| 1. Domain | 1.1 Scope of the Domain | Boundaries | The range of phenomena the science includes and excludes. | Focuses on experimental methods and technological platforms used to manipulate, measure, detect, visualize, sequence, modify, engineer, or isolate molecular components. Includes sequencing technologies, imaging systems, molecular manipulation tools, purification techniques, analytical platforms, and genome-editing systems. Excludes large-scale physiology, ecology, and organism-level processes except when directly dependent on molecular techniques. |
| | Scale | The spatial, temporal, or organizational level at which the science operates (e.g., quantum, cellular, social, cosmic). | Operates at molecular and subcellular scales: nucleotides, proteins, complexes, chromatin, membranes, and synthetic constructs. Covers nanometer to micrometer spatial scales and microsecond to hour-long temporal scales for molecular reactions and measurements. |
| 1.2 Ontological Commitments | Entities | The kinds of things assumed to exist within the domain (particles, organisms, agents, fields, etc.). | Instruments, reagents, sequencing platforms, imaging systems, molecular tags, probes, enzymes, synthetic constructs, engineered nucleic acids/proteins, reporter systems, calibration standards, and computational analysis tools. |
| | Properties | The fundamental attributes these entities possess (mass, charge, genotype, preference, etc.). | Sensitivity, specificity, resolution, throughput, fidelity, reaction kinetics, probe intensity, enzymatic efficiency, labeling accuracy, signal-to-noise ratio, and compatibility with biological samples. |
| | Categories | The basic ontological types used to classify domain elements (substances, processes, relations, structures). | Analytical methods, sequencing technologies, imaging modalities, amplification systems, gene-editing tools, purification platforms, single-molecule systems, microfluidics, biosensors, and synthetic-biology toolkits. |
| 1.3 State-Variables | Variables | The measurable or definable properties that describe system conditions. | Instrument settings, reaction conditions, probe concentrations, amplification cycles, read-depth, signal intensities, noise levels, detection thresholds, reagent states, and calibration parameters. |
| | Parameterization | How variables encode and represent the system’s state. | State encoded through machine parameters, thermocycler programs, imaging exposure settings, sequencing run metrics, barcoding schemes, enzyme kinetics, standard curves, and computational processing pipelines. |
| 1.4 Admissible Idealizations | Simplifications | Conceptual reductions used to make the domain tractable (point masses, rational agents, perfect gases). | Treating instruments as error-free, assuming uniform reaction conditions, modeling assays as perfectly efficient, ignoring batch effects, reducing complex workflows to linear steps, or approximating biological samples as homogeneous. |
| | Validity Conditions | The limits and contexts in which idealizations hold or break down. | Simplifications fail when instruments drift, reactions exhibit nonlinear behavior, samples vary in quality, noise becomes non-negligible, or multiparameter interactions influence assay performance. |
| 1.5 Domain Assumptions | Structural Assumptions | Background ontological stances such as determinism, continuity, randomness, discreteness. | Assumes physical laws governing molecular detection are stable, instrumentation behaves predictably, calibration is possible, and measurement outputs correlate reliably with biological reality. |
| | Implicit Commitments | Unstated but necessary assumptions that shape the field’s conceptual structure. | Assumes instrument readouts are interpretable, sample preparation is representative, workflow steps are reproducible, reagents behave consistently, and computational processing accurately transforms raw signals into usable outputs. |
| 1.6 Internal Coherence Requirements | Consistency | The demand that domain concepts do not contradict one another. | Instrument behavior, reagent function, sample preparation, and computational processing must align without contradicting each other or empirical performance metrics. |
| | Compatibility | The requirement that entities, variables, and assumptions fit together into a unified descriptive framework. | Entities (instruments, reagents, probes), variables (settings, signals), and assumptions (fidelity, reproducibility) must fit into a unified framework ensuring that technologies generate interpretable, valid molecular information. |
| 2. Evidence Layer | 2.1 Observable Phenomena | Observables | The aspects of the domain that can produce detectable signals accessible to measurement. | Detectable signals include fluorescence emission, absorbance spectra, sequencing reads, electrophoretic band patterns, mass-spec peaks, imaging contrast, probe binding intensity, molecular mobility, and reaction kinetics. |
| | Detection Limits | The boundaries of what can be resolved or sensed by current instruments or methods. | Sensitivity thresholds for minimum detectable fluorescence, minimal sequence depth, smallest measurable mass-spec peak, optical-resolution limits, minimum detectable concentration, and lower bounds for single-molecule detection. |
| 2.2 Measurement Systems | Units | Standardized quantifications (meters, seconds, volts, decibels, dollars, etc.) necessary for consistent comparison. | Fluorescence intensity units, absorbance (A.U.), sequencing read counts, mass/charge (m/z), reaction rates (s⁻¹), concentration (nM–µM), imaging resolution (nm), and calibration-standard units. |
| | Instruments | Devices and tools (microscopes, spectrometers, sensors, surveys, detectors) used to produce measurements. | Sequencers (short/long-read), PCR/qPCR machines, mass spectrometers, spectrophotometers, fluorescence microscopes, confocal systems, cryo-EM, microfluidic devices, flow cytometers, biosensors, and single-molecule imaging platforms. |
| 2.3 Operational Definitions | Definitions | Terms defined by specific measurement procedures, ensuring empirical clarity. | Operational definitions for expression quantification, amplification success, probe binding, sequencing quality, imaging signal thresholds, detection confidence, reaction completion, or calibration standards. |
| | Procedures | The explicit steps required to perform a measurement in a reproducible way. | Standardized workflows such as library preparation, PCR cycling, gel electrophoresis, imaging exposure protocols, proteomics sample prep, hybridization procedures, microfluidic handling, and reagent calibration steps. |
| 2.4 Data Acquisition | Protocols | Formal processes for gathering data under controlled or standardized conditions. | Sequencing runs, imaging time-lapses, fluorescence measurements, MS/MS fragmentation cycles, thermal cycling programs, microfluidic sampling sequences, and multi-replicate instrument runs under controlled conditions. |
| | Sampling | Rules determining which subset of the domain is measured and how representative it is. | Selecting molecules, cell samples, regions of interest, time points, barcodes, or analytic windows to represent molecular states; ensuring adequate depth, replicates, and coverage. |
| 2.5 Data Character & Format | Data Types | The form raw evidence takes (time series, spectra, images, counts, qualitative records). | Spectra (MS, NMR), sequencing FASTQ files, images (TIFF), electropherograms, fluorescence traces, chromatograms, calibration curves, kinetic time series, and spatial-intensity matrices. |
| | Resolution | The granularity or precision with which data is captured. | Instrument-specific limits: Å-scale structural resolution, nm-scale imaging resolution, single-nucleotide sequencing resolution, mass-spec resolution for distinguishing peptides, and temporal resolution for reaction monitoring. |
| 2.6 Reliability & Calibration | Calibration | Adjustment procedures ensuring instruments produce accurate results. | Instrument calibration using standards, fluorophore reference curves, mass-spec mass-calibration mixtures, sequencing spike-ins, known-concentration controls, imaging intensity references, and thermal cycler calibration. |
| | Error Characterization | Identification and quantification of noise, uncertainty, bias, and measurement error. | Quantifying noise from detector drift, sequencing bias, PCR errors, fluorescence photobleaching, mass-spec misidentification, imaging noise, microfluidic flow variability, and batch effects in reagent performance. |
| 3. Structural Layer | 3.1 Patterns & Regularities | Laws / Relations | Stable, repeatable patterns governing how observables behave across conditions. | Recurring relationships such as amplification kinetics in PCR, sequencing error distributions, probe–target binding curves, fluorescence–intensity linearity, enzymatic reaction laws, and instrument resolution constraints. |
| | Invariants | Quantities or properties that remain constant under transformations (symmetries, conservation laws). | Stable properties such as constant calibration standards, reproducible instrument response curves, conserved kinetic parameters, consistent wavelength–intensity relationships, and invariant spectral signatures for known molecules. |
| 3.2 Causal Architecture | Mechanisms | Underlying processes or structures that produce the observed regularities. | Mechanisms include probe hybridization, enzymatic amplification, optical excitation/emission, mass-to-charge separation, sequencing-by-synthesis reactions, molecular labeling, signal amplification, and microfluidic flow dynamics. |
| | Pathways | Organized sequences of interactions forming a causal chain or network. | Ordered steps such as sample prep → amplification → detection → analysis; or excitation → emission → signal capture → computational extraction; or barcoding → sequencing → alignment → variant calling. |
| 3.3 Theoretical Vocabulary | Concepts | Core terms that encode the domain’s structure (force, gene, equilibrium, field). | Core terms include sensitivity, specificity, fidelity, throughput, resolution, amplification efficiency, signal-to-noise ratio, calibration curves, hybridization kinetics, and sequencing depth. |
| | Classifications | Taxonomies, categories, or typologies that organize entities and relations. | Categories such as sequencing methods (short-read, long-read), imaging modalities (fluorescence, confocal, super-resolution), analytical platforms (MS, NMR), amplification systems (PCR, isothermal), and microfluidic device types. |
| 3.4 Formal Representations | Equations | Mathematical constructs expressing laws, relations, or mechanisms. | PCR amplification equations (exponential/efficiency-based), binding isotherms, Beer–Lambert law for absorbance, fluorescence emission equations, signal-to-noise ratios, kinetic rate laws, and calibration-curve equations. |
| | Models | Structured representations—mathematical, computational, or conceptual—used to predict and explain phenomena. | Computational pipelines, signal-processing models, error-correction algorithms, kinetic models of amplification, optical models of imaging, mass-spec fragmentation models, and microfluidic flow simulations. |
| 3.5 Idealized Structures | Simplified Models | Purposeful abstractions that capture essential dynamics while omitting irrelevant detail. | Treating amplification as perfect, modeling signals as noise-free, approximating optical paths as ideal, using simplified reaction kinetics, or representing sequencing errors with uniform distributions. |
| | Limit Conditions | Regimes where specific models or approximations hold (classical vs. quantum, linear vs. nonlinear). | Valid under stable instrument conditions, uniform sample quality, predictable kinetics, and typical signal strengths; break down under high noise, poor sample prep, low-abundance targets, extreme temperatures, or nonlinear detector behavior. |
| 3.6 Integrative Frameworks | Unifying Theories | Higher-order structures that connect disparate laws or mechanisms under a coherent whole. | Integrative frameworks include information-processing theory, signal-transduction and detection theory, systems for scalable molecular measurement, and unified computational–experimental pipelines that convert molecular events into interpretable data. |
| | Interdisciplinary Links | Points where the theory connects to adjacent sciences or larger explanatory systems. | Links to biophysics, chemistry, engineering, computer science, statistics, and synthetic biology through shared principles of detection, measurement, signal processing, and instrument–sample interactions. |
| 4. Method Layer | 4.1 Inquiry Design | Experimental Design | Structured plans for manipulating variables to test causal claims. | Designing manipulations of reaction conditions, amplification cycles, probe concentrations, imaging parameters, sequencing platforms, or microfluidic flows to test detection efficiency, fidelity, or measurement accuracy. |
| | Observational Design | Systematic approaches for gathering non-manipulated data (surveys, field studies, natural experiments). | Collecting non-manipulated measurements such as natural expression levels, spontaneous molecular dynamics, baseline instrument signals, native spectra, and unperturbed sequencing or imaging outputs. |
| 4.2 Testing & Validation | Hypothesis Testing | Procedures for evaluating whether evidence supports or contradicts specific claims. | Testing performance claims—e.g., whether a platform increases sensitivity, whether a new probe improves specificity, whether an algorithm reduces noise—through controlled comparisons and benchmarking datasets. |
| | Replication | The requirement that results be independently reproducible under similar conditions. | Repeating sequencing runs, imaging experiments, PCR cycles, MS analyses, and microfluidic operations across independent replicates, batches, and devices to ensure reproducibility and robustness. |
| 4.3 Inference & Evaluation | Statistical Inference | Rules for drawing conclusions from noisy or incomplete data. | Using statistical models to interpret noisy molecular data: evaluating confidence in measurements, deriving error distributions, assessing detection thresholds, and estimating parameter values from signal profiles. |
| | Model Comparison | Criteria (fit, simplicity, predictive accuracy, robustness) used to evaluate competing models. | Comparing alternative detection models, noise models, amplification kinetics, alignment algorithms, structural reconstruction pipelines, or imaging-processing models for accuracy, fit, robustness, and predictive power. |
| 4.4 Error Management | Error Analysis | Identification and quantification of random and systematic errors. | Quantifying PCR error rates, sequencing miscalls, imaging noise, mass-spec ambiguity, detector drift, probe cross-reactivity, calibration deviations, and microfluidic variability. |
| | Bias Control | Methods for minimizing subjective, instrumental, or procedural biases. | Managing bias via randomized sample handling, blinded processing, standardized reagent batches, validated calibration standards, internal spike-ins, and consistent signal-processing workflows. |
| 4.5 Adjudication & Revision | Peer Scrutiny | Collective evaluation of claims through critique, review, and debate. | Independent evaluation of instrument performance, algorithmic pipelines, calibration methods, experimental workflows, and measurement claims through peer review and collaborative benchmarking. |
| | Theory Revision | Procedures for modifying, replacing, or discarding models based on new evidence. | Updating measurement theories, detection algorithms, amplification models, or imaging frameworks when new evidence, improved technologies, or unforeseen artifacts challenge existing assumptions. |
| 4.6 Integrity Conditions | Transparency | Requirements to disclose methods, data, assumptions, and limitations. | Full disclosure of instrument settings, sample-prep steps, amplification programs, imaging parameters, calibration curves, run metadata, computational pipelines, assumptions, and limitations. |
| | Ethical Standards | Norms ensuring responsible conduct in experimentation, data handling, and publication. | Responsible handling of molecular data, rigorous reporting of platform performance, avoidance of data manipulation or selective filtering, compliance with biosafety standards, and ethical deployment of emerging molecular technologies. |