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REVIEW 3 major objections 3 minor 182 references

Computational modelling of biological systems now and then: revisiting tools and visions from the beginning of the century

T0 review · 3 major / 3 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read The paper argues that computational modelling of biological systems has matured into an integral part of biomedical research, and that the next advances will come from deliberately combining mechanistic, first-principles models with…

desk verdict A useful, well-referenced perspective on 25 years of cardiac M&S that is honest about its limits; the 'maturity' claim is asserted more than proven, but this is advocacy, not fraud. read the letter →

arxiv 2501.13142 v1 pith:4NGKL7W3 submitted 2025-01-22 q-bio.QM

Axel Loewe , Peter J. Hunter , Peter Kohl This is my paper · ORCID
classification q-bio.QM
keywords computationalmodellinginsilicomedicinedigitaltwinsclinicaltrialsmechanisticmachinelearningcardiacelectrophysiologyFAIRdata
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

This review article argues that computational modelling and simulation (M&S) of biological systems has matured over the past quarter century into an integral part of basic and clinical research, with cardiology as the leading example. The authors claim that the old reductionism-versus-integrationism debate has largely settled, and that the newer split between mechanistic, first-principles models and data-driven machine learning is a false dichotomy: the two approaches complement each other, because mechanistic models generalise and respect physical laws while data-driven models stay anchored to real observations. They document this maturity in concrete forms: personalised digital twins, synthetic virtual cohorts ("digital chimeras") that make in silico clinical trials possible, synthetic data that trains machine-learning classifiers, and open standards that make models interchangeable. The stake is practical: if the review is right, further progress in computational medicine depends less on choosing between physics-based and data-driven methods than on deliberately integrating them, backed by shared data and quality-controlled workflows.

What carries the argument

The conceptual centrepiece is the "mountain and village" pairing, integrationist panorama versus reductionist close-up, which the authors repurpose into the modern pairing of mechanistic (village) and data-driven (mountain-top) modelling. The concrete machinery that carries the technical claims is the multi-scale cardiac electrophysiology hierarchy: ion-channel kinetics described by ordinary differential equations, cell-level models that couple currents into action potentials, tissue-level reaction-diffusion partial differential equations that propagate excitation, and Poisson's equation mapping the resulting fields to a body-surface electrocardiogram. This hierarchy is what makes digital twins personalisable, digital chimeras sampleable, and synthetic labelled training data producible at scale. Around it, the paper places an ecosystem layer of markup languages, FAIR data and software practices, and credibility frameworks, which is the mechanism that makes models interoperable and trustworthy in regulatory settings.

What would settle it

A head-to-head comparison in which machine-learning classifiers trained on synthetic data from mechanistic cardiac models are outperformed, on out-of-sample clinical data, by classifiers trained on equal-sized clinical datasets for the same diagnostic task would undermine the synergy claim. So would a post-market audit showing that in silico trial predictions—for instance, proarrhythmia risk classifications—fail to correlate with adverse outcomes in subsequent real clinical trials.

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Extended reading notes

Core claim

On its own terms, the paper's central claim is that "in silico methods" have become "an integral part of physiological and medical research" and that the next wave of progress will come from a synergy between mechanistic and data-driven modelling. The review traces a shift away from the early ambition of one universal model of "the human" toward two complementary targets: digital twins, which personalise a baseline mechanistic model to a single patient, and digital chimeras, synthetic individuals sampled from shape and parameter distributions so that statistically representative cohorts can undergo simulated interventions. It further claims that machine learning does not replace mechanistic modelling but is strengthened by it: validated multi-scale simulations supply large, class-balanced, precisely labelled synthetic training data; physics-informed neural networks constrain learning with known laws; and hybrid simulated-plus-clinical datasets improve classifiers in atrial fibrillation, flutter, fibrosis, and electrolyte disorders. The paper's corollary is that the binding constraints on future progress are data standardisation, uncertainty quantification, reproducibility, and community infrastructure rather than computational power.

Load-bearing premise

The load-bearing premise is that the selected success stories—rising publication counts, digital-twin pilots, regulatory uptake, and synthetic-data machine-learning demos—are representative enough to prove that modelling and simulation has matured into an integral part of biomedical research, rather than being early or atypical exceptions.

Editorial extensions

If this is right

  • In silico trials built on cohorts of digital chimeras can reduce, refine, or replace some animal and early human experiments, and regulators are already beginning to accept simulation evidence.
  • Cardiac multi-scale models can supply large, well-labelled, class-balanced synthetic datasets that improve machine-learning classifiers beyond what scarce clinical data alone allow.
  • Physics-informed neural networks and statistical emulators will become routine tools for parameter identification, sensitivity analysis, and uncertainty quantification.
  • Interoperable standards and open, FAIR data and code are prerequisites for building and validating models across scales, diseases, and contexts of use.
  • The controlled in silico environment lets researchers isolate confounding factors and test cause-and-effect hypotheses in ways wet-lab experiments cannot easily match.

Reading between the lines

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

  • A fair quantitative test of the maturation narrative would be a longitudinal analysis of how many M&S publications in cardiology report prospective clinical implementation, as opposed to retrospective simulation studies; the ratio would show whether adoption is broad or confined to showcases.
  • If the synergy claim generalises, the same digital-chimera-plus-machine-learning recipe should transfer to non-cardiac fields such as oncology, neurology, and immunology, where mechanistic models are less mature; demonstrating benefit there would be a strong external confirmation.
  • The paper's own caveat that no data quantify M&S's contribution to rising life expectancy implies a concrete evaluative gap: a cost-effectiveness meta-analysis of in silico-guided versus conventional drug and device development would directly test whether the promised savings in time and money actually materialise.
  • An implicit, testable consequence is that classifiers trained on synthetic data should fail gracefully when a simulation omits a silent variable such as age; a benchmark that introduces such variables in held-out clinical sets would probe the limits of the whole synthetic-data strategy.
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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 / 3 minor

Summary. This perspective article revisits the 2000 vision of Kohl, Noble, Winslow, and Hunter on computational modelling of biological systems, with a focus on the cardiovascular system. It argues that computational modelling and simulation (M&S) has matured into an integral part of physiological and medical research, that the old reductionism/integrationism debate has been superseded by a mechanistic-vs-data-driven dichotomy, and that the two approaches can and should be combined synergistically. The paper surveys recent developments in digital twins, in silico clinical trials, machine learning, open tools and standards, and selected cardiology applications, and it concludes with near-term frontiers and community-oriented recommendations. The article is explicitly a perspective/review and does not present new quantitative analyses.

Significance. If the central narrative is accepted, this paper provides a useful synthesis and agenda for the field, connecting historical visions to current tooling and identifying actionable needs such as FAIR data, metadata and software standards, uncertainty quantification, and better incentives for sharing. Its strengths include an extensive and largely accurate reference base, a clear description of the Physiome/VPH lineage, and an unusually honest treatment of limitations: the 'plausibility trap' in synthetic training data, domain gaps between simulated and real-world data, missing evidence for population-level health impact, and unresolved issues in digital twin personalisation. The paper's main value is as a statement of current consensus and future priorities by three leaders in the field, rather than as an empirical demonstration of clinical impact. Its central claims therefore need to be phrased with a level of confidence commensurate with the evidence presented.

major comments (3)
  1. [Section 2 (life-expectancy paragraph)] The sentence 'The millennial promise of reducing morbidity and mortality turned out to be true: life expectancy in the EU rose...' is not supported by the evidence that follows, because the authors immediately concede that 'we are not aware of data that would allow one to quantify the contribution of M&S to this improvement.' This assertion is load-bearing for the paper's broader maturity-and-benefit narrative. I recommend either replacing this passage with concrete adoption evidence (e.g., the ICH E14/S7B update, regulatory qualification examples, or prospective clinical trials using model-derived predictions) or explicitly softening the claim to state that anticipated benefits are becoming plausible and are beginning to materialize in specific contexts, rather than that the promise has been fulfilled.
  2. [Section 2 opening and Section 6] The assertion that M&S 'has become an integral part of physiological and medical research' is supported mainly by a five-fold increase in a PubMed fraction (reaching 2.2% in 2023) and by selected success stories, several of which originate from the authors' own groups (e.g., MedalCare-XL, openCARP, the bi-atrial statistical shape model). Publication counts measure research activity, not clinical integration or patient benefit, and the examples are not demonstrated to be representative of the field as a whole. Please add indicators of adoption beyond publication metrics (regulatory decisions, clinical guidelines, reimbursement, or numbers of patients reached) or reframe the conclusion as 'increasingly established in research and emerging in specific clinical contexts,' which would be better matched to the evidence actually presented.
  3. [Section 2.2 (Digital Twin Approaches)] The statement that digital twins 'have proven valuable across diverse medical applications' is stronger than the cited reviews support. The two prospective trials referenced later in Section 4 ([140,141]) are encouraging but small and restricted to ablation guidance, and the other cited sources are position papers or reviews. Because clinical utility is central to the maturity narrative, I recommend replacing 'have proven valuable' with 'have shown promise in early clinical evaluations' or 'are being evaluated in prospective clinical settings.'
minor comments (3)
  1. [Section 2.4] The phrase 'in silico generated data' should be hyphenated as 'in silico-generated data' for consistency with standard usage.
  2. [Section 3] There are two consecutive 'Acknowledgments' blocks after the author contributions; these should be merged into a single section.
  3. [Section 2.1] The sentence stating that in silico research has an 'absence of inherent variability, obviating the need for repeated experiments' is overstated for numerical simulations, which still face discretization error and stochastic algorithm variability; the later discussion of uncertainty quantification should be reflected in this sentence as well.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: the paper is a narrative review whose claims rest on external evidence, and self-citations are illustrative rather than load-bearing.

full rationale

This manuscript is a perspective/review, not a derivation. Its central claims, that computational modelling has matured, that mechanistic and data-driven modelling can synergize, and that open standards and sharing are needed, are supported by publication statistics, external benchmark initiatives such as CiPA and the Physiome/VPH programme, and a broad set of independent literature. The authors explicitly disclaim quantitative attribution of health-outcome improvements to modelling, writing in Section 2 that “we are not aware of data that would allow one to quantify the contribution of M&S to this improvement”; this is an evidentiary limitation, not a circular step. Author-affiliated work appears among many examples (e.g., MedalCare-XL [39], openCARP [78], a bi-atrial shape model [70]), but the review’s conclusions do not reduce to those citations, and removing them would not change the structure of the argument. No equations are derived, no fitted parameter is renamed as a prediction, and no uniqueness theorem from the authors is invoked to force a conclusion. The paper is self-contained as a narrative assessment, so the circularity score is 0.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

No free parameters or invented entities. Two domain assumptions underpin the review: representativeness of the cited literature, and the utility of the mechanistic/data-driven framing. The paper introduces no new mathematical objects or predictions.

assumptions (2)
  • domain assumption The selected citations and examples are representative of the field's development since 2000.
    Sections 2 and 4 use publication counts and spotlight examples to conclude that M&S has matured; this is an interpretive assumption, not a measured fact.
  • domain assumption The dichotomy between mechanistic and data-driven modelling is a meaningful organizing frame.
    Section 1 defines the paper's narrative; the value of the review depends on this framing being accepted.

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Cite this review

Pith. "Pith review of Computational modelling of biological systems now and then: revisiting tools and visions from the beginning of the century." pith.science (2026). https://pith.science/paper/4NGKL7W3

@misc{pith2026250113142,
  author       = {Pith},
  title        = {Pith review of: Computational modelling of biological systems now and then: revisiting tools and visions from the beginning of the century},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4NGKL7W3}},
  note         = {Machine review of arXiv:2501.13142}
}
read the original abstract

Since the turn of the millennium, computational modelling of biological systems has evolved remarkably and sees matured use spanning basic and clinical research. While the topic of the peri-millennial debate about the virtues and limitations of 'reductionism and integrationism' seems less controversial today, a new apparent dichotomy dominates discussions: mechanistic vs. data-driven modelling. In light of this distinction, we provide an overview of recent achievements and new challenges with a focus on the cardiovascular system. Attention has shifted from generating a universal model of the human to either models of individual humans (digital twins) or entire cohorts of models representative of clinical populations to enable in silico clinical trials. Disease-specific parameterisation, inter-individual and intra-individual variability, uncertainty quantification as well as interoperable, standardised, and quality-controlled data are important issues today, which call for open tools, data and metadata standards, as well as strong community interactions. The quantitative, biophysical, and highly controlled approach provided by in silico methods has become an integral part of physiological and medical research. In silico methods have the potential to accelerate future progress also in the fields of integrated multi-physics modelling, multi-scale models, virtual cohort studies, and machine learning beyond what is feasible today. In fact, mechanistic and data-driven modelling can complement each other synergistically and fuel tomorrow's artificial intelligence applications to further our understanding of physiology and disease mechanisms, to generate new hypotheses and assess their plausibility, and thus to contribute to the evolution of preventive, diagnostic, and therapeutic approaches.

Figures

Figures reproduced from arXiv: 2501.13142 by the authors.

Figure 1
Figure 1. Synergy of mechanistic and statistical (data-driven) models. Reproduced from Corral-Acero et al. [2] under the Creative Commons Attribution License 4.0. 2 The Millennium View from Today’s Perspective The vision presented by Kohl, Noble, Winslow & Hunter [1] has proven to be remarkably accurate overall. Today, computational M&S have matured significantly, witnessing widespread adoption in both basic and clinical rese… view at source ↗
Figure 2
Figure 2. Hierarchy of multiscale cardiac electrophysiology models ranging from ion channels (A) via integrated cell (B) and tissue level models (C) to the body surface and electrocardiogram (D). The simulation system allows one to investigate what-if scenarios by changing input parameters of the model (top row) and analysing the effect on simulation outputs on numerous scales (bottom row) in comparison with wet lab and clini… view at source ↗
Figure 3
Figure 3. Digital twin workflow. A baseline model builds the basis for the digital twin. Often, it is a bottom-up mechanistic model, informed by biophysical first principles and population-level knowledge. Anatomical and functional personalization are performed based on individual patient measurements (e.g., computed tomography or magnetic resonance imaging for anatomical twinning and ECG for functional twinning). The paramet… view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Computational M&S approaches. Representing an individual as well as possible with a computational digital twin model requires both anatomical and functional personalization (A). Once a digital twin is established, different interventions can be evaluated in silico, for…

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Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.