{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T10:01:36Z","timestamp":1789984896613,"version":"4.0.1"},"reference-count":111,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,20]],"date-time":"2026-09-20T00:00:00Z","timestamp":1789862400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cells"],"abstract":"<jats:p>The term virtual cell now covers objects ranging from curated biochemical simulators to atlas-trained neural representations, yet the decisive empirical question is narrower: whether a model built to obey known biochemical rules predicts the effects of new drugs or genetic changes better than an otherwise identical model that lacks them. Formally, this Perspective asks whether a biologically specified mechanistic operator improves out-of-distribution prediction of single-cell perturbation responses beyond strong simple, relational, transport-based, and learned-dynamical comparators. Recent benchmarks show that rankings depend on partition, statistical unit, effect-size distribution, gene set, and metric, and that several deep and foundation-model approaches fail to exceed no-change, mean-effect, additive, or linear controls. Set-based, knowledge-graph, transport, and learned state-equation models can improve defined tasks. No identified study isolates the incremental value of a biologically specified operator against late fusion, a capacity-matched static model, a capacity-matched learned-dynamical operator, and topology-rewired controls under one locked evaluation. Six source categories are distinguished, with the operator category divided into biologically specified and learned-dynamical subclasses. A minimum falsification framework is specified for the unfolded protein response with a declared capacity ledger, endpoint times chosen by a prespecified Stage 1 rule, and a locked comparator set. The framework is prospective: no new data, model implementation, or performance result is reported, and every threshold is a design choice awaiting empirical test.<\/jats:p>","DOI":"10.3390\/cells15181711","type":"journal-article","created":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T09:01:38Z","timestamp":1789981298000},"page":"1711","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Explicit Mechanistic Operators in Predictive Virtual Cells: Evidence, Failure Modes, and a Minimum Falsification Framework for Single-Cell Perturbation Prediction"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-3931-0566","authenticated-orcid":false,"given":"Miko\u0142aj","family":"Sta\u0144czak","sequence":"first","affiliation":[{"name":"School of Mathematical and Computer Science, Heriot-Watt University, Edinburgh EH14 4AS, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0513-0336","authenticated-orcid":false,"given":"Sarfaraz K.","family":"Niazi","sequence":"additional","affiliation":[{"name":"College of Pharmacy and Pharmaceutical Sciences, Washington State University, Spokane, WA 99202, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1016\/S0167-7799(01)01740-1","article-title":"The Virtual Cell: A software environment for computational cell biology","volume":"19","author":"Loew","year":"2001","journal-title":"Trends Biotechnol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1016\/j.cell.2012.05.044","article-title":"A whole-cell computational model predicts phenotype from genotype","volume":"150","author":"Karr","year":"2012","journal-title":"Cell"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1016\/j.cell.2021.12.025","article-title":"Fundamental behaviors emerge from simulations of a living minimal cell","volume":"185","author":"Thornburg","year":"2022","journal-title":"Cell"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"eaav3751","DOI":"10.1126\/science.aav3751","article-title":"Simultaneous cross-evaluation of heterogeneous E. coli datasets via mechanistic simulation","volume":"369","author":"Macklin","year":"2020","journal-title":"Science"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"7045","DOI":"10.1016\/j.cell.2024.11.015","article-title":"How to build the virtual cell with artificial intelligence: Priorities and opportunities","volume":"187","author":"Bunne","year":"2024","journal-title":"Cell"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1038\/s41746-022-00610-z","article-title":"Building digital twins of the human immune system: Toward a roadmap","volume":"5","author":"Laubenbacher","year":"2022","journal-title":"npj Digit. 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