Control Laws in Aging and Longevity: A Control Theory of Aging for Gerotherapeutic Drug Discovery

Alex Zhavoronkov; Bud Mishra

Control Laws in Aging and Longevity: A Control Theory of Aging for Gerotherapeutic Drug Discovery

T0 review · 4 major / 0 minor · reviewed 2026-07-12 · grok-4.5

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Aging is progressive loss of safe controllability, and biological age is the minimum safe cost of restoring function under available interventions.

desk verdict Solid control-theory packaging of gerotherapeutics with a clean toy Lie bracket and a real falsifiable agenda; the headline claim that control-value beats Hallmarks is still untested and currently uncomputable from data. read the letter →

arxiv 2605.16781 v3 pith:WC25VHXO submitted 2026-05-16 q-bio.MN q-bio.PE

Alex Zhavoronkov , Bud Mishra This is my paper · ORCID

classification q-bio.MNq-bio.PE

keywords agingcontroltheorybiologicalagedrugdiscoverygerotherapeuticsvectorfieldsLiebracketsviabilityset

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Most aging theories describe what changes with age but do not say which intervention to use, at what dose, in what order, or under what safety limits. This paper casts aging as a constrained control problem: the body has a latent state that drifts out of a functional viability region, and drugs are state-dependent vector fields that push that state. Biological age is redefined as how hard it is—under safety constraints—to restore or keep function. Because those vector fields need not commute, sequence can change outcome, and Lie brackets quantify when order matters. The claim that would reorganize discovery is operational: ranking candidates by how much they cut that restoration cost should predict translational success better than Hallmark labels or biomarker reversal alone.

What carries the argument

Control biological age: BAcontrol = φ(V), where V is the minimum safe control cost of functional restoration under an admissible intervention library. Drugs enter as vector fields g_j(x); their Lie brackets [g_A, g_B] identify new reachable directions and order-dependent protocols. Safety is first-class via a state-dependent admissible set and a viability kernel of states that can still be steered into function.

What would settle it

Pre-register a frozen compound list and intervention library; score each candidate by Hallmark annotation and by estimated control-value reduction; test which score better predicts translational advancement, functional efficacy, and toxicity-adjusted index in aged organoid or animal panels. If control-value does not outperform Hallmark or network scores, the central claim fails.

Watch

Extended reading notes

Core claim

The paper defines aging as progressive loss of safe controllability and defines control biological age as a monotone map of the minimum expected cost of a safe policy that returns a measured state to a functional viability set. Drugs are modeled as state-dependent vector fields; nonzero Lie brackets between fields predict order-dependent outcomes. The central empirical claim is that control-value reduction—the estimated drop in that restoration cost—ranks targets and protocols for translational success better than Hallmark annotation or clock reversal alone, and that the same formalism yields ranked targets, sequences, safety-constrained protocols, and falsifiable predictions usable in gerot

Load-bearing premise

The framework assumes that state-dependent effects of real interventions can be estimated well enough from today’s sparse, biased laboratory data to rank drugs and sequences for aged, frail humans—even though the paper treats that estimation as unfinished future work.

Editorial extensions

If this is right

Target ranking by estimated drop in restoration cost V should beat Hallmark membership for predicting which programs advance.
In fibrotic or inflamed aged tissue, senolysis before reprogramming should restore function better than the reverse order when the Lie bracket is nonzero.
Some high-control-value targets will lie outside canonical Hallmarks, and multi-dimensional agents can outrank single-pathway drugs.
Closed-loop adaptive dosing under safety constraints should beat fixed schedules at matched cumulative exposure.
States can cross biological deadlines beyond which no admissible protocol restores viability, shifting policy from restoration to compensation or palliation.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If intervention vector fields can be learned from multi-omics perturbation data, the same ranking logic could prioritize combination schedules for multi-morbidity, not only single-organ aging.
The viability-kernel idea suggests trial designs that enroll and stratify by estimated control cost rather than chronological age or clock score alone.
Treating irreversible coordinates as absorbing boundaries reframes when durable modalities (gene or cell therapy) become justified versus reversible small molecules.
The paper’s claim of roughly 20% state-space coverage under the current library implies an explicit discovery loop: map control gaps and design modalities for currently uncontrollable dimensions.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The thing to know: this is a framework-and-program paper, not a demonstrated discovery law. They define aging as progressive loss of safe controllability and biological age as the minimum safe control cost V of restoring a measured state to a functional viability set, then treat drugs as state-dependent vector fields whose order effects are Lie brackets. That language is operational and useful.

What is actually new is the synthesis, not the individual tools. Network controllability, viability kernels, systems pharmacology, and Hallmarks are all prior art they cite. The package that is new is BA as intervention-relative min safe cost, modality-aware admissible sets with a reversibility hierarchy, an explicit claim that control-value reduction should beat Hallmark or clock scoring, twenty enumerated predictions, and an epoch-stratified intervention scoreboard. The five-variable aged-liver ODE is fully specified; the analytic bracket [gsen, grep] is correct for the assumed fields (reprogramming gain ∝ (1−s)r), and the protocol ranking with bootstrap CIs is reproducible from the equations. They are honest that field estimation is future/companion work and that the current library covers ~20% of state space. Citations are appropriate; self-cites are to their own AI/clock work and sit in context.

Soft spots, in proportion. The central claim is untested: without calibrated g_j(x) you cannot compute the V of Eq. (8)–(10) for real candidates, so the proposed head-to-head against Hallmarks and the LIS table are rubric proxies, not the object defined in the theory. In the toy model non-commutativity is built into the ODEs, then recovered—so that showcase is partly by construction. Free parameters (cost weights, rates, epoch weights, α, T) are many; SI tables are not fully inspectable in the main text. None of that is algebraic error; it is a claim–evidence gap the authors mostly flag themselves.

Who it is for: people who design aging drug programs, score targets, or run combination/order experiments. A serious referee should see it. I would engage—read the SI, pressure the validation plan, and watch whether they actually estimate the fields and pre-register the comparison. I would not yet reorganize a portfolio around control-value scores.

Referee Report

4 major / 0 minor

Summary. The manuscript proposes a control-theoretic framework for aging and gerotherapeutic discovery: aging is progressive loss of safe controllability; biological age is the minimum safe control cost V of restoring a measured state to a functional viability set; drugs are state-dependent vector fields whose non-commutativity (Lie brackets) predicts order-dependent outcomes. The authors supply a five-dimensional aged-liver ODE with an analytic Lie bracket for senolysis versus reprogramming, a modality-aware admissible-control layer, three translational case sketches, a closed-loop MPC architecture with power analysis, an epoch-stratified Lifetime Integrated Score (LIS) ranking of interventions, and twenty falsifiable predictions. The central empirical claim is that control-value reduction will predict translational success better than Hallmark annotation or biomarker/clock reversal alone; the paper presents this as a program to be validated rather than as a completed demonstration.

Significance. If the framework can be calibrated so that estimated reductions in V actually rank targets and sequences better than Hallmark or clock scores, it would supply the missing interventional layer between descriptive aging biology and rational protocol design—ordering, non-responder identification, and state-dependent sign of effect—that Hallmark ontologies do not provide. Strengths that should be credited include: an explicit analytic Lie-bracket derivation for the toy fields (Eq. 21); a clear formal separation of viability set, viability kernel, and absorbing boundaries; twenty pre-specified falsifiable predictions with a formal-object mapping (Table 2); an honest Limitations section that flags identifiability, data bias, and that vector-field estimation is future work; and a power analysis for the proposed retrospective testbed. These are real contributions of a theory paper even before the central ranking claim is tested.

major comments (4)

[Abstract; Validation; Prediction 14; Empirical Application] Abstract, Validation section, and Prediction 14 state that control-value reduction predicts translational success better than Hallmark annotation. Computing that score requires calibrated state-dependent fields g_j(x) so that V in Eqs. (8)–(10) can be evaluated under a shared U_safe, cost, and horizon. The manuscript itself states that estimation of those fields is future/companion work (Introduction; S5), that the present library covers only ~20% of state space (Empirical Application), and that the liver example is a hand-specified finite-protocol approximation, not a data-learned field (Worked Example; Materials). The LIS table (Table 3) and the proposed scoring of TNIK/PHD1/2 programs therefore use a rubric proxy, not the object defined by Eqs. (8)–(10). This claim–evidence gap is load-bearing: either (i) reframe the central claim as a hypothesis to be tested once fields exist and rem
[Worked Computational Example; Eq. (21)] In the worked example, g_rep is defined to scale with (1−s)r while g_sen does not depend on e (Eq. 21 and surrounding text). The nonzero Lie bracket and the Protocol A ≻ B ranking are therefore largely by construction of the ODEs, not an independent prediction recovered from data. The Falsifiability paragraph correctly notes what would make the bracket vanish, but the main text still presents the ordering result as evidence for the framework’s geometric content. Please state explicitly that the example demonstrates internal consistency of the formalism under assumed fields, not empirical support for order-dependence, and move any stronger language to the prospective Experimental Test box.
[Eqs. (8)–(10); Worked Example; Empirical Application; Table 3] The value function V and BAcontrol depend on free choices that are not fixed by the theory: terminal weights (w_s,…,w_f), running-cost weights, viability thresholds θ_i / G(x), risk level α, horizon T, and the map φ (Eqs. 2–4, 8–10; S5). The epoch-specific weights w_{k,i} in Composite_k / LIS are likewise free. Without a sensitivity analysis showing that protocol ranking and the top LIS interventions are stable under reasonable reweighting, the quantitative outputs (J_A = 0.184 vs J_B = 0.257; Table 3 ranks) cannot be treated as robust predictions. A short sensitivity or leave-one-weight-out analysis in the main text or S5 is needed for the load-bearing numerical claims.
[Validation and Power Analysis; Supplementary S8] The Validation plan (power analysis for Spearman ~0.45, n≈36–40 compounds; four-quadrant Hallmark-vs-control-value design) is appropriate in principle, but it presupposes that a control-value score can already be computed for the Insilico/public testbed compounds. As written, that score is not yet available. Either restrict the validation section to a pre-registration template that freezes how g_j will be estimated before any ranking is produced, or drop the implication that the existing dual-purpose target programs already constitute a retrospective test of control-value scoring. Without that clarification the section overstates current testability.

Circularity Check

3 steps flagged · score 4.0 of 10

Mild circularity confined to the toy liver example (order effect built into the ODE ansatz) and to hand-scored LIS rankings presented as empirical predictions; core aging/BA statements are explicit definitions, not claimed derivations.

self definitional [Worked Computational Example; Eq. (21) and preceding vector-field definitions]
"the reprogramming field is grep(x) = (0,0,µe(1−s)r,0,0)⊤. Because grep depends on s while gsen does not, the Lie bracket is [gsen,grep](x) = (0,0, βsµesr,0,0)⊤ ≠ 0 whenever s>0, r>0. ... The bracket is positive in the epigenetic-integrity coordinate because senolysis increases the effective gain of reprogramming"

Non-commutativity and the A≻B ordering are encoded by writing reprogramming efficacy as proportional to (1−s)r. Once that factor is in the ansatz, [gsen,grep]≠0 and the simulated protocol ranking follow by algebra/integration; they are not independent recoveries from data. The example therefore demonstrates the formalism on a model built to exhibit the effect rather than predicting order from an unconstrained fit.
fitted input called prediction [Empirical Application: Scoring Interventions Across Biological Epochs; Table 3; Composite/LIS definitions]
"Each intervention was mapped onto a 20-dimensional biological state space ... with a vector field gi∈R20, and scored on eight axes (control leverage, safety margin, controllability expansion, durability, reversibility, combinability, evidence tier, sequence position) at each of five biological epochs. The composite score at epoch k is Compositek = 100×∑8 i=1 wk,i·Axisi ... and the Lifetime Integrated Score (LIS) is ∑5 k=1 Compositek. ... Table 3 reports the top twenty interventions by LIS."

Axis scores and epoch weights are assigned by the authors under the same control-theoretic rubric. LIS is defined as the weighted sum of those scores, so the published ranking is the rubric applied to itself, not a prediction tested against held-out translational outcomes. Presenting the table as ‘actionable, quantitative predictions’ and ‘empirical application’ treats the scoring output as external evidence for the framework that produced the scores.

1 more flagged steps

self definitional [Abstract; Biological age as control cost: the core definition; Eqs. (8)–(10)]
"Aging is defined as progressive loss of safe controllability; biological age is the minimum safe control cost of functional restoration. ... We define control biological age as BAcontrol(x0) = φ(V(x0,T))"

These are explicit definitions, not results. Mild circularity arises only when the same objects are later listed among the framework’s ‘core claims’ and ‘central claim’ side-by-side with the still-untested empirical hypothesis that control-value reduction predicts translational success—inviting the definitional objects to be read as demonstrated content. The paper does mark them as definitions, so this step is weaker than the two above.

full rationale

The paper is primarily a framework proposal, not a closed derivation of empirical laws from independent axioms. Aging-as-loss-of-safe-controllability and BA_control = φ(V) are stated as definitions (Abstract; Core definition), not as theorems derived from something that already contains them; the central claim that control-value reduction outperforms Hallmark scoring is explicitly a hypothesis to be validated (Validation; Prediction 14), and the paper repeatedly flags vector-field estimation as future/companion work. That is not circularity under the rules. Two concrete reductions remain. (1) In the five-variable liver model the reprogramming field is written with an explicit (1−s) factor, so [g_sen, g_rep] ≠ 0 and Protocol A ≻ B are algebraic consequences of the ansatz rather than independent predictions recovered from data; the falsifiability clause acknowledges this but the section still presents the ordering as a worked ‘proof’ of the framework. (2) The Lifetime Integrated Score table is the direct output of author-assigned axis scores and epoch weights under the same control rubric being advertised, so the ranking is the scoring by construction, not an external empirical test. Self-citations (prior Zhavoronkov AI/clock/PandaOmics work) supply context and a proposed testbed but do not carry a uniqueness theorem that forces the present claims. Net: partial, local circularity in the illustrative and scoring sections; the main control-theoretic proposal is not forced by self-reference or by definition of the target result.

Assumptions & free parameters 6 free parameters · 7 assumptions · 4 invented entities

The central claim rests on redefining aging and biological age in control-theoretic terms, assuming a latent state and learnable state-dependent drug fields, and on many hand-set weights and thresholds in both the toy ODE and the LIS rubric. Standard geometric control is imported cleanly; the load-bearing novelty is definitional and parametric rather than derived from data.

free parameters (6)

Terminal cost weights (ws, wd, we, wr, wf) and running-cost weights in V
Chosen for the worked example and value-function approximation; not fit to a held-out functional endpoint; determine protocol ranking JA < JC < JB.
ODE rate parameters (e.g. βs, μe and full S5 parameterization)
Described as literature-calibrated illustrative values; set the magnitude of the Lie bracket (0.0072 at x0) and trajectory shapes.
Epoch-specific axis weights wk,i in Compositek and LIS
Hand-specified per biological epoch; directly drive top-20 rankings (CR, exercise, SGLT2, GLP-1).
Viability thresholds θi / joint G(x) and forbidden-region risk level α
Define V, K, and Usafe; change who is a non-responder and when a biological deadline is declared.
Horizon T and map φ from V to BAcontrol
Problem-specification choices that rescale biological age and trial-style objectives.
Initial aged state x0 = (0.15, 0.40, 0.55, 0.40, 0.30)
Sets the operating point for all protocol comparisons and the bracket evaluation.

assumptions (7)

ad hoc to paper Aging is progressive loss of safe controllability under an admissible intervention library and safety constraints.
Core definitional move in Introduction and Control Theory sections; not derived from data.
domain assumption A latent finite-dimensional state x and observation model y = h(x)+ε adequately represent multi-scale aging for control design.
Stated in Biological state space; identifiability flagged as open in Limitations.
domain assumption Interventions act as (possibly state-dependent) control vector fields in affine form dx = f dt + G(x)u dt + Σ dW.
Standard control modeling choice; maps drugs to gj(x) throughout.
standard math Local accessibility and order effects are governed by the controllability Lie algebra / Lie brackets of intervention fields.
Invokes Kalman, Nijmeijer–van der Schaft, Isidori; used for sequence predictions.
ad hoc to paper Reprogramming efficacy in the liver model scales with (1−s)r, inducing [gsen, grep] ≠ 0 whenever s,r > 0.
Structural assumption that produces the headline order effect in Section 5 / Eq. (21).
domain assumption Safety is encoded by a state-dependent Usafe, hard/soft constraints, and terminal penalties on future controllability and cancer/immune risk.
Section on modality classes and safety; defines admissible policies.
domain assumption Control-value scores can be compared to Hallmark scores as predictors of translational advancement.
Validation and power-analysis program; assumed measurable and decisive.

invented entities (4)

Control biological age BAcontrol = φ(V(x0,T))
purpose: Replace correlative clocks with an intervention-relative restoration-cost age.
Defined in Core definition; no independent clinical validation in this paper.
Control-value score (estimated reduction in V) for target prioritization
purpose: Rank targets/protocols against Hallmark and network scores.
Central comparative object in Validation; not yet computed on a frozen public portfolio with outcomes.
Lifetime Integrated Score (LIS) over five biological epochs
purpose: Produce a single ranking of lifestyle and drug interventions from a 20-D hand-mapped state.
Empirical Application / Table 3; depends on rubric weights internal to the paper.
Modality-annotated control uj = (mj, dj, τj, ρj, ηj, σj) with reversibility hierarchy
purpose: Gate durable modalities behind evidence thresholds in MPC.
Table 1 and safety section; operational proposal without clinical trial evidence here.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Control Laws in Aging and Longevity: A Control Theory of Aging for Gerotherapeutic Drug Discovery." pith.science (2026). https://pith.science/paper/WC25VHXO

@misc{pith2026260516781,
  author       = {Pith},
  title        = {Pith review of: Control Laws in Aging and Longevity: A Control Theory of Aging for Gerotherapeutic Drug Discovery},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WC25VHXO}},
  note         = {Machine review of arXiv:2605.16781}
}

read the original abstract

Existing aging theories describe what changes with age but do not prescribe how to intervene. We propose a control-theoretic framework that is not merely descriptive but prescriptive: it specifies which intervention, at which dose and sequence, under which safety constraints, will restore a measured biological state to a functional region. Aging is defined as progressive loss of safe controllability; biological age is the minimum safe control cost of functional restoration. Drugs are modeled as vector fields on biological state space whose non-commutativity, quantified by Lie brackets, predicts that intervention order determines outcome. The core differentiation from prior theories is operational: the framework outputs ranked targets, optimal sequences, safety-constrained protocols, and falsifiable predictions directly usable in drug discovery, rather than mechanistic ontologies or correlative biomarkers. We present a five-dimensional ODE model with analytic Lie-bracket derivation, a modality-aware control layer, three translational case studies, an implementation architecture with power analysis, and empirical scoring of aging interventions across five biological epochs. Twenty falsifiable predictions are enumerated. The central claim is that control-value reduction predicts translational success better than Hallmark annotation or biomarker reversal alone. If validated, this provides the missing interventional layer connecting aging biology to rational gerotherapeutic discovery.

Figures

Figures reproduced from arXiv: 2605.16781 by the authors.

**Figure 1.** FIG. 1: Symmetric convolution of channel [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗

**Figure 2.** Thirteen-hallmark network of aging with driver and follower classification. [PITH_FULL_IMAGE:figures/full_fig_p017_2.png] view at source ↗

Figures from the paper (8 more)

**Figure 2.** Figure 2: Thirteen-hallmark network of aging with driver and follower classification. [PITH_FULL_IMAGE:figures/full_fig_p020_2.png]

**Figure 2.** Figure 2: for a visual depiction. Using the expression in the penultimate line of (9) for linear bosonic channels, we obtain the no-signalling FIG. 2: Extension of the 2-fold symmetric convolution. property that the channel N˜ 𝐴1 𝐵1 is independent of the input on 𝐴2. By definiti…

**Figure 3.** Figure 3: Worked-example composite aging score V (x(t)) over a 12-week horizon for four intervention policies in the aged murine liver model (4-variable DSER surrogate, Euler–Maruyama SDE integration, n = 500 stochastic realizations per policy). Solid curve: no intervention. Das…

**Figure 4.** Figure 4: Loss of controllability with age. Upper panel: reachable safe-set fraction (the [PITH_FULL_IMAGE:figures/full_fig_p031_4.png]

**Figure 4.** Figure 4: Loss of controllability with age. Upper panel: reachable safe-set fraction (the [PITH_FULL_IMAGE:figures/full_fig_p037_4.png]

**Figure 5.** Figure 5: Closed-loop implementation architecture: measurement [PITH_FULL_IMAGE:figures/full_fig_p036_5.png]

**Figure 5.** Figure 5: Closed-loop implementation architecture: measurement [PITH_FULL_IMAGE:figures/full_fig_p042_5.png]

Review history (3 revisions) →

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

91 extracted references · 5 canonical work pages

[1]

Gladyshev, Benjamin Anderson, Hanna Barlit, Benjamin Barré, Samuel Beck, Bahareh Behrouz, Daniel W

Vadim N. Gladyshev, Benjamin Anderson, Hanna Barlit, Benjamin Barré, Samuel Beck, Bahareh Behrouz, Daniel W. Belsky, Amandine Chaix, Manish Chamoli, Brian H. Chen, et al. Disagreement on foundational principles of biological aging. PNAS Nexus, 3(12):pgae499, 2024. doi: 10.1093/pnasnexus/pgae499

doi:10.1093/pnasnexus/pgae499 2024
[2]

P. B. Medawar.An Unsolved Problem of Biology. H. K. Lewis, London, 1952

1952
[3]

G. C. Williams. Pleiotropy, natural selection, and the evolution of senescence. Evolution, 11:398–411, 1957

1957
[4]

W. D. Hamilton. The moulding of senescence by natural selection.Journal of Theoretical Biology, 12:12–45, 1966

1966
[5]

D. Harman. Aging: a theory based on free radical and radiation chemistry.Journal of Gerontology, 11:298–300, 1956

1956
[6]

T. B. L. Kirkwood. Evolution of ageing.Nature, 270:301–304, 1977

1977
[7]

López-Otín, M

C. López-Otín, M. A. Blasco, L. Partridge, M. Serrano, and G. Kroemer. The hallmarks of aging.Cell, 153:1194–1217, 2013

2013
[8]

López-Otín, M

C. López-Otín, M. A. Blasco, L. Partridge, M. Serrano, and G. Kroemer. Hallmarks of aging: an expanding universe.Cell, 186:243–278, 2023

2023

Show all 91 references

[9]

A. D. N. J. de Grey, B. N. Ames, J. K. Andersen, et al. Time to talk SENS: critiquing the immutability of human aging.Annals of the New York Academy of Sciences, 959: 452–462, 2002

2002
[10]

Y. Lu, B. Brommer, X. Tian, et al. Reprogramming to recover youthful epigenetic information and restore vision.Nature, 588:124–129, 2020

2020
[11]

J.-H. Yang, M. Hayano, P. T. Griffin, et al. Loss of epigenetic information as a cause of mammalian aging.Cell, 186:305–326, 2023

2023
[12]

B. K. Kennedy, S. L. Berger, A. Brunet, et al. Geroscience: linking aging to chronic disease.Cell, 159:709–713, 2014

2014
[13]

M. V. Blagosklonny. Aging and immortality: quasi-programmed senescence and its pharmacologic inhibition.Cell Cycle, 5:2087–2102, 2006
[14]

Bellman.Dynamic Programming

R. Bellman.Dynamic Programming. Princeton University Press, 1957

1957
[15]

R. E. Kalman. A new approach to linear filtering and prediction problems.Journal of Basic Engineering, 82:35–45, 1960. 28

1960
[16]

D. E. Kirk.Optimal Control Theory: An Introduction. Prentice-Hall, 1970

1970
[17]

E. D. Sontag.Mathematical Control Theory: Deterministic Finite-Dimensional Systems. Springer, 1998

1998
[18]

R. E. Kalman. Mathematical description of linear dynamical systems.Journal of the Society for Industrial and Applied Mathematics, Series A: Control, 1(2):152–192, 1963

1963
[19]

Nijmeijer and A

H. Nijmeijer and A. van der Schaft.Nonlinear Dynamical Control Systems. Springer, 1990

1990
[20]

Isidori.Nonlinear Control Systems

A. Isidori.Nonlinear Control Systems. Springer, 3 edition, 1995

1995
[21]

J. Lamb, E. D. Crawford, D. Peck, et al. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease.Science, 313:1929–1935, 2006

1929
[22]

Subramanian, R

A. Subramanian, R. Narayan, S. M. Corsello, et al. A next generation connectivity map: L1000 platform and the first 1,000,000 profiles.Cell, 171:1437–1452, 2017

2017
[23]

Zhavoronkov, Y

A. Zhavoronkov, Y. A. Ivanenkov, A. Aliper, et al. Deep learning enables rapid identification of potent DDR1 kinase inhibitors.Nature Biotechnology, 37:1038–1040, 2019

2019
[24]

Zhavoronkov, P

A. Zhavoronkov, P. Mamoshina, Q. Vanhaelen, et al. Artificial intelligence for aging and longevity research: recent advances and perspectives.Ageing Research Reviews, 49:49–66, 2019

2019
[25]

Zhavoronkov and P

A. Zhavoronkov and P. Mamoshina. Deep aging clocks: the emergence of AI-based biomarkers of aging and longevity.Trends in Pharmacological Sciences, 40:546–549, 2019

2019
[26]

Artificial intelligence for aging and longevity research: Recent advances and perspectives.Ageing Research Reviews, 49:49–66,

Alex Zhavoronkov, Polina Mamoshina, Quentin Vanhaelen, Morten Scheibye-Knudsen, Alexey Moskalev, and Alex Aliper. Artificial intelligence for aging and longevity research: Recent advances and perspectives.Ageing Research Reviews, 49:49–66,
[27]

doi: 10.1016/j.arr.2018.11.003

2018 doi
[28]

Buzdin, Andrey V

Alex Zhavoronkov, Anton A. Buzdin, Andrey V. Garazha, Nikolay M. Borisov, and Alexey A. Moskalev. Signaling pathway cloud regulation for in silico screening and ranking of the potential geroprotective drugs.Frontiers in Genetics, 5:49, 2014. doi: 10.3389/fgene.2014.00049. 29

2014 doi
[29]

Pun, Alex Aliper, Feng Ren, and Alex Zhavoronkov

Jianjiu Chen, Geoffrey Ho Duen Leung, Howell Leung, Alina Ustiugova, Anastasia Shneyderman, Mike Korzinkin, David Gennert, Frank W. Pun, Alex Aliper, Feng Ren, and Alex Zhavoronkov. From clock to clock: Therapeutic target discovery for aging and age-related diseases.Ageing Res...

2025 doi
[30]

T. B. L. Kirkwood and R. Holliday. The evolution of ageing and longevity.Proceedings of the Royal Society B, 205:531–546, 1979

1979
[31]

D. Harman. The biologic clock: the mitochondria?Journal of the American Geriatrics Society, 20:145–147, 1972

1972
[32]

D. C. Wallace. A mitochondrial paradigm of metabolic and degenerative diseases, aging, and cancer.Annual Review of Genetics, 39:359–407, 2005

2005
[33]

A. D. N. J. de Grey.Ending Aging. St. Martin’s Press, 2007

2007
[34]

D. A. Sinclair and M. D. LaPlante.Lifespan: Why We Age—and Why We Don’t Have To. Atria Books, 2019

2019
[35]

Ocampo, P

A. Ocampo, P. Reddy, P. Martinez-Redondo, et al. In vivo amelioration of age- associated hallmarks by partial reprogramming.Cell, 167:1719–1733, 2016

2016
[36]

D. Gill, A. Parry, F. Santos, et al. Multi-omic rejuvenation of human cells by maturation phase transient reprogramming.eLife, 11:e71624, 2022

2022
[37]

Sierra and R

F. Sierra and R. Kohanski. Geroscience and the trans-NIH Geroscience Interest Group, GSIG.GeroScience, 39:1–5, 2017

2017
[38]

Barzilai, A

N. Barzilai, A. M. Cuervo, and S. Austad. Aging as a biological target for prevention and therapy.JAMA, 320:1321–1322, 2018

2018
[39]

M. V. Blagosklonny. Aging is not programmed: genetic pseudo-program is a shadow of developmental growth.Cell Cycle, 12:3736–3742, 2013

2013
[40]

D. E. Harrison, R. Strong, Z. D. Sharp, et al. Rapamycin fed late in life extends lifespan in genetically heterogeneous mice.Nature, 460:392–395, 2009

2009
[41]

R. A. Miller, D. E. Harrison, C. M. Astle, et al. Rapamycin, but not resveratrol or simvastatin, extends lifespan of genetically heterogeneous mice.Journals of Gerontology Series A, 66:191–201, 2011

2011
[42]

D. W. Lamming, L. Ye, D. M. Sabatini, and J. A. Baur. Rapalogs and mTOR inhibitors as anti-aging therapeutics.Journal of Clinical Investigation, 123:980–989, 2013. 30

2013
[43]

B. K. Kennedy and D. W. Lamming. The mechanistic target of rapamycin: the grand conductor of metabolism and aging.Cell Metabolism, 23:990–1003, 2016

2016
[44]

L. A. Gavrilov and N. S. Gavrilova.The Biology of Life Span: A Quantitative Approach. Harwood Academic, 1991

1991
[45]

Gompertz

B. Gompertz. On the nature of the function expressive of the law of human mortality. Philosophical Transactions of the Royal Society of London, 115:513–585, 1825
[46]

Scheffer, J

M. Scheffer, J. Bascompte, W. A. Brock, et al. Early-warning signals for critical transitions.Nature, 461:53–59, 2009

2009
[47]

Scheffer, S

M. Scheffer, S. R. Carpenter, T. M. Lenton, et al. Anticipating critical transitions. Science, 338:344–348, 2012

2012
[48]

J. Gao, B. Barzel, and A.-L. Barabási. Universal resilience patterns in complex networks.Nature, 530:307–312, 2016

2016
[49]

M. S. Hipp, P. Kasturi, and F. U. Hartl. The proteostasis network and its decline in ageing.Nature Reviews Molecular Cell Biology, 20:421–435, 2019

2019
[50]

Bahar, C

R. Bahar, C. H. Hartmann, K. A. Rodriguez, et al. Increased cell-to-cell variation in gene expression in ageing mouse heart.Nature, 441:1011–1014, 2006

2006
[51]

M. Enge, H. E. Arda, M. Mignardi, et al. Single-cell analysis of human pancreas reveals transcriptional signatures of aging.Cell, 171:321–330, 2017

2017
[52]

C. P. Martinez-Jimenez, N. Eling, H.-C. Chen, et al. Aging increases cell-to-cell transcriptional variability upon immune stimulation.Science, 355:1433–1436, 2017

2017
[53]

Demaria, N

M. Demaria, N. Ohtani, S. A. Youssef, et al. An essential role for senescent cells in optimal wound healing through secretion of PDGF-AA.Developmental Cell, 31: 722–733, 2014

2014
[54]

Ritschka, M

B. Ritschka, M. Storer, A. Mas, et al. The senescence-associated secretory phenotype induces cellular plasticity and tissue regeneration.Genes & Development, 31:172–183, 2017

2017
[55]

López-Otín, L

C. López-Otín, L. Galluzzi, J. M. P. Freije, F. Madeo, and G. Kroemer. Metabolic control of longevity.Cell, 166:802–821, 2016

2016
[56]

Hannum, J

G. Hannum, J. Guinney, L. Zhao, et al. Genome-wide methylation profiles reveal quantitative views of human aging rates.Molecular Cell, 49:359–367, 2013

2013
[57]

S. Horvath. DNA methylation age of human tissues and cell types.Genome Biology, 14:R115, 2013. 31

2013
[58]

M. E. Levine, A. T. Lu, A. Quach, et al. An epigenetic biomarker of aging for lifespan and healthspan.Aging, 10:573–591, 2018

2018
[59]

A. T. Lu, A. Quach, J. G. Wilson, et al. DNA methylation GrimAge strongly predicts lifespan and healthspan.Aging, 11:303–327, 2019

2019
[60]

D. R. Cox. Regression models and life-tables.Journal of the Royal Statistical Society, Series B, 34(2):187–220, 1972

1972
[61]

O. Aalen. Nonparametric inference for a family of counting processes.Annals of Statistics, 6(4):701–726, 1978

1978
[62]

J. D. Kalbfleisch and R. L. Prentice.The Statistical Analysis of Failure Time Data. Wiley, 2 edition, 2002

2002
[63]

Liu, J.-J

Y.-Y. Liu, J.-J. Slotine, and A.-L. Barabási. Controllability of complex networks. Nature, 473:167–173, 2011

2011
[64]

S. E. Luria and M. Delbrück. Mutations of bacteria from virus sensitivity to virus resistance.Genetics, 28(6):491–511, 1943

1943
[65]

J. Jee, A. Rasouly, I. Shamovsky, Y. Akivis, S. R. Steinman, B. Mishra, and E. Nudler. Rates and mechanisms of bacterial mutagenesis from maximum-depth sequencing. Nature, 534(7609):693–696, 2016

2016
[66]

Lotfollahi, A

M. Lotfollahi, A. K. Susmelj, C. De Donno, et al. Predicting cellular responses to complex perturbations with combinatorial autoencoders.Nature Biotechnology, 2023

2023
[67]

N. H. Holford and L. B. Sheiner. Understanding the dose-effect relationship: clinical application of pharmacokinetic-pharmacodynamic models.Clinical Pharmacokinetics, 6:429–453, 1981

1981
[68]

U. S. Bhalla and R. Iyengar. Emergent properties of networks of biological signaling pathways.Science, 283:381–387, 1999

1999
[69]

P. K. Sorger, S. R. Allerheiligen, D. R. Abernethy, et al. Quantitative and systems pharmacology in the post-genomic era: new approaches to discovering drugs and understanding therapeutic mechanisms. Technical report, NIH QSP White Paper, 2011

2011
[70]

D. J. Baker, T. Wijshake, T. Tchkonia, et al. Clearance of p16Ink4a-positive senescent cells delays ageing-associated disorders.Nature, 479:232–236, 2011

2011
[71]

M. Xu, T. Pirtskhalava, J. N. Farr, et al. Senolytics improve physical function and increase lifespan in old age.Nature Medicine, 24:1246–1256, 2018. 32

2018
[72]

Rashid, Juan Carlos Acosta, Sida Li, Carlos F

Ana Banito, Sheikh T. Rashid, Juan Carlos Acosta, Sida Li, Carlos F. Pereira, Imbisaat Geti, Sandra Pinho, Jose C. Silva, Veronique Azuara, Michael Walsh, Ludovic Vallier, and Jesus Gil. Senescence impairs successful reprogramming to pluripotent stem cells.Genes & Development,...

2009
[73]

Maríon, Dafni Chondrona- siou, Miguel Rovira, Pablo J

Lluc Mosteiro, Cristina Pantoja, Noelia Alcazar, Rosa M. Maríon, Dafni Chondrona- siou, Miguel Rovira, Pablo J. Fernandez-Marcos, Maribel Muñoz-Martín, Carmen Blanco-Aparicio, Joaquin Pastor, Gonzalo Gómez-López, Alba De Martino, Maria A. Blasco, Maria Abad, and Manuel Serrano...

2016 doi
[74]

Petrashen, Amy E

Marco De Cecco, Takahiro Ito, Anna P. Petrashen, Amy E. Elias, Nicholas J. Skvir, Steven W. Criscione, Alberto Caligiana, Greta Brocculi, Emily M. Adney, Jef D. Boeke, Oanh Le, Christian Beausejour, Jayakrishna Ambati, Kameshwari Ambati, Matthew Simon, Andrei Seluanov, Vera Go...

2019 doi
[75]

Gonzalez, Tomoko Taguchi, Marco De Cecco, Katerina I

Matthew Simon, Michael Van Meter, Julia Ablaeva, Zhonghe Ke, Raul S. Gonzalez, Tomoko Taguchi, Marco De Cecco, Katerina I. Leonova, Valeria Kogan, Stephen L. Helfand, Nicola Neretti, Asael Roichman, Haim Y. Cohen, Margarita V. Meer, Vadim N. Gladyshev, Marina P. Antoch, Andrei...

2019 doi
[76]

McIntyre, Yasmine J

Rebecca L. McIntyre, Yasmine J. Liu, Man Hu, Brian J. Morris, Bradley J. Willcox, Timothy A. Donlon, Riekelt H. Houtkooper, and Georges E. Janssens. Pharmacologi- cal and genetic interventions targeting LINE-1 retrotransposition extend longevity via ATF-4.Cell Reports, 42(1):1...

2023 doi
[77]

Yariv Kanfi, Shoshana Naiman, Gail Amir, Victoria Peshti, Guy Zinman, Liat Nahum, Ziv Bar-Joseph, and Haim Y. Cohen. The sirtuin SIRT6 regulates lifespan in male mice.Nature, 483(7388):218–221, 2012. doi: 10.1038/nature10815

2012 doi
[78]

J. N. Justice, A. M. Nambiar, T. Tchkonia, et al. Senolytics in idiopathic pulmonary fibrosis: results from a first-in-human, open-label, pilot study.EBioMedicine, 40: 554–563, 2019. 33

2019
[79]

G. M. Fahy, R. T. Brooke, J. P. Watson, et al. Reversal of epigenetic aging and immunosenescent trends in humans.Aging Cell, 18:e13028, 2019

2019
[80]

R. T. Q. Chen, Y. Rubanova, J. Bettencourt, and D. Duvenaud. Neural ordinary differential equations. InNeurIPS, 2018

2018
[81]

Rajman, K

L. Rajman, K. Chwalek, and D. A. Sinclair. Therapeutic potential of NAD-boosting molecules: in vivo evidence.Cell Metabolism, 27:529–547, 2018

2018
[82]

Partridge, M

L. Partridge, M. Fuentealba, and B. K. Kennedy. The quest to slow ageing through drug discovery.Nature Reviews Drug Discovery, 19:513–532, 2020

2020
[83]

Pun, Geoffrey Ho Duen Leung, Hoi Wing Leung, Bonnie Hei Man Liu, Xi Long, Ivan V

Frank W. Pun, Geoffrey Ho Duen Leung, Hoi Wing Leung, Bonnie Hei Man Liu, Xi Long, Ivan V. Ozerov, Ju Wang, Feng Ren, Alexander Aliper, Evgeny Izum- chenko, Alexey Moskalev, João Pedro de Magalhães, and Alex Zhavoronkov. Hall- marks of aging-based dual-purpose disease and age-...

2022 doi
[84]

T. A. Henzinger. The theory of hybrid automata. InVerification of Digital and Hybrid Systems, NATO ASI Series, pages 265–292. Springer, 2000

2000
[85]

Olde Loohuis, A

L. Olde Loohuis, A. Witzel, and B. Mishra. Cancer hybrid automata: model, beliefs and therapy.Information and Computation, 236:68–86, 2014

2014
[86]

Olde Loohuis, A

L. Olde Loohuis, A. Witzel, and B. Mishra. Cancer hybrid automata. InProceedings of the 4th International Workshop on Hybrid Systems and Biology (HSB), 2012

2012
[87]

S. E. Massey and B. Mishra. Origin of biomolecular games: deception and molecular evolution.Journal of the Royal Society Interface, 15(148):20180429, 2018

2018
[88]

M. J. Casey, S. E. Massey, and B. Mishra. When good DNA repair brings cancer: signaling games as a framework for understanding mimicry and deception in oncoge- nesis. InProceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology, and Health In...

2020
[89]

H. E. Bryant, N. Schultz, H. D. Thomas, K. M. Parker, D. Flower, E. Lopez, S. Kyle, M. Meuth, N. J. Curtin, and T. Helleday. Specific killing of BRCA2-deficient tumours with inhibitors of poly(ADP-ribose) polymerase.Nature, 434(7035):913–917, 2005

2005
[90]

W. G. Kaelin. Synthetic lethality: a framework for the development of wiser cancer therapeutics.Genome Medicine, 1(99), 2009. 34

2009
[91]

ICMJE. Recommendations for the conduct, reporting, editing, and publication of scholarly work in medical journals: updated recommendations on disclosures on use of AI-assisted technologies, 2024. Updated May 2024. 35 Table 1: Modality classes and control properties. Modality R...

2024

Pith tools

Reviewed July 12, 2026 · model on record in the stance chip above.

[1] [1]

Gladyshev, Benjamin Anderson, Hanna Barlit, Benjamin Barré, Samuel Beck, Bahareh Behrouz, Daniel W

Vadim N. Gladyshev, Benjamin Anderson, Hanna Barlit, Benjamin Barré, Samuel Beck, Bahareh Behrouz, Daniel W. Belsky, Amandine Chaix, Manish Chamoli, Brian H. Chen, et al. Disagreement on foundational principles of biological aging. PNAS Nexus, 3(12):pgae499, 2024. doi: 10.1093/pnasnexus/pgae499

doi:10.1093/pnasnexus/pgae499 2024

[2] [2]

P. B. Medawar.An Unsolved Problem of Biology. H. K. Lewis, London, 1952

1952

[3] [3]

G. C. Williams. Pleiotropy, natural selection, and the evolution of senescence. Evolution, 11:398–411, 1957

1957

[4] [4]

W. D. Hamilton. The moulding of senescence by natural selection.Journal of Theoretical Biology, 12:12–45, 1966

1966

[5] [5]

D. Harman. Aging: a theory based on free radical and radiation chemistry.Journal of Gerontology, 11:298–300, 1956

1956

[6] [6]

T. B. L. Kirkwood. Evolution of ageing.Nature, 270:301–304, 1977

1977

[7] [7]

López-Otín, M

C. López-Otín, M. A. Blasco, L. Partridge, M. Serrano, and G. Kroemer. The hallmarks of aging.Cell, 153:1194–1217, 2013

2013

[8] [8]

López-Otín, M

C. López-Otín, M. A. Blasco, L. Partridge, M. Serrano, and G. Kroemer. Hallmarks of aging: an expanding universe.Cell, 186:243–278, 2023

2023