| 1. Domain | 1.1 Scope of the Domain | Boundaries | The range of phenomena the science includes and excludes. | Studies how genetic variation influences biochemical pathways, enzyme function, metabolic phenotypes, molecular defects, inheritance patterns, and disease mechanisms; excludes purely structural genetics or purely metabolic biochemistry not tied to genotype–phenotype relationships. |
| | Scale | The spatial, temporal, or organizational level at which the science operates (e.g., quantum, cellular, social, cosmic). | Operates from DNA sequence changes (SNPs, insertions/deletions, mutations) to altered proteins/enzymes, disrupted pathways, cellular metabolic consequences, organismal phenotypes, and population-level inheritance patterns. |
| 1.2 Ontological Commitments | Entities | The kinds of things assumed to exist within the domain (particles, organisms, agents, fields, etc.). | Genes, alleles, mutations, enzymes, metabolites, RNA transcripts, regulatory elements, protein complexes, biochemical pathways, inheritance units, molecular defects, compensatory pathways, modifier genes. |
| | Properties | The fundamental attributes these entities possess (mass, charge, genotype, preference, etc.). | Catalytic efficiency, binding affinity, enzyme stability, PTM susceptibility, metabolite abundance, allele penetrance, expressivity, biochemical flux, mutation severity, genotype–phenotype strength. |
| | Categories | The basic ontological types used to classify domain elements (substances, processes, relations, structures). | Mutation types (missense, nonsense, frameshift), inheritance types (dominant, recessive, X-linked, mitochondrial), metabolic disorders, enzyme-deficiency classes, pathway modules, regulatory mutations, modifier genes. |
| 1.3 State-Variables | Variables | The measurable or definable properties that describe system conditions. | Gene expression level, allele dosage, mutation frequency, enzyme activity, pathway flux, metabolite concentrations, redox balance, compensation capacity, developmental stage, environmental influences. |
| | Parameterization | How variables encode and represent the system’s state. | States encoded via kinetic constants (Km, kcat), pathway flux distributions, allelic expression ratios, metabolic profiling maps, variant effect predictions, penetrance models, stoichiometric matrices, genotype–phenotype curves. |
| 1.4 Admissible Idealizations | Simplifications | Conceptual reductions used to make the domain tractable (point masses, rational agents, perfect gases). | Single-gene single-effect assumptions, linear genotype–phenotype mapping, ignoring epistasis, assuming constant environment, ignoring stochastic gene expression, treating pathways as isolated modules without cross-talk. |
| | Validity Conditions | The limits and contexts in which idealizations hold or break down. | Valid for strong-effect mutations, isolated metabolic blocks, Mendelian disorders; breaks down for polygenic traits, network-level compensation, regulatory mutations with context dependence, or environmentally modulated phenotypes. |
| 1.5 Domain Assumptions | Structural Assumptions | Background ontological stances such as determinism, continuity, randomness, discreteness. | DNA sequence determines biochemical capacity; mutation changes protein chemistry; altered biochemical function drives phenotype; inheritance rules reflect molecular causes; biochemical networks respond predictably to perturbation. |
| | Implicit Commitments | Unstated but necessary assumptions that shape the field’s conceptual structure. | Assumes stable genotype–phenotype relationships, conserved pathway architecture, well-defined enzyme functions, reliable kinetic modeling, interpretable metabolic signatures, and heritable molecular mechanisms. |
| 1.6 Internal Coherence Requirements | Consistency | The demand that domain concepts do not contradict one another. | Requires agreement among genotype, enzyme kinetics, metabolic flux, cellular phenotype, tissue physiology, and inheritance pattern without contradiction. |
| | Compatibility | The requirement that entities, variables, and assumptions fit together into a unified descriptive framework. | Demands alignment between molecular biology, protein chemistry, enzymology, metabolism, genetics, systems biology, and evolutionary constraints within a unified genotype→biochemistry→phenotype framework. |
| 2. Evidence Layer | 2.1 Observable Phenomena | Observables | The aspects of the domain that can produce detectable signals accessible to measurement. | Altered metabolite levels, abnormal enzyme activity, shifted kinetic curves, accumulation of toxic intermediates, misfolded proteins, altered PTM patterns, aberrant RNA expression, variant-specific protein stability, organelle dysfunction, phenotypic traits arising from biochemical defects. |
| | Detection Limits | The boundaries of what can be resolved or sensed by current instruments or methods. | Limited by low metabolite abundance, weak enzyme activity changes, incomplete variant expression, low-frequency alleles, tissue heterogeneity, MS/sequence noise, unstable intermediates, and limited sensitivity for rare mitochondrial variants. |
| 2.2 Measurement Systems | Units | Standardized quantifications (meters, seconds, volts, decibels, dollars, etc.) necessary for consistent comparison. | Concentration (µM–mM), enzyme activity (µmol/min/mg), kinetic constants (Km, kcat), flux rates, allele frequency (%), metabolite fold-change, expression counts (TPM/RPKM), protein abundance (copies/cell), variant frequency, redox ratios. |
| | Instruments | Devices and tools (microscopes, spectrometers, sensors, surveys, detectors) used to produce measurements. | Sequencers (NGS), qPCR machines, mass spectrometers (metabolomics/proteomics), HPLC/UPLC, NMR metabolomics rigs, enzyme assay plate readers, Western blot imagers, CRISPR genotyping tools, structural MS, single-cell sequencing platforms. |
| 2.3 Operational Definitions | Definitions | Terms defined by specific measurement procedures, ensuring empirical clarity. | Mutation impact defined by change in enzyme kinetics or protein stability; metabolic block defined by accumulation/depletion patterns; genotype defined by sequencing; penetrance defined by phenotype occurrence relative to genotype; allele dosage defined by expression ratio. |
| | Procedures | The explicit steps required to perform a measurement in a reproducible way. | DNA/RNA extraction, variant calling workflows, allele-specific expression assays, enzyme-activity assays, metabolite profiling, proteomic PTM mapping, genetic rescue experiments, CRISPR perturbation assays, linkage/association analyses. |
| 2.4 Data Acquisition | Protocols | Formal processes for gathering data under controlled or standardized conditions. | Sequencing runs, metabolomics time courses, enzyme kinetics curves, proteomic PTM scans, allele-dose response curves, transcriptional profiling, CRISPR perturbation timepoints, family-based genetic sampling, cohort phenotyping. |
| | Sampling | Rules determining which subset of the domain is measured and how representative it is. | Biological replicates, multigenerational samples, tissue-specific sampling, single-cell sampling, variant-specific sampling, cohort-level sampling, multiple developmental stages, longitudinal clinical sampling. |
| 2.5 Data Character & Format | Data Types | The form raw evidence takes (time series, spectra, images, counts, qualitative records). | Variant tables, FASTQ/VCF files, metabolomics spectra, kinetic plots, PTM mass maps, expression matrices, phenotypic trait tables, linkage maps, inheritance diagrams, allele-frequency distributions. |
| | Resolution | The granularity or precision with which data is captured. | Determined by sequencing depth, MS sensitivity, kinetic sampling frequency, tissue/cell purity, allele-detection thresholds, variant-calling accuracy, and noise in metabolite quantification. |
| 2.6 Reliability & Calibration | Calibration | Adjustment procedures ensuring instruments produce accurate results. | Sequencer calibration, variant-calling QC filters, MS mass-axis calibration, enzyme assay standardization, metabolite standards, qPCR standard curves, genotyping controls, allele-frequency calibration using reference samples. |
| | Error Characterization | Identification and quantification of noise, uncertainty, bias, and measurement error. | Sequencing errors, false positives/negatives in variant calling, allele dropout, MS ion suppression, protein degradation, metabolic instability, sample heterogeneity, misannotation, batch effects, and statistical noise in low-frequency variant detection. |
| 3. Structural Layer | 3.1 Patterns & Regularities | Laws / Relations | Stable, repeatable patterns governing how observables behave across conditions. | Genotype → enzyme defect → metabolic alteration → phenotype is the core mapping; Mendelian inheritance patterns; conserved pathway stoichiometry; mutation–activity correlations; dosage-sensitive gene effects; classical metabolic block relationships (precursor accumulation, product deficiency). |
| | Invariants | Quantities or properties that remain constant under transformations (symmetries, conservation laws). | Conserved catalytic residues in enzyme families, invariant pathway topology across species, stable allele-segregation ratios, conserved biochemical rules for how mutation type affects protein chemistry, consistent dominance/recessiveness logic for loss-/gain-of-function mutations. |
| 3.2 Causal Architecture | Mechanisms | Underlying processes or structures that produce the observed regularities. | Mutation impacts protein structure → altered enzyme kinetics → pathway imbalance → cellular stress → organismal phenotype; mechanisms include misfolding, instability, reduced binding affinity, catalytic impairment, aberrant PTMs, haploinsufficiency, dominant-negative interference, or toxic gain-of-function. |
| | Pathways | Organized sequences of interactions forming a causal chain or network. | Metabolic pathways (glycolysis, urea cycle, amino-acid metabolism), DNA repair pathways, RNA processing pathways, mitochondrial inheritance pathways, cofactor-processing pathways, compensatory metabolic rewiring, and multi-step genotype→biochemistry→phenotype cascades. |
| 3.3 Theoretical Vocabulary | Concepts | Core terms that encode the domain’s structure (force, gene, equilibrium, field). | Penetrance, expressivity, allelic series, modifier genes, genotype–phenotype map, biochemical block, flux reduction, loss-/gain-of-function, haploinsufficiency, dominance, recessivity, metabolic thresholds, epistasis, pleiotropy. |
| | Classifications | Taxonomies, categories, or typologies that organize entities and relations. | Mutation classes (missense, nonsense, frameshift, splice-site), inheritance classes (autosomal, X-linked, mitochondrial), biochemical disorder classes (enzyme deficiencies, transport defects, receptor mutations), genotype–effect categories (null, hypomorphic, hypermorphic, neomorphic). |
| 3.4 Formal Representations | Equations | Mathematical constructs expressing laws, relations, or mechanisms. | Michaelis–Menten relations for mutant enzymes, ΔG and stability equations, genotype–penetrance models, Hardy-Weinberg equations, metabolic-flux equations, allele-dosage models, epistasis interaction terms, quantitative trait equations. |
| | Models | Structured representations—mathematical, computational, or conceptual—used to predict and explain phenomena. | Genotype–phenotype mapping models, metabolic network simulations, enzyme-kinetic mutation models, protein-stability mutation models, polygenic-risk models, Mendelian segregation models, mitochondrial inheritance models. |
| 3.5 Idealized Structures | Simplified Models | Purposeful abstractions that capture essential dynamics while omitting irrelevant detail. | Single-gene Mendelian models, direct linear genotype→phenotype mapping, ignoring modifier genes, perfect enzyme deficiency assumptions, isolated pathways without cross-talk, uniform tissue expression, no environmental modulation. |
| | Limit Conditions | Regimes where specific models or approximations hold (classical vs. quantum, linear vs. nonlinear). | Fail for polygenic traits, complex metabolic networks, environmental influences, tissue-specific effects, partial compensation by paralogs, mosaicism, mitochondrial heteroplasmy, non-linear dose effects, and stochastic expression. |
| 3.6 Integrative Frameworks | Unifying Theories | Higher-order structures that connect disparate laws or mechanisms under a coherent whole. | Integrates genetics, enzymology, protein chemistry, metabolism, and systems biology to create unified genotype→biochemical mechanism→cellular phenotype→organismal phenotype frameworks; ties molecular defects to inheritance patterns and evolutionary dynamics. |
| | Interdisciplinary Links | Points where the theory connects to adjacent sciences or larger explanatory systems. | Connects to medical genetics, molecular biology, clinical biochemistry, evolutionary genetics, computational biology, systems medicine, pharmacogenomics, and developmental biology. |
| 4. Method Layer | 4.1 Inquiry Design | Experimental Design | Structured plans for manipulating variables to test causal claims. | Controlling genotype (CRISPR edits, knockouts, knock-ins), allele dosage, expression levels, enzyme concentrations, nutrient availability, metabolic load, environmental stressors, and developmental timing to test causal genotype→biochemistry→phenotype hypotheses. |
| | Observational Design | Systematic approaches for gathering non-manipulated data (surveys, field studies, natural experiments). | Monitoring natural variant frequency, spontaneous metabolic imbalances, baseline expression variation, endogenous PTM patterns, unperturbed phenotypic drift, and inheritance outcomes without imposed manipulation. |
| 4.2 Testing & Validation | Hypothesis Testing | Procedures for evaluating whether evidence supports or contradicts specific claims. | Comparing predicted biochemical effects of mutations with experimentally measured enzyme kinetics, metabolite levels, stability changes, pathway flux, expression profiles, and phenotypic outcomes across genotypes. |
| | Replication | The requirement that results be independently reproducible under similar conditions. | Repeating genotyping runs, enzyme assays, metabolomics measurements, expression quantification, PTM analysis, family-based sampling, and functional rescue experiments across technical and biological replicates. |
| 4.3 Inference & Evaluation | Statistical Inference | Rules for drawing conclusions from noisy or incomplete data. | Estimating penetrance, expressivity, effect sizes, kinetic parameter confidence intervals, metabolite-level variance, allele-frequency distributions, and likelihoods of genotype–phenotype associations; performing linkage/association statistics. |
| | Model Comparison | Criteria (fit, simplicity, predictive accuracy, robustness) used to evaluate competing models. | Evaluating competing genotype–phenotype mapping models, enzyme kinetic models, metabolic network simulations, inheritance models, and variant-effect predictions (structural models vs statistical models vs machine-learning models). |
| 4.4 Error Management | Error Analysis | Identification and quantification of random and systematic errors. | Identifying sequencing noise, variant miscalls, allele dropout, enzyme-prep instability, metabolite degradation, MS ion suppression, tissue heterogeneity, mosaicism, batch effects, and environmental confounders. |
| | Bias Control | Methods for minimizing subjective, instrumental, or procedural biases. | Blinding genotype labels, randomizing sample order, balancing family/cohort structure, using internal standards, matching tissue/cell type, normalizing expression/metabolite loads, correcting population stratification. |
| 4.5 Adjudication & Revision | Peer Scrutiny | Collective evaluation of claims through critique, review, and debate. | Independent evaluation of variant calls, kinetic interpretations, metabolic profiles, inheritance assignments, linkage results, computational predictions, and claims about genotype–phenotype causality. |
| | Theory Revision | Procedures for modifying, replacing, or discarding models based on new evidence. | Updating mutation-impact models, revising enzyme-defect mechanisms, redefining metabolic blocks, adjusting inheritance expectations, incorporating modifier genes, replacing false genotype–phenotype links with validated models. |
| 4.6 Integrity Conditions | Transparency | Requirements to disclose methods, data, assumptions, and limitations. | Full reporting of sequencing/mapping parameters, variant filters, QC metrics, enzyme-assay conditions, metabolomics pipelines, statistical models, assumptions, normalization procedures, and variant-interpretation criteria. |
| | Ethical Standards | Norms ensuring responsible conduct in experimentation, data handling, and publication. | Honest reporting of uncertain variant effects, ambiguous inheritance patterns, failed validation, negative results, patient-data limitations, and compliance with genetic ethics, privacy, and clinical research standards. |