Beyond Phantom Baths: Balanced Physical Fidelity for Responsive Virtual Cells

1. The Responsive Virtual Cell

“Virtual Cell,” “AI Cell,” and “digital twin” now appear across discussions of structural atlases, whole-cell models, AI-enabled cellular prediction platforms, and multiscale simulation programs. [1,2,3,4] That breadth reflects genuine progress: experimental platforms yield richer multimodal measurements, machine-learning systems extract predictive regularities from previously intractable data volumes, and mechanistic simulation tools can treat larger and more heterogeneous cellular subsystems than before. Yet it conflates fundamentally different scientific ambitions. A divide runs through all of these efforts, largely unacknowledged: between virtual cells that project cellular states onto low-dimensional statistical manifolds and ones that propagate them forward in time under explicit physical forces. Statistical projections capture correlations within their training domains but are silent on the rate-dependent, causally structured dynamics through which cells sense and respond to their environments. Mechanistic models generate those dynamics but face a challenge that statistical models do not: every gain in interior fidelity raises the bar for the physical realism of environmental coupling at the simulation boundary. Making that challenge explicit—and proposing a framework for addressing it—is the purpose of this perspective.

The landmark 4D whole-cell simulation of the genetically minimal bacterium JCVI-syn3A by Thornburg et al. [5] provides a compelling motivating example of responsive mechanistic modeling: a complete ∼100-minute cell cycle integrating DNA replication, transcription, translation, metabolism, lipid synthesis, and cell division was simulated through explicit stochastic dynamics, validated against experimental measurements of doubling time, mRNA half-lives, protein distributions, and chromosome segregation. The implication is clear: a responsive virtual cell must propagate cell-state forward in time under explicit forces, not infer it from a learned manifold. By contrast, cell foundation models such as scGPT, [6] Geneformer, [7] scFoundation, [8] and X-Cell [9] now represent the dominant approach to AI-driven virtual-cell science. These models have achieved remarkable success at capturing correlations across cellular states, imputing missing measurements, and predicting perturbation responses within their training domains. Neither achievement is diminished by recognizing that the two approaches embody fundamentally different levels of physical detail; the distinction matters precisely because only the mechanistic approach can capture the nonequilibrium dynamics of living systems.

Incorporating progressively more realistic dynamics is the natural next frontier, but interior simulation fidelity is advancing far faster than environmental coupling. Feynman path-integral methods now incorporate nuclear quantum effects into atomistic simulations of biological relevance, [10] with quantum thermal-bath extensions further bridging quantum and classical descriptions of heat transport. [11,12] Machine-learned interatomic potentials such as MACE [13,14] now approach density-functional-theory accuracy at a fraction of the cost. Google’s Willow quantum processor has demonstrated verifiable quantum advantage in molecular simulation, [15] and machine-learning frameworks are being coupled to path-integral engines for efficient quantum-nuclear dynamics. [16] Computational barriers to large-scale, high-fidelity simulation are therefore falling faster than the methodological barriers to physically faithful environmental coupling. As system sizes and physical resolution grow, the distortions introduced by algorithmically imposed environmental control will be exposed, not eliminated, by greater computational power—unless the community simultaneously addresses the imbalance at the boundary (see Supplementary Note S6 for extended discussion).

This widening gap makes explicit a principle that applies to every mechanistic virtual cell: it must be faithful on both sides of its simulation boundary—the internal dynamics that generate cellular responses and the environmental coupling through which the cell exchanges energy, momentum, and matter with its surroundings. [17,18] These two representations are not independent—advancing one without the other concentrates artifacts at the boundary. This is the principle of balanced physical fidelity: the representation of interior dynamics and the representation of environmental coupling must advance together.

2. Why Mechanistic Virtual Cells Require Dynamics

The crux of the requirement for explicit dynamics is the difference between instantaneous state functions and rate-dependent, energy-integrating responses. A cell subjected to a brief, intense heat pulse does not respond equivalently to a prolonged, moderate elevation delivering the same total thermal energy, because heat-shock response pathways, protein unfolding kinetics, and repair mechanisms are activated with rate-dependent thresholds. [19] The outcome depends not only on the integral of energy flux over time but on the rate profile relative to the cell’s dissipation and repair capacity. Analogous rate-history dependence pervades cellular physiology—from signaling kinetics and mechanosensitive channel gating to stress-history-dependent morphogenesis and nonequilibrium phase behavior. [20,21] Any model that reduces cells to state-function descriptions—including purely statistical or projection-based representations—systematically omits this rate-integrated biology and cannot, on its own, support mechanistically grounded responsiveness (see Supplementary Note S1 for extended discussion).

The practical urgency of this requirement is sharpest for the biological transitions of greatest biomedical and biotechnological relevance: the onset of pathology from normal physiology, [22] the activation of quiescent cells, [23] and stress adaptation in cultivated organisms. [24] These transitions traverse regions of state space far from any steady-state attractor, where path dependence and transient fluxes dominate the outcome: epithelial–mesenchymal transition, for example, exhibits hysteresis that makes the trajectory itself—not merely the endpoints—a determinant of the resulting cell state. [25,26] Only explicit dynamics can follow the cell as it crosses between basins. Moreover, the environmental signals that trigger or modulate these transitions—extracellular matrix stiffening, [27] hypoxia gradients, [28] thermal and osmotic shock [24]—are themselves spatially structured and rate-dependent, making physically grounded environmental coupling a practical prerequisite for simulation-guided intervention in medicine and agriculture alike.

3. The Physical-Fidelity Continuum

We propose that virtual-cell approaches are best understood as positions on a continuous axis ordered by the density of physical information explicitly encoded in the model. Crucially, advancing on this continuum increases demands on both the representation of interior dynamics and the representation of environmental coupling.

1.

Representational. Organizes structural and compositional information without dynamics. No explicit environmental coupling is required or meaningful at this level.

2.

Predictive/statistical (projection model). Fitted to data; predicts perturbation responses or phenotypes. Captures a principled low-dimensional projection of the full mechanistic description onto observable subsets. Exemplified by cell foundation models such as scGPT, [6] Geneformer, [7] scFoundation, [8] and X-Cell. [9] Environmental context is encoded implicitly through training data rather than through explicit physical exchange, so the balance question does not yet arise.

3.

Stochastic mechanistic. Propagates explicit physical reactions, diffusion, and forces through stochastic trajectories. Exemplified by Thornburg et al. [5] Environmental coupling begins to matter explicitly: boundary conditions and thermal fluctuations can no longer be absorbed into training statistics.

4.

Focus-dynamic hybrid. One or more regions of biological interest are modeled with explicit dynamical fidelity while their surroundings are represented at a coarser level of physical detail—the cellular-scale analogue of the QM/MM paradigm. [29,30,31] As a balance-aware architecture, the focus-dynamic hybrid explicitly matches the environmental representation to the fidelity demands of each dynamically resolved region.

5.

Fully physical. All degrees of freedom are propagated under explicit physical forces—whether through classical force fields, quantum-mechanical Hamiltonians, or combinations thereof—with no component of the system projected onto a statistical summary. Because no component is statistically projected, the environmental coupling must reproduce the relevant physical exchange—fluctuation spectra, response functions, and conservation laws—at a fidelity commensurate with the interior, whether through explicit physical modeling, stochastic methods that faithfully emulate real boundary forces, or AI-learned coupling schemes validated against experimental measurements. The accuracy of the overall representation is ultimately bounded by quantum-computational approximations and available experimental validation.

Moving toward the mechanistic end of the continuum demands that environmental coupling advance in parallel with interior detail. Statistical models do not face this demand because they do not claim explicit dynamical exchange with any environment—but they remain indispensable as principled projections whose authority is bounded by the dimensions they retain. [32,33,34] The central challenge is to understand the principles by which biological constraints arise—the generative understanding that mechanistic models aim to provide.

4. The Phantom-Bath Approximation

Algorithmic devices such as thermostats and barostats are the clearest current embodiment of the imbalance between internal fidelity and environmental coupling. These tools control temperature and pressure by coupling every degree of freedom to a single global variable—a friction coefficient, a piston mass—that enforces the target thermodynamic state instantaneously and homogeneously. A physical bath acts through local material contact and finite propagation speeds: heat flows diffusively, mechanical stress propagates through material with finite speed and molecular dissipation, and chemical species exchange through membranes and channels at rates set by molecular kinetics. A phantom bath, by contrast, acts through algorithmic global coupling with no material mediator: temperature is enforced by rescaling velocities or adjusting a friction variable; pressure is enforced by rescaling atomic coordinates and box dimensions. The phantom bath has no spatial extent, no transport delay, no conservation of local momentum or energy flux—it is an algorithmic reservoir that replaces physically mediated exchange with instantaneous global control.

4.1. How Standard Algorithms Violate Balanced Fidelity

Standard thermostats and barostats both exhibit the phantom-bath structure defined above, though through different mechanisms. Thermostats: Nosé–Hoover dynamics introduces a single global friction variable coupled to the entire system. [35,36] If the total kinetic energy exceeds the target thermal energy, the friction coefficient increases, scaling down all velocities uniformly; if kinetic energy falls short, the friction reverses. Langevin and velocity-rescaling thermostats differ in implementation but share the same structural property: thermal coupling is global and instantaneous (see Supplementary Note S2 for equations). Barostats: Standard barostats (e.g., Andersen and MTK) treat the simulation-box volume as a dynamic variable coupled to a “piston.” [37,38] This coupling acts on every atom simultaneously through uniform box rescaling, so that any localized pressure perturbation is redistributed globally rather than propagating as a mechanical disturbance (see Supplementary Note S3 for equations). The resulting fidelity gap is quantifiable. The characteristic time for thermal relaxation over a subcellular length L scales as $L^2/\alpha$, where $\alpha$ is the thermal diffusivity of cytoplasm. For $L=1\mu$m, the physical relaxation time is on the order of microseconds; a Nosé–Hoover thermostat with a typical coupling time of ∼0.1–1 ps acts three to four orders of magnitude faster, and it acts globally rather than locally. The mismatch for mechanical stress is comparably severe (see Supplementary Note S4 for derivations). This gap is not a minor quantitative correction—it is a qualitative difference between physically mediated exchange and algorithmic imposition.

4.2. Empirical Evidence of Phantom-Bath Artifacts

Empirical studies have documented several artifacts arising from phantom-bath coupling:

• Harvey et al. [39] demonstrated that velocity-rescaling thermostats induce the “flying ice cube” effect—violations of equipartition due to excessive energy accumulation in low-frequency translational modes at the expense of intramolecular vibrations.
• Patra et al. [40] showed that the Berendsen barostat generates nonphysical ordering artifacts in lipid bilayers, distorting area per lipid and lateral pressure profiles.
• Best and Hummer [41] found that Langevin dynamics alters protein folding rates—too little friction underestimates barrier recrossing, while excessive friction overdamps transitions, shifting folding kinetics by an order of magnitude.
• Sääskilahti et al. [42] demonstrated that thermostats introduce spurious thermal resistance near interfaces, creating artificial barriers to heat flow absent in physically realistic simulations.

Each artifact reflects the same structural mismatch: interior dynamics resolved at one level of physical detail, environmental coupling operating at a much coarser level (see Supplementary Note S5 for extended discussion).

4.3. Alternative Environmental Coupling Strategies

Practitioners have developed several strategies that improve environmental coupling beyond naive phantom baths, and these represent genuine progress toward balanced fidelity.

Spatially partitioned thermostats. Assigning separate Nosé–Hoover or Langevin thermostats to distinct spatial zones introduces a coarse spatial resolution absent from a single global coupling. [35] This improves the spatial structure of thermal coupling but still imposes algorithmic temperature control within each zone rather than allowing heat to flow through physical conduction.

Momentum-conserving local coupling. Dissipative particle dynamics (DPD) preserves pairwise momentum exchange and supports the hydrodynamic modes that Langevin damping suppresses, bringing environmental dissipation into closer fidelity with real fluid coupling. [43]

Explicit environmental modeling. The most direct approach is to model more of the environment explicitly, thermostatting remote boundary regions rather than the cellular interior, so that interior dynamics equilibrates through physically realistic transport rather than artificial damping. [44]

Interface-mediated pressure control. Replacing global box rescaling with deformable boundaries, wall potentials, or explicit extracellular mechanical elements makes internal pressure an emergent local quantity rather than a prescribed scalar.

Open-system and hybrid methods. Grand-canonical schemes, boundary insertion-deletion, and particle-continuum hybrids offer routes to boundary conditions whose chemical-potential and hydrodynamic accuracy matches the mechanistic level of the interior. [45,46]

Adaptive-resolution methods. These interpolate between atomistic and coarse-grained representations, allowing different regions to be treated at different levels of fidelity. [30,44] They reduce phantom-bath severity for some observables but introduce their own interface artifacts unless carefully matched.

Each of these strategies helps to the extent that it better matches environmental coupling to the modeled interior. None eliminates the fundamental imbalance by itself: the coupling within each zone, across each resolution boundary, or at each open-system interface remains an approximation whose fidelity must be assessed against the interior physics it serves.

4.4. Cellular Processes Vulnerable to Phantom-Bath Distortions

The processes most vulnerable to phantom-bath distortion are those in which environmental exchange—heat flow, momentum transfer, volume coupling, and electrochemical dissipation—is not incidental noise but a constitutive part of the mechanism producing the biological response. In each case, the common distortion is the same: a global algorithm redistributes energy, momentum, or volume across the entire system on algorithmic timescales orders of magnitude shorter than the physical transport process, replacing a spatially structured, rate-dependent exchange with an effectively uniform one. What differs is the biology that this replacement disrupts.

Localized metabolic heat production. Metabolic free-energy transduction is spatially heterogeneous: ATP synthesis, respiratory-chain turnover, and proton pumping localize dissipation to specific membranes and complexes. [47] The biological response—organelle temperature gradients, heat-shock signaling, and thermosensitive enzyme regulation—depends on where heat is injected and how it propagates through the cellular medium.

Active matter and cytoskeletal mechanics. The cytoskeleton is an ATP-fueled active material whose long-range mechanical organization depends on momentum being generated locally and propagated through the cytoskeletal network and surrounding medium. [17,18,48] Suppressing local momentum conservation eliminates the hydrodynamic and elastic modes through which motors at one location drive correlated motion elsewhere.

Phase separation and condensate dynamics. Biomolecular condensates require active maintenance away from equilibrium. [20,21] Their assembly, composition, and lifetime are regulated by ATP-dependent processes—enzymatic remodeling, post-translational modification cycles, and active transport—that continuously consume chemical energy. [?] The local thermal and mechanical conditions under which phase boundaries are maintained depend on where and how intensely these energy-dissipating reactions operate.

Membrane mechanics and osmotic regulation. Cells regulate volume, membrane tension, and pressure through a sequential physical process: localized solute imbalances drive local membrane stress, which drives water transport and shape change, which feeds back on tension and signaling. [49,50] Each link in this chain is spatially localized and rate-dependent.

Electrochemical coupling. Cells maintain electrochemical gradients that drive proton motive force, membrane voltage propagation, and ion-channel activity. [51,52] These processes depend on the local partitioning of energy among electrical work, dissipation, and mechanical motion—a partitioning that requires physically realistic dissipation timescales to remain interpretable.

5. Toward Physically Grounded Environmental Modeling

These vulnerabilities call for architectures in which environmental exchange is represented through physically grounded mechanisms—local transport, finite propagation, conservation laws, and material boundary conditions—at a level of realism commensurate with the interior. Numerical stabilization remains a legitimate engineering task, but it must be kept clearly separate from physical environmental representation and deliberately minimized.

The focus-dynamic hybrid strategy. Atomistic simulation of small bacterial cells is already computationally feasible, but whole-cell atomistic resolution under phantom-bath coupling does not, by itself, yield mechanistically interpretable dynamics. A more productive strategy is to achieve locally balanced fidelity where responsiveness matters most: model one or more cellular regions of biological interest with explicit dynamics at the appropriate level of physical fidelity, while the remaining cellular context is represented at a coarser but physically grounded level of detail. In its simplest form, this is a two-level scheme analogous to QM/MM: [29,30,31] a dynamically treated focus embedded in a physically grounded background. A mitochondrial-membrane focus could model proton translocation and ATP-synthase mechanics at atomistic or coarse-grained resolution while representing the rest of the cell as a thermally and chemically realistic reservoir; a cytoskeletal-cortex focus could treat actin-myosin dynamics with momentum-conserving methods while the cytoplasmic interior is projected onto a continuum description (see Supplementary Note S7 for detailed examples with computational and experimental foundations). The focus need not be spatially localized—it can equally be a molecular process such as ribosome translation or a biochemical pathway modeled with explicit dynamics while surrounding processes are projected onto their statistical descriptions. More generally, the architecture is flexible in both the number of levels and the resolution at each level. The choice is dictated by the scientific question, not by cell size: a two-level scheme with a coarse-grained focus embedded in a continuum background may be entirely appropriate for a mammalian cell if the question concerns mesoscale mechanics, while a hierarchical scheme with multiple levels of progressively coarsened representation may be necessary when the scales separating a focal region from the cell boundary span distinct physical regimes. In either case, each interface must satisfy balanced fidelity locally so that no single boundary concentrates a fidelity cliff.

This framework defines a fertile ground for algorithm development. The static molecular-mechanical backgrounds of classical QM/MM—fixed force fields with no capacity for history dependence or adaptive context sensitivity—are insufficient for cellular-scale applications where the background must represent living, responsive environments. Constructing coarse-grained representations that satisfy conservation laws while capturing context-dependent, history-sensitive behavior; managing adaptive interfaces that shift as the simulation evolves; and coordinating multiple levels of a hierarchical scheme are open algorithmic challenges. Machine learning offers promising paths toward each of these capabilities, but so do physically motivated approaches such as adaptive-resolution methods, [44] fluctuating hydrodynamic closures, [53,54] and data-informed continuum models. [55] What matters is not any single enabling technology but the richness of the design space: the background levels can, in principle, encode path-dependent environmental context that static force fields cannot. Whichever algorithmic path is pursued, physical grounding remains essential: learned or fitted components must be validated against physical targets such as response functions, conservation laws, and fluctuation spectra, not only steady-state pattern matching. The strategic value of this architecture is cumulative: as successive focal regions are brought under explicit dynamical control, the background is progressively displaced—advancing incrementally toward the full physically dynamical cell.

6. Experimental Foundations for Balanced Fidelity

The widening imbalance identified above is not merely a methodological gap—it reflects an asymmetry in the experimental foundations available to each side. Since all force fields and simulation methods are ultimately parameterized and validated against experimental data, computational innovation alone cannot close the gap. This is not a special requirement of balanced fidelity—it is the foundational epistemology of physical science. What makes the constraint specifically acute here is that the two sides are at very different stages of maturity: the experimental data needed to parameterize and validate interior dynamics—spectroscopic, crystallographic, calorimetric, and scattering measurements of molecular interactions—is comparatively mature, which is precisely why interior fidelity has advanced so rapidly. By contrast, the experimental data needed to parameterize and validate environmental coupling at cellular interfaces—spatially resolved thermal transport, momentum transfer and viscoelastic response across organelle boundaries, nonequilibrium fluctuation landscapes in living cells—is comparatively immature.

Closing this gap requires investment in specific experimental frontiers. Spatially resolved thermal measurements inside cells remain at the frontier of nanothermometry; [47,56] mechanical response at organelle interfaces is only beginning to be characterized by microrheology and force-spectrum microscopy; [48,57] and the rate-dependent fluctuation spectra that govern condensate dynamics, mechanotransduction, and electrochemical coupling are largely unmapped at the spatiotemporal resolution that simulation validation demands. These physical validation targets are themselves experimental deliverables: spatially resolved fluctuation spectra and interface response functions must be measured at the relevant spatiotemporal scales before they can serve as training or validation data for any computational approach, whether physics-based or learned.

The most productive path forward co-develops simulation capability and experimental measurement: each focal region brought under dynamical control should be paired with targeted experiments that provide the validation data its interfaces demand. Without this pairing, learned or fitted components risk reconstructing phantom-bath distortions in less transparent form (see Supplementary Note S8 for discussion of AI-learned environmental models and their validation challenges), and benchmark problems remain theoretical exercises without experimental ground truth. The benchmarks and standards proposed in the next section operationalize this experimental foundation: they can serve as genuine tests of environmental fidelity only when paired with the measurement data identified here.

7. Implications for Benchmarks, Standards, and Funding

Realizing this design principle requires not only new methods but complementary changes in how the field evaluates, reports, and funds virtual-cell work. The physical-fidelity continuum and the focus-dynamic hybrid architecture both imply that a single model may combine representations at very different levels of physical detail—statistical projections for some processes, explicit dynamics for others, and coarser intermediate descriptions in between. The relevant question is therefore not whether a model is “statistical” or “physical” as a whole, but whether the claims made about specific observables are commensurate with the fidelity actually achieved in the part of the model that produces them. [1,2] A community that calibrates evaluation criteria to the fidelity level demonstrated—process by process and claim by claim—can reward genuine progress at every point on the continuum. Without such claim-specific evaluation, a model’s strongest components can be used to justify conclusions that depend on its weakest.

Reporting standards. Any virtual-cell claim should state where on the physical-fidelity continuum the model sits, and benchmarks should distinguish between claims appropriate for statistical projections and those for mechanistic models. Studies making strong mechanistic claims should further disclose: (a) the environmental coupling algorithms used; (b) characteristic coupling and relaxation times; (c) whether coupling is global or spatially localized; and (d) how the bath or boundary treatment should be classified. A practical vocabulary clarifies this last point: physical environmental models (explicit solvent shells, extracellular space, open reservoirs) represent the environment through material degrees of freedom; reduced effective models (DPD coupling, fluctuating hydrodynamic closures, generalized Langevin boundaries) encode physical transport properties without full material resolution; numerical stabilizers (weak remote thermostats, shadow-energy corrections) serve only computational stability. Each class is legitimate; the error is allowing them to blur—treating a numerical stabilizer as a faithful environmental mechanism conflates physical resolution with algorithmic convenience. Critically, environmental-coupling assumptions must be commensurate with the level of interior dynamics represented: a model that resolves nonequilibrium mechanistic processes internally cannot satisfy its own claims with an environmental representation that suppresses those processes at the boundary. Sensitivity analyses—varying thermostat type, coupling strength, and boundary conditions—should be reported for all key conclusions, so that reviewers and readers can distinguish results that are robust from those that depend on bath assumptions.

A benchmark hierarchy for balanced fidelity. Evaluation requires benchmarks organized specifically around the interior–environment interface. We propose three benchmark classes, each probing a different aspect of environmental-coupling fidelity: (i) equilibrium and near-equilibrium controls that verify baseline thermodynamic and structural correctness; (ii) transport and exchange fidelity tests that verify whether heat, momentum, matter, and relaxation spectra propagate through physically realistic mechanisms—finite-speed transport, conservation laws, and correct boundary exchange—rather than being imposed or absorbed by algorithmic control; and (iii) responsive-robustness tests that verify whether mechanistic conclusions survive variation of environmental coupling algorithms and boundary assumptions. Concrete challenge problems—such as a localized metabolic heat source in a subcellular water box, or a pressure pulse propagating through a membrane–solvent interface—can anchor cross-method comparison within each class, provided that experimental ground truth—spatially resolved fluctuation spectra, response functions, and propagation profiles at the relevant interfaces—is developed in parallel (see Supplementary Note S7 for representative challenge problems). Funding programs should treat environmental modeling as core scientific infrastructure for physical fidelity, reserving explicit space for benchmark consortia, shared test problems, and reference implementations of localized coupling schemes. Funding agencies should ask: “Does this proposal specify its position on the continuum, and do its environmental-modeling assumptions match the dynamical fidelity of the represented interior?”

8. Conclusion

The core principle of this paper is balanced physical fidelity: a dynamical focus and its environment must be represented at commensurate levels of physical detail, because accuracy that is high on one side of an interface and low on the other does not average to adequate—it concentrates artifacts at the boundary. Although phantom baths at the cell–environment boundary are the most visible instance, the principle applies to every interface in a multiscale architecture—statistical, continuum, or coarse-grained—making the coupling at each boundary an explicit object of scientific design.

Balanced fidelity does more than refine computational methodology—it opens biological questions that current approaches cannot pose. A virtual cell with commensurate accuracy across its interfaces can address problems at the heart of biomedicine and biotechnology: follow an epithelial–mesenchymal transition driven by matrix stiffening, predict how a therapeutic agent redistributes energy dissipation across organelles, or guide the engineering of synthetic circuits under realistic nonequilibrium conditions.