Yulong Sun

Single-cell perturbation data have enabled computational models that predict intervention-associated cellular responses. This narrative review organizes explicit gene-regulatory-network simulations, latent and compositional response models, graph, optimal-transport, and dynamical approaches, foundation-model representations, and emerging spatial or cross-modal settings through a virtual-knockout lens. We distinguish representation-level ablation from causal genetic intervention and separate held-out regimes for genes, cell types or contexts, perturbation combinations, chemical structures, and doses. We compare representative methods, experimental resources, metrics, and benchmark frameworks and operationally define a prospective holographic virtual cell by its modalities, regulatory and physical constraints, intervention-conditioned outputs with calibrated uncertainty, and prospective validation requirements. The holographic virtual cell and Design−Build−Test−Learn roadmap are presented as future-facing criteria rather than as an achieved clinical capability.

Bioelectrical signals, particularly endogenous direct-current electric fields (EFs), constitute a pivotal yet often underappreciated class of physical cues within the cellular microenvironment. Macrophages, as central effectors of the innate immune system, exhibit remarkable plasticity in response to physicochemical stimuli. Accumulating evidence indicates that both endogenous and applied direct current EFs can significantly modulate key macrophage behaviors, including electrotaxis, phenotypic polarization (M1/M2), and phagocytic activity. While the precise molecular architecture remains to be fully delineated, current research suggests a multi-layered regulatory network involving surface charge redistribution, polarized activation of growth factor receptors, and signal transduction mediated by voltage-gated or mechanosensitive ion channels, which subsequently converge on downstream pathways such as phosphoinositide 3-kinase/protein kinase B (PI3K/Akt), mitogen-activated protein kinase (MAPK), and nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB). A rigorous dissection of the biophysical and molecular mechanisms governing these responses is essential for elucidating the role of physical signaling in tissue repair, chronic inflammation, and the tumor microenvironment. Furthermore, distinguishing the mechanistic nuances between physiological endogenous fields and therapeutic applied fields provides a critical theoretical foundation for developing next-generation, bio-mimetic immunomodulatory strategies. Future efforts should focus on characterizing the in vivo spatiotemporal dynamics of EFs, unraveling cell-type-specific response mechanisms, and assessing the translational feasibility of these fundamental insights.

Tumor Treating Fields (TTFields) and pulsed electric fields (PEF) have emerged as a fourth modality in oncological treatment, succeeding surgery, radiotherapy, and pharmacotherapy. However, the fundamental mechanisms through which physical electrical stress is precisely transduced into biochemical signals remain to be fully elucidated. By integrating molecular dynamics simulations, multi-omics profiling, and the latest clinical trial data, this review systematically explores how electric fields, functioning as physical signals, initiate intracellular transduction by inducing conformational kinetic changes in G protein-coupled receptors (GPCRs), such as NPFFR2. Furthermore, we discuss how the modulation of waveform parameters enables the precise programming of apoptosis and pyroptosis patterns, thereby inducing immunogenic cell death (ICD) and activating the cGAS-STING pathway. Additionally, proteomics-based insights are employed to reveal the mechanisms of electric-field-induced compensatory resistance, and the pivotal role of electric fields in remodeling the tumor immune microenvironment and reversing resistance to immune checkpoint inhibitors is discussed. This review aims to provide refined theoretical guidance for the clinical translation of the emerging field of the “electro-immunome”.

The therapeutic efficacy in malignant pleural mesothelioma (MPM) is severely hindered by its dense stromal barrier and immunosuppressive microenvironment. Although Tumor Treating Fields (TTFields) have demonstrated significant survival benefits in clinical trials, their underlying mechanisms extend well beyond simple mitotic arrest. This review delineates a “multidimensional biophysical remodeling” atlas of TTFields in MPM. First, we highlight that TTFields physically disrupt actin-dependent intercellular “tunneling nanotubes, ” thereby severing the tumor’s communication and metabolic rescue networks. Second, physical stress downregulates the Fanconi Anemia-BRCA pathway, inducing a state of “conditional BRCAness” that specifically sensitizes tumors to DNA-damaging agents. Finally, this cascade triggers immunogenic cell death and orchestrates a chemokine storm, which is hypothesized to facilitate the conversion of the tumor microenvironment from an immune “cold” to a “hot” phenotype. Collectively, by integrating the physical disintegration of subcellular structures with immunological remodeling, TTFields may offer a hypothetical theoretical framework for overcoming therapeutic resistance in MPM.

Atopic dermatitis (AD) is a chronic inflammatory dermatosis characterised by skin barrier disruption and immune dysregulation. Current clinical scoring systems (e.g., SCORAD) often fail to quantify subclinical pathophysiology or characterise the biopharmaceutical interface. This review synthesises the ‘bioelectric profile’ of AD, integrating electrical impedance spectroscopy (EIS) and current perception threshold (CPT) to construct a precision phenotyping framework. Evidence indicates that EIS non‐invasively quantifies barrier integrity, with specific parameters (e.g., EIS ^diff ) that correlate positively with terminal differentiation proteins such as filaggrin, serving as a surrogate marker of molecular permeability. Concurrently, neuroselective CPT assessment reveals abnormal C‐fibre sensitisation in non‐lesional skin, distinguishing extrinsic from intrinsic AD phenotypes. Furthermore, we explore reciprocal interactions between bioelectric parameters, Th2/Th22 cytokines (e.g., IL‐31, IL‐13) and the microbiome. Finally, we discuss translating these signatures into closed‐loop theranostic strategies for feedback‐controlled drug delivery. This bioelectric panorama provides a unique biophysical perspective on AD pathogenesis and a theoretical foundation for future precision medicine.

The high heterogeneity of the disease microenvironment is a critical factor contributing to therapeutic failure and the emergence of drug resistance; however, predicting drug responses with precision at single-cell resolution remains a substantial challenge. Traditional pharmacogenomic studies are constrained by averaged signals at the population level, which frequently obscure rare yet lethal resistant subpopulations. This article reviews a closed-loop strategy that integrates computational pharmacology with cell biology to address this dilemma. First, we explore computational frameworks based on Deep Transfer Learning and Domain Adaptation, such as scDEAL and SCAD. These algorithms can transfer pharmacological knowledge from large-scale cell lines to clinical single-cell data, thereby enabling virtual prediction of cellular drug sensitivity in the absence of experimental labels. Second, based on algorithmic predictions, we elucidate chemotherapy-induced Transcriptional Stress States and their co-evolutionary mechanisms with inflammatory stromal cells, as well as interactions that construct an immunosuppressive barrier protecting residual disease. Finally, we demonstrate the feasibility of reprogramming these specific pathological states using small-molecule drugs (e.g., decitabine, benzofuran derivatives), including the reversal of macrophage polarization imbalance in spinal cord injury and the amelioration of osteogenic differentiation disorders in osteoporosis. This integrated “algorithm prediction–mechanism elucidation–drug intervention” strategy provides a novel paradigm for precision therapy to reverse disease-associated cell fates.