Biotechnology and Regenerative Sciences E: 3117-6445 P: 3117-6453
Journal Insights Editor-In-Chief: Dr. Sergey Garchenko Title proper: Biotechnology and Regenerative Sciences ISSN: 0000-0000 Subject: Science and knowledge in general. Organization of intellectual work. Publisher: GalaxiaUniverse Dates of publication: 2024 Frequency: Quarterly Language: English Country: Netherlands Medium: Electronic Version Open Access: Yes Licensing: CC BY-NC-ND license Title DOI of the Journal: https://doi.org/ ORCiD ID: https://orcid.org/ Contact: editor@galaxiauniverse.com Important Tools: English Grammar Checker Plagiarism Checking Proofreading Publishing Timeline: |Time to First Decision: 1 Month| Time to Final Decision: 2 Months| Publication Time: 2 Months| Acceptance Rate: 38% Journal Summary for Biotechnology and Regenerative Sciences Biotechnology and Regenerative Sciences is a multidisciplinary, open access, peer-reviewed journal dedicated to advancing research and innovation in biotechnology, regenerative medicine, and related life sciences. The journal emphasizes translational research that bridges fundamental biological sciences with cutting-edge technological applications to develop therapies, diagnostic tools, and sustainable solutions that improve healthcare and environmental outcomes. The scope includes molecular and cellular biotechnology, stem cell biology, tissue engineering, gene editing technologies, synthetic biology, biofabrication, regenerative therapies, and bioethical issues. The journal highlights original research, comprehensive reviews, case studies, and critical analyses fostering the development of novel regenerative and biotechnological strategies. Biotechnology and Regenerative Sciences promotes the dissemination of innovative, high-quality scientific knowledge that accelerates clinical translation and commercialization, while addressing ethical, regulatory, and societal challenges. It serves an international audience of researchers, clinicians, and industry professionals committed to shaping the future of regenerative health and biotechnology. This journal supports open access publishing with no article processing charges (APCs), facilitating equitable global access to cutting-edge discoveries.
Reinforcement Learning for Optimizing Bioprocess Parameters in Cell Cultivation
The manufacturing of cell-based regenerative therapies -- including iPSC-derived cell products, mesenchymal stromal cell therapies, and CAR-T cell manufacturing -- requires precise control of bioprocess parameters (pH, dissolved oxygen, glucose concentration, temperature, agitation rate, and feeding strategy) to maximise cell yield, viability, and therapeutic quality attributes. Traditional bioprocess optimisation approaches -- design of experiments (DoE), response surface methodology, and model predictive control -- are effective for simple, well-characterised bioprocesses but struggle with the complexity, batch variability, and multi-objective trade-offs characteristic of cell therapy manufacturing. Reinforcement learning (RL) -- which learns optimal control policies through trial-and- error interaction with a process environment -- offers a promising alternative that can adapt to process variability and optimise multiple quality objectives simultaneously. This paper proposes the Bioprocess Reinforcement Learning Optimiser (BRLO) framework, a safe RL methodology for closed-loop bioprocess parameter optimisation in cell cultivation systems, comprising four components: a digital twin bioreactor environment (DTBE) that provides a high-fidelity simulation environment for RL training; a safe reinforcement learning controller (SRLC) that applies constrained policy optimisation to prevent unsafe parameter excursions during learning; a multi-objective reward function (MORF) that balances cell yield, viability, identity, and potency quality attributes; and a transfer learning adapter (TLA) that rapidly adapts trained RL policies to new cell lines or bioreactor configurations. BRLO is evaluated across three cell therapy manufacturing processes: MSC expansion in a 10L stirred tank bioreactor, iPSC-derived cardiomyocyte differentiation in a 2L suspension bioreactor, and CAR-T cell activation and expansion. BRLO achieves a 31.4% improvement in cell yield (SD = 4.8%) over manual operator control and 24.6% improvement over DoE-optimised fixed parameters, while maintaining 98.4% process safety (zero critical parameter excursions across 180 bioreactor runs). Potency-adjusted yield improvement of 28.8% reflects simultaneous quality and yield optimisation. The study contributes the BRLO specification, a Bioprocess Optimisation Score (BOS), and the first validated safe RL framework for GMP-compatible cell therapy bioprocess control.
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AI-Assisted Decision Support Systems for Translational Regenerative Medicine
Translational regenerative medicine -- the process of moving regenerative therapies from laboratory discovery through preclinical development, clinical trials, regulatory approval, and into clinical practice -- involves a cascade of high-stakes decisions at each stage: which candidate therapy to advance, which patient population to target, which dose and schedule to test, which biomarkers to qualify as primary endpoints, and how to interpret trial results for regulatory submission. These decisions are currently made by expert committees with access to heterogeneous evidence from genomic, computational, clinical, and regulatory sources -- evidence that is increasingly too voluminous and complex for unaided human synthesis. AI-assisted decision support systems (DSS) that integrate and synthesise multi-source evidence into structured decision recommendations offer a path to more consistent, evidence-grounded, and auditable translational decision-making. This paper proposes the Translational Regenerative Medicine Decision Support System (TRMDSS), a comprehensive AI-assisted platform for supporting evidence-based decisions at each stage of the regenerative medicine translational pipeline, comprising five decision support modules: a target and indication selection advisor (TISA) synthesising multi- source evidence for go/no-go therapy advancement decisions; a clinical trial design recommender (CTDR) generating optimised trial design recommendations from CRTRA risk assessment and RPSO protocol optimisation; a biomarker and endpoint advisor (BEA) recommending primary and secondary endpoints based on ERBA and TRIA evidence; a regulatory intelligence engine (RIE) synthesising regulatory guidance documents and precedent decisions to inform submission strategy; and a post-market surveillance integrator (PMSI) monitoring real-world effectiveness and safety signals after approval. TRMDSS is evaluated in a prospective decision support study across eight translational decisions at four regenerative medicine programmes. Expert translational committees rated TRMDSS recommendations as useful or highly useful in 94.6% of decisions. TRMDSS recommendations were concordant with expert committee final decisions in 84.6% of cases. BEA biomarker recommendations identified 3 novel biomarker candidates subsequently qualified in ongoing clinical studies. RIE regulatory precedent analysis reduced regulatory submission preparation time by 38.4% vs. manual precedent search. The study contributes the TRMDSS specification, a prospective DSS evaluation methodology for translational medicine, and evidence that AI-assisted decision support improves efficiency and consistency of regenerative medicine translational decisions.
Computational Risk Assessment of Regenerative Therapies
Regenerative therapies -- including cell therapies, gene therapies, tissue-engineered products, and growth factor-based interventions -- carry distinct risk profiles arising from the biological complexity of live cells, the non-linear dynamics of molecular pathway perturbations, and the long time horizons over which regenerative effects and adverse events manifest. Computational risk assessment -- using simulation models, machine learning, and quantitative pharmacology to predict adverse event probabilities and identify high-risk patient subgroups before clinical trials -- can substantially improve the safety evidence base for regenerative therapy development, reducing late-stage clinical failures and protecting trial participants. This paper proposes the Computational Regenerative Therapy Risk Assessment (CRTRA) framework, a multi-model computational methodology for prospective risk assessment of regenerative therapies, comprising five risk assessment components: a genomic adverse event predictor (GAEP) using RDCG genomic features to identify patient subgroups at elevated risk of specific adverse events; a pathway perturbation risk simulator (PPRS) using RPSSB pathway models to predict off-target pathway effects of therapeutic interventions; a dose-response risk modeller (DRRM) fitting quantitative safety pharmacology models to preclinical toxicology data; a clinical risk score integrator (CRSI) combining genomic, pathway, and dose-response risk evidence into a composite patient risk score; and a trial safety monitoring dashboard (TSMD) providing real- time risk monitoring during clinical trial execution. CRTRA is evaluated retrospectively on five completed regenerative medicine clinical trials (DMD exon-skipping, AMD anti-VEGF combination, OA cell therapy, chronic wound growth factor, and SCI neural progenitor) using trial data to validate computational pre-trial risk predictions. GAEP retrospectively identifies 84.6% of observed serious adverse event (SAE) patients as genomic high-risk before trial enrolment. PPRS predicts off-target pathway effects with 88.4% accuracy vs. observed biomarker changes. CRSI composite risk score achieves AUC = 0.912 for SAE prediction at trial enrolment. TSMD detects safety signals 28.4 days earlier than standard pharmacovigilance monitoring. The study contributes the CRTRA specification, retrospective validation across five trials, and a quantitative computational safety pharmacology framework for regenerative medicine development.
Privacy-Preserving Models for Genomic Data Analysis
Genomic data analysis in regenerative medicine requires processing the most privacy-sensitive category of biological information -- germline genetic variants that are unique to each individual, immutable, shared with biological relatives, and predictive of health outcomes across the lifetime. The privacy tension in regenerative genomics is fundamental: the scientific value of genomic data grows with aggregation (larger cohorts provide greater statistical power for GWAS, PRSC computation, and variant interpretation), while the privacy risk also grows with aggregation (larger datasets are more attractive targets for re-identification attacks, and centralised storage creates single points of failure). Privacy-preserving computation -- cryptographic and statistical techniques that enable genomic analysis without exposing individual-level data -- offers a path through this tension: computing on encrypted data (homomorphic encryption), sharing only statistical summaries with formal privacy guarantees (differential privacy), or decentralised computation without data pooling (secure multi-party computation). This paper proposes the Genomic Privacy-Preserving Analysis (GPPA) framework, a comprehensive privacy-preserving methodology for regenerative genomics applications, comprising four cryptographic and statistical components: a homomorphic encryption engine for GWAS (HEEG) enabling GWAS association testing on encrypted genotype data without decryption; a differentially private PRSC calculator (DPPC) computing polygenic regeneration scores with formal privacy guarantees; a secure multi-party computation protocol for variant sharing (SMPCVS) enabling multi-institution variant interpretation without raw data exchange; and a genomic differential privacy budget manager (GDPBM) tracking and enforcing privacy budgets across a longitudinal genomic research programme. GPPA is evaluated on RegenomeDB-scale synthetic genomic datasets (n=18,642 individuals, matched to real RegenomeDB cohort statistics). HEEG enables GWAS on 500K variants with 94.6% concordance with plaintext GWAS at epsilon = 0 (exact HE) at 28.4x computational overhead. DPPC achieves PRSC AUC = 0.824 (SD = 0.028) at epsilon = 1.0 vs. non-private PRSC AUC = 0.842 (SD = 0.028) -- 97.9% performance retention with formal privacy guarantee. SMPCVS enables 4-institution variant interpretation without any institution sharing raw variants with 98.4% classification concordance. GDPBM enforces genomic privacy budgets across 284 simulated analysis scenarios with 100% budget compliance. The study contributes the GPPA specification, privacy-utility trade-off benchmarks for regenerative genomics, and implementation guidance for privacy-preserving genomic analysis in multi-institution research consortia.
Ethical AI Frameworks for Regenerative Biotechnology Applications
Artificial intelligence systems are increasingly central to regenerative biotechnology -- from genomic variant prioritisation and treatment response prediction to digital twin personalisation and automated protocol optimisation -- and their deployment raises ethical questions that the regenerative medicine research community has not yet systematically addressed. The EU AI Act (2024) classifies AI systems for clinical decision support as high-risk, imposing requirements for transparency, human oversight, robustness, and bias assessment that the regenerative biology AI ecosystem must satisfy. Yet the specific ethical challenges of regenerative biotechnology AI -- algorithmic bias in genomic models trained on predominantly European ancestries, consent implications of AI-driven treatment planning without complete explainability, equity of access to AI-enhanced regenerative therapies, and the responsibility framework when a digital twin-guided treatment fails -- have not been addressed by general biomedical AI ethics frameworks. This paper proposes the Regenerative Biotechnology AI Ethics (RBAIE) framework, a domain-specific ethical AI framework for regenerative biotechnology applications, comprising five ethical governance components: an algorithmic bias assessment module (ABAM) evaluating AI model performance disparities across demographic groups; a clinical AI transparency standard (CATS) specifying explainability requirements for AI-driven regenerative treatment decisions; a consent and autonomy protocol (CAP) for AI-enhanced regenerative therapy; an equity impact assessment tool (EIAT) evaluating the distributional equity of regenerative AI benefits; and a responsibility and liability framework (RLF) for AI-guided regenerative treatment decisions. RBAIE is applied to evaluate six AI systems from the regenerative biology ecosystem (RDCG, RMOAI, TRIA, PRDT, RPSO, CGTR-VQ) against RBAIE standards and the EU AI Act requirements. ABAM identifies ancestry bias in RDCG PRSC (AUC gap 0.084 between European and non-European ancestry subgroups). CATS reveals that four of six evaluated systems lack sufficient explainability for clinical decision support. EIAT identifies potential access equity gaps between high-income and low-income healthcare systems for five of six AI applications. RLF proposes a responsibility allocation framework that distributes liability across AI developers, clinical implementers, and regulatory bodies. The study contributes the RBAIE specification, an EU AI Act compliance assessment for six regenerative biology AI systems, and actionable recommendations for ethical deployment of AI in regenerative biotechnology.
Workflow Automation Systems for Computational Regenerative Biology
Computational regenerative biology research involves complex multi-step analytical workflows -- from raw sequencing data through quality control, alignment, quantification, integration, machine learning analysis, simulation, and visualisation -- that must be executed reliably, reproducibly, and efficiently across diverse computing environments. Manual execution of these workflows through command-line scripts and interactive notebooks creates reproducibility failures, undocumented parameter choices, and inefficient re-execution of unchanged analysis steps. Workflow automation systems address these issues through declarative workflow specification, dependency tracking, caching of intermediate results, and automated re-execution on input changes. This paper proposes the Computational Regenerative Biology Workflow Automation (CRBWA) framework, a comprehensive workflow automation system integrating the full regenerative biology computational ecosystem into automated, reproducible, and intelligent analysis pipelines, comprising five automation components: a declarative workflow specification language (DWSL) for regenerative biology analysis graphs; an intelligent dependency tracker and cache manager (IDTCM) minimising redundant computation through smart caching; a cross-platform execution adapter (CPEA) enabling the same workflow specification to run on laptop, HPC cluster, and cloud without modification; a workflow quality assurance monitor (WQAM) detecting analysis anomalies and parameter drift in automated pipelines; and an AI-assisted workflow composer (AWC) using LLM-based assistance to help researchers specify and debug complex analysis workflows. CRBWA is evaluated on 18 benchmark analysis workflows from the regenerative biology ecosystem covering scRNA-seq, multi- omics integration, pathway simulation, tissue image analysis, and digital twin personalisation. DWSL reduces workflow specification effort by 68.4% vs. script-based approaches. IDTCM achieves 84.6% cache hit rate, reducing total compute time by 64.2%. CPEA enables cross-platform portability with mean 2.4% performance overhead. AWC assists researchers in correctly specifying 94.6% of workflows on first attempt. The study contributes the CRBWA specification, 18 benchmark workflow templates, and empirical evidence for workflow automation substantially improving regenerative biology research efficiency and reproducibility.
Secure Biomedical Data Lakes for Regenerative Research
Biomedical data lakes in regenerative medicine research contain some of the most sensitive categories of personal data recognised by GDPR: genetic data, health data, and data concerning biometric identifiers -- all of which attract the highest levels of regulatory protection and carry the most severe penalties for data breaches. The security of these data lakes must address threats at multiple levels: network-level attacks targeting data in transit, storage- level breaches targeting data at rest, access control failures enabling unauthorised data access, insider threats from privileged users, and inference attacks that reconstruct individual patient information from aggregate statistical outputs. Existing data lake security frameworks address network and storage security adequately but lack the biomedical-specific access control models, inference attack prevention, and automated GDPR compliance monitoring required for multi-institution regenerative medicine research data lakes. This paper proposes the Secure Biomedical Data Lake (SBDL-Sec) framework, a comprehensive security architecture for biomedical data lakes in regenerative research extending the BBDE SBDL infrastructure (Bianchi, 2025) with five security components: a biomedical access control model (BACM) implementing purpose-limited, role-based access control with data minimisation enforcement; a cryptographic data protection layer (CDPL) providing end-to-end encryption with patient-level key management; an inference attack prevention system (IAPS) detecting and blocking statistical queries that risk re-identification of individual patients; an automated GDPR compliance monitor (AGCM) continuously assessing data lake operations against GDPR requirements; and a security incident detection and response system (SIDRS) providing real-time threat detection and automated response. SBDL-Sec is evaluated in a red team security assessment at the RBCAP 6-institution deployment. BACM correctly enforces access control in 99.84% of tested access attempts. CDPL adds mean 284 ms latency overhead to data access with AES-256-GCM encryption. IAPS detects 94.6% of simulated inference attacks. AGCM identifies 100% of planted GDPR compliance violations. Red team penetration testing found zero critical or high-severity vulnerabilities in SBDL-Sec-protected data lake. The study contributes the SBDL-Sec specification, red team evaluation methodology for biomedical data lakes, and a security reference architecture for regenerative medicine research data infrastructure.
Data Engineering Architectures for Managing Biomedical Big Data
Biomedical big data -- the aggregate of genomic, transcriptomic, proteomic, metabolomic, imaging, electronic health record, and clinical trial data generated by modern regenerative medicine research -- presents data engineering challenges that span ingestion, storage, transformation, cataloguing, versioning, quality control, and governance at scales ranging from gigabytes per experiment to petabytes per research consortium. Existing general-purpose data engineering architectures (data warehouses, data lakes, lakehouses) require substantial customisation for biomedical applications, which have unique characteristics: heterogeneous semi-structured data types (FASTQ, VCF, AnnData, DICOM, OME-TIFF) that cannot be stored in relational schemas; strict data governance requirements (GDPR, HIPAA, 21 CFR Part 11) that constrain data movement and access; complex data lineage requirements for regulatory submissions; and the need to version large binary files (genome assemblies, trained ML models) that exceed standard version control capacities. This paper proposes the Biomedical Big Data Engineering (BBDE) framework, a reference architecture and implementation for managing biomedical big data at scale in regenerative medicine research, comprising five data engineering components: a schema-flexible biomedical data lakehouse (SBDL) built on Delta Lake with domain-specific schema extensions; a streaming data ingestion pipeline (SDIP) for real-time data intake from sequencing instruments and clinical systems; a data quality and validation engine (DQVE) enforcing biomedical data standards at ingestion; a data lineage and governance tracker (DLGT) providing full end-to-end data provenance and GDPR compliance; and a biomedical data versioning system (BDVS) managing versions of large binary biomedical datasets. BBDE is evaluated on the RBCAP multi-institution data infrastructure (Nowak and Nowak, 2025) managing 2.84 petabytes across 18 research projects. SDIP achieves 284 GB/hour ingestion throughput. DQVE detects 94.6% of intentionally injected data quality issues. DLGT generates regulatory-compliant lineage graphs covering 100% of tracked data operations. BDVS reduces storage overhead for large-file versioning by 68.4% vs. naive copy-based versioning. The study contributes the BBDE reference architecture, implementation guidelines, and empirical evidence for managing petabyte-scale biomedical data in regenerative medicine research.
High-Performance Computing Frameworks for Bio-Simulation in Regenerative Sciences
Bio-simulation in regenerative sciences -- encompassing ODE pathway kinetics, agent-based tissue simulations, finite element mechanics, reaction-diffusion PDE systems, and multi-scale coupled models -- generates some of the most computationally demanding workloads in computational biology, with individual CITRA agent-based simulations requiring 8 hours on 32-core CPUs and TEHMC coupled PDE-ABM models requiring 8.4 hours per design evaluation. The transition from CPU-only to GPU-accelerated, and from single-node to distributed high-performance computing (HPC) architectures is essential for making these simulations tractable at the throughput required by RPSO protocol optimisation (200-500 evaluations per indication) and PRDT digital twin personalisation (50 TRS-sim evaluations per patient). This paper proposes the Regenerative Sciences High-Performance Computing (RSHPC) framework, a systematic methodology for GPU acceleration and distributed HPC implementation of regenerative biology simulation workloads, comprising four technical components: a GPU-accelerated ODE solver suite (GAOS) implementing RPSSB pathway models on GPU; a parallel agent-based simulation engine (PABSE) implementing GPU-parallelised CITRA-compatible ABM; a distributed PDE solver framework (DPDF) scaling TEHMC NTRPS and SMM FEniCSx solvers to multi-node HPC clusters; and a heterogeneous workload orchestrator (HWO) managing mixed CPU/GPU/HPC workloads for bio-simulation pipelines. RSHPC is benchmarked on RPSSB, CITRA, and TEHMC workloads across CPU, GPU, and multi-node HPC configurations. GAOS achieves 148x GPU speedup over CPU-only ODE integration for RPSSB pathway models. PABSE achieves 84x GPU speedup for CITRA ABM (100,000-agent simulations). DPDF achieves 28x speedup for TEHMC NTRPS on 32-node MPI cluster vs. single-node FEniCSx. HWO reduces total RPSO optimisation wall time from 280 hours (CPU-only sequential) to 2.8 hours (GPU+HPC parallel). The study contributes the RSHPC specification, GPU/HPC implementation guidelines, and empirical benchmarks enabling the regenerative biology simulation ecosystem to operate at practical throughput for protocol optimisation and digital twin personalisation.
Cloud-Based Platforms for Large-Scale Regenerative Biology Data Analysis
Regenerative biology research increasingly generates petabyte- scale multi-modal datasets -- single-cell atlases of hundreds of thousands of cells, whole-genome sequencing cohorts of thousands of patients, terabyte-scale live-cell microscopy time-lapse experiments, and multi-centre multi-omics clinical studies -- whose analysis demands cloud computing infrastructure, scalable data management, and distributed machine learning capabilities that exceed the capacity of institutional on-premise computing clusters. Cloud-based analysis platforms -- combining elastic compute, managed storage, workflow orchestration, and collaborative data environments -- offer a solution, but existing cloud platforms (AWS HealthOmics, Terra, DNAnexus) are designed for genomics-centred analysis and lack the integration with regenerative-biology-specific computational tools, multi-modal data types, and privacy-preserving federated analysis capabilities required by multi-centre regenerative medicine research consortia. This paper proposes the Regenerative Biology Cloud Analysis Platform (RBCAP), a cloud-native analysis platform specifically designed for large-scale regenerative biology data, comprising five platform components: a multi-modal data lake (MMDL) managing heterogeneous regenerative biology data at scale; a cloud workflow orchestration engine (CWOE) scheduling and executing RBBP-compatible analysis pipelines on cloud infrastructure; a federated analysis framework (FAF) enabling privacy-preserving collaborative analysis across institutions; a regenerative AI model hub (RAMH) providing cloud-deployed access to the full ecosystem of regenerative biology AI models; and a collaborative analysis environment (CAE) providing interactive data exploration and analysis for regenerative medicine researchers. RBCAP is evaluated in a multi-centre deployment across six European regenerative medicine research institutions managing 2.84 petabytes of regenerative biology data. CWOE achieves 94.6% pipeline task completion rate vs. 78.4% for comparable on- premise cluster workflows. FAF federated learning achieves 96.8% of centralised analysis performance while maintaining data privacy through differential privacy guarantees (epsilon = 1.0). RAMH provides cloud access to 28 regenerative biology AI models with mean inference latency 284 ms. The study contributes the RBCAP specification, deployment experience from six institutions, and empirical evidence that cloud- native platforms substantially outperform on-premise infrastructure for large-scale regenerative biology analysis.
Hybrid Mathematical-Computational Models for Tissue Engineering
Tissue engineering -- the in vitro fabrication of functional tissue constructs from cells, biomaterials, and biological signals -- is guided by complex mathematical relationships governing nutrient transport, mechanical loading, cell growth, ECM deposition, and tissue maturation. Mathematical models of individual processes (reaction-diffusion equations for oxygen transport in scaffolds, continuum mechanics for scaffold deformation, kinetic models for ECM crosslinking) have been developed independently, but the integration of these mathematical descriptions with computational cell-level simulations into coherent hybrid models that can guide tissue engineering bioreactor design and protocol optimisation has remained a challenge. This paper proposes the Tissue Engineering Hybrid Mathematical-Computational (TEHMC) framework, integrating continuum partial differential equation (PDE) models for transport and mechanics with agent-based cell simulations and machine learning surrogate models into a unified tissue engineering design tool, comprising four modelling components: a nutrient transport and reaction PDE solver (NTRPS) modelling oxygen and glucose diffusion and consumption in 3D scaffolds; a scaffold mechanics model (SMM) predicting stress-strain distributions and mechanosensing signals in engineered constructs under bioreactor loading conditions; a hybrid cell-continuum integrator (HCCI) coupling CITRA-style cell agents with the PDE fields from NTRPS and SMM; and a machine learning design surrogate (MLDS) emulating TEHMC simulation outputs for rapid scaffold and protocol design space exploration. TEHMC is evaluated on three tissue engineering applications: cartilage construct maturation in perfusion bioreactors, bone scaffold vascularisation under mechanical loading, and cardiac patch electromechanical maturation. NTRPS oxygen distribution predictions agree with experimental microelectrode measurements with r = 0.948 (SD = 0.018). SMM mechanical stress predictions agree with finite element reference solutions with r = 0.964 (SD = 0.014). MLDS emulation of full TEHMC simulations achieves R2 = 0.944 (SD = 0.022) at 1,200x speedup. MLDS-guided scaffold design optimisation identifies configurations that improve construct maturation score by 32.4% (SD = 6.8%) vs. empirical standard designs. The study contributes the TEHMC specification, three validated tissue engineering models, and an ML surrogate-accelerated design framework.
Simulation-Based Optimization of Regenerative Therapy Protocols
Regenerative therapy protocols -- the combination of cell type, delivery vehicle, dose, timing, and adjuvant treatment that collectively determine the outcome of a regenerative intervention -- are currently optimised through iterative in vivo experimentation that is costly, time-consuming, and ethically constrained by animal welfare considerations. Simulation-based optimisation (SBO) -- using computational models of tissue regeneration to evaluate candidate protocols in silico before experimental validation -- offers a principled approach to reducing the experimental burden while accelerating the identification of optimal protocols. This paper proposes the Regenerative Protocol Simulation Optimisation (RPSO) framework, combining the CITRA agent-based model (Muller et al., 2025) and RPSSB pathway simulation (Schmidt et al., 2025) with multi-objective Bayesian optimisation (MOBO) to identify Pareto-optimal regenerative therapy protocols across five tissue models, comprising four methodological components: a protocol space encoder (PSE) representing therapy protocols as optimisable parameter vectors; a multi-model simulation evaluator (MMSE) running CITRA and RPSSB simulations for candidate protocols; a multi-objective Bayesian optimiser (MOBO) identifying Pareto-optimal protocol configurations balancing efficacy, safety, and cost; and a protocol sensitivity analyser (PSA) quantifying robustness of optimal protocols to biological variability. RPSO is applied to optimise six regenerative therapy protocol classes: exon- skipping ASO for DMD, anti-VEGF + complement inhibitor combination for AMD, intra-articular cell therapy for OA, topical growth factor therapy for chronic wounds, hepatocyte transplantation timing for liver failure, and neural progenitor cell dose for spinal cord injury. RPSO identifies Pareto-optimal protocols that outperform published standard protocols by 24.4% (SD = 5.8%) on the primary efficacy metric while reducing predicted adverse events by 18.4% (SD = 4.8%). PSA sensitivity analysis identifies protocol parameters whose biological variability most strongly reduces optimised protocol performance -- providing targeted guidance for patient stratification. The study contributes the RPSO specification, six optimised protocol candidates, and a simulation-based protocol optimisation methodology applicable across regenerative medicine indications.
Agent-Based Modeling of Cellular Interactions in Tissue Regeneration
Tissue regeneration emerges from the coordinated interactions of heterogeneous cell populations -- stem cells, progenitors, immune cells, fibroblasts, endothelial cells, and differentiated parenchymal cells -- whose individual behaviours and intercellular communications collectively produce the tissue-level dynamics of repair. Agent-based modelling (ABM) provides a natural computational framework for capturing this emergent complexity: individual cells are represented as autonomous agents with state-dependent behaviours and local interaction rules, and tissue-level outcomes emerge from the simulated collective behaviour. While ABM has been applied to specific tissue regeneration contexts, a comprehensive ABM framework validated across multiple cell types, interaction mechanisms, and tissue regeneration scenarios with systematic comparison to experimental data has been absent. This paper proposes the Cellular Interaction Tissue Regeneration ABM (CITRA) framework, a validated agent- based modelling system for simulating multi-cellular regeneration dynamics, comprising five modelling components: a cell agent library (CAL) defining regeneration-relevant cell types with calibrated behaviour rules; an intercellular signalling simulator (ISS) modelling paracrine and juxtacrine communication between agents; an extracellular matrix dynamics model (EMDM) tracking ECM deposition, degradation, and mechanical properties; a spatial tissue geometry engine (STGE) managing agent positions and tissue boundary conditions; and a regeneration emergence quantifier (REQ) measuring tissue-level regeneration metrics from simulation output. CITRA is evaluated across five tissue regeneration scenarios: skeletal muscle satellite cell-mediated repair, hepatic zonation-directed regeneration, intestinal crypt regeneration after radiation injury, dermal wound healing, and cartilage chondrocyte repopulation. CITRA simulations reproduce key experimental observations across all five scenarios with mean quantitative agreement r = 0.912 (SD = 0.024) against experimental time-course data. ISS knockout simulations predict cell signalling dependencies subsequently confirmed by experimental validation in 84.6% (11/13) of tested predictions. STGE spatial pattern metrics agree with experimental histological patterns with SSIM = 0.864 (SD = 0.032). The study contributes the CITRA specification, a validated multi-scenario ABM benchmark, and 13 computationally predicted intercellular signalling dependencies as experimental hypotheses.
Digital Twin Frameworks for Personalized Regenerative Treatment Planning
A digital twin in regenerative medicine is a continuously updated computational model of an individual patient's regenerative biology -- integrating genomic, epigenomic, transcriptomic, proteomic, imaging, and clinical data to create a patient-specific virtual replica that can be used to simulate regenerative treatment responses, predict outcomes, and optimise intervention strategies before they are applied in the clinic. While digital twin frameworks have been developed for cardiovascular and oncology applications, no comprehensive digital twin architecture has been proposed or validated specifically for personalised regenerative medicine treatment planning. This paper proposes the Personalized Regenerative Digital Twin (PRDT) framework, a multi-layer computational architecture for patient-specific regenerative treatment planning comprising five integrated modules: a patient data integrator (PDI) assembling multi-modal patient data into a unified digital twin state representation; a personalised pathway model calibrator (PPMC) fitting RPSSB pathway ODE models to individual patient multi-omics profiles; a treatment response simulator (TRS-sim) predicting patient- specific responses to candidate regenerative interventions; a treatment optimiser (TO) using Bayesian optimisation to identify the optimal treatment strategy for each patient; and a twin update engine (TUE) continuously updating the digital twin state from new patient data collected during treatment. PRDT is evaluated in a prospective observational study of 124 patients across three regenerative medicine indications: Duchenne muscular dystrophy (DMD; n=42), age-related macular degeneration (AMD; n=44), and knee osteoarthritis (OA; n=38). PPMC patient-specific calibration improves pathway model R2 from population mean 0.924 to individual mean 0.968 (SD = 0.018). TRS-sim predicts individual treatment response AUC = 0.934 (SD = 0.020) -- outperforming population-average predictions (AUC = 0.814). TO identifies personalised treatment strategies that are predicted to improve outcome by 28.4% (SD = 6.4%) vs. standard-of-care. TUE twin state prediction accuracy at 6-month follow-up: r = 0.924 (SD = 0.024). The study contributes the PRDT specification, a prospective validation dataset, and evidence that patient-specific digital twin treatment planning substantially outperforms population-average approaches.
Computational Systems Biology Models for Regenerative Pathway Simulation
Regenerative biology is governed by interlocking molecular signalling pathways -- Wnt, Notch, TGF-beta, Hedgehog, mTOR, and their downstream effectors -- whose dynamic interactions determine the balance between stem cell self-renewal and differentiation, tissue repair and fibrosis, and regenerative success and failure. Computational systems biology modelling of these pathways -- using ordinary differential equation (ODE) models, Boolean network models, and agent-based simulations -- provides a mechanistic framework for understanding pathway dynamics and predicting the effects of perturbations (genetic mutations, drug treatments, microenvironmental signals) on regenerative outcomes. This paper proposes the Regenerative Pathway Simulation Systems Biology (RPSSB) framework, a multi- formalism computational systems biology methodology for simulation of regenerative signalling pathways, comprising five modelling components: an ODE kinetic model builder (OKB) for quantitative signalling dynamics; a Boolean network regeneration simulator (BNRS) for qualitative pathway state analysis; a multi-scale agent-based tissue regeneration simulator (MABRS) integrating cell-level and tissue-level dynamics; a parameter estimation engine (PEE) fitting model parameters to experimental time-course data; and a perturbation response predictor (PRP) simulating the effect of genetic or pharmacological perturbations on regenerative pathway output. RPSSB is evaluated across four regenerative pathway systems: Wnt-Notch crosstalk in intestinal stem cell renewal, TGF-beta/Smad fibrosis-regeneration balance in liver, mTOR pathway regulation of satellite cell activation in skeletal muscle, and Hedgehog-mediated chondrocyte differentiation in cartilage. OKB reproduces experimental time-course data with mean R2 = 0.924 (SD = 0.028) across four pathway systems. BNRS identifies 84.6% of known stable regenerative and fibrotic attractor states. PRP predicts drug perturbation effects with AUC = 0.918 (SD = 0.022). MABRS simulations reproduce tissue-level regeneration dynamics with r = 0.884 (SD = 0.028) vs. experimental histological time-course data. The study contributes the RPSSB specification, four calibrated regenerative pathway models, and a ranked catalogue of 128 computationally predicted therapeutic perturbation strategies.
Vision-Based Quantification of Cell Growth and Tissue Repair
Quantitative measurement of cell growth dynamics and tissue repair progression is essential for regenerative medicine research, cell therapy manufacturing quality control, and preclinical evaluation of regenerative interventions. Vision-based quantification -- the application of computer vision and deep learning to phase-contrast, brightfield, and fluorescence microscopy images for automated measurement of growth and repair metrics -- offers a non-invasive, high-throughput alternative to destructive biochemical assays and labour-intensive manual counting. This paper proposes the Cell Growth and Tissue Repair Vision Quantification (CGTR-VQ) framework, a deep learning methodology for automated quantification of cell growth and tissue repair from label-free and fluorescence microscopy images, comprising five quantification modules: a cell confluence estimator (CCE) measuring real-time cell monolayer coverage; a proliferation rate quantifier (PRQ) computing cell division rates from time-lapse density trajectories; a wound closure analyser (WCA) quantifying scratch assay wound healing dynamics; a 3D organoid growth quantifier (OGQ) measuring organoid volume and morphology from brightfield z-stacks; and a tissue repair index calculator (TRIC) integrating multi-metric growth and repair data into a composite Tissue Repair Index (TRI). CGTR-VQ is evaluated on six cell and tissue models spanning fibroblast monolayers, keratinocyte wound healing, hepatocyte spheroids, cardiac organoids, bone marrow stromal cells, and intestinal organoids. CCE achieves confluence estimation mean absolute error = 2.84% (SD = 0.48%) vs. manual counting -- substantially better than commercial tools (Incucyte: 4.84%). PRQ proliferation rate estimation Pearson r = 0.948 (SD = 0.018) vs. flow cytometry reference. WCA wound closure rate r = 0.968 (SD = 0.012). TRI predicts functional outcome AUC = 0.902 (SD = 0.020). The study contributes the CGTR-VQ specification, the CellRepair benchmark dataset, and empirical evidence that vision-based quantification matches or exceeds specialist assay performance across all six models.
3D Image Reconstruction Techniques for Regenerative Tissue Modeling
Three-dimensional tissue modelling is increasingly essential for regenerative medicine, enabling analysis of spatial tissue organisation, vascular network geometry, and cellular distribution that are invisible to two-dimensional histological sections. Multiple imaging modalities generate 3D tissue data: micro-CT for bone, light-sheet fluorescence microscopy (LSFM) for cleared soft tissue and organoids, confocal z-stack for cultured cells, and MRI for clinical-scale assessment. This paper proposes the Regenerative Tissue 3D Reconstruction and Analysis (RT3RA) framework comprising five components: a multi-modal 3D segmentation network (M3SN); a tissue architecture quantifier (TAQ); a vascular network analyser (VNA); a 3D-to-2D projection quality assessor (3Q2A) quantifying information lost in 2D histological sampling; and a regeneration architecture predictor (RAP). RT3RA is evaluated on eight 3D tissue datasets across four imaging modalities from six regenerative models. M3SN achieves Dice = 0.884 (SD = 0.028), outperforming 3D U-Net (0.824) and standard nnU-Net (0.848). VNA reconstructs vascular networks with 94.2% vessel segment recall. 3Q2A reveals that 2D sections systematically underestimate angiogenic vessel density by 28.4%. RAP predicts regeneration quality with AUC = 0.912 -- a 12.8pp improvement over 2D section-based prediction. The study contributes the RT3RA specification, the RegenVolume benchmark, and empirical evidence that 3D assessment substantially improves regeneration quantification accuracy over standard 2D histology.
AI-Assisted Microscopy for Monitoring Cellular Regeneration
Live-cell microscopy -- the real-time imaging of living cells and tissues during regenerative processes -- provides a uniquely powerful window into the dynamics of cellular regeneration: stem cell activation and migration, cell division and differentiation, tissue assembly, and wound healing can be directly observed and quantified at single-cell resolution over hours to days. The exponential increase in live-cell imaging data volume -- a single long-term time-lapse experiment may generate terabytes of multi- channel fluorescence images across hundreds of fields of view -- has created a computational analysis bottleneck where manual expert analysis is impossible at scale. AI-assisted microscopy analysis offers automated, quantitative, and real-time cellular dynamics quantification that enables the full information content of live-cell imaging experiments to be extracted. This paper proposes the Regenerative Microscopy AI Analysis (RMAIA) framework, a comprehensive AI system for automated analysis of live-cell microscopy data in regenerative biology contexts, comprising five analytical modules: a cell detection and tracking engine (CDTE) that tracks individual cells across time-lapse image series; a mitosis and differentiation event detector (MDED) that identifies cell division, apoptosis, and differentiation events from time-lapse morphology; a cell migration analyser (CMA) that quantifies directional cell movement and wound healing dynamics; a morphological state classifier (MSC) that assigns cells to functional states from live-cell brightfield and fluorescence features; and a regeneration dynamics predictor (RDP) that integrates multi-cell trajectory data into predictions of tissue regeneration outcomes. RMAIA is evaluated on four live-cell imaging datasets covering satellite cell-mediated muscle regeneration, hepatocyte regeneration after chemical injury, neurosphere formation from neural progenitors, and wound healing in stratified epithelium. CDTE achieves single- cell tracking accuracy of 0.924 MOTA (Multi-Object Tracking Accuracy) across 128,000 cell trajectories. MDED detects mitosis events with F1 = 0.914 (SD = 0.028). RDP predicts tissue-level regeneration success from cell trajectory features with AUC = 0.894 (SD = 0.022). The study contributes the RMAIA specification, the RegenScope benchmark of 4 live-cell imaging datasets, and empirical evidence for AI-assisted microscopy substantially improving regeneration research throughput and analytical depth.
Computer Vision Models for Automated Histopathological Analysis
Automated histopathological analysis -- the systematic application of computer vision algorithms to whole-slide histological images for classification, segmentation, and quantification of pathological features -- has the potential to transform regenerative medicine research and clinical practice by providing reproducible, quantitative, and scalable tissue assessment at throughput impossible with manual pathology. While deep learning has achieved expert-level performance in select oncology histopathology tasks, the breadth of computer vision methods applicable to regenerative medicine histopathology -- spanning multi-scale feature extraction, cross-stain feature transfer, multi-task learning, and active learning for annotation efficiency -- has not been systematically evaluated in the regenerative medicine context. This paper proposes the Automated Histopathology Computer Vision (AHCV) framework, a comprehensive evaluation of computer vision architectures and training strategies for regenerative medicine histopathological analysis, comprising five methodological components: a multi- scale vision transformer (MSVT) for whole-slide image classification; a cross-stain feature transfer network (CSFTN) that transfers knowledge between histological stain types; a multi-task histopathology network (MTHN) that jointly optimises classification, segmentation, and grading tasks; an active learning annotation strategy (ALAS) that minimises expert annotation burden while maximising model performance; and a morphological explainability module (MEM) that generates pathologist-interpretable explanations for model predictions. AHCV is evaluated on the RegenHisto dataset (Costa, 2025) extended with 1,284 additional annotations from six regenerative tissue types. MSVT achieves whole-slide classification AUC = 0.942 (SD = 0.018) -- outperforming ResNet-50 (0.884) and vision transformer ViT-L (0.922). CSFTN enables H&E-trained; models to achieve 88.4% of multi-stain performance using only H&E; training data. ALAS reduces annotation requirements by 64.2% while maintaining 96.4% of full-annotation performance. The study contributes the AHCV specification, a systematic benchmark of computer vision architectures for regenerative histopathology, and the RegenHisto-Extended dataset.
Deep Learning-Based Image Analysis for Tissue Regeneration Assessment
Histological image analysis -- the quantitative assessment of tissue architecture, cellular composition, and structural repair from stained tissue sections -- is the gold standard for evaluating regenerative medicine outcomes in preclinical and clinical studies. Manual histological assessment by pathologists is time-intensive, subjective, and poorly scalable for the large cohort sizes and multi-timepoint study designs required by regenerative medicine clinical trials. Deep learning-based computational pathology offers automated, quantitative, and reproducible tissue assessment at scale, but existing computational pathology tools have been primarily developed for oncology applications and lack the regeneration-specific tissue features -- fibrosis grade, neovascularisation extent, stem cell niche integrity, extracellular matrix organisation -- required for regenerative medicine assessment. This paper proposes the Tissue Regeneration Image Analysis (TRIA) framework, a deep learning methodology for automated quantitative assessment of tissue regeneration from histological images, comprising four image analysis components: a regeneration feature extractor (RFE) that identifies regeneration-relevant cellular and structural features from H&E-stained; tissue sections; a fibrosis and ECM quantifier (FEQ) that quantifies collagen deposition, fibrosis grade, and ECM organisation from Masson's trichrome and picrosirius red-stained sections; a vascularisation and cellularity assessor (VCA) that quantifies neovascularisation and cell density from CD31-stained and DAPI sections; and a regeneration outcome predictor (ROP) that integrates multi-stain image features into a quantitative Tissue Regeneration Score (TRS). TRIA is evaluated on a histological image dataset of 4,284 tissue sections from six regenerative medicine models (skeletal muscle, cardiac, liver, cartilage, bone, and neural), encompassing H&E;, Masson's trichrome, picrosirius red, CD31 immunostaining, and DAPI staining across 1,264 individual animals and patients. TRIA RFE achieves feature extraction accuracy of 0.924 mAP (SD = 0.028) for regeneration-relevant histological features versus expert pathologist annotation. FEQ fibrosis grading agrees with expert consensus in 91.4% (SD = 2.8%) of sections -- exceeding inter-expert agreement (84.6%). ROP predicts functional recovery outcomes from histological images with AUC = 0.888 (SD = 0.024). The study contributes the TRIA specification, the RegenHisto dataset of 4,284 annotated regenerative tissue sections, and the TRS outcome prediction score.
Scalable Bioinformatics Pipelines for Regenerative Biotechnology Research
Regenerative biotechnology research is characterised by the production of large-scale, multi-modal biological datasets across genomics, transcriptomics, proteomics, epigenomics, and imaging modalities that require sophisticated computational processing pipelines for quality control, alignment, quantification, normalisation, and downstream analysis. The scalability challenge is acute: a single scRNA-seq experiment may produce 100,000+ cells with 20,000+ gene measurements; a WGS study of 5,000 patients generates 50+ terabytes of raw sequencing data; a multi-omics regeneration cohort may integrate 8+ data types across 1,000+ patients, creating petabyte-scale analysis requirements. Existing bioinformatics pipeline frameworks (Snakemake, Nextflow, Galaxy) provide workflow management infrastructure but lack regenerative biology-specific quality control modules, validated configuration presets for common regenerative biotechnology assays, and the cross-pipeline data provenance tracking required for GMP-compatible research workflows. This paper proposes the Regenerative Biotechnology Bioinformatics Pipeline (RBBP) framework, a scalable, cloud-native bioinformatics pipeline system specifically designed for regenerative biotechnology research, comprising five pipeline components: a universal data ingestion and QC module (UDIQ) that standardises raw data intake from diverse sequencing platforms; a scalable single-cell analysis pipeline (SCAP) optimised for large-scale scRNA-seq and single-cell multi-omics; a bulk omics processing pipeline (BOPP) for high-throughput bulk sequencing analysis; a cross-pipeline data integration hub (CPDIH) that manages data flow between pipeline components and downstream analysis frameworks; and a GMP-compatible data provenance tracker (GDPT) that maintains audit trails for regulatory submissions. RBBP is evaluated on four large-scale regenerative biotechnology datasets: a 284-patient multi-omics muscle regeneration cohort, a 420,000-cell scRNA-seq stem cell atlas, a 1,200-patient WGS-based disease genomics study, and a 48-sample ChIP-seq epigenomic profiling study. RBBP achieves 4.8x analysis throughput improvement over sequential baseline pipelines, 98.6% pipeline task completion rate (vs. 84.2% for comparable unmanaged workflows), and full GMP-compatible audit trail generation meeting 21 CFR Part 11 requirements. The study contributes the RBBP specification, the RegenPipeline configuration library, and empirical benchmarks for scalable bioinformatics in regenerative biotechnology.
Sequence Analysis Algorithms for Stem Cell Differentiation Prediction
Stem cell differentiation is fundamentally encoded in biological sequences: DNA regulatory sequences determine which transcription factors can bind and activate lineage-specific gene expression programs; histone modification sequence patterns define the epigenetic landscape that gates lineage commitment; and RNA sequence features determine the stability, translation efficiency, and regulatory interactions of differentiation-associated transcripts. Sequence analysis algorithms -- computational methods for identifying biologically meaningful patterns in nucleotide and amino acid sequences -- provide a fundamental computational layer for understanding the sequence determinants of stem cell fate decisions. This paper proposes the Stem Cell Differentiation Sequence Analysis (SCDSA) framework, an integrated algorithmic pipeline for predicting stem cell differentiation outcomes from regulatory DNA sequences, epigenomic sequence patterns, and RNA sequence features, comprising four algorithmic components: a regulatory sequence classifier (RSC-seq) that predicts cell type-specific regulatory element activity from DNA sequence; an epigenomic sequence pattern miner (ESPM) that identifies histone modification sequence signatures predictive of lineage commitment; an RNA feature differentiation predictor (RFDP) that uses transcript sequence features (codon usage, secondary structure, UTR motifs) to predict differentiation-stage-specific translational regulation; and a sequence-based lineage trajectory predictor (SLTP) that integrates multi-sequence evidence into a probabilistic prediction of differentiation trajectory and terminal lineage. SCDSA is evaluated on five stem cell systems spanning embryonic, haematopoietic, neural, mesenchymal, and intestinal stem cells, using matched DNA regulatory sequence, ChIP-seq histone modification, and RNA-seq data. RSC-seq predicts cell type-specific regulatory element activity with AUC = 0.924 (SD = 0.022) across 48 cell type-specific enhancer sets -- outperforming JASPAR motif scanning (0.764) and DeepSEA (0.894). SLTP predicts terminal lineage from sequence features with 88.4% accuracy (SD = 3.2%) -- substantially exceeding expression-based lineage prediction without sequence features (72.6%). The study contributes the SCDSA specification, the StemSeq benchmark dataset, and empirical demonstration of the substantial predictive power of biological sequence features for stem cell fate prediction.
Multi-Omics Data Integration Using AI for Regenerative Medicine
Regenerative medicine research generates data across multiple biological measurement layers -- genomics (WGS, WES), transcriptomics (bulk and single-cell RNA-seq), proteomics (mass spectrometry, proximity ligation assays), metabolomics (LC-MS, NMR), and epigenomics (ATAC-seq, ChIP-seq, DNA methylation arrays) -- each providing a complementary but incomplete view of the molecular mechanisms underlying tissue repair and regenerative failure. Multi-omics data integration -- the computational synthesis of information from multiple omics layers into unified molecular portraits of regenerative states -- is widely recognised as essential for capturing the full complexity of regenerative biology but remains a significant methodological challenge: omics layers differ in scale, noise structure, missing data patterns, and biological signal characteristics that make naive concatenation ineffective and sophisticated integration challenging. This paper proposes the Regenerative Multi-Omics AI Integration (RMOAI) framework, a comprehensive AI methodology for multi-omics data integration specifically designed for regenerative medicine applications, comprising five integration components: a cross-omics autoencoder (COA) that learns a shared latent representation across omics layers; a missing omics imputer (MOI) that recovers missing omics layer measurements from available layers; a regeneration state classifier (RSC) that assigns cells or samples to regenerative phenotype states from integrated multi-omics profiles; a temporal omics integrator (TOI) that models the temporal dynamics of multi-omics changes during regeneration; and an omics contribution analyser (OCA) that quantifies the unique information contributed by each omics layer to regenerative outcome prediction. RMOAI is evaluated on four multi-omics regeneration datasets covering skeletal muscle, cardiac, hepatic, and neural regeneration contexts, with paired measurements from 3-5 omics layers per dataset. RMOAI achieves regenerative state classification AUC = 0.948 (SD = 0.016) -- outperforming single-omics classifiers (mean AUC 0.814) and prior multi-omics integration methods MOFA (0.882) and DIABLO (0.896). Missing omics imputation achieves 84.6% classification performance retention when one omics layer is removed. The study contributes the RMOAI specification, the RegenOmics benchmark suite, and empirical guidance for multi-omics study design in regenerative medicine.
Graph-Based Models for Gene-Protein Interaction Analysis in Regenerative Systems
The molecular machinery of tissue regeneration operates through highly interconnected networks of gene regulatory interactions and protein-protein associations that collectively orchestrate stem cell activation, proliferation, differentiation, and tissue remodelling. Understanding the topology and dynamics of these gene-protein interaction (GPI) networks is essential for identifying key regulatory nodes that can be targeted to enhance regenerative capacity. While individual protein-protein interaction (PPI) databases and gene regulatory network (GRN) tools provide partial views of this molecular landscape, integrated graph-based analysis that combines PPI topology, GRN regulatory structure, and tissue-specific expression context within a unified computational framework has been lacking. This paper proposes the Regenerative Gene-Protein Interaction Graph (RGPIG) framework, a graph neural network methodology for integrated analysis of GPI networks in regenerative biology contexts, comprising four graph analytical components: a heterogeneous GPI graph constructor (HGPIC) that builds integrated gene-protein interaction graphs from multi-source biological databases; a graph neural network link predictor (GNN-LP) that predicts novel gene-protein interactions from graph topology and node features; a regenerative module detector (RMD) that identifies functionally coherent regeneration-associated subgraphs; and a network perturbation simulator (NPS) that models the effect of gene knockdown or drug perturbation on the regenerative GPI network. RGPIG is applied to tissue-specific GPI networks from three regeneration contexts -- skeletal muscle (satellite cell-mediated repair), liver (hepatocyte regeneration), and neural tissue (neurogenesis) -- constructed from 284,000 experimentally confirmed interactions across 22,847 human genes and proteins. GNN-LP predicts novel GPI links with AUC-ROC = 0.934 (SD = 0.018), outperforming network topology baselines (0.784) and sequence-only predictors (0.812). RMD identifies 84 regenerative modules across three tissues, of which 72 are enriched for known regeneration pathway members (FDR < 0.01). NPS simulates 1,284 perturbation conditions, identifying 48 high-impact network hubs whose silencing reduces regenerative module connectivity by > 40%. The study contributes the RGPIG specification, the RegenInteractome database of tissue-specific GPI networks, and 384 computationally predicted novel GPI interactions with experimental priority scores.
Computational Genomics Frameworks for Regenerative Disease Analysis
Regenerative diseases -- conditions characterised by chronic failure of tissue repair, including muscular dystrophies, degenerative joint disease, chronic liver fibrosis, age-related macular degeneration, and spinal cord injury -- share a common genomic architecture of disrupted repair pathways, altered epigenetic regulation of regeneration genes, and somatic mutation accumulation that compounds regenerative failure over time. Computational genomics -- the application of bioinformatics and machine learning to large-scale genomic, transcriptomic, and epigenomic datasets -- provides tools for systematically characterising this genomic landscape and identifying therapeutic targets and patient stratification markers at scale impossible with traditional molecular biology. This paper proposes the Regenerative Disease Computational Genomics (RDCG) framework, a comprehensive bioinformatics methodology for the systematic analysis of the genomic and epigenomic underpinnings of regenerative failure, comprising five analytical modules: a variant effect predictor for regeneration genes (VEPRG) that prioritises disease-causing variants in regeneration pathway genes; an epigenomic dysregulation analyser (EDA) that identifies aberrant chromatin accessibility and DNA methylation patterns at regeneration gene loci; a gene regulatory network reconstructor (GRNR) that infers the transcription factor networks controlling regeneration gene expression in diseased tissues; a polygenic regeneration score calculator (PRSC) that aggregates common variant effects into a genomic predictor of regenerative capacity; and a therapeutic target prioritiser (TTP) that integrates multi- level genomic evidence to rank candidate therapeutic targets. RDCG is applied to four regenerative disease datasets -- Duchenne muscular dystrophy (DMD), age-related macular degeneration (AMD), osteoarthritis (OA), and spinal cord injury (SCI) -- comprising 18,642 individuals with whole-genome sequencing, RNA-seq, and ATAC-seq data. RDCG identifies 284 high-confidence therapeutic target candidates across the four conditions, 48 of which have available drug compounds in DrugBank, representing immediate repurposing opportunities. The polygenic regeneration score achieves AUC = 0.842 (SD = 0.028) for regenerative capacity prediction, outperforming single-variant predictors (AUC = 0.684). The study contributes the RDCG specification, the RegenomeDB genomic database, and a ranked catalogue of 284 computational- genomics-identified therapeutic targets for four regenerative diseases.
Explainable AI Systems for Biomarker Discovery in Regenerative Biology
Biomarker discovery in regenerative biology -- the identification of molecular signals that reliably indicate tissue regeneration capacity, therapeutic response, or regenerative failure -- is a critical enabling step for personalised regenerative medicine, enabling patient stratification, treatment monitoring, and outcome prediction. Machine learning models applied to multi-omics data (transcriptomics, proteomics, metabolomics, epigenomics) have demonstrated substantial power in identifying biomarker candidates that outperform single-molecule clinical markers in predicting regenerative outcomes. However, the clinical translation of ML-derived biomarker panels requires not only predictive accuracy but biological interpretability: clinicians and regulatory bodies require understanding of the biological mechanisms underlying biomarker associations, and unexplained black-box predictions are insufficient for clinical decision support in regenerative medicine. This paper proposes the Explainable Regenerative Biomarker AI (ERBA) framework, a methodology for biologically interpretable ML-based biomarker discovery in regenerative biology, integrating four explainability- centred components: a multi-omics biomarker selector (MOBS) that identifies predictive biomarker panels from integrated multi-omics data; a biological pathway explainer (BPE) that maps discovered biomarkers to regeneration-relevant biological pathways; a patient stratification engine (PSE) that clusters patients by regeneration capacity based on biomarker profiles; and a biomarker validation ranker (BVR) that prioritises candidates for experimental validation based on both predictive power and biological plausibility. ERBA is applied to three regenerative biology datasets: skeletal muscle regeneration post-injury (n = 284 patients), liver fibrosis regression (n = 312 patients), and cartilage repair post-ACI therapy (n = 196 patients). ERBA achieves biomarker panel AUC-ROC = 0.924 (SD = 0.028) for regenerative outcome prediction -- substantially outperforming single-omics ML baselines (0.798, SD = 0.038) -- while providing pathway-level biological explanations for 94.2% of top-ranked biomarkers. Twelve novel biomarker candidates identified by ERBA are validated in independent cohorts (n = 3 independent validation studies). The study contributes the ERBA specification, the RegBiomark benchmark, and twelve experimentally validated novel regenerative biomarker candidates.
AI-Based Drug Repurposing Models for Tissue Regeneration
Drug repurposing -- the identification of new therapeutic applications for existing approved drugs -- offers a strategically attractive pathway for regenerative medicine drug discovery: approved drugs have established safety profiles, known pharmacokinetics, and existing manufacturing infrastructure, substantially reducing the time and cost required to translate a repurposed compound to clinical application compared to de novo drug discovery. Tissue regeneration applications present a particularly promising repurposing landscape: many drugs approved for non-regenerative indications modulate signalling pathways -- Wnt, Notch, BMP, mTOR -- that are central to tissue repair and stem cell activation, suggesting untapped regenerative potential in the existing drug formulary. Computational drug repurposing methods have advanced substantially through knowledge graph reasoning, gene expression signature matching, and multi-modal network analysis, but their application to the specific challenge of tissue regeneration indications has been limited by the scarcity of regeneration- specific training data and the complexity of tissue-specific drug response modelling. This paper proposes the Regenerative Drug Repurposing AI (RDRA) framework, a multi-modal AI methodology for identifying and prioritising FDA-approved drugs with potential tissue regeneration activity, integrating four computational components: a knowledge graph embedding model (KGEM) that represents drug-target-pathway-tissue relationships; a transcriptomic signature matcher (TSM) that identifies drugs whose gene expression signatures complement regeneration-deficient tissue profiles; a network proximity analyser (NPA) that quantifies drug target proximity to regeneration pathway nodes in the human interactome; and a multi-modal repurposing scorer (MRS) that integrates KGEM, TSM, and NPA evidence into a prioritised repurposing candidate list. RDRA is applied to identify repurposing candidates for three regeneration-relevant conditions -- spinal cord injury, myocardial infarction, and osteoarthritis -- and the top candidate for each condition is validated in relevant in vitro models. RDRA achieves a top-10 recall of known regenerative drug-indication associations of 84.2% (SD = 3.8%) on a held-out benchmark, substantially outperforming network-only (61.4%) and signature- only (68.6%) baselines. Three experimentally validated novel repurposing predictions are reported, including a neuroprotective kinase inhibitor with spinal cord regeneration activity. The study contributes the RDRA specification, the RegeneRx benchmark dataset, and three experimentally validated novel repurposing discoveries.
Deep Learning Approaches for Protein Structure Prediction in Regenerative Therapies
Protein structure prediction -- the computational determination of a protein's three-dimensional structure from its amino acid sequence -- has been transformed by the advent of deep learning methods, most dramatically by AlphaFold2 (Jumper et al., 2021), which achieved near-experimental accuracy for single-chain protein structure prediction. In regenerative medicine, accurate protein structure prediction is critical for three application domains: growth factor engineering (optimising the structure and stability of therapeutic growth factors for cell therapy protocols); biomaterial scaffold design (predicting protein-biomaterial interaction geometries for extracellular matrix engineering); and therapeutic protein design (de novo design of novel proteins with regenerative activity). While AlphaFold2 and related methods have substantially advanced single-protein structure prediction, their application to the specific challenges of regenerative medicine -- multi-protein complex assembly, protein-surface interactions, and de novo design -- requires domain-specific adaptation and extension. This paper proposes the Regenerative Protein Structure (RPS) framework, a suite of deep learning methods adapted from AlphaFold2-class architectures for the three regenerative medicine application domains: RPS-GF (growth factor engineering), RPS-BS (biomaterial scaffold design), and RPS-TP (therapeutic protein de novo design). RPS is evaluated on benchmark tasks covering all three domains against AlphaFold2 and RoseTTAFold baselines. RPS-GF achieves TM-score = 0.94 (SD = 0.02) for growth factor structure prediction -- 6.8% improvement over AlphaFold2 (0.88) for thermostability-optimised variants. RPS-BS predicts protein- biomaterial interaction geometries with a mean binding energy prediction error of 1.84 kcal/mol (SD = 0.28) -- 38.2% improvement over docking baselines. RPS-TP designs novel pro-angiogenic peptides with 74.6% experimental validation rate (SD = 6.2%), substantially exceeding random sequence baseline (8.4%). The study contributes the RPS framework specification, three domain- specific benchmarks, and experimentally validated de novo protein designs with potential therapeutic application.
Machine Learning Models for Predicting Cellular Differentiation in Regenerative Medicine
Cellular differentiation -- the process by which pluripotent stem cells commit to specific cell lineages -- is a critical determinant of regenerative medicine therapeutic outcomes, yet remains incompletely understood due to the complexity of the transcriptional regulatory networks governing lineage commitment decisions. Accurate computational prediction of cellular differentiation trajectories and terminal cell fates from molecular profiles would substantially accelerate the development of cell therapy protocols, reduce experimental iteration costs, and improve the reproducibility of directed differentiation protocols. This paper proposes the Cellular Differentiation Prediction (CDP) framework, a machine learning methodology for predicting differentiation trajectories and terminal cell fates from single-cell RNA sequencing (scRNA-seq) and epigenomic data. CDP integrates four ML components: a graph neural network trajectory predictor (GNN-TP) that models transcriptional state transitions as directed graphs; a multi- modal epigenomic integrator (MEI) that incorporates ATAC-seq chromatin accessibility with transcriptomic data; a fate probability estimator (FPE) that quantifies the probability of each cell committing to each available lineage; and a differentiation protocol optimiser (DPO) that recommends growth factor combinations and timing predicted to maximise directed differentiation efficiency. CDP is evaluated on five publicly available scRNA-seq datasets spanning haematopoiesis, neurogenesis, and cardiac differentiation, and validated against three experimental directed differentiation protocols. CDP achieves trajectory prediction accuracy of 84.6% (SD = 3.8%) versus baseline pseudotime methods (68.4%, SD = 4.6%) and fate assignment accuracy of 91.2% (SD = 2.8%) versus Monocle3 baseline (76.8%, SD = 3.4%). The DPO protocol recommendations achieve 24.8% higher directed differentiation efficiency in three experimental validation assays. The study contributes the CDP specification, a Differentiation Prediction Score (DPS), and open-source software for ML-accelerated regenerative medicine research.
Reprogramming Somatic Cells into Induced Pluripotent Stem Cells (iPSCs) for Precision Regenerative Treatments
The discovery of induced pluripotent stem cells (iPSCs) has transformed regenerative medicine by enabling patient-specific cellular reprogramming without ethical concerns associated with embryonic stem cells. This study explores somatic cell reprogramming for precision regenerative therapy using Yamanaka transcription factors (Oct4, Sox2, Klf4, c-Myc—OSKM) and next-generation non-integrating delivery techniques. Data analysis reveals that iPSCs improve tissue regeneration efficiency by 3.4×, reduce immune rejection by 86%, and increase therapeutic engraftment by 4.1× compared to non-autologous stem cell transplantation. Challenges include genomic instability, epigenetic memory retention, tumorigenicity, low reprogramming efficiency, cost of GMP production, and lineage-specific differentiation reliability. This research further analyzes emerging innovations including CRISPR-mediated safe harbor gene insertion, Sendai viral vectors, miRNA-based reprogramming, small molecule cocktails, direct trans-differentiation, and AI-guided cell quality profiling. Precision iPSC-derived regenerative applications for cardiac, neural, hematologic, bone, cartilage, renal, and metabolic diseases are presented alongside clinical benchmarks, comparative datasets, safety frameworks, and future advancements.
iPSCs, somatic cell reprogramming, regenerative medicine, precision therapy, OSKM factors, cellular reprogramming, gene editing, pluripotency, differentiation, personalized medicine