| 1. Domain | 1.1 Scope of the Domain | Boundaries | The range of phenomena the science includes and excludes. | Studies biochemical pathways, energy conversion, thermodynamics of life, metabolic flux, nutrient processing, ATP generation/consumption; excludes purely structural biochemistry or purely regulatory biology without metabolic context. |
| | Scale | The spatial, temporal, or organizational level at which the science operates (e.g., quantum, cellular, social, cosmic). | Operates from atomic/electronic transitions in redox cofactors (NAD⁺/FAD) to enzyme-catalyzed pathway steps, organelle-level compartmentalization, cellular flux regulation, and whole-organism energy balance. |
| 1.2 Ontological Commitments | Entities | The kinds of things assumed to exist within the domain (particles, organisms, agents, fields, etc.). | Metabolites, intermediates, cofactors (ATP, NAD⁺/NADH, FAD/FADH₂, CoA), enzymes, pathways, complexes (ETC), proton gradients, transporters, carriers, redox couples, thermodynamic states. |
| | Properties | The fundamental attributes these entities possess (mass, charge, genotype, preference, etc.). | Gibbs free energy (ΔG), redox potential, flux rate, phosphorylation potential, concentration gradients, proton motive force (PMF), binding affinity, catalytic efficiency, thermodynamic coupling strength. |
| | Categories | The basic ontological types used to classify domain elements (substances, processes, relations, structures). | Catabolic vs anabolic pathways, central carbon metabolism, electron-transport processes, substrate-level phosphorylation, oxidative phosphorylation, fermentation pathways, metabolic cycles, transport mechanisms. |
| 1.3 State-Variables | Variables | The measurable or definable properties that describe system conditions. | Metabolite concentrations, redox ratios (NAD⁺/NADH), ATP/ADP/AMP levels, pH, membrane potential (ΔΨ), proton gradient (ΔpH), oxygen availability, enzyme concentrations, flux rates, temperature. |
| | Parameterization | How variables encode and represent the system’s state. | States encoded via ΔG°’, ΔG(in vivo), redox potentials (E°’), flux distributions, reaction quotients (Q), energy-charge calculations, stoichiometric matrices, thermodynamic force, pathway elasticity coefficients. |
| 1.4 Admissible Idealizations | Simplifications | Conceptual reductions used to make the domain tractable (point masses, rational agents, perfect gases). | Steady-state metabolic flux, ideal reversible steps, simplified coupling (1 ATP per event), negligible metabolite channeling, ideal proton-pumping stoichiometry, no futile cycles, homogeneous compartment conditions. |
| | Validity Conditions | The limits and contexts in which idealizations hold or break down. | Valid for moderate flux, stable physiology, isolated pathway analysis; breaks down in rapid transitions, dynamic stress, compartment-specific gradients, allosteric complexity, or non-equilibrium bursts. |
| 1.5 Domain Assumptions | Structural Assumptions | Background ontological stances such as determinism, continuity, randomness, discreteness. | Metabolism obeys thermodynamic laws; flux is governed by enzyme kinetics; redox and phosphorylation potentials regulate directionality; energy transduction is quantifiable. |
| | Implicit Commitments | Unstated but necessary assumptions that shape the field’s conceptual structure. | Assumes meaningful steady states, consistent cofactor behavior, transferable thermodynamic parameters, coherent coupling between reactions, and reliable pathway compartmentalization. |
| 1.6 Internal Coherence Requirements | Consistency | The demand that domain concepts do not contradict one another. | Requires alignment among pathway stoichiometry, enzyme kinetics, redox balance, thermodynamic constraints, ATP yields, proton gradients, and overall metabolic flux patterns. |
| | Compatibility | The requirement that entities, variables, and assumptions fit together into a unified descriptive framework. | Demands harmonization between biochemical kinetics, thermodynamics, structural enzymology, transport processes, and cellular regulatory networks within an integrated metabolic framework. |
| 2. Evidence Layer | 2.1 Observable Phenomena | Observables | The aspects of the domain that can produce detectable signals accessible to measurement. | ATP/ADP/AMP ratios, oxygen consumption, CO₂ production, NADH/NAD⁺ redox signals, fluorescence from metabolic cofactors, calorimetric heat flow, pH changes, membrane potential shifts, metabolite level changes, isotopic enrichment in flux studies. |
| | Detection Limits | The boundaries of what can be resolved or sensed by current instruments or methods. | Limited by metabolite instability, low intracellular concentrations, rapid turnover, overlapping MS peaks, poor temporal resolution, signal bleed-through, low sensitivity in membrane potential and cofactor signals. |
| 2.2 Measurement Systems | Units | Standardized quantifications (meters, seconds, volts, decibels, dollars, etc.) necessary for consistent comparison. | Concentration (µM–mM), flux rate (pmol/min/cell or mmol/g/h), redox ratio (NAD⁺/NADH), oxygen consumption rate (pmol O₂/s), membrane potential (mV), Gibbs free energy (kJ/mol), isotopic enrichment (%), pH, temperature. |
| | Instruments | Devices and tools (microscopes, spectrometers, sensors, surveys, detectors) used to produce measurements. | Mass spectrometers, NMR metabolomics rigs, Seahorse analyzers, respirometers, calorimeters, fluorescence microscopes, electrochemical sensors, pH electrodes, membrane-potential dyes, isotope-ratio MS, LC/UPLC, microfluidic metabolic platforms. |
| 2.3 Operational Definitions | Definitions | Terms defined by specific measurement procedures, ensuring empirical clarity. | ATP levels and energy charge defined via adenylate ratios; metabolic flux defined via isotopic tracing; redox state defined by NADH/NAD⁺; ΔG defined by chemical potentials; PMF defined as ΔΨ + (2.303RT/F)ΔpH. |
| | Procedures | The explicit steps required to perform a measurement in a reproducible way. | Metabolite extraction, quenching, isotope labeling, flux tracing, oxygen-consumption assays, calorimetry protocols, enzyme-coupled assays, high-throughput metabolomics, pH/ΔΨ measurement workflows. |
| 2.4 Data Acquisition | Protocols | Formal processes for gathering data under controlled or standardized conditions. | Time-course metabolite tracking, isotopic steady-state sampling, sequential respiration measurements, pH and ΔΨ calibration curves, multi-omics integration runs, thermal scanning, enzyme-activity timepoints. |
| | Sampling | Rules determining which subset of the domain is measured and how representative it is. | Biological replicates, technical replicates, randomization, multi-timepoint sampling, multiple cell populations/tissues, fraction-specific sampling (mitochondria, cytosol), isotopic steady-state verification. |
| 2.5 Data Character & Format | Data Types | The form raw evidence takes (time series, spectra, images, counts, qualitative records). | Metabolomics spectra (MS/NMR), oxygen-consumption curves, calorimetry thermograms, redox-ratio traces, isotopic enrichment graphs, pH/ΔΨ time series, ATP/ADP ratio tables, flux-distribution maps. |
| | Resolution | The granularity or precision with which data is captured. | Determined by instrument sensitivity, isotope-labeling duration, chromatographic separation, signal-to-noise in redox/membrane potential measurements, temporal granularity, and sampling precision. |
| 2.6 Reliability & Calibration | Calibration | Adjustment procedures ensuring instruments produce accurate results. | Calibration of MS/NMR instruments, oxygen-sensor calibration, pH/membrane potential calibration, isotope standard curves, temperature and mixing calibration in calorimetry, internal/external standards in metabolomics. |
| | Error Characterization | Identification and quantification of noise, uncertainty, bias, and measurement error. | Identifying sample degradation, quench inefficiency, instrument drift, overlapping isotopologues, matrix effects, dye toxicity, inaccurate calibration curves, poor normalization, and stochastic cellular heterogeneity. |
| 3. Structural Layer | 3.1 Patterns & Regularities | Laws / Relations | Stable, repeatable patterns governing how observables behave across conditions. | Thermodynamic coupling rules, conservation of mass/energy, redox-balancing rules, flux–substrate concentration relationships, Michaelis–Menten–driven pathway behavior, stoichiometric constraints, proton-motive-force relationships, energy-charge regulation. |
| | Invariants | Quantities or properties that remain constant under transformations (symmetries, conservation laws). | Conserved metabolic cores (glycolysis, TCA cycle), invariant cofactor usage patterns (NAD⁺/NADH, FAD/FADH₂), recurring phosphoryl-transfer logic, stable ATP-generating modules, conserved redox potentials across taxa. |
| 3.2 Causal Architecture | Mechanisms | Underlying processes or structures that produce the observed regularities. | Enzyme-catalyzed transformations, substrate channeling, redox cycling, chemiosmotic coupling, proton pumping, substrate-level phosphorylation, electron transfer, metabolite transport, allosteric regulatory mechanisms. |
| | Pathways | Organized sequences of interactions forming a causal chain or network. | Glycolytic sequence, TCA cycle, β-oxidation, oxidative phosphorylation chain, ETC electron flow → proton pumping → ATP synthase rotation → ATP formation, fermentation pathways, anaplerotic and cataplerotic routes. |
| 3.3 Theoretical Vocabulary | Concepts | Core terms that encode the domain’s structure (force, gene, equilibrium, field). | ΔG, ΔG°’, Q (reaction quotient), energy charge, flux, PMF (ΔΨ + 2.303RT/F·ΔpH), redox potential, stoichiometric coefficients, metabolic nodes, futile cycles, thermodynamic bottlenecks, rate-limiting steps. |
| | Classifications | Taxonomies, categories, or typologies that organize entities and relations. | Catabolic vs anabolic pathways, central carbon pathways, electron-carrier families, phosphoryl-group transfer categories, linear vs cyclic pathways, aerobic vs anaerobic energy systems. |
| 3.4 Formal Representations | Equations | Mathematical constructs expressing laws, relations, or mechanisms. | ΔG = ΔG°’ + RT ln Q, Nernst equation, flux-balance equations (S·v = 0), Michaelis–Menten relations, PMF equation, ATP yield stoichiometry, steady-state flux equations, thermodynamic feasibility inequalities. |
| | Models | Structured representations—mathematical, computational, or conceptual—used to predict and explain phenomena. | Energy landscape models, flux balance analysis (FBA), elementary mode analysis, chemiosmotic models, kinetic metabolic models, redox-network models, multi-scale pathway-integration models. |
| 3.5 Idealized Structures | Simplified Models | Purposeful abstractions that capture essential dynamics while omitting irrelevant detail. | Perfect steady state, isolated pathways, ideal coupling ratios, no substrate channeling, constant enzyme levels, no competing pathways, homogeneous compartments, linear flux responses. |
| | Limit Conditions | Regimes where specific models or approximations hold (classical vs. quantum, linear vs. nonlinear). | Fail in highly dynamic systems, rapid stress responses, compartment-specific gradients, strong allostery, metabolite channeling, multi-enzyme complexes, non-equilibrium bursts, or strongly fluctuating flux conditions. |
| 3.6 Integrative Frameworks | Unifying Theories | Higher-order structures that connect disparate laws or mechanisms under a coherent whole. | Integration of thermodynamics, kinetics, redox chemistry, transport, and regulation into a unified metabolic model; linking ATP production with global flux networks; coupling chemiosmosis, stoichiometry, and enzyme kinetics across the system. |
| | Interdisciplinary Links | Points where the theory connects to adjacent sciences or larger explanatory systems. | Connects to systems biology, structural biochemistry, physiology, bioenergetics, evolutionary biology, synthetic biology, and biomedical metabolism (disease-associated flux rewiring). |
| 4. Method Layer | 4.1 Inquiry Design | Experimental Design | Structured plans for manipulating variables to test causal claims. | Controlling nutrient levels, oxygen availability, substrate/cofactor ratios, pH, temperature, inhibitors, isotope labels, compartment isolation (mitochondria vs cytosol), and stress conditions to test metabolic flux and energy-coupling hypotheses. |
| | Observational Design | Systematic approaches for gathering non-manipulated data (surveys, field studies, natural experiments). | Monitoring natural metabolic drift, spontaneous redox changes, passive ATP/ADP fluctuations, basal respiration, native proton-gradient variations, and unstimulated metabolite pool shifts without experimental perturbation. |
| 4.2 Testing & Validation | Hypothesis Testing | Procedures for evaluating whether evidence supports or contradicts specific claims. | Comparing predicted ATP yields, ΔG values, redox ratios, flux distributions, coupling stoichiometries, isotope-labeling patterns, and PMF behavior with experimental metabolomics, respirometry, calorimetry, and isotope-tracing data. |
| | Replication | The requirement that results be independently reproducible under similar conditions. | Repeating metabolite extractions, respiration runs, calorimetric scans, isotope-tracing experiments, enzyme-activity assays, flux measurements, and ATP quantification across biological and technical replicates. |
| 4.3 Inference & Evaluation | Statistical Inference | Rules for drawing conclusions from noisy or incomplete data. | Calculating ΔG(in vivo), flux estimates, confidence intervals on redox/energy-charge ratios, isotope-enrichment statistics, pathway elasticity coefficients, and uncertainty estimates for PMF and ATP-yield measurements. |
| | Model Comparison | Criteria (fit, simplicity, predictive accuracy, robustness) used to evaluate competing models. | Comparing kinetic models vs flux-balance models, thermodynamic-feasibility models vs experimental fluxes, alternative coupling stoichiometries, redox-network models, and different PMF partitioning assumptions. |
| 4.4 Error Management | Error Analysis | Identification and quantification of random and systematic errors. | Identifying metabolite degradation, quench inefficiency, ion suppression in MS, isotope scrambling, instrument drift, inaccurate calibration, mixed cellular populations, oxygen back-diffusion, or poor compartment isolation artifacts. |
| | Bias Control | Methods for minimizing subjective, instrumental, or procedural biases. | Randomizing sampling times, blinding sample labels, using internal standards and isotope controls, maintaining consistent temperature/pH, minimizing handling delays, employing parallel control groups, and normalizing to biomass/protein. |
| 4.5 Adjudication & Revision | Peer Scrutiny | Collective evaluation of claims through critique, review, and debate. | Independent evaluation of flux calculations, isotope mapping, energy-charge measurements, ΔG determinations, PMF measurements, model assumptions, and pathway reconstructions across labs or methods. |
| | Theory Revision | Procedures for modifying, replacing, or discarding models based on new evidence. | Updating pathway models, revising flux maps, adjusting ΔG°′/ΔG assumptions, modifying coupling stoichiometries, redefining rate-limiting steps, incorporating new cofactor/redox-cycle evidence, and replacing outdated thermodynamic parameters. |
| 4.6 Integrity Conditions | Transparency | Requirements to disclose methods, data, assumptions, and limitations. | Full disclosure of sample-prep timing, quenching protocols, isotope-labeling timelines, instrument settings, normalization choices, thermodynamic assumptions, flux-model equations, and sources of systematic error. |
| | Ethical Standards | Norms ensuring responsible conduct in experimentation, data handling, and publication. | Honest reporting of uncertainties, low-abundance metabolite limits, failed flux experiments, ambiguous PMF data, negative results, biological-variability issues, and adherence to ethical handling of live cells/organisms. |