine:
- Narrow, hand-curated corpus — landmark mTOR studies, deep and low-noise rather than broad and shallow
- Explicit evidence tier on every claim — so an answer visibly separates what rests on a human RCT from what rests on a cell-culture experiment
- A gap layer — points to where the evidence is thin, contradictory, or missing entirely, and reframes those gaps as testable hypotheses
So you can ask "what's the actual evidence, at what tier, that rapamycin extends lifespan in humans and not just mice?" and get a direct answer with the receipts attached — and, just as important, an honest map of what nobody has shown yet.
Built narrow, starting with mTOR, by someone still early enough in their scientific training to insist that every claim traces back to a real paper.
How it's built and kept current
Every study enters the Atlas through the same four-step pipeline:
1. Source
Candidate papers are found via PubMed, prioritizing landmark discovery papers, systematic reviews, and large human RCTs over secondary commentary.
2. Verify
Each citation's PMID, DOI, year, and journal are confirmed against PubMed metadata before the entry is written — no citation is added from memory alone.
3. Grade
Every study gets an evidence tier (A = systematic review of human data, down through animal, invertebrate, and mechanistic-only) based on the strength of the model system, not the size of the headline finding.
4. Link
Entities mentioned in the study (genes, drugs, outcomes) are connected to it in the graph, so the same paper surfaces wherever any of its subjects is explored.
Every entity and study in the Atlas is sourced from primary, checkable resources — never from secondary write-ups or memory.
PubMed
The NIH's biomedical literature index. Primary source for study metadata — title, authors, journal, date, and DOI are pulled and verified against PubMed's own record before an entry is written.
bioRxiv
bioRxiv
Source for preprints — studies not yet peer-reviewed. These are explicitly flagged as preprints, never treated as equivalent to published work.
ClinicalTrials.gov
ClinicalTrials.gov
U.S. registry of clinical trials. Lets the Atlas show ongoing human studies and what they're testing, even before results exist.
Publisher DOIs
Every DOI resolves to the publisher's own page — Nature, Cell, Science, NEJM, Lancet, and others — so any claim traces back to the original paper in one click.
How studies are chosen
"Relevant" has a specific meaning in this Atlas: a study earns a place only if it does at least one of four things —
- establishes a landmark mechanism in mTOR biology
- supplies the strongest available human evidence for a claim (a systematic review or a large RCT, not an isolated small trial)
- fills in representative animal or mechanistic work for a pathway node that would otherwise be uncovered
- reports a negative or null result that the rest of the literature tends to bury
That last category is deliberate — the Interventions Testing Program's null findings for resveratrol, curcumin, and simvastatin are included with the same visibility as the positive rapamycin results, because a database that only shows what worked misrepresents the actual state of the science.
Every candidate is then run through the four-step pipeline above (Source → Verify → Grade → Link) before it's added. So "relevant" is a curation judgment made once, up front against those four criteria — not a search-time popularity ranking, and not simply everything PubMed returns for "mTOR."