| 1. Domain | 1.1 Scope of the Domain | Boundaries | The range of phenomena the science includes and excludes. | Studies organic chemistry within biological contexts: biomolecule structure, reactivity, mechanistic behavior, and synthetic mimicry; excludes purely inorganic biochemistry or non-organic catalysis. |
| | Scale | The spatial, temporal, or organizational level at which the science operates (e.g., quantum, cellular, social, cosmic). | Operates from electronic/atomic scales (bond formation, stereoelectronics) to biomolecular and cellular scales (enzyme active sites, metabolic pathways, supramolecular assemblies). |
| 1.2 Ontological Commitments | Entities | The kinds of things assumed to exist within the domain (particles, organisms, agents, fields, etc.). | Biomolecules (amino acids, peptides, nucleotides, sugars, lipids), cofactors, enzymes, substrates, intermediates, transition states, catalytic residues, reactive oxygen species, synthetic analogs. |
| | Properties | The fundamental attributes these entities possess (mass, charge, genotype, preference, etc.). | Chirality, stereoelectronic preferences, acidity/basicity, redox characteristics, hydrogen-bonding capacity, hydrophobicity, conformational flexibility, biochemical compatibility/reactivity. |
| | Categories | The basic ontological types used to classify domain elements (substances, processes, relations, structures). | Biomolecular families (peptides, carbohydrates, nucleic acids, lipids), enzyme reaction types, cofactors, metabolic intermediates, bioorthogonal reactions, biomimetic catalysts. |
| 1.3 State-Variables | Variables | The measurable or definable properties that describe system conditions. | pH, ionic strength, temperature, concentration, redox state, conformational populations, protonation states, solvent polarity (aqueous vs mixed), binding affinities. |
| | Parameterization | How variables encode and represent the system’s state. | States encoded via pKa profiles, Michaelis–Menten parameters, binding constants, conformational energy surfaces, stereoelectronic descriptors, redox potentials, hydrogen-bonding patterns. |
| 1.4 Admissible Idealizations | Simplifications | Conceptual reductions used to make the domain tractable (point masses, rational agents, perfect gases). | Idealized active-site geometries, neglect of competing pathways, isolated reaction models, simplified solvent environments, static conformations, truncated biomolecular models. |
| | Validity Conditions | The limits and contexts in which idealizations hold or break down. | Valid under controlled biological or biomimetic conditions; break down in crowded environments, highly dynamic conformational states, multi-enzyme coupling, or extreme ionic/thermal regimes. |
| 1.5 Domain Assumptions | Structural Assumptions | Background ontological stances such as determinism, continuity, randomness, discreteness. | Organic reactivity principles govern biomolecular transformations; enzyme active sites enforce predictable stereochemistry and transition-state stabilization. |
| | Implicit Commitments | Unstated but necessary assumptions that shape the field’s conceptual structure. | Assumes transferability of mechanistic logic from organic chemistry to biology, stable functional-group behavior in aqueous media, meaningful TS analogs, and interpretable conformational preferences. |
| 1.6 Internal Coherence Requirements | Consistency | The demand that domain concepts do not contradict one another. | Requires compatibility among stereoelectronic models, enzymatic mechanisms, biomolecular structure, kinetic parameters, and thermodynamic landscapes. |
| | Compatibility | The requirement that entities, variables, and assumptions fit together into a unified descriptive framework. | Demands integrative alignment between organic mechanism, enzyme structure/function, solution chemistry, supramolecular interactions, and cellular biochemical constraints. |
| 2. Evidence Layer | 2.1 Observable Phenomena | Observables | The aspects of the domain that can produce detectable signals accessible to measurement. | Reaction rates in enzyme or biomimetic systems, binding events, pH-dependent reactivity, conformational changes, fluorescence signals, UV–Vis shifts, redox transitions, product profiles. |
| | Detection Limits | The boundaries of what can be resolved or sensed by current instruments or methods. | Limited by ability to detect low-abundance intermediates, transient enzyme–substrate complexes, small conformational changes, weak fluorescence, or fast biological turnover events. |
| 2.2 Measurement Systems | Units | Standardized quantifications (meters, seconds, volts, decibels, dollars, etc.) necessary for consistent comparison. | Rate constants (s⁻¹, M⁻¹ s⁻¹), binding constants (Kd), Michaelis–Menten parameters (Km, kcat), pKa, ΔG/ΔH/ΔS, absorbance (a.u.), wavelength (nm), chemical shift (ppm), mass (m/z). |
| | Instruments | Devices and tools (microscopes, spectrometers, sensors, surveys, detectors) used to produce measurements. | NMR, IR, UV–Vis, fluorescence spectrometers, stopped-flow instruments, calorimeters (ITC/DSC), mass spectrometers, CD spectrometers, HPLC/LC-MS, enzyme assay platforms, cryostats. |
| 2.3 Operational Definitions | Definitions | Terms defined by specific measurement procedures, ensuring empirical clarity. | Binding defined via Kd; catalytic efficiency via kcat/Km; pKa via titration curves; conformational states by NMR/fluorescence signatures; redox states via electrochemical potentials. |
| | Procedures | The explicit steps required to perform a measurement in a reproducible way. | Controlled enzyme assays, titrations, spectroscopic monitoring, quenching protocols, buffer preparation standards, reproducible substrate addition, equilibrium/steady-state measurements. |
| 2.4 Data Acquisition | Protocols | Formal processes for gathering data under controlled or standardized conditions. | Time-resolved enzymatic sampling, temperature/pH ramps, multi-wavelength fluorescence monitoring, rapid-mix kinetics, ITC titration cycles, LC-MS product tracking. |
| | Sampling | Rules determining which subset of the domain is measured and how representative it is. | Repeated aliquots across time, multiple substrate concentrations, conformational sampling via NMR, replicate binding experiments, multi-angle fluorescence sampling. |
| 2.5 Data Character & Format | Data Types | The form raw evidence takes (time series, spectra, images, counts, qualitative records). | Kinetic curves, pH-rate profiles, fluorescence emission spectra, UV–Vis traces, CD spectra, binding isotherms, MS product patterns, NMR structural data, sequence of intermediates. |
| | Resolution | The granularity or precision with which data is captured. | Determined by spectrometer bandwidth, detector sensitivity, temperature/pH stability, mixing speed, fluorescence lifetime resolution, and mass accuracy for biomolecular fragments. |
| 2.6 Reliability & Calibration | Calibration | Adjustment procedures ensuring instruments produce accurate results. | Calibration of pH meters, spectrometer baselines, fluorescence intensity, thermal control in ITC/DSC, mass calibration in MS, NMR referencing, enzyme concentration standardization. |
| | Error Characterization | Identification and quantification of noise, uncertainty, bias, and measurement error. | Noise, detector drift, buffer impurities, enzyme instability, substrate degradation, inner-filter effects in fluorescence, peak overlap, fitting uncertainty in kinetic/binding models. |
| 3. Structural Layer | 3.1 Patterns & Regularities | Laws / Relations | Stable, repeatable patterns governing how observables behave across conditions. | Structure–reactivity relationships in biomolecules, stereoelectronic effects in enzymatic catalysis, pH-rate profiles, hydrogen-bonding patterns, Michaelis–Menten relationships, binding cooperativity laws. |
| | Invariants | Quantities or properties that remain constant under transformations (symmetries, conservation laws). | Conserved stereochemical relationships in enzymatic reactions, invariant hydrogen-bonding motifs, preserved catalytic residue roles, invariant scaffold–function relationships across homologous systems. |
| 3.2 Causal Architecture | Mechanisms | Underlying processes or structures that produce the observed regularities. | Enzyme-catalyzed proton transfers, covalent catalysis, general acid/base catalysis, metal-mediated activation, radical pathways, nucleophilic/electrophilic attack in biomolecular contexts. |
| | Pathways | Organized sequences of interactions forming a causal chain or network. | Enzymatic catalytic cycles, metabolic routes, conformational gating, multi-step substrate binding → reaction → release sequences, biomimetic reaction pathways, cofactor-assisted cycles. |
| 3.3 Theoretical Vocabulary | Concepts | Core terms that encode the domain’s structure (force, gene, equilibrium, field). | Transition-state stabilization, binding affinity, induced fit, conformational selection, catalytic residues, cofactors, allostery, hydrophobic collapse, bioorthogonal reactivity, kinetic vs thermodynamic control. |
| | Classifications | Taxonomies, categories, or typologies that organize entities and relations. | Reaction types (hydrolysis, oxidation, reduction, group transfer, elimination/addition), enzyme classes, cofactor families, biomimetic catalysts, supramolecular host–guest binding categories. |
| 3.4 Formal Representations | Equations | Mathematical constructs expressing laws, relations, or mechanisms. | Michaelis–Menten equation, Lineweaver–Burk and Eadie–Hofstee transforms, Henderson–Hasselbalch relationships, binding isotherms, rate equations for multi-step enzymatic mechanisms. |
| | Models | Structured representations—mathematical, computational, or conceptual—used to predict and explain phenomena. | Enzyme active-site models, TS-analog models, conformational energy surfaces, molecular docking models, computational QM/MM mechanistic models, binding site interaction maps. |
| 3.5 Idealized Structures | Simplified Models | Purposeful abstractions that capture essential dynamics while omitting irrelevant detail. | Rigid active-site models, minimal-residue catalytic motifs, perfect TS mimics, simplified hydrogen-bond networks, truncated biomolecules, single-conformation reaction-coordinate models. |
| | Limit Conditions | Regimes where specific models or approximations hold (classical vs. quantum, linear vs. nonlinear). | Break down in highly flexible biomolecules, crowded cellular environments, multi-conformation ensembles, radical biochemistry, non-classical mechanisms, or large dynamic conformational shifts. |
| 3.6 Integrative Frameworks | Unifying Theories | Higher-order structures that connect disparate laws or mechanisms under a coherent whole. | Integration of organic reactivity with biological structure; unified enzyme–mechanism relationships; coupling of binding, catalysis, and conformational dynamics; biomimetic translational frameworks. |
| | Interdisciplinary Links | Points where the theory connects to adjacent sciences or larger explanatory systems. | Connects to biochemistry, enzymology, medicinal chemistry, molecular biology, chemical biology, supramolecular chemistry, and computational biophysics. |
| 4. Method Layer | 4.1 Inquiry Design | Experimental Design | Structured plans for manipulating variables to test causal claims. | Controlling pH, temperature, substrate concentration, cofactor levels, ionic strength, solvent composition, and enzyme/catalyst loading to probe mechanism, binding, and catalysis. |
| | Observational Design | Systematic approaches for gathering non-manipulated data (surveys, field studies, natural experiments). | Monitoring spontaneous conformational changes, natural binding equilibria, in vivo reaction progression, unperturbed redox or proton-transfer processes, and native folding/unfolding. |
| 4.2 Testing & Validation | Hypothesis Testing | Procedures for evaluating whether evidence supports or contradicts specific claims. | Comparing predicted binding affinities, catalytic efficiencies, isotope effects, pH-rate profiles, and substrate selectivity patterns with experimental kinetic and structural data. |
| | Replication | The requirement that results be independently reproducible under similar conditions. | Repeating enzyme assays, binding assays, fluorescence spectra, NMR assignments, LC-MS product analyses, calorimetric measurements, and pH-dependent kinetics across replicates and labs. |
| 4.3 Inference & Evaluation | Statistical Inference | Rules for drawing conclusions from noisy or incomplete data. | Extracting Km, kcat, ΔG‡, ΔG_binding, pKa values, equilibrium constants, and rate constants from noisy data; fitting Michaelis–Menten and binding models; interpreting isotopic perturbations. |
| | Model Comparison | Criteria (fit, simplicity, predictive accuracy, robustness) used to evaluate competing models. | Evaluating competing enzyme mechanisms, binding models (induced fit vs conformational selection), TS-stabilization models, QM/MM predictions, and mechanistic interpretations of rate data. |
| 4.4 Error Management | Error Analysis | Identification and quantification of random and systematic errors. | Identifying noise sources (fluorescence/refractive), temperature instability, buffer impurities, enzyme degradation, photobleaching, scattering artifacts, spectral overlap, and fitting uncertainty. |
| | Bias Control | Methods for minimizing subjective, instrumental, or procedural biases. | Randomizing substrate orders, blinding fluorescence/NMR interpretation when possible, maintaining identical buffer conditions, consistent enzyme prep, strict timing control in kinetics. |
| 4.5 Adjudication & Revision | Peer Scrutiny | Collective evaluation of claims through critique, review, and debate. | Independent evaluation of mechanistic proposals, structural assignments, binding models, kinetic fits, TS-analog interpretations, and computational predictions. |
| | Theory Revision | Procedures for modifying, replacing, or discarding models based on new evidence. | Updating mechanistic frameworks, revising binding models, adjusting pH-rate interpretations, refining TS structures, and reinterpreting catalytic roles based on new biochemical evidence. |
| 4.6 Integrity Conditions | Transparency | Requirements to disclose methods, data, assumptions, and limitations. | Full disclosure of buffer compositions, pH calibration, enzyme purity, assay conditions, instrumental settings, model assumptions, computational levels of theory, and data-processing methods. |
| | Ethical Standards | Norms ensuring responsible conduct in experimentation, data handling, and publication. | Honest reporting of activity data, uncertainties, failed assays, anomalous binding/catalysis results, careful handling of biological samples, and maintaining reproducibility standards. |