| 1. Domain | 1.1 Scope of the Domain | Boundaries | The range of phenomena the science includes and excludes. | Focuses on the mechanisms controlling gene expression, chromatin state, transcriptional activation/repression, epigenetic marks, and heritable regulatory patterns. Excludes protein-level functional outcomes, broad metabolic pathways, and organism-level physiology except where they directly depend on regulatory control. |
| | Scale | The spatial, temporal, or organizational level at which the science operates (e.g., quantum, cellular, social, cosmic). | Operates at the molecular, chromatin, and nuclear scales: regulatory DNA elements, transcription factors, nucleosomes, epigenetic marks, chromatin loops, long-range interactions, and multi-minute to generational timescales for stable epigenetic inheritance. |
| 1.2 Ontological Commitments | Entities | The kinds of things assumed to exist within the domain (particles, organisms, agents, fields, etc.). | Regulatory DNA sequences (promoters, enhancers, silencers, insulators), transcription factors, chromatin remodelers, nucleosomes, histones, histone modifiers, noncoding RNAs, DNA methylation machinery, and 3D chromatin structures. |
| | Properties | The fundamental attributes these entities possess (mass, charge, genotype, preference, etc.). | Binding affinity, transcriptional activity, chromatin accessibility, methylation levels, histone-modification states, nucleosome positioning, cooperative factor binding, and regulatory plasticity across conditions or cell types. |
| | Categories | The basic ontological types used to classify domain elements (substances, processes, relations, structures). | Cis-regulatory elements, trans-acting factors, activating vs repressing histone marks, open vs closed chromatin, short-term vs long-term regulation, locus-specific vs genome-wide epigenetic states, and heritable vs reversible regulatory mechanisms. |
| 1.3 State-Variables | Variables | The measurable or definable properties that describe system conditions. | Chromatin accessibility, histone-mark densities, methylation percentages, transcription factor occupancy, enhancer–promoter interaction frequency, transcription rates, and regulatory-RNA abundance. |
| | Parameterization | How variables encode and represent the system’s state. | Regulatory state encoded through ATAC-seq profiles, ChIP-seq enrichment, methylation maps, Hi-C contact matrices, RNA expression levels, TF-binding curves, and genome-wide annotation sets. |
| 1.4 Admissible Idealizations | Simplifications | Conceptual reductions used to make the domain tractable (point masses, rational agents, perfect gases). | Treating chromatin as binary “open/closed,” collapsing complex histone codes into single activating/repressing classes, modeling transcription-factor binding with simplified kinetics, or reducing 3D genome architecture to coarse interaction domains. |
| | Validity Conditions | The limits and contexts in which idealizations hold or break down. | Simplifications break when marks interact combinatorially, when 3D structure dominates regulation, under strong developmental or stress conditions, or when factor binding is highly cooperative or context-dependent. |
| 1.5 Domain Assumptions | Structural Assumptions | Background ontological stances such as determinism, continuity, randomness, discreteness. | Assumes information flow from regulatory elements to transcriptional output, stable chemical behavior of epigenetic marks, continuity of chromatin remodeling, and predictable TF–DNA interaction principles. |
| | Implicit Commitments | Unstated but necessary assumptions that shape the field’s conceptual structure. | Assumes that marks carry functional meaning, regulatory interactions are interpretable, chromatin accessibility correlates with expression capacity, and that regulatory logic can be stably represented across similar cellular contexts. |
| 1.6 Internal Coherence Requirements | Consistency | The demand that domain concepts do not contradict one another. | Regulatory mechanisms, chromatin architecture, epigenetic marks, and transcriptional outcomes must not contradict each other across assays, sequences, or conditions. |
| | Compatibility | The requirement that entities, variables, and assumptions fit together into a unified descriptive framework. | Entities (regulatory elements, factors, chromatin), variables (marks, accessibility), and assumptions (specificity, stability) must jointly form a unified regulatory framework explaining gene-expression control. |
| 2. Evidence Layer | 2.1 Observable Phenomena | Observables | The aspects of the domain that can produce detectable signals accessible to measurement. | Detectable features include chromatin accessibility, histone marks, DNA methylation levels, transcription-factor binding, regulatory RNA abundance, enhancer–promoter interactions, transcriptional output, and chromatin compaction states. |
| | Detection Limits | The boundaries of what can be resolved or sensed by current instruments or methods. | Limits of ATAC-seq sensitivity, minimum ChIP enrichment required for mark detection, lowest measurable methylation fraction, single-cell detection thresholds, minimal interaction frequencies detectable in Hi-C, and optical resolution of chromatin imaging. |
| 2.2 Measurement Systems | Units | Standardized quantifications (meters, seconds, volts, decibels, dollars, etc.) necessary for consistent comparison. | Quantifications expressed in normalized read counts, fragments per kilobase (FPKM/TPM), ChIP enrichment scores, methylation percentages, accessibility indices, contact-frequency units, fluorescence intensity, and fold-change expression values. |
| | Instruments | Devices and tools (microscopes, spectrometers, sensors, surveys, detectors) used to produce measurements. | Sequencers, qPCR machines, ChIP-seq platforms, bisulfite sequencing systems, single-molecule imaging setups, confocal microscopes, ATAC-seq workflows, Hi-C instrumentation, and platforms for CUT&RUN or CUT&Tag. |
| 2.3 Operational Definitions | Definitions | Terms defined by specific measurement procedures, ensuring empirical clarity. | Operational definitions for accessibility, TF occupancy, histone-mark presence, methylation status, interaction frequency, promoter activity, and enhancer strength based on assay-specific thresholds and normalization rules. |
| | Procedures | The explicit steps required to perform a measurement in a reproducible way. | Protocol steps for ChIP, ATAC-seq, bisulfite conversion, RNA-seq library prep, Hi-C ligation workflows, immunofluorescence staining, and single-cell regulatory profiling. |
| 2.4 Data Acquisition | Protocols | Formal processes for gathering data under controlled or standardized conditions. | Controlled acquisition through sequencing runs, immunoprecipitation workflows, fragmentation and tagging, crosslinking procedures, chromatin capture steps, and standardized pipelines for replicates and controls. |
| | Sampling | Rules determining which subset of the domain is measured and how representative it is. | Selection strategies for genomic regions, cell types, developmental stages, environmental conditions, or single-cell subsets to ensure representativeness of regulatory or epigenetic states. |
| 2.5 Data Character & Format | Data Types | The form raw evidence takes (time series, spectra, images, counts, qualitative records). | ChIP-seq peaks, ATAC profiles, methylation maps, Hi-C matrices, RNA expression tables, TF-binding motifs, fluorescence images, and single-cell regulatory-state vectors. |
| | Resolution | The granularity or precision with which data is captured. | Base-pair resolution for sequencing, single-nucleotide methylation resolution, kilobase-scale for Hi-C loops, single-cell resolution for scATAC/scRNA data, and imaging spatial resolution for chromatin structure. |
| 2.6 Reliability & Calibration | Calibration | Adjustment procedures ensuring instruments produce accurate results. | Normalization using spike-in controls, calibrating antibody specificity for ChIP, controlling for enzyme efficiency in bisulfite conversion, adjusting sequencing depth, and standardizing imaging intensities. |
| | Error Characterization | Identification and quantification of noise, uncertainty, bias, and measurement error. | Characterization of ChIP antibody bias, sequencing noise, PCR amplification artifacts, incomplete bisulfite conversion, mapping ambiguity, batch effects in regulatory assays, and variability in single-cell measurements. |
| 3. Structural Layer | 3.1 Patterns & Regularities | Laws / Relations | Stable, repeatable patterns governing how observables behave across conditions. | Core regulatory patterns include transcription-factor binding rules, enhancer–promoter communication, nucleosome–DNA interaction principles, histone-mark activation/repression rules, and methylation–silencing relationships. |
| | Invariants | Quantities or properties that remain constant under transformations (symmetries, conservation laws). | Stable features such as conserved regulatory motifs, persistent epigenetic marks across cell divisions, invariant chromatin domains, predictable TF-binding hierarchies, and long-term maintenance of methylation states. |
| 3.2 Causal Architecture | Mechanisms | Underlying processes or structures that produce the observed regularities. | Mechanisms include transcription-factor recruitment, chromatin remodeling, histone modification cascades, DNA methylation deposition/removal, insulator-mediated domain formation, and long-range chromatin-loop regulation. |
| | Pathways | Organized sequences of interactions forming a causal chain or network. | Ordered chains such as signal → TF activation → DNA binding → chromatin modification → transcriptional change; or methylation loss → chromatin opening → transcription initiation; or enhancer activation → promoter engagement → gene upregulation. |
| 3.3 Theoretical Vocabulary | Concepts | Core terms that encode the domain’s structure (force, gene, equilibrium, field). | Core terms include promoters, enhancers, silencers, insulators, chromatin accessibility, histone marks, epigenetic memory, regulatory state, TF occupancy, cis/trans regulation, and regulatory plasticity. |
| | Classifications | Taxonomies, categories, or typologies that organize entities and relations. | Taxonomies of activating vs repressing marks, classes of regulatory elements, TF families, types of regulatory RNAs, chromatin-state models, topologically associating domains (TADs), and genome-wide regulatory categories. |
| 3.4 Formal Representations | Equations | Mathematical constructs expressing laws, relations, or mechanisms. | Quantitative representations of TF-binding kinetics, Hill-type activation functions, methylation/demethylation rate equations, chromatin accessibility models, and statistical equations for enhancer–promoter contact probability. |
| | Models | Structured representations—mathematical, computational, or conceptual—used to predict and explain phenomena. | Conceptual and computational models such as chromatin-state models, TF-binding thermodynamic models, stochastic transcription-bursting models, regulatory-network models, 3D genome-looping models, and epigenetic-inheritance models. |
| 3.5 Idealized Structures | Simplified Models | Purposeful abstractions that capture essential dynamics while omitting irrelevant detail. | Simplified regulatory circuits, binary “active/inactive” promoter states, minimal histone-code categories, coarse-grained chromatin loops, reduced TF-binding motifs, and simplified methylation landscapes. |
| | Limit Conditions | Regimes where specific models or approximations hold (classical vs. quantum, linear vs. nonlinear). | Approximations hold under stable cellular conditions, moderate TF concentrations, intact chromatin structure, and typical methylation patterns; they fail under stress, developmentally dynamic states, or complex multi-factor interactions. |
| 3.6 Integrative Frameworks | Unifying Theories | Higher-order structures that connect disparate laws or mechanisms under a coherent whole. | Unifying frameworks include the regulatory logic of the central dogma, epigenetic inheritance models, sequence–structure–expression coupling, chromatin-state theory, and genome-wide regulatory network architecture. |
| | Interdisciplinary Links | Points where the theory connects to adjacent sciences or larger explanatory systems. | Links to genetics, developmental biology, molecular biology, neuroscience, cancer biology, evolutionary biology, and systems biology through shared principles of gene control, chromatin dynamics, and information regulation. |
| 4. Method Layer | 4.1 Inquiry Design | Experimental Design | Structured plans for manipulating variables to test causal claims. | Manipulating regulatory elements, TF levels, chromatin states, or epigenetic marks using CRISPR editing, TF overexpression/knockdown, histone-modifier perturbation, chromatin-remodeler inhibition, or targeted methylation/demethylation. |
| | Observational Design | Systematic approaches for gathering non-manipulated data (surveys, field studies, natural experiments). | Non-manipulative approaches such as profiling native chromatin states (ATAC-seq, DNase-seq), mapping histone marks (ChIP-seq), assessing methylation, measuring TF binding, or observing natural transcriptional variation across conditions or cell types. |
| 4.2 Testing & Validation | Hypothesis Testing | Procedures for evaluating whether evidence supports or contradicts specific claims. | Testing claims about regulatory effects by comparing expression changes after enhancer disruption, verifying methylation–silencing relationships, validating predicted TF-binding motifs, or assessing chromatin-state transitions. |
| | Replication | The requirement that results be independently reproducible under similar conditions. | Repeating ChIP-seq, ATAC-seq, methylation assays, RNA expression measures, and chromatin-interaction experiments across independent replicates, batches, or laboratories to ensure robustness. |
| 4.3 Inference & Evaluation | Statistical Inference | Rules for drawing conclusions from noisy or incomplete data. | Inferring regulatory interactions from noisy genomic data using differential-accessibility tests, ChIP enrichment statistics, methylation distributions, expression variance models, and Bayesian regulatory-network inference. |
| | Model Comparison | Criteria (fit, simplicity, predictive accuracy, robustness) used to evaluate competing models. | Evaluating competing regulatory models based on fit to expression profiles, accuracy of TF-binding predictions, correlation with chromatin states, predictive success in perturbation experiments, and generalizability across cell types. |
| 4.4 Error Management | Error Analysis | Identification and quantification of random and systematic errors. | Quantifying biases and errors in ChIP antibody specificity, sequencing noise, PCR amplification artifacts, mapping errors, batch effects in epigenomic assays, and stochastic variability in single-cell regulatory measurements. |
| | Bias Control | Methods for minimizing subjective, instrumental, or procedural biases. | Controlling bias through spike-in references, randomized sample handling, strict antibody validation, balanced library preparation, controlled crosslinking duration, and consistent normalization pipelines. |
| 4.5 Adjudication & Revision | Peer Scrutiny | Collective evaluation of claims through critique, review, and debate. | Review by independent scientists of regulatory interpretations, chromatin maps, epigenetic claims, modeling assumptions, and methodology to verify validity and reproducibility. |
| | Theory Revision | Procedures for modifying, replacing, or discarding models based on new evidence. | Updating regulatory theories when new evidence reveals novel chromatin states, new regulatory elements, unexpected TF cooperativity, noncanonical epigenetic inheritance, or rewrites existing regulatory hierarchies. |
| 4.6 Integrity Conditions | Transparency | Requirements to disclose methods, data, assumptions, and limitations. | Full disclosure of sequencing parameters, antibody details, data-preprocessing steps, normalization methods, perturbation conditions, sample metadata, and model assumptions. |
| | Ethical Standards | Norms ensuring responsible conduct in experimentation, data handling, and publication. | Ensuring responsible handling of genomic/epigenomic data, adherence to privacy norms for human samples, accurate reporting, prevention of data manipulation, and ethical interpretation of heritable regulatory findings. |