| 1. Domain | 1.1 Scope of the Domain | Boundaries | The range of phenomena the science includes and excludes. | Studies how genetic information is stored, replicated, transcribed, processed, translated, and regulated; excludes pure evolutionary biology without molecular mechanisms, and structural biochemistry without gene-expression context. |
| | Scale | The spatial, temporal, or organizational level at which the science operates (e.g., quantum, cellular, social, cosmic). | Operates from atomic interactions in DNA/RNA bases to molecular machines (polymerases, ribosomes), to cellular-level gene-expression programs, and organismal regulatory networks. |
| 1.2 Ontological Commitments | Entities | The kinds of things assumed to exist within the domain (particles, organisms, agents, fields, etc.). | DNA, RNA, nucleotides, genes, promoters, enhancers, transcription factors, RNA polymerases, ribosomes, nucleosomes, epigenetic marks, regulatory RNAs, spliceosomes, chaperones, replication complexes. |
| | Properties | The fundamental attributes these entities possess (mass, charge, genotype, preference, etc.). | Base-pair identity, sequence-specific binding affinity, transcription rate, translation efficiency, epigenetic modification state, chromatin accessibility, RNA stability, codon usage bias, regulatory strength. |
| | Categories | The basic ontological types used to classify domain elements (substances, processes, relations, structures). | Genetic elements (genes, operons, enhancers, insulators), RNA types (mRNA, tRNA, rRNA, ncRNA, miRNA, lncRNA), regulatory systems (activators, repressors), chromatin states, transcriptional/epigenetic mechanisms. |
| 1.3 State-Variables | Variables | The measurable or definable properties that describe system conditions. | Gene expression level, transcript abundance, promoter occupancy, epigenetic modification density, chromatin compaction, ribosome loading, transcription rate, splicing efficiency, RNA turnover rate. |
| | Parameterization | How variables encode and represent the system’s state. | States encoded via transcript counts, promoter strength metrics, chromatin marks, transcription-factor occupancy maps, ribosome profiling, RNA half-lives, polymerase speed, binding constants, codon usage indices. |
| 1.4 Admissible Idealizations | Simplifications | Conceptual reductions used to make the domain tractable (point masses, rational agents, perfect gases). | Treating genes as independent units, ignoring chromatin complexity, idealized promoter–TF interactions, two-state ON/OFF transcription models, single-isoform assumptions, linear transcription–translation coupling. |
| | Validity Conditions | The limits and contexts in which idealizations hold or break down. | Valid in controlled systems with isolated genes or purified components; break down in complex chromatin, multi-enhancer regulation, RNA processing diversity, cellular stress responses, or stochastic low-copy regimes. |
| 1.5 Domain Assumptions | Structural Assumptions | Background ontological stances such as determinism, continuity, randomness, discreteness. | DNA sequence determines regulatory logic; RNA mediates information transfer; protein synthesis follows universal ribosomal mechanisms; regulation is encoded via molecular interactions. |
| | Implicit Commitments | Unstated but necessary assumptions that shape the field’s conceptual structure. | Assumes stable base-pairing rules, reliable protein–DNA/RNA recognition, consistent polymerase behavior, interpretable regulatory networks, and coherent mapping between genotype → transcript → protein. |
| 1.6 Internal Coherence Requirements | Consistency | The demand that domain concepts do not contradict one another. | Requires consistency among DNA sequence, chromatin context, transcription rates, RNA processing, translational output, and protein regulation without contradictions. |
| | Compatibility | The requirement that entities, variables, and assumptions fit together into a unified descriptive framework. | Demands alignment between transcription, splicing, translation, chromatin structure, signaling pathways, metabolic state, and cellular/organismal regulation within a unified gene-expression framework. |
| 2. Evidence Layer | 2.1 Observable Phenomena | Observables | The aspects of the domain that can produce detectable signals accessible to measurement. | Transcript abundance changes, promoter activation, protein-expression levels, fluorescent reporter output, chromatin accessibility, DNA–protein binding events, ribosome occupancy, RNA splicing patterns, RNA degradation curves, epigenetic modifications. |
| | Detection Limits | The boundaries of what can be resolved or sensed by current instruments or methods. | Limited by low-abundance transcripts, sequencing depth, noise in single-cell data, antibody sensitivity, cross-reactivity, short-lived intermediates, incomplete chromatin fragmentation, low-affinity binding detection limits. |
| 2.2 Measurement Systems | Units | Standardized quantifications (meters, seconds, volts, decibels, dollars, etc.) necessary for consistent comparison. | Transcript counts (TPM, RPKM, CPM), protein abundance (a.u. or copies/cell), fluorescence intensity, ChIP enrichment (fold-change), ribosome footprints (reads), methylation (percentage), binding affinities (Kd), time (s–hr). |
| | Instruments | Devices and tools (microscopes, spectrometers, sensors, surveys, detectors) used to produce measurements. | qPCR, RT-PCR, RNA-seq, ChIP-seq, ATAC-seq, bisulfite sequencing rigs, single-cell sequencing platforms, ribosome profiling systems, fluorescence microscopes, flow cytometers, Western blot imagers, mass spectrometers. |
| 2.3 Operational Definitions | Definitions | Terms defined by specific measurement procedures, ensuring empirical clarity. | Gene expression defined by transcript abundance; promoter activity by reporter fluorescence or ChIP enrichment; translation by ribosome density; regulation by TF occupancy; epigenetic state by methylation/acetylation patterns. |
| | Procedures | The explicit steps required to perform a measurement in a reproducible way. | RNA extraction, reverse transcription, library preparation, antibody pulldowns (ChIP), chromatin fragmentation, single-cell isolation, electrophoresis, sample-barcode processing, sequencing, ribosome-footprint protection assays. |
| 2.4 Data Acquisition | Protocols | Formal processes for gathering data under controlled or standardized conditions. | Sequencing runs, fluorescence time-lapse imaging, flow cytometry gating workflows, replicate ChIP-seq libraries, ribosome profiling timepoints, single-cell multiomic sampling, parallel RNA-protein assays. |
| | Sampling | Rules determining which subset of the domain is measured and how representative it is. | Replicate biological samples, technical replicates, multi-timepoint sampling, multi-condition sampling (stimulus vs control), cell-type–specific sampling, nuclei vs cytosol fractionation, transcript isoform sampling. |
| 2.5 Data Character & Format | Data Types | The form raw evidence takes (time series, spectra, images, counts, qualitative records). | Sequencing reads, count matrices, gene-expression profiles, ChIP peaks, ATAC-seq accessibility profiles, ribosome-footprint distributions, methylation maps, fluorescence images, flow-cytometry plots, protein bands/traces. |
| | Resolution | The granularity or precision with which data is captured. | Determined by sequencing depth, read length, imaging pixel size, antibody specificity, chromatin fragmentation efficiency, footprint resolution, single-cell capture efficiency, and time-lapse acquisition frequency. |
| 2.6 Reliability & Calibration | Calibration | Adjustment procedures ensuring instruments produce accurate results. | Sequencer calibration, fluorescence intensity standardization, flow cytometer compensation, qPCR standard curves, antibody specificity controls, spike-in RNA standards, mapping-quality filters, batch-effect correction. |
| | Error Characterization | Identification and quantification of noise, uncertainty, bias, and measurement error. | Sequencing noise, PCR bias, dropouts in scRNA-seq, antibody cross-reactivity, false ChIP peaks, ribosome-stall artifacts, mapping ambiguity, degradation bias, GC-content bias, batch effects, and sampling variance. |
| 3. Structural Layer | 3.1 Patterns & Regularities | Laws / Relations | Stable, repeatable patterns governing how observables behave across conditions. | Central Dogma flow (DNA→RNA→protein), promoter–TF binding rules, enhancer–promoter communication patterns, chromatin accessibility–expression relationships, codon-usage influences on translation, cooperative transcription-factor binding. |
| | Invariants | Quantities or properties that remain constant under transformations (symmetries, conservation laws). | Conserved promoter motifs (TATA, CpG islands), invariant base-pairing rules, conserved splice-site motifs (GU–AG), stable ribosomal decoding logic, universal genetic code (with rare exceptions), consistent polymerase catalytic mechanisms. |
| 3.2 Causal Architecture | Mechanisms | Underlying processes or structures that produce the observed regularities. | DNA replication, transcription initiation/elongation/termination, RNA processing (splicing, capping, polyadenylation), chromatin remodeling, enhancer activation, transcription-factor binding, translation initiation/elongation, RNA degradation pathways, epigenetic modification cycles. |
| | Pathways | Organized sequences of interactions forming a causal chain or network. | Gene activation pathways, transcription–splicing–export cascade, RNA surveillance pathways (NMD), translation cycles, regulatory feedback loops, epigenetic reinforcement pathways, operon-based transcription sequences (prokaryotes). |
| 3.3 Theoretical Vocabulary | Concepts | Core terms that encode the domain’s structure (force, gene, equilibrium, field). | Promoter strength, enhancer looping, chromatin accessibility, TF-binding affinity, transcriptional bursting, epigenetic marks, operons, cis/trans regulation, splicing code, RNA stability elements, ribosome profiling metrics, codon optimality. |
| | Classifications | Taxonomies, categories, or typologies that organize entities and relations. | Gene classes (housekeeping, inducible, repressible), regulatory RNA types (miRNA, lncRNA, siRNA), promoter types, enhancer classes, chromatin states, transcription-factor families, operon structures, RNA polymerase systems (I/II/III). |
| 3.4 Formal Representations | Equations | Mathematical constructs expressing laws, relations, or mechanisms. | Gene-expression models: transcription rate equations, Hill-type TF-binding equations, burst frequency/size equations, chromatin accessibility–expression models, translation-rate equations, degradation kinetics (first-order decay). |
| | Models | Structured representations—mathematical, computational, or conceptual—used to predict and explain phenomena. | Gene regulatory network models, stochastic transcription models, chromatin-state models, TF–DNA binding energy models, splicing-decision models, ribosome-traffic models, epigenetic Markov-state frameworks. |
| 3.5 Idealized Structures | Simplified Models | Purposeful abstractions that capture essential dynamics while omitting irrelevant detail. | Two-state ON/OFF transcription models, uniform-chromatin assumptions, perfect TF-binding specificity, no cross-talk between enhancers, single isoform per gene, deterministic transcription, no RNA secondary-structure effects. |
| | Limit Conditions | Regimes where specific models or approximations hold (classical vs. quantum, linear vs. nonlinear). | Break down in heterogeneous chromatin, noisy low-copy expression, multi-enhancer regulation, alternative splicing, overlapping transcription units, RNA structural regulation, multi-gene operons, or highly dynamic signal-responsive systems. |
| 3.6 Integrative Frameworks | Unifying Theories | Higher-order structures that connect disparate laws or mechanisms under a coherent whole. | Integration of DNA sequence, chromatin state, TF networks, RNA processing, translation, and degradation into unified gene-expression models; linking sequence → structure → regulatory logic → transcript → protein output. |
| | Interdisciplinary Links | Points where the theory connects to adjacent sciences or larger explanatory systems. | Connects to genetics, epigenetics, systems biology, structural biology, bioinformatics, synthetic biology, developmental biology, cancer biology, and evolutionary genomics. |
| 4. Method Layer | 4.1 Inquiry Design | Experimental Design | Structured plans for manipulating variables to test causal claims. | Controlling stimulus conditions, promoter constructs, TF concentrations, chromatin-state modulators, knockout/knockdown settings, time-course sampling, and reporter design to test causal gene-expression hypotheses. |
| | Observational Design | Systematic approaches for gathering non-manipulated data (surveys, field studies, natural experiments). | Monitoring spontaneous transcriptional bursting, natural chromatin fluctuations, passive RNA decay, unregulated promoter occupancy, native transcription–translation coupling, and baseline epigenetic drift without perturbation. |
| 4.2 Testing & Validation | Hypothesis Testing | Procedures for evaluating whether evidence supports or contradicts specific claims. | Comparing predicted expression patterns, TF-binding profiles, enhancer–promoter interactions, isoform ratios, chromatin accessibility, and ribosome loading with experimental data from qPCR, RNA-seq, ChIP-seq, ATAC-seq, and ribosome profiling. |
| | Replication | The requirement that results be independently reproducible under similar conditions. | Performing replicate RNA extractions, sequencing libraries, reporter assays, ChIP pulldowns, ATAC-seq replicates, flow-cytometry runs, imaging replicates, and parallel cell-culture experiments across days or batches. |
| 4.3 Inference & Evaluation | Statistical Inference | Rules for drawing conclusions from noisy or incomplete data. | Calculating differential expression, promoter-strength estimates, confidence intervals for TF occupancy, splicing probabilities, gene regulatory network edges, noise decomposition (intrinsic vs extrinsic), and transcript-decay constants. |
| | Model Comparison | Criteria (fit, simplicity, predictive accuracy, robustness) used to evaluate competing models. | Evaluating stochastic vs deterministic transcription models, competing TF-binding models, GRN structures, splicing-decision models, burst-frequency vs burst-size models, and chromatin-state transition frameworks. |
| 4.4 Error Management | Error Analysis | Identification and quantification of random and systematic errors. | Identifying sequencing errors, PCR bias, dropout effects (scRNA-seq), antibody cross-reactivity, mapping errors, batch effects, chromatin-fragmentation artifacts, isoform misquantification, and reporter-background signal. |
| | Bias Control | Methods for minimizing subjective, instrumental, or procedural biases. | Randomizing sample order, barcode balancing, blinding sample identity, using spike-ins and ERCC controls, applying batch correction and normalization, validating antibodies, and using orthogonal readouts (e.g., qPCR confirmation). |
| 4.5 Adjudication & Revision | Peer Scrutiny | Collective evaluation of claims through critique, review, and debate. | Independent review of expression profiles, ChIP/ATAC peak calls, TF-binding assignments, GRN inference, isoform quantification, and claims of regulatory interactions or gene-function discoveries. |
| | Theory Revision | Procedures for modifying, replacing, or discarding models based on new evidence. | Updating GRN models, revising transcriptional-burst parameters, correcting promoter/enhancer annotations, adjusting chromatin-state models, redefining regulatory interactions, and incorporating contradictory multi-omics evidence. |
| 4.6 Integrity Conditions | Transparency | Requirements to disclose methods, data, assumptions, and limitations. | Full disclosure of sequencing parameters, mapping methods, normalization pipelines, antibody validation data, chromatin-prep conditions, reporter constructs, statistical thresholds, and computational workflows. |
| | Ethical Standards | Norms ensuring responsible conduct in experimentation, data handling, and publication. | Honest reporting of uncertain peaks, low-confidence transcripts, ambiguous isoforms, failed controls, negative results, data exclusions, and compliance with ethical regulations for genetic manipulation. |