| 1. Domain | 1.1 Scope of the Domain | Boundaries | The range of phenomena the science includes and excludes. | Includes the application of physical principles to biological structures, dynamics, and functions across all scales; covers molecular motors, protein folding, membranes, ion channels, neural signaling, biomechanics, cellular mechanics, electrophysiology, population-level dynamics, and biological energy transfer. Excludes purely chemical biology, ecological macro-systems not grounded in physical laws, and medical physics unless tied directly to biological function. |
| | Scale | The spatial, temporal, or organizational level at which the science operates (e.g., quantum, cellular, social, cosmic). | Operates from angstrom-scale molecular interactions to cellular-scale mechanics, tissue-level biomechanics, and organism-level physiology; time scales from femtosecond molecular motions to multi-year biological adaptation processes. |
| 1.2 Ontological Commitments | Entities | The kinds of things assumed to exist within the domain (particles, organisms, agents, fields, etc.). | Biomolecules, proteins, DNA, RNA, lipids, cells, membranes, cytoskeletal elements, ion channels, molecular motors, mechanical forces, energy fields, neural signals, and biological macroscopic structures. |
| | Properties | The fundamental attributes these entities possess (mass, charge, genotype, preference, etc.). | Charge, mass, binding affinity, diffusion coefficient, elasticity, viscosity, membrane potential, conformational state, energy consumption rate, and reaction kinetics. |
| | Categories | The basic ontological types used to classify domain elements (substances, processes, relations, structures). | Structural biology elements, dynamic biochemical processes, transport and signaling processes, mechanical systems, electrophysiological systems, and emergent collective behaviors. |
| 1.3 State-Variables | Variables | The measurable or definable properties that describe system conditions. | Concentration, membrane potential, force, displacement, reaction rates, probability of conformational states, diffusion rates, firing rates, pressure, elasticity, and strain. |
| | Parameterization | How variables encode and represent the system’s state. | States encoded by energy landscapes, rate constants, diffusion coefficients, mechanical stiffness coefficients, charge distributions, molecular conformations, and boundary conditions determined by biological structure. |
| 1.4 Admissible Idealizations | Simplifications | Conceptual reductions used to make the domain tractable (point masses, rational agents, perfect gases). | Coarse graining molecular structure, treating cells as homogeneous compartments, linearizing elasticity, assuming equilibrium for non equilibrium systems, using simplified binding kinetics, ignoring stochastic noise, and approximating membranes as continuous surfaces. |
| | Validity Conditions | The limits and contexts in which idealizations hold or break down. | Valid when microscopic details are not dominant, when biochemical networks evolve slowly relative to molecular fluctuations, when elasticity remains in linear regimes, or when stochastic noise does not dominate signaling; breaks down in highly nonlinear, strongly fluctuating, or single molecule regimes. |
| 1.5 Domain Assumptions | Structural Assumptions | Background ontological stances such as determinism, continuity, randomness, discreteness. | Assumes biological systems obey physical laws, energy is conserved, forces and diffusion govern interactions, reaction kinetics are physically constrained, and emergent biological processes arise from underlying physical substrates. |
| | Implicit Commitments | Unstated but necessary assumptions that shape the field’s conceptual structure. | Assumes statistical physics captures biological fluctuations, continuum mechanics can approximate cellular or tissue mechanics, coarse graining does not erase critical biological features, and molecular interactions follow consistent physical laws. |
| 1.6 Internal Coherence Requirements | Consistency | The demand that domain concepts do not contradict one another. | Requires compatibility between biochemical kinetics, mechanical models, electromagnetic models, stochastic models, and observed biological behavior; no contradictions between molecular, cellular, and organismal physics. |
| | Compatibility | The requirement that entities, variables, and assumptions fit together into a unified descriptive framework. | Entities, variables, and assumptions must form a unified description linking physical forces, molecular structure, biochemical reaction networks, mechanical properties, and emergent biological function. |
| 2. Evidence Layer | 2.1 Observable Phenomena | Observables | The aspects of the domain that can produce detectable signals accessible to measurement. | Observable signals include ion channel currents, membrane potentials, molecular binding rates, fluorescence intensities, protein folding transitions, cellular forces, diffusion trajectories, structural conformations, biomechanical deformations, neural firing patterns, and optical or mechanical responses of biological tissues. |
| | Detection Limits | The boundaries of what can be resolved or sensed by current instruments or methods. | Limited by spatial resolution of microscopy, signal to noise in electrophysiology, fluorophore brightness, detector sensitivity, temporal resolution for fast molecular motions, depth penetration limits in tissues, and noise in mechanical force probes. |
| 2.2 Measurement Systems | Units | Standardized quantifications (meters, seconds, volts, decibels, dollars, etc.) necessary for consistent comparison. | Uses meters, seconds, newtons, pascals, volts, millivolts, amperes, hertz, molar concentration, diffusion coefficients, elasticity units, probability, and fluorescence intensity counts. |
| | Instruments | Devices and tools (microscopes, spectrometers, sensors, surveys, detectors) used to produce measurements. | Instruments include fluorescence microscopes, confocal microscopes, electron microscopes, patch clamp amplifiers, optical tweezers, atomic force microscopes, mass spectrometers, electrophysiology rigs, spectroscopy systems, microfluidic devices, and high sensitivity cameras. |
| 2.3 Operational Definitions | Definitions | Terms defined by specific measurement procedures, ensuring empirical clarity. | Terms such as binding constant, reaction rate, membrane potential, diffusion coefficient, stiffness, firing rate, and folding transition midpoint are defined using standardized measurement protocols. |
| | Procedures | The explicit steps required to perform a measurement in a reproducible way. | Procedures include patch clamp recordings, force spectroscopy pulls, fluorescence excitation and emission scans, time resolved imaging, protein unfolding assays, microfluidic flow measurements, and mechanical indentation tests. |
| 2.4 Data Acquisition | Protocols | Formal processes for gathering data under controlled or standardized conditions. | Data gathered through continuous imaging, time series recording, synchronized electrical and optical sampling, repeated mechanical probing, calibrated chemical perturbations, and controlled environmental conditions. |
| | Sampling | Rules determining which subset of the domain is measured and how representative it is. | Sampling rules include fixed temporal sampling for dynamics, spatial sampling across structures, repeated measurements for stochastic processes, ensemble measurements for populations, and multi region sampling for heterogeneous tissues. |
| 2.5 Data Character & Format | Data Types | The form raw evidence takes (time series, spectra, images, counts, qualitative records). | Data appears as electrophysiology traces, fluorescence time series, spectra, force curves, diffusion tracks, structural images, reaction progress curves, neural spike rasters, and biomechanical maps. |
| | Resolution | The granularity or precision with which data is captured. | Determined by camera frame rate, pixel size, numerical aperture, electronic bandwidth of amplifiers, force sensor precision, fluorescence signal level, and noise thresholds of detectors. |
| 2.6 Reliability & Calibration | Calibration | Adjustment procedures ensuring instruments produce accurate results. | Calibration uses reference fluorophores, force standards, electrophysiology calibration signals, temperature controls, pH standards, known diffusion markers, and imaging resolution calibration grids. |
| | Error Characterization | Identification and quantification of noise, uncertainty, bias, and measurement error. | Errors arise from photobleaching, drift, electronic noise, thermal fluctuations, force probe misalignment, imperfect sample preparation, motion artifacts, and stochastic variability inherent to biological systems. |
| 3. Structural Layer | 3.1 Patterns & Regularities | Laws / Relations | Stable, repeatable patterns governing how observables behave across conditions. | Stable patterns include diffusion laws, force–extension curves of biomolecules, ion channel conductance laws, membrane voltage dynamics, enzyme kinetics, molecular motor stepping behavior, viscoelastic cellular responses, conformational transitions, and scaling laws across biological structures. |
| | Invariants | Quantities or properties that remain constant under transformations (symmetries, conservation laws). | Invariants include conservation of energy in biochemical cycles, constant charge within membrane domains, stable reaction stoichiometries, conserved molecular architecture motifs, and statistically repeatable fluctuations described by thermodynamic or stochastic principles. |
| 3.2 Causal Architecture | Mechanisms | Underlying processes or structures that produce the observed regularities. | Mechanisms arise from electrostatic interactions, hydrogen bonding, mechanical elasticity, thermal fluctuations, chemical reaction pathways, ion gradient driven forces, cytoskeletal polymerization, ligand binding dynamics, and neural electrochemical signaling. |
| | Pathways | Organized sequences of interactions forming a causal chain or network. | Pathways include signal transduction cascades, allosteric transitions, motor protein stepping cycles, neural firing sequences, energy transfer in photosynthesis, diffusion and active transport, and biomechanical deformation or relaxation processes. |
| 3.3 Theoretical Vocabulary | Concepts | Core terms that encode the domain’s structure (force, gene, equilibrium, field). | Core terms include energy landscape, diffusion, binding affinity, conformational state, membrane potential, gating kinetics, spring constant, reaction rate, stochastic fluctuation, and mechanotransduction. |
| | Classifications | Taxonomies, categories, or typologies that organize entities and relations. | Classifies systems by structural level (molecular, cellular, tissue), by mechanism (mechanical, electrical, chemical), by dynamics (elastic, viscoelastic, stochastic), and by interaction type (binding, transport, signaling, motor activity). |
| 3.4 Formal Representations | Equations | Mathematical constructs expressing laws, relations, or mechanisms. | Includes diffusion equations, Langevin equations, kinetic rate equations, Poisson–Boltzmann equations, Hodgkin–Huxley equations, polymer elasticity laws, force–extension models, and stochastic master equations. |
| | Models | Structured representations—mathematical, computational, or conceptual—used to predict and explain phenomena. | Uses molecular dynamics models, coarse-grained biomolecular models, electrophysiological models, biomechanical models, reaction–diffusion models, network models of signaling or neural activity, and thermodynamic free energy models. |
| 3.5 Idealized Structures | Simplified Models | Purposeful abstractions that capture essential dynamics while omitting irrelevant detail. | Idealizations include treating biomolecules as springs, cells as homogeneous elastic bodies, ignoring molecular crowding, assuming thermal equilibrium, linearizing membrane dynamics, neglecting stochastic noise, and coarse-graining complex biochemical networks. |
| | Limit Conditions | Regimes where specific models or approximations hold (classical vs. quantum, linear vs. nonlinear). | Valid when deformation is small, chemical networks evolve slowly, crowding is moderate, fluctuations are not dominant, and system dimensionality can be reduced; breaks down at single-molecule limits, highly nonlinear regimes, strong crowding, or far-from-equilibrium conditions. |
| 3.6 Integrative Frameworks | Unifying Theories | Higher-order structures that connect disparate laws or mechanisms under a coherent whole. | Includes frameworks connecting mechanics, chemistry, electromagnetism, thermodynamics, and stochastic physics into unified models of biological function across molecular to organismal scales. |
| | Interdisciplinary Links | Points where the theory connects to adjacent sciences or larger explanatory systems. | Links to molecular biology, chemistry, neuroscience, biomechanics, soft-matter physics, systems biology, computational biology, and medical physics. |
| 4. Method Layer | 4.1 Inquiry Design | Experimental Design | Structured plans for manipulating variables to test causal claims. | Experiments manipulate concentration, voltage, force, temperature, ligand exposure, structural mutation, membrane composition, or applied mechanical loads to test causal effects on molecular binding, electrophysiology, signaling, conformation, biomechanics, or cellular responses. |
| | Observational Design | Systematic approaches for gathering non-manipulated data (surveys, field studies, natural experiments). | Observational methods track spontaneous cellular activity, molecule diffusion, neural firing, mechanical deformation, protein folding, or structural fluctuations without externally applied control parameters. |
| 4.2 Testing & Validation | Hypothesis Testing | Procedures for evaluating whether evidence supports or contradicts specific claims. | Hypotheses tested by comparing measured reaction rates, force curves, firing rates, ionic currents, conformational distributions, diffusion profiles, or mechanical responses with predictions from biophysical models. |
| | Replication | The requirement that results be independently reproducible under similar conditions. | Replication achieved by repeating measurements across cells, molecules, tissues, or organisms; confirming results with separate instruments; using independent sample preparations; and verifying responses under varied but equivalent conditions. |
| 4.3 Inference & Evaluation | Statistical Inference | Rules for drawing conclusions from noisy or incomplete data. | Statistical methods include nonlinear curve fitting, Markov state analysis, diffusion coefficient estimation, spike train statistics, ensemble averaging, bootstrapping, hidden state inference, and probabilistic modeling of noise or molecular transitions. |
| | Model Comparison | Criteria (fit, simplicity, predictive accuracy, robustness) used to evaluate competing models. | Models compared based on ability to reproduce electrophysiology dynamics, viscoelastic responses, conformational transitions, binding kinetics, motor stepping behavior, or diffusion traces across experimental regimes. |
| 4.4 Error Management | Error Analysis | Identification and quantification of random and systematic errors. | Errors arise from photobleaching, camera noise, thermal drift, electrode noise, force probe calibration error, molecular heterogeneity, stochastic fluctuations, sample degradation, and imperfect environmental control. |
| | Bias Control | Methods for minimizing subjective, instrumental, or procedural biases. | Bias minimized through blind data processing, independent calibration, consistent sample preparation, randomized measurement order, control experiments, and cross validation with imaging or mechanical data. |
| 4.5 Adjudication & Revision | Peer Scrutiny | Collective evaluation of claims through critique, review, and debate. | Findings evaluated through replication by other laboratories, interdisciplinary peer review, cross validation with computational simulations, and comparison to known biophysical constraints and conservation laws. |
| | Theory Revision | Procedures for modifying, replacing, or discarding models based on new evidence. | Theories revised when experimental results show unexpected kinetics, anomalous diffusion, nonlinear mechanical responses, atypical channel behavior, or structural transitions incompatible with existing biophysical models. |
| 4.6 Integrity Conditions | Transparency | Requirements to disclose methods, data, assumptions, and limitations. | Requires full disclosure of experimental procedures, data processing steps, calibration methods, sample preparation details, environmental conditions, and limitations of measurement techniques. |
| | Ethical Standards | Norms ensuring responsible conduct in experimentation, data handling, and publication. | Requires responsible handling of biological samples, adherence to safety protocols, accurate data reporting, avoidance of selective data omission, and compliance with ethical standards for animal or human biological material when applicable. |