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A CRISP approach to QSP: XAI enabling fit-for-purpose models

T0 review · 3 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read CRISP is a workflow that turns over-parameterized, literature-derived QSP models into query-specific reduced models that retain predictive fidelity and mechanistic interpretability, and demonstrates the payoff on the coagulation cascade…

desk verdict A useful workflow paper whose headline data-efficiency claim overstates what was actually tested. read the letter →

arxiv 2505.02750 v3 pith:IFU4AT6W submitted 2025-05-05 q-bio.QM q-bio.MNstat.AP

Noah DeTal , Christian N. K. Anderson , Mark K. Transtrum This is my paper · ORCID
classification q-bio.QMq-bio.MNstat.AP
keywords quantitativesystemspharmacologymodelreductionManifoldBoundaryApproximationMethodparameteridentifiabilitysloppymodelsvirtualpopulationsbroadlyneutralizingantibodiescoagulationcascade
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

The paper introduces CRISP (Contextualized Reduction for Identifiability and Scientific Precision), a workflow that starts from a deliberately over-comprehensive, literature-derived mechanistic model, calibrates it to data, and then uses the Manifold Boundary Approximation Method (MBAM) to strip away parameter combinations that are irrelevant to specific 'queries'—the predictions a modeler actually cares about. The central claim is that the resulting reduced-order models remain accurate where it matters, are far cheaper and numerically more stable to simulate, and reveal the causal structure that drives a response. In the coagulation case study, fitting the reduced model to a single thrombin profile predicts the key metrics of the other 27 profiles, a 94% reduction in data demand. In the SHIV case study, the workflow produces separate reduced models for responders and nonresponders, identifies latent-cell dynamics and immune exhaustion as the distinguishing mechanisms, and ranks drug targets by how small a perturbation induces a clinical response. If correct, CRISP gives QSP modelers a general, automated way to turn unwieldy mechanism-rich models into tools that are precise, interpretable, and suitable for regulatory and clinical decisions.

What carries the argument

The load-bearing mechanism is the Manifold Boundary Approximation Method (MBAM). Treating the model's predictions as a Riemannian manifold with the Fisher Information as metric, MBAM starts at a calibrated parameter vector and follows a geodesic in the least-sensitive parameter direction until the model hits a boundary—a limiting approximation such as a reaction becoming instantaneous, a rate vanishing, or a Michaelis constant diverging. That approximation is then applied analytically to the equations, and the process repeats, carving out a reduced model that is a genuine special case of the full model. The 'query' formulation is the other half of the machinery: by specifying which predictions matter, CRISP ensures the reduction keeps parameters needed for extrapolative predictions even if they are not constrained by fitting data.

What would settle it

Re-run the SHIV reduction from a different statistically valid nonresponder fit—the paper itself states such fits exist—and check whether the supremum model still singles out latent-cell dynamics and the same target parameters; if the reduced mechanism changes, the workflow's mechanistic conclusions are fit-dependent rather than data-fixed.

Watch

Extended reading notes

Core claim

The paper's central discovery is that context-specific model reduction can certify fit-for-purpose QSP models: a model reduced with MBAM is formally a special case of the full model, restricted to the parameter combinations that actively propagate information from calibration data to the chosen queries. The reduced model inherits the full model's predictions for those queries while making the relevant mechanisms explicit, and the retained parameters are the ones that must be tightly constrained for accurate prediction. Applied to coagulation, the reduction compresses a detailed cascade model to a minimal core that preserves the adaptive thrombin response and reproduces the full model's bias; applied to SHIV, it yields a 'supremum' model containing the approximations common to four calibrated fits, and the differences between responder and nonresponder reductions point to immune exhaustion and latent-cell reservoirs as the mechanistic basis of treatment failure. The paper also claims that target discovery on the reduced model identifies parameter perturbations smaller than 15% that convert a nonresponder into a responder, and that Bayesian sampling of a reduced model produces a 7,200-member virtual population with clinically distinguishable response categories.

Load-bearing premise

The load-bearing premise is that the literature-derived starting model already contains the mechanisms that actually drive the system, so that a reduction of that model reveals true biology rather than merely the model's own assumptions.

Editorial extensions

If this is right

  • Reduced models can be used for the standard QSP tasks—virtual population generation, experimental design, toxicology, target discovery—at far lower computational cost and with none of the solver-dependent instability the paper reports for the full coagulation model.
  • Calibrating the reduced coagulation model to one thrombin profile is enough to predict the summary metrics of the other 27 profiles, implying a 94% reduction in data demand for that task.
  • The SHIV supremum model identifies mechanisms common to responder and nonresponder fits, and the optimization on it ranks clinically actionable targets that require less than a 15% parameter change to flip a nonresponder outcome.
  • Model-based conclusions about mechanism become explicit: latent-cell reservoirs mark nonresponders, transient effector exhaustion marks responders, and single-drug pharmacokinetics suffice to describe the bNAb therapy's effect on viral load.
  • Virtual populations generated from the reduced model can be categorized by dynamics—monostable versus bistable, then responder, nonresponder, immune, or adverse—and machine-learned classifiers on the retained parameters predict these categories with high cross-validated accuracy.

Reading between the lines

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

  • A natural extension would be to store a full literature-derived model together with a library of query-specific reductions, since the same full model can legitimately produce different minimal models for different questions.
  • A direct test of the workflow's mechanistic claims is to add a mechanism the current SHIV model omits—for example, more detailed memory-cell kinetics or an additional drug interaction—and check whether the reduced models and target rankings change.
  • The 94% data-demand figure is demonstrated within the original 28-profile experimental grid; a stronger test would fit the reduced model to one profile and predict conditions outside that grid, such as different tissue-factor concentrations.
  • Because the paper itself notes that different statistically valid fits can activate different mechanisms, the workflow may be more reliably viewed as a generator of competing mechanistic hypotheses than as a unique identifier of the true mechanism; regulatory use should make that distinction explicit.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The paper proposes CRISP, a QSP workflow built on the Manifold Boundary Approximation Method (MBAM), and demonstrates it on two case studies: a coagulation cascade model, where a 5-parameter reduced model is claimed to predict 27 thrombin profiles after calibrating to one profile (a "94% reduction in data demand"), and a SHIV model, where reduced models are used to identify mechanistic differences between responders and nonresponders, to propose drug targets, and to generate a virtual population via Bayesian sampling. The central claim is that CRISP produces parsimonious, mechanistically interpretable models that retain predictive fidelity, thereby offering a general-purpose approach to QSP model simplification.

Significance. If fully established, CRISP would be a valuable contribution to QSP practice, providing a principled, query-driven alternative to ad hoc model simplification. The paper is clearly written, presents explicit reduced-model equations (Eqs. 1–12) and a coherent mechanistic narrative (the incoherent feed-forward loop in coagulation), and is unusually transparent about limitations (e.g., prior-dependence of virtual population proportions in §2.3.4). The use of MBAM is state-of-the-art, and the SHIV case study offers a complete end-to-end illustration. However, as detailed below, the headline data-efficiency claim is weakened by a structural information leak, and the SHIV mechanistic conclusions rest on manually selected fits without withheld validation. These issues are central to the paper's claims and require revision.

major comments (3)
  1. [§2.2.1–2.2.2, Fig 3] The "94% reduction in data demand" claim is not supported by the reported validation. Section 2.2.1 states that the 28 thrombin time series are "the same time series used in model calibration" and that these 28 series constitute the MBAM query set. MBAM uses the query set to compute the Fisher information matrix and to follow geodesics to manifold boundaries, so the structure of the reduced model—including which parameters remain and the incoherent feed-forward loop interpretation—is selected using information from all 28 profiles. Section 2.2.2 then calibrates only the final parameter values to one profile and calls the other 27 "unobserved." The ability of the reduced model to reproduce the full model's behavior on those 27 profiles is therefore a consistency check within the query set, not a test of generalization from one profile to unseen conditions. The "94% reduction" conflates parameter estimation with model selection. To support the data-efficiency claim, the reduction should be performed with only one profile as the query, or the claim should be reframed as a reduction in fitted parameters per profile with the structural information contributed by all profiles explicitly acknowledged.
  2. [§2.3.1–2.3.3, §4.3.2] The SHIV mechanistic conclusions (latent cells present in nonresponders but not responders; exhaustion dynamics in responders but not nonresponders; the target list in Fig 6) are derived from a small number of manually selected fits. Section 2.3.1 admits that "it is possible to find sub-optimal fits that are statistically significant that activate different mechanisms in the model," and the authors select two nonresponder fits (DF06A, DF06B) by their own criteria without a formal model-selection or robustness analysis. Section 4.3.2 states that DEWL and DF06 were selected as "typical" representatives and asserts that similar results would be obtained with other representatives, but no evidence is provided. Because the reduced models inherit the mechanisms of their parent fits, alternative statistically valid fits could yield different reduced structures (e.g., removal of the latent compartment in nonresponders), which would change the central target-identification conclusions. The workflow, as presented, does not independently test the completeness of the literature-derived model or the representativeness of the chosen fits. I recommend adding a robustness analysis (e.g., refitting with multiple initializations or subjects, bootstrapping the data, or reporting the spread of reduced-model structures across fits) before claiming that these mechanistic differences explain treatment failure.
  3. [§2.3.4, Table 1] The virtual population statistics (e.g., 6.75% responders, 0.14% adverse responders in Table 1) are presented as substantive results, yet the authors acknowledge that these proportions are determined by an arbitrarily broad flat prior spanning roughly 16 orders of magnitude in parameter space. The paper correctly notes that the distribution "does not necessarily reflect the relative distribution of responses in real patients," but this caveat is confined to the text of §2.3.4 and is absent from the abstract and discussion, where the virtual population is cited as a demonstration of CRISP's utility for regulatory decisions. As a result, readers may misinterpret the 7% responder figure as a predictive prevalence. I suggest either adding an explicit warning in the abstract/discussion or reframing the virtual population example as a demonstration of the workflow's mechanics (i.e., how to generate and categorize a population under a stated prior) rather than as a quantitative prediction.
minor comments (5)
  1. [§2.2.1 and Fig 2 caption] The number of parameters in the coagulation reduced model is stated inconsistently: §2.2.1 says "five parameters," while the same section later says "five dynamical variables and four parameters," and Fig 2 says "5 parameters." Eq. (1) has four θ parameters plus the initial condition for II; please clarify the count.
  2. [§4.3.6] The MCMC description says the chain ran for 10,000 steps after a burn-in of 1,000 and was sampled every 100 steps, which with 120 walkers yields 12,000 posterior samples, yet Table 1 reports a 7,200-member virtual population. Please explain how 7,200 was obtained or correct the text.
  3. [Eq. (13) and §4.3.5] The regularization parameter λ is described as a "Lagrange multiplier," but in a sparse optimization context it is typically a hyperparameter controlling the sparsity-accuracy tradeoff. Please clarify how λ is chosen and whether the reported target sets are robust to its value.
  4. [Tables 2 and 3] Several entries group multiple limits in one row without separation (e.g., "mI, mP→∞, λE, bE→0" in Table 3). Please format each approximation individually or use explicit separators for readability.
  5. [General] The manuscript does not include a data or code availability statement. Given that the paper presents a computational workflow and validation results, a statement on availability of the reduced-model equations, fitting code, and virtual population generation code would improve reproducibility.

Circularity Check

1 steps flagged · score 6.0 of 10

Coagulation validation is partly circular: all 28 profiles were MBAM queries used to select the reduced structure, so the 27 'unseen' profiles were not independent, undercutting the 94% data-demand claim.

  1. fitted input called prediction [Section 2.2.1 (Minimal Model), Section 2.2.2 (Predictive Power of the Minimal Model), and Fig. 3 caption]
    "The queries we chose are thrombin time series (Factor IIa) in response to various doses of Factor VIIa and Factor Xa added to both normal and Factor VII-deficient human plasma, for a total of 28 time series. These are the same time series used in model calibration. ... Here, we calibrate the reduced model to data for one of the 28 time series and then test its ability to predict key quantities of interest for the remaining 27 unobserved time series. ... The model accurately predicts this trade-off, even when only one thrombin profile is used as a data query (a 94% reduction in data demand)."

    MBAM selects the reduced model by computing the Fisher Information matrix and geodesics over the query set. The query set here is all 28 thrombin profiles, and the paper explicitly says these are the same time series used in model calibration. Thus the five-parameter reduced structure was chosen to reproduce all 28 profiles, including the 27 that are later labeled 'unobserved.' Holding out only the parameter fit to one profile does not hold out model selection: the 27 'unseen' profiles contributed structural information through the queries. The reported prediction of the remaining 27 profiles is therefore not a test of generalization to unseen conditions; it is an evaluation of a model whose structure was constructed using those same profiles.

full rationale

The core CRISP/MBAM reduction is not circular: it is an information-geometric algorithm applied to a literature-derived model, and the SHIV target and virtual-population analyses are model-internal sensitivity and uncertainty-quantification exercises rather than derivations from their own outputs. The principal circularity is in the coagulation validation. MBAM computes its Fisher Information matrix and geodesic reductions over the query set, and the paper states that the 28 thrombin profiles used as queries are the same time series used in calibration. Therefore the reduced model's structure was selected using all 28 profiles, including the 27 later called 'unobserved' when one profile is used for parameter fitting. The 94% data-demand claim conflates parameter estimation with model selection. No self-citation chain is load-bearing here: MBAM [32] and the supremum construction [91] are used as methodological tools, not as evidence for the target conclusions. The SHIV mechanistic claims, such as latent cells in nonresponders, are interpretations of reduced fits and align with prior work; they are not presented as predictions from held-out data, so that is a validation-completeness concern rather than circularity.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The central claim rests on MBAM as a trusted reduction tool, on the completeness of the literature-derived models, and on hand-selected calibrated fits. The free parameters are numerous but their values are largely absent from the text. No new physical entities are introduced.

free parameters (4)
  • Coagulation reduced-model parameters theta_1, theta_2, theta_3, theta_4 (plus initial II) = not reported in text
    Fit to a single thrombin profile in the validation; exact values and units are not listed, so the central 94% data-reduction claim cannot be re-fit from the paper alone.
  • SHIV literature-derived model's 45 tunable parameters = not reported in text
    Calibrated to Nishimura et al. viral load data; no parameter table or fitted values are given, so independent calibration/reduction is not possible.
  • Virtual population prior width (flat prior over log-parameters, +/-8) = ±8 log units
    Chosen by hand; subpopulation fractions (7% responders, 0.13% adverse) depend on this prior, as the paper acknowledges in Section 2.3.4.
  • Target-identification regularization lambda in Eq. (13) = not specified
    The sparse optimization result depends on the unspecified Lagrange multiplier; no value or selection procedure is provided.
assumptions (4)
  • domain assumption The Manifold Boundary Approximation Method correctly identifies limiting approximations and the reduced model remains a valid special case of the full model.
    Invoked throughout Section 2.1 and Methods 4.1, relying on refs [32,33] rather than proofs in this paper.
  • domain assumption The literature-derived SHIV model is sufficiently comprehensive and epistemically neutral for the queries considered.
    Section 2.3.1 assembles mechanisms from [80-85]; if relevant mechanisms are absent, reduction cannot recover them.
  • domain assumption The selected calibrated fits (untreated aggregate, DEWL, DF06A, DF06B) are representative of the population and of responder/nonresponder mechanisms.
    Section 4.3.2-4.3.3 selects these by hand and notes alternative fits are possible.
  • standard math The ODE models are well-posed and numerical solutions used for fitting/reduction are reliable.
    The paper notes the full coagulation model required high-precision solvers; no solver tolerances or verification of SHIV numerics are given.

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Cite this review

Pith. "Pith review of A CRISP approach to QSP: XAI enabling fit-for-purpose models." pith.science (2026). https://pith.science/paper/IFU4AT6W

@misc{pith2026250502750,
  author       = {Pith},
  title        = {Pith review of: A CRISP approach to QSP: XAI enabling fit-for-purpose models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/IFU4AT6W}},
  note         = {Machine review of arXiv:2505.02750}
}
read the original abstract

Quantitative Systems Pharmacology (QSP) promises to accelerate drug development, enable personalized medicine, and improve the predictability of clinical outcomes. Realizing this potential requires effectively managing the complexity of mathematical models representing biological systems. Here, we present and validate a novel QSP workflow--CRISP (Contextualized Reduction for Identifiability and Scientific Precision)--that addresses a central challenge in QSP: the problem of complexity and over-parameterization, in which models contain irrelevant parameters that obscure interpretation and hinder predictive reliability. The CRISP workflow begins with a literature-derived model, constructed to be comprehensive and unbiased by integrating prior mechanistic insights. At the core of the workflow is the Manifold Boundary Approximation Method (MBAM), a reduction technique that simplifies models while preserving mechanistic structure and predictive fidelity. By applying MBAM in a context-specific manner, CRISP links parsimonious models directly to predictions of interest, clarifying causal structure and enhancing interpretability. The resulting models are computationally efficient and well-suited to key QSP tasks, including virtual population generation, experimental design, toxicology, and target discovery. We demonstrate the utility of CRISP on case studies involving the coagulation cascade and SHIV infection, and identify promising directions for improving the efficacy of bNAb therapies for HIV. Together, these results establish CRISP as a general-purpose QSP workflow for turning complex mechanistic models into tools for precise scientific reasoning to guide pharmacological and regulatory decision-making.

Figures

Figures reproduced from arXiv: 2505.02750 by the authors.

Figure 1
Figure 1. Contextualized Reduction for Identifiability and Scientific Precision [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. The model integrates the classical pathway elements into a detailed mechanistic [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗
Figure 3
Figure 3. Predictions of the reduced order coagulation model compared to [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: SHIV Literature-derived model: Viral load interacts with helper CD4+ T-cells, resulting in four classes of infected cells: actively Infected, Latently, Persistently, and Defectively infected cells. Such cells are camouflaged until the immune system is “trained” by broa…
Figure 5
Figure 5. Figure 5: Model fits to the aggregate untreated data (A), responder DEWL data (B), and two fits to the nonresponder DF06 data showing a good fit (C) and an inferior but statistically significant fit that activates different mechanisms (D). 2.3.2 Reduced Models and Mechanistic In…
Figure 6
Figure 6. Figure 6: Drug target parameters for inducing a clinical response in a nonresponder. The bars show the fold-change required for each individual parameter to induce response, with no change exceeding 15% of the nominal value. 2.3.3 Drug target identification The insight from the …
Figure 7
Figure 7. Figure 7: Typical responses for each subpopulation [PITH_FULL_IMAGE:figures/full_fig_p017_7.png]
Figure 8
Figure 8. Figure 8: Subpopulations and various parameter values [PITH_FULL_IMAGE:figures/full_fig_p018_8.png]

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Reference graph

Works this paper leans on

100 extracted references · 69 canonical work pages

  1. [1]

    Quantitative and systems pharmacology in the post-genomic era: new September 4, 2025 30/38 approaches to discovering drugs and understanding therapeutic mechanisms

    Sorger PK, Allerheiligen SR, Abernethy DR, Altman RB, Brouwer KL, Califano A, et al. Quantitative and systems pharmacology in the post-genomic era: new September 4, 2025 30/38 approaches to discovering drugs and understanding therapeutic mechanisms. In: An NIH white paper by the QSP workshop group. vol. 48. NIH Bethesda Bethesda; 2011. p. 1–47

    2025

  2. [2]

    Quantitative Systems Pharmacology: a Case for Disease Models

    Musante C, Ramanujan S, Schmidt B, Ghobrial O, Lu J, Heatherington A. Quantitative Systems Pharmacology: a Case for Disease Models. Clinical Pharmacology & Therapeutics. 2016;101(1):24–27. doi:10.1002/cpt.528

    work page doi:10.1002/cpt.528 2016

  3. [3]

    Quantitative Systems Pharmacology: a Regulatory Perspective on Translation

    Zineh I. Quantitative Systems Pharmacology: a Regulatory Perspective on Translation. CPT: Pharmacometrics & Systems Pharmacology. 2019;8(6):336–339. doi:10.1002/psp4.12403

    work page doi:10.1002/psp4.12403 2019

  4. [4]

    A Perspective on Quantitative Systems Pharmacology Applications To Clinical Drug Development

    Bai JPF, Earp JC, Strauss DG, Zhu H. A Perspective on Quantitative Systems Pharmacology Applications To Clinical Drug Development. CPT: Pharmacometrcs & Systems Pharmacology. 2020;9(12):675–677. doi:10.1002/psp4.12567

    work page doi:10.1002/psp4.12567 2020

  5. [5]

    Review: Role of Model-Informed Drug Development Approaches in the Lifecycle of Drug Development and Regulatory Decision-Making

    Madabushi R, Seo P, Zhao L, Tegenge M, Zhu H. Review: Role of Model-Informed Drug Development Approaches in the Lifecycle of Drug Development and Regulatory Decision-Making. Pharmaceutical Research. 2022;39(8):1669–1680. doi:10.1007/s11095-022-03288-w

    work page doi:10.1007/s11095-022-03288-w 2022

  6. [6]

    Quantitative Systems Pharmacology: an Exemplar Model-building Workflow With Applications in Cardiovascular, Metabolic, and Oncology Drug Development

    Helmlinger G, Sokolov V, Peskov K, Hallow KM, Kosinsky Y, Voronova V, et al. Quantitative Systems Pharmacology: an Exemplar Model-building Workflow With Applications in Cardiovascular, Metabolic, and Oncology Drug Development. CPT: Pharmacometrics & Systems Pharmacology. 2019;8(6):380–395. doi:10.1002/psp4.12426

    work page doi:10.1002/psp4.12426 2019

  7. [7]

    No Recipe for Quantitative Systems Pharmacology Model Validation, But a Balancing Act Between Risk and Cost

    Lemaire V, Hu C, van der Graaf PH, Chang S, Wang W. No Recipe for Quantitative Systems Pharmacology Model Validation, But a Balancing Act Between Risk and Cost. Clinical Pharmacology & Therapeutics. 2023;115(1):25–28. doi:10.1002/cpt.3082

    work page doi:10.1002/cpt.3082 2023

  8. [8]

    Assessing the Performance of Qsp Models: Biology As the Driver for Validation

    Singh FA, Afzal N, Smithline SJ, Thalhauser CJ. Assessing the Performance of Qsp Models: Biology As the Driver for Validation. Journal of Pharmacokinetics and Pharmacodynamics. 2023;51(5):533–542. doi:10.1007/s10928-023-09871-x

    work page doi:10.1007/s10928-023-09871-x 2023

Show all 100 references
  1. [9]

    A Framework for Quantitative Systems Pharmacology Model Execution

    Sokolov V, Peskov K, Helmlinger G. A Framework for Quantitative Systems Pharmacology Model Execution. In: Handbook of Experimental Pharmacology. Handbook of Experimental Pharmacology. Springer Berlin Heidelberg; 2025. p. 1–46. Available from:http://dx.doi.org/10.1007/164_2024_738

    2025 doi

  2. [10]

    A Quantitative Systems Pharmacology Perspective on the Importance of Parameter Identifiability

    Sher A, Niederer SA, Mirams GR, Kirpichnikova A, Allen R, Pathmanathan P, et al. A Quantitative Systems Pharmacology Perspective on the Importance of Parameter Identifiability. Bulletin of mathematical biology. 2022

    2022

  3. [11]

    Navigating the landscape of parameter identifiability methods: A workflow recommendation for model development

    van Noort M, Ruppert M, DeJongh J, Marostica E, Bosch R, Meˇ si´ c E, et al. Navigating the landscape of parameter identifiability methods: A workflow recommendation for model development. CPT: Pharmacometrics & Systems Pharmacology. 2024;13(7):1170–1179. doi:https://doi.org/1...

    2024 doi

  4. [12]

    A Review of Quantitative Systems Pharmacology Models of the Coagulation Cascade: Opportunities for Improved Usability

    Chung D, Bakshi S, van der Graaf PH. A Review of Quantitative Systems Pharmacology Models of the Coagulation Cascade: Opportunities for Improved Usability. Pharmaceutics. 2023;15(3):918. doi:10.3390/pharmaceutics15030918

    2023 doi

  5. [13]

    The Spectrum of Mechanism-Oriented Models and Methods for Explanations of Biological Phenomena

    Hunt C, Erdemir A, Lytton W, Gabhann FM, Sander E, Transtrum M, et al. The Spectrum of Mechanism-Oriented Models and Methods for Explanations of Biological Phenomena. Processes. 2018;6(5):56. doi:10.3390/pr6050056. September 4, 2025 31/38

    2018 doi

  6. [14]

    Markov models for ion channels: versatility versus identifiability and speed

    Fink M, Noble D. Markov models for ion channels: versatility versus identifiability and speed. Philosophical transactions Series A, Mathematical, physical, and engineering sciences. 2009;doi:http://doi.org/10.1098/rsta.2008.0301

    2009

  7. [15]

    Determining Identifiable Parameter Combinations Using Subset Profiling

    Eisenberg MC, Hayashi MAL. Determining Identifiable Parameter Combinations Using Subset Profiling. Mathematical Biosciences. 2014;256:116–126. doi:10.1016/j.mbs.2014.08.008

    2014 doi

  8. [16]

    Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood

    Raue A, Kreutz C, Maiwald T, Bachmann J, Schilling M, Klingm¨ uller U, et al. Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood. Bioinformatics. 2009;25(15):1923–1929. doi:10.1093/bioinformatics/btp358

    2009 doi

  9. [17]

    Alternate virtual populations elucidate the type I interferon signature predictive of the response to rituximab in rheumatoid arthritis

    Schmidt BJ, Casey FP, Paterson T, Chan JR. Alternate virtual populations elucidate the type I interferon signature predictive of the response to rituximab in rheumatoid arthritis. BMC Bioinformatics. 2013;14(1):221. doi:10.1186/1471-2105-14-221

    2013 doi

  10. [18]

    Efficient Generation and Selection of Virtual Populations in Quantitative Systems Pharmacology Models

    Allen R, Rieger T, Musante C. Efficient Generation and Selection of Virtual Populations in Quantitative Systems Pharmacology Models. CPT: Pharmacometrics & Systems Pharmacology. 2016;5(3):140–146. doi:https://doi.org/10.1002/psp4.12063

    2016 doi

  11. [19]

    An integrated quantitative systems pharmacology virtual population approach for calibration with oncology efficacy endpoints

    Braniff N, Joshi T, Cassidy T, Trogdon M, Kumar R, Poels K, et al. An integrated quantitative systems pharmacology virtual population approach for calibration with oncology efficacy endpoints. CPT: Pharmacometrics & Systems Pharmacology. 2025;14(2):268–278. doi:https://doi.org...

    2025 doi

  12. [20]

    Identifiable Reparametrizations of Linear Compartment Models

    Meshkat N, Sullivant S. Identifiable Reparametrizations of Linear Compartment Models. Journal of Symbolic Computation. 2014;63:46–67. doi:10.1016/j.jsc.2013.11.002

    2014 doi

  13. [21]

    Optimal Experimental Design in an Epidermal Growth Factor Receptor Signalling and Down-Regulation Model

    Casey FP, Gutenkunst RN, Myers CR, Baird D, Brown KS, Waterfall JJ, et al. Optimal Experimental Design in an Epidermal Growth Factor Receptor Signalling and Down-Regulation Model. IET Systems Biology. 2007;1(3):190–202. doi:10.1049/iet-syb:20060065

    2007 doi

  14. [22]

    Sloppy Models, Parameter Uncertainty, and the Role of Experimental Design

    Apgar JF, Witmer DK, White FM, Tidor B. Sloppy Models, Parameter Uncertainty, and the Role of Experimental Design. Molecular BioSystems. 2010;6(10):1890. doi:10.1039/b918098b

    2010 doi

  15. [23]

    Experimental Design for Biological Systems

    Chung M, Haber E. Experimental Design for Biological Systems. SIAM Journal on Control and Optimization. 2012;50(1):471–489. doi:10.1137/100791063

    2012 doi

  16. [24]

    The Limitations of Model-Based Experimental Design and Parameter Estimation in Sloppy Systems

    White A, Tolman M, Thames HD, Withers HR, Mason KA, Transtrum MK. The Limitations of Model-Based Experimental Design and Parameter Estimation in Sloppy Systems. PLOS Computational Biology. 2016;12(12):e1005227. doi:10.1371/journal.pcbi.1005227

    2016 doi

  17. [25]

    Scale Reduction of a Systems Coagulation Model With an Application To Modeling Pharmacokinetic-Pharmacodynamic Data

    Gulati A, Isbister G, Duffull S. Scale Reduction of a Systems Coagulation Model With an Application To Modeling Pharmacokinetic-Pharmacodynamic Data. CPT: Pharmacometrics & Systems Pharmacology. 2014;3(1):1–8. doi:10.1038/psp.2013.67

    2014 doi

  18. [26]

    Automated Scale Reduction of Nonlinear QSP Models With an Illustrative Application To a Bone Biology System

    Hasegawa C, Duffull SB. Automated Scale Reduction of Nonlinear QSP Models With an Illustrative Application To a Bone Biology System. CPT: Pharmacometrics & Systems Pharmacology. 2018;7(9):562–572. doi:10.1002/psp4.12324. September 4, 2025 32/38

    2018 doi

  19. [27]

    Predicting Nonlinear Changes in Bone Mineral Density Over Time Using a Multiscale Systems Pharmacology Model

    Peterson M, Riggs M. Predicting Nonlinear Changes in Bone Mineral Density Over Time Using a Multiscale Systems Pharmacology Model. CPT: Pharmacometrics & Systems Pharmacology. 2012;1(11):1–8. doi:10.1038/psp.2012.15

    2012 doi

  20. [28]

    A Structure Preserving Model Order Reduction Method for Calcium Homeostatic System

    Biswal B, Sen S, Maka S. A Structure Preserving Model Order Reduction Method for Calcium Homeostatic System. Mathematical Biosciences. 2019;312:8–22. doi:10.1016/j.mbs.2019.03.002

    2019 doi

  21. [29]

    Conservation Analysis of Large Biochemical Networks

    Vallabhajosyula RR, Chickarmane V, Sauro HM. Conservation Analysis of Large Biochemical Networks. Bioinformatics. 2005;22(3):346–353. doi:10.1093/bioinformatics/bti800

    2005 doi

  22. [30]

    An Improved Method for Nonlinear Model Reduction Using Balancing of Empirical Gramians

    Hahn J, Edgar TF. An Improved Method for Nonlinear Model Reduction Using Balancing of Empirical Gramians. Computers & Chemical Engineering. 2002;26(10):1379–1397. doi:10.1016/s0098-1354(02)00120-5

    2002 doi

  23. [31]

    Model Reduction in Mathematical Pharmacology

    Snowden TJ, van der Graaf PH, Tindall MJ. Model Reduction in Mathematical Pharmacology. Journal of Pharmacokinetics and Pharmacodynamics. 2018;45(4):537–555. doi:10.1007/s10928-018-9584-y

    2018 doi

  24. [32]

    Model Reduction By Manifold Boundaries

    Transtrum MK, Qiu P. Model Reduction By Manifold Boundaries. Physical Review Letters. 2014;113(9):098701. doi:10.1103/physrevlett.113.098701

    2014 doi

  25. [33]

    Bridging Mechanistic and Phenomenological Models of Complex Biological Systems

    Transtrum MK, Qiu P. Bridging Mechanistic and Phenomenological Models of Complex Biological Systems. PLOS Computational Biology. 2016;12(5):e1004915. doi:10.1371/journal.pcbi.1004915

    2016 doi

  26. [34]

    Model Boundary Approximation Method As a Unifying Framework for Balanced Truncation and Singular Perturbation Approximation

    Pare PE, Grimsman D, Wilson AT, Transtrum MK, Warnick S. Model Boundary Approximation Method As a Unifying Framework for Balanced Truncation and Singular Perturbation Approximation. IEEE Transactions on Automatic Control. 2019;64(11):4796–4802. doi:10.1109/tac.2019.2908523

    2019

  27. [35]

    Measurement-Directed Reduction of Dynamic Models in Power Systems

    Transtrum MK, Saric AT, Stankovic AM. Measurement-Directed Reduction of Dynamic Models in Power Systems. IEEE Transactions on Power Systems. 2017;32(3):2243–2253. doi:10.1109/tpwrs.2016.2611511

    2017

  28. [36]

    Sobol Sensitivity Analysis: A Tool to Guide the Development and Evaluation of Systems Pharmacology Models

    Zhang XY, Trame MN, Lesko LJ, Schmidt S. Sobol Sensitivity Analysis: A Tool to Guide the Development and Evaluation of Systems Pharmacology Models. CPT: pharmacometrics & systems pharmacology. 2015;4(2):69–79. doi:10.1002/psp4.6

    2015 doi

  29. [37]

    Sensitivity Estimates for Nonlinear Mathematical Models

    Sobol’ IM. Sensitivity Estimates for Nonlinear Mathematical Models. Mathematical Modeling in Civil Engineering. 1993;1(4):407–414

    1993

  30. [38]

    Importance measures in global sensitivity analysis of nonlinear models

    Homma T, Saltelli A. Importance measures in global sensitivity analysis of nonlinear models. Reliability Engineering and System Safety. 1996;52(1):1–17. doi:10.1016/0951-8320(96)00002-6

    1996 doi

  31. [39]

    Factorial Sampling Plans for Preliminary Computational Experiments

    Morris MD. Factorial Sampling Plans for Preliminary Computational Experiments. Technometrics. 1991;33(2):161–174. doi:10.1080/00401706.1991.10484804

    1991

  32. [40]

    Statistical Mechanical Approaches To Models With Many Poorly Known Parameters

    Brown KS, Sethna JP. Statistical Mechanical Approaches To Models With Many Poorly Known Parameters. Physical Review E. 2003;68(2):021904. doi:10.1103/physreve.68.021904. September 4, 2025 33/38

    2003 doi

  33. [41]

    Perspective: Sloppiness and Emergent Theories in Physics, Biology, and Beyond

    Transtrum MK, Machta BB, Brown KS, Daniels BC, Myers CR, Sethna JP. Perspective: Sloppiness and Emergent Theories in Physics, Biology, and Beyond. The Journal of Chemical Physics. 2015;143(1):010901. doi:10.1063/1.4923066

    2015 doi

  34. [42]

    Information geometry for multiparameter models: new perspectives on the origin of simplicity

    Quinn KN, Abbott MC, Transtrum MK, Machta BB, Sethna JP. Information geometry for multiparameter models: new perspectives on the origin of simplicity. Reports on Progress in Physics. 2022;86(3):035901. doi:10.1088/1361-6633/aca6f8

    2022 doi

  35. [43]

    The Statistical Mechanics of Complex Signaling Networks: Nerve Growth Factor Signaling

    Brown KS, Hill CC, Calero GA, Myers CR, Lee KH, Sethna JP, et al. The Statistical Mechanics of Complex Signaling Networks: Nerve Growth Factor Signaling. Physical Biology. 2004;1(3):184–195. doi:10.1088/1478-3967/1/3/006

    2004 doi

  36. [44]

    More is different

    Anderson PW, et al. More is different. Science. 1972;177(4047):393–396

    1972

  37. [45]

    Renormalization Group and Critical Phenomena

    Wilson KG. Renormalization Group and Critical Phenomena. I. Renormalization Group and the Kadanoff Scaling Picture. Physical Review B. 1971;4(9):3174–3183. doi:10.1103/physrevb.4.3174

    1971 doi

  38. [46]

    Simple Lessons From Complexity

    Goldenfeld N. Simple Lessons From Complexity. Science. 1999;284(5411):87–89. doi:10.1126/science.284.5411.87

    1999 doi

  39. [47]

    From the Cover: The Theory of Everything

    Laughlin RB, Pines D. From the Cover: The Theory of Everything. Proceedings of the National Academy of Sciences of the United States of America. 2000;97(1):28

    2000

  40. [48]

    The Devil in the Details: Asymptotic Reasoning in Explanation, Reduction, and Emergence

    Batterman RW. The Devil in the Details: Asymptotic Reasoning in Explanation, Reduction, and Emergence. Oxford University Press; 2001

    2001

  41. [49]

    Parameter Space Compression Underlies Emergent Theories and Predictive Models

    Machta BB, Chachra R, Transtrum MK, Sethna JP. Parameter Space Compression Underlies Emergent Theories and Predictive Models. Science. 2013;342(6158):604–607. doi:10.1126/science.1238723

    2013 doi

  42. [50]

    Sloppy Models, Renormalization Group Realism, and the Success of Science

    Freeborn D. Sloppy Models, Renormalization Group Realism, and the Success of Science. Erkenntnis. 2023;doi:10.1007/s10670-023-00728-w

    2023 doi

  43. [51]

    Why Are Nonlinear Fits To Data So Challenging? Physical Review Letters

    Transtrum MK, Machta BB, Sethna JP. Why Are Nonlinear Fits To Data So Challenging? Physical Review Letters. 2010;104(6):060201. doi:10.1103/physrevlett.104.060201

    2010 doi

  44. [52]

    Geometry of Nonlinear Least Squares With Applications To Sloppy Models and Optimization

    Transtrum MK, Machta BB, Sethna JP. Geometry of Nonlinear Least Squares With Applications To Sloppy Models and Optimization. Physical Review E. 2011;83(3):036701. doi:10.1103/physreve.83.036701

    2011 doi

  45. [53]

    Structural Susceptibility and Separation of Time Scales in the Van Der Pol Oscillator

    Chachra R, Transtrum MK, Sethna JP. Structural Susceptibility and Separation of Time Scales in the Van Der Pol Oscillator. Physical Review E. 2012;86(2):026712. doi:10.1103/physreve.86.026712

    2012 doi

  46. [54]

    Sloppy Model Analysis Identifies Bifurcation Parameters Without Normal Form Analysis

    Anderson CNK, Transtrum MK. Sloppy Model Analysis Identifies Bifurcation Parameters Without Normal Form Analysis. Physical Review E. 2023;108(6):064215. doi:10.1103/physreve.108.064215

    2023 doi

  47. [55]

    Improvements To the Levenberg-Marquardt Algorithm for Nonlinear Least-Squares Minimization

    Transtrum MK, Sethna JP. Improvements To the Levenberg-Marquardt Algorithm for Nonlinear Least-Squares Minimization. CoRR. 2012

    2012

  48. [56]

    Riemann Manifold Langevin and Hamiltonian Monte Carlo Methods

    Girolami M, Calderhead B. Riemann Manifold Langevin and Hamiltonian Monte Carlo Methods. Journal of the Royal Statistical Society: Series B (Statistical Methodology). 2011;73(2):123–214. doi:10.1111/j.1467-9868.2010.00765.x. September 4, 2025 34/38

    2011

  49. [57]

    Maximizing the information learned from finite data selects a simple model

    Mattingly HH, Transtrum MK, Abbott MC, Machta BB. Maximizing the information learned from finite data selects a simple model. Proceedings of the National Academy of Sciences. 2018;115(8):1760–1765

    2018

  50. [58]

    Visualizing probabilistic models and data with Intensive Principal Component Analysis

    Quinn KN, Clement CB, De Bernardis F, Niemack MD, Sethna JP. Visualizing probabilistic models and data with Intensive Principal Component Analysis. Proceedings of the National Academy of Sciences. 2019;116(28):13762–13767. doi:10.1073/pnas.1817218116

    2019 doi

  51. [59]

    Visualizing probabilistic models in Minkowski space with intensive symmetrized Kullback-Leibler embedding

    Teoh HK, Quinn KN, Kent-Dobias J, Clement CB, Xu Q, Sethna JP. Visualizing probabilistic models in Minkowski space with intensive symmetrized Kullback-Leibler embedding. Physical Review Research. 2020;2(3):033221. doi:10.1103/PhysRevResearch.2.033221

    2020 doi

  52. [60]

    Translational Quantitative Systems Pharmacology in Drug Development: From Current Landscape To Good Practices

    Bai JPF, Earp JC, Pillai VC. Translational Quantitative Systems Pharmacology in Drug Development: From Current Landscape To Good Practices. The AAPS Journal. 2019;21(4):72. doi:10.1208/s12248-019-0339-5

    2019 doi

  53. [61]

    Evaluation Framework for Systems Models

    Braakman S, Pathmanathan P, Moore H. Evaluation Framework for Systems Models. CPT: Pharmacometrics & Systems Pharmacology. 2022;11(3):264–289. doi:10.1002/psp4.12755

    2022 doi

  54. [62]

    A Six-stage Workflow for Robust Application of Systems Pharmacology

    Gadkar K, Kirouac D, Mager D, van der Graaf P, Ramanujan S. A Six-stage Workflow for Robust Application of Systems Pharmacology. CPT: Pharmacometrics & Systems Pharmacology. 2016;5(5):235–249. doi:10.1002/psp4.12071

    2016 doi

  55. [63]

    Methodologies for Quantitative Systems Pharmacology (QSP) Models: Design and Estimation

    Ribba B, Grimm H, Agoram B, Davies M, Gadkar K, Niederer S, et al. Methodologies for Quantitative Systems Pharmacology (QSP) Models: Design and Estimation. CPT: Pharmacometrics & Systems Pharmacology. 2017;6(8):496–498. doi:https://doi.org/10.1002/psp4.12206

    2017 doi

  56. [64]

    Towards a Comprehensive Assessment of Qsp Models: What Would It Take? Journal of Pharmacokinetics and Pharmacodynamics

    Androulakis IP. Towards a Comprehensive Assessment of Qsp Models: What Would It Take? Journal of Pharmacokinetics and Pharmacodynamics. 2022;51(5):521–531. doi:10.1007/s10928-022-09820-0

    2022 doi

  57. [65]

    Quantitative systems pharmacology in the age of artificial intelligence

    Ribba B. Quantitative systems pharmacology in the age of artificial intelligence. CPT: Pharmacometrics & Systems Pharmacology. 2023;doi:10.1002/psp4.13047

    2023 doi

  58. [66]

    Qsp Toolbox: Computational Implementation of Integrated Workflow Components for Deploying Multi-Scale Mechanistic Models

    Cheng Y, Thalhauser CJ, Smithline S, Pagidala J, Miladinov M, Vezina HE, et al. Qsp Toolbox: Computational Implementation of Integrated Workflow Components for Deploying Multi-Scale Mechanistic Models. The AAPS Journal. 2017;19(4):1002–1016. doi:10.1208/s12248-017-0100-x

    2017 doi

  59. [67]

    A Quantitative Systems Pharmacology Workflow Toward Optimal Design and Biomarker Stratification of Atopic Dermatitis Clinical Trials

    Go N, Ars` ene S, Faddeenkov I, Galland T, B SM, Lefaudeux D, et al. A Quantitative Systems Pharmacology Workflow Toward Optimal Design and Biomarker Stratification of Atopic Dermatitis Clinical Trials. Journal of Allergy and Clinical Immunology. 2024;153(5):1330–1343. doi:10....

    2024 doi

  60. [68]

    Judgment under Uncertainty: Heuristics and Biases

    Tversky A, Kahneman D. Judgment under Uncertainty: Heuristics and Biases. Science (New York, NY). 1974;185(4157):1124–1131. doi:10.1126/science.185.4157.1124

    1974

  61. [69]

    Heuristics and Biases: The Psychology of Intuitive Judgment

    Gilovich T, Griffin D, Kahneman D. Heuristics and Biases: The Psychology of Intuitive Judgment. Cambridge University Press; 2002. September 4, 2025 35/38

    2002

  62. [70]

    Applying Deutsch’s concept of good explanations to artificial intelligence and neuroscience – An initial exploration

    Elton DC. Applying Deutsch’s concept of good explanations to artificial intelligence and neuroscience – An initial exploration. Cognitive Systems Research. 2021;67:9–17. doi:10.1016/j.cogsys.2020.12.002

    2021 doi

  63. [71]

    The Beginning of Infinity: Explanations that Transform the World

    Deutsch D. The Beginning of Infinity: Explanations that Transform the World. New York: Penguin Books; 2012

    2012

  64. [72]

    Clinical Impact of Coagulation and Fibrinolysis Markers for Predicting Postoperative Venous Thromboembolism in Total Joint Arthroplasty Patients

    Cheng Y, Liu J, Su Y, Zhao H, Zhao Y, Wen M, et al. Clinical Impact of Coagulation and Fibrinolysis Markers for Predicting Postoperative Venous Thromboembolism in Total Joint Arthroplasty Patients. Clinical and Applied Thrombosis/Hemostasis. 2019;25. doi:10.1177/1076029619877458

    2019 doi

  65. [73]

    Critical Evaluation of Kinetic Schemes for Coagulation

    Ranc A, Bru S, Mendez S, Giansily-Blaizot M, Nicoud F, Rojano RM. Critical Evaluation of Kinetic Schemes for Coagulation. PLOS ONE. 2023;18(8):e0290531. doi:10.1371/journal.pone.0290531

    2023 doi

  66. [74]

    Using a Systems Pharmacology Model of the Blood Coagulation Network to Predict the Effects of Various Therapies on Biomarkers

    Nayak S, Lee D, Patel-Hett S, Pittman D, Martin S, Heatherington A, et al. Using a Systems Pharmacology Model of the Blood Coagulation Network to Predict the Effects of Various Therapies on Biomarkers. CPT: Pharmacometrics & Systems Pharmacology. 2015;4(7):396–405. doi:10.1002/psp4.50

    2015 doi

  67. [75]

    A Novel Simplified Model for Blood Coagulation: a Piecewise Dynamical Model for Thrombin With Robust Predictive Capabilities

    Arumugam J, Srinivasa A. A Novel Simplified Model for Blood Coagulation: a Piecewise Dynamical Model for Thrombin With Robust Predictive Capabilities. CoRR. 2017

    2017

  68. [76]

    Automated Reduction of Blood Coagulation Models

    Hansen KB, Shadden SC. Automated Reduction of Blood Coagulation Models. International Journal for Numerical Methods in Biomedical Engineering. 2019;35(10). doi:10.1002/cnm.3220

    2019 doi

  69. [77]

    Quantitative Systems Pharmacology Models: Potential Tools for Advancing Drug Development for Rare Diseases

    Neves-Zaph S, Kaddi C. Quantitative Systems Pharmacology Models: Potential Tools for Advancing Drug Development for Rare Diseases. Clinical Pharmacology & Therapeutics. 2024;116(6):1442–1451. doi:10.1002/cpt.3451

    2024 doi

  70. [78]

    Multistep Model Reduction of Coagulation Schemes

    Chen J, Caz` eres Q, Riber E, Nicoud F. Multistep Model Reduction of Coagulation Schemes. Biomechanics and Modeling in Mechanobiology. 2025;doi:10.1007/s10237-025-01944-9

    2025 doi

  71. [79]

    Defining Network Topologies That Can Achieve Biochemical Adaptation

    Ma W, Trusina A, El-Samad H, Lim WA, Tang C. Defining Network Topologies That Can Achieve Biochemical Adaptation. Cell. 2009;138(4):760–773. doi:10.1016/j.cell.2009.06.013

    2009 doi

  72. [80]

    Early exposure to broadly neutralizing antibodies may trigger a dynamical switch from progressive disease to lasting control of SHIV infection

    Desikan R, Raja R, Dixit NM. Early exposure to broadly neutralizing antibodies may trigger a dynamical switch from progressive disease to lasting control of SHIV infection. PLOS Computational Biology. 2020;16(8):1–30. doi:10.1371/journal.pcbi.1008064

    2020 doi

  73. [81]

    Quantification of in vivo replicative capacity of HIV-1 in different compartments of infected cells

    Funk GA, Fischer M, Joos B, Opravil M, G¨ unthard HF, Ledergerber B, et al. Quantification of in vivo replicative capacity of HIV-1 in different compartments of infected cells. J Acquir Immune Defic Syndr. 2001;26(5):397–404

    2001

  74. [82]

    Vaccination Alters the Balance between Protective Immunity, Exhaustion, Escape, and Death in Chronic Infections

    Johnson PLF, Kochin BF, McAfee MS, Stromnes IM, Regoes RR, Ahmed R, et al. Vaccination Alters the Balance between Protective Immunity, Exhaustion, Escape, and Death in Chronic Infections. Journal of Virology. 2011;85(11):5565–5570. doi:10.1128/JVI.00166-11. September 4, 2025 36/38

    2011 doi

  75. [83]

    Modelling Immune Memory for Prediction and Computation

    Wilson WO, Garrett SM. Modelling Immune Memory for Prediction and Computation. In: Hutchison D, Kanade T, Kittler J, Kleinberg JM, Mattern F, Mitchell JC, et al., editors. Artificial Immune Systems. vol. 3239. Berlin, Heidelberg: Springer Berlin Heidelberg; 2004. p. 386–399. A...

    2004 doi

  76. [84]

    Modelling T Cell Memory

    McLean AR. Modelling T Cell Memory. Journal of Theoretical Biology. 1994;170(1):63–74. doi:10.1006/jtbi.1994.1168

    1994

  77. [85]

    Quantifying Drug Combination Synergy along Potency and Efficacy Axes

    Meyer CT, Wooten DJ, Paudel BB, Bauer J, Hardeman KN, Westover D, et al. Quantifying Drug Combination Synergy along Potency and Efficacy Axes. Cell Systems. 2019;8(2):97–108.e16. doi:10.1016/j.cels.2019.01.003

    2019 doi

  78. [86]

    Early antibody therapy can induce long-lasting immunity to SHIV

    Nishimura Y, Gautam R, Chun TW, Sadjadpour R, Foulds KE, Shingai M, et al. Early antibody therapy can induce long-lasting immunity to SHIV. Nature. 2017;543(7646):559–563. doi:10.1038/nature21435

    2017 doi

  79. [87]

    Cytokines Elevated in HIV Elite Controllers Reduce HIV Replication In Vitro and Modulate HIV Restriction Factor Expression

    Jacobs ES, Keating SM, Abdel-Mohsen M, Gibb SL, Heitman JW, Inglis HC, et al. Cytokines Elevated in HIV Elite Controllers Reduce HIV Replication In Vitro and Modulate HIV Restriction Factor Expression. Journal of Virology. 2017;91(6):e02051–16. doi:10.1128/JVI.02051-16

    2017 doi

  80. [88]

    A high prevalence of potential HIV elite controllers identified over 30 years in Democratic Republic of Congo

    Berg MG, Olivo A, Harris BJ, Rodgers MA, James L, Mampunza S, et al. A high prevalence of potential HIV elite controllers identified over 30 years in Democratic Republic of Congo. EBioMedicine. 2021;65:103258. doi:10.1016/j.ebiom.2021.103258

    2021

  81. [89]

    Long noncoding RNAMIR4435-2HGenhances metabolic function of myeloid dendritic cells from HIV-1 elite controllers

    Hartana CA, Rassadkina Y, Gao C, Martin-Gayo E, Walker BD, Lichterfeld M, et al. Long noncoding RNAMIR4435-2HGenhances metabolic function of myeloid dendritic cells from HIV-1 elite controllers. The Journal of Clinical Investigation. 2021;131(9). doi:10.1172/JCI146136

    2021 doi

  82. [90]

    Learning to Be Elite: Lessons From HIV-1 Controllers and Animal Models on Trained Innate Immunity and Virus Suppression

    Sugawara S, Reeves RK, Jost S. Learning to Be Elite: Lessons From HIV-1 Controllers and Animal Models on Trained Innate Immunity and Virus Suppression. Frontiers in Immunology. 2022;13:858383. doi:10.3389/fimmu.2022.858383

    2022

  83. [91]

    Selecting Simple, Transferable Models With the Supremum Principle

    Petrie C, Anderson C, Maekawa C, Maekawa T, Transtrum MK. Selecting Simple, Transferable Models With the Supremum Principle. Physical Review Research. 2022;4(3):L032044. doi:10.1103/physrevresearch.4.l032044

    2022 doi

  84. [92]

    Bryostatin Modulates Latent HIV-1 Infection via PKC and AMPK Signaling but Inhibits Acute Infection in a Receptor Independent Manner

    Mehla R, Bivalkar-Mehla S, Zhang R, Handy I, Albrecht H, Giri S, et al. Bryostatin Modulates Latent HIV-1 Infection via PKC and AMPK Signaling but Inhibits Acute Infection in a Receptor Independent Manner. PLOS ONE. 2010;5(6):e11160. doi:10.1371/journal.pone.0011160

    2010 doi

  85. [93]

    kick” and “kill

    Marsden MD, Loy BA, Wu X, Ramirez CM, Schrier AJ, Murray D, et al. In vivo activation of latent HIV with a synthetic bryostatin analog effects both latent cell “kick” and “kill” in strategy for virus eradication. PLOS Pathogens. 2017;13(9):e1006575. doi:10.1371/journal.ppat.1006575

    2017 doi

  86. [94]

    Synthesis and preclinical evaluation of tigilanol tiglate analogs as latency-reversing agents for the eradication of HIV

    Gentry ZO, McAteer OD, Hamad JL, Moran JA, Kim JT, Marsden MD, et al. Synthesis and preclinical evaluation of tigilanol tiglate analogs as latency-reversing agents for the eradication of HIV. Science Advances. 2025;11(4):eads1911. doi:10.1126/sciadv.ads1911. September 4, 2025 37/38

    2025 doi

  87. [95]

    A dynamical motif comprising the interactions between antigens and CD8 T cells may underlie the outcomes of viral infections

    Baral S, Antia R, Dixit NM. A dynamical motif comprising the interactions between antigens and CD8 T cells may underlie the outcomes of viral infections. Proceedings of the National Academy of Sciences. 2019;116(35):17393–17398. doi:10.1073/pnas.1902178116

    2019 doi

  88. [96]

    A Framework for Simplification of Quantitative Systems Pharmacology Models in Clinical Pharmacology

    Derbalah A, Al-Sallami H, Hasegawa C, Gulati A, Duffull SB. A Framework for Simplification of Quantitative Systems Pharmacology Models in Clinical Pharmacology. British Journal of Clinical Pharmacology. 2020;88(4):1430–1440. doi:10.1111/bcp.14451

    2020 doi

  89. [97]

    Of Mice, Macaques, and Men: Broadly Neutralizing Antibody Immunotherapy for HIV-1

    Nishimura Y, Martin MA. Of Mice, Macaques, and Men: Broadly Neutralizing Antibody Immunotherapy for HIV-1. Cell Host & Microbe. 2017;22(2):207–216. doi:10.1016/j.chom.2017.07.010

    2017 doi

  90. [98]

    Basic Framework and Main Methods of Uncertainty Quantification

    Zhang J, Yin J, Wang R. Basic Framework and Main Methods of Uncertainty Quantification. Mathematical Problems in Engineering. 2020;2020(1). doi:10.1155/2020/6068203

    2020 doi

  91. [99]

    Parameter estimation and uncertainty quantification using information geometry

    Sharp JA, Browning AP, Burrage K, Simpson MJ. Parameter estimation and uncertainty quantification using information geometry. Journal of The Royal Society Interface. 2022;19(189):20210940. doi:10.1098/rsif.2021.0940

    2022

  92. [100]

    Ensemble samplers with affine invariance

    Goodman J, Weare J. Ensemble samplers with affine invariance. Communications in Applied Mathematics and Computational Science. 2010;5(1):65–80. doi:10.2140/camcos.2010.5.65. September 4, 2025 38/38

    2010 doi

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.