| 1. Domain | 1.1 Scope of the Domain | Boundaries | The range of phenomena the science includes and excludes. | The field examines multi-molecular assemblies—protein complexes, nucleoprotein machines, membrane-bound assemblies, ribonucleoproteins, and signaling complexes—and the ways they transmit, transform, and integrate biochemical information. Excludes isolated single-molecule behavior except when acting as part of a larger information-processing system. |
| | Scale | The spatial, temporal, or organizational level at which the science operates (e.g., quantum, cellular, social, cosmic). | Operates at supramolecular and mesoscale levels: multimeric complexes, chromatin domains, ribosomes, transcriptional factories, replisomes, signaling platforms, membrane microdomains, and dynamic assemblies across millisecond-to-cell-cycle timescales. |
| 1.2 Ontological Commitments | Entities | The kinds of things assumed to exist within the domain (particles, organisms, agents, fields, etc.). | Protein complexes, ribosomes, replisomes, spliceosomes, chromatin remodelers, transcriptional hubs, signaling complexes, scaffold proteins, membrane microdomains, regulatory RNPs, and dynamic phase-separated condensates. |
| | Properties | The fundamental attributes these entities possess (mass, charge, genotype, preference, etc.). | Stoichiometry, assembly state, interaction strength, conformational flexibility, information throughput, catalytic capacity, modularity, spatial localization, allosteric coupling, and signal-to-noise fidelity. |
| | Categories | The basic ontological types used to classify domain elements (substances, processes, relations, structures). | Catalytic complexes, structural scaffolds, information-processing hubs, regulatory condensates, membrane-associated complexes, genome-maintenance machines, and dynamic signaling assemblies. |
| 1.3 State-Variables | Variables | The measurable or definable properties that describe system conditions. | Complex composition, assembly/disassembly rate, conformational state, occupancy of subunits, interaction frequencies, information-transfer rate, post-translational or nucleic-acid modifications, and spatial position within the cell. |
| | Parameterization | How variables encode and represent the system’s state. | State encoded through stoichiometric maps, interaction networks, single-particle tracking, structural coordinates, binding-kinetic constants, expression/activity profiles, condensate-formation thresholds, and spatial-distribution models. |
| 1.4 Admissible Idealizations | Simplifications | Conceptual reductions used to make the domain tractable (point masses, rational agents, perfect gases). | Treating complexes as static, modeling them as rigid units, simplifying multistep signaling cascades to single-step modules, ignoring rare conformational states, approximating phase-separated bodies as homogeneous droplets, or reducing dynamic assembly cycles to binary on/off states. |
| | Validity Conditions | The limits and contexts in which idealizations hold or break down. | Simplifications fail when complexes undergo rapid conformational switching, exhibit multi-level cooperativity, display heterogeneous subunit composition, form transient assemblies, or behave differently under active-cellular-crowding conditions. |
| 1.5 Domain Assumptions | Structural Assumptions | Background ontological stances such as determinism, continuity, randomness, discreteness. | Assumes that information flow is mediated by structural organization, binding specificity, reaction kinetics, spatial compartmentalization, and thermodynamically predictable assembly behaviors. |
| | Implicit Commitments | Unstated but necessary assumptions that shape the field’s conceptual structure. | Assumes complexes encode functional logic, information transfer is interpretable and not random, assembly states correlate with regulatory outcomes, and emergent properties arise consistently from subunit interactions. |
| 1.6 Internal Coherence Requirements | Consistency | The demand that domain concepts do not contradict one another. | Principles of assembly, stoichiometry, information transfer, allostery, signaling fidelity, and structural dynamics must not contradict biochemical rules or mechanistic models across different complexes. |
| | Compatibility | The requirement that entities, variables, and assumptions fit together into a unified descriptive framework. | Entities (complexes, subunits, nucleic acids), variables (conformation, assembly state), and assumptions (specificity, modularity, stability) must integrate into a unified framework explaining coordinated information flow across molecular systems. |
| 2. Evidence Layer | 2.1 Observable Phenomena | Observables | The aspects of the domain that can produce detectable signals accessible to measurement. | Detectable features include complex assembly/disassembly, interaction frequencies, conformational shifts, signal-transduction events, spatial localization patterns, phase-separation behavior, and throughput of biochemical information. |
| | Detection Limits | The boundaries of what can be resolved or sensed by current instruments or methods. | Sensitivity thresholds for detecting low-abundance complexes, minimal resolvable conformational changes, lower bounds for interaction-detection frequency, resolution limits of super-resolution imaging, and mass-spec detection limits for subunit composition. |
| 2.2 Measurement Systems | Units | Standardized quantifications (meters, seconds, volts, decibels, dollars, etc.) necessary for consistent comparison. | Stoichiometric ratios, binding affinities (Kd), assembly rates (s⁻¹), interaction frequencies, structural resolution (Å), fluorescence intensities, localization coordinates, and information-transfer rates. |
| | Instruments | Devices and tools (microscopes, spectrometers, sensors, surveys, detectors) used to produce measurements. | Cryo-EM, super-resolution microscopy, mass spectrometry, single-molecule fluorescence systems, crosslinking mass spec, FRET/FLIM setups, Hi-C/HiChIP platforms, proximity-labeling tools (BioID/APEX), and live-cell tracking microscopes. |
| 2.3 Operational Definitions | Definitions | Terms defined by specific measurement procedures, ensuring empirical clarity. | Operational definitions for assembly state, interaction strength, complex occupancy, signaling activation, phase-separation status, and conformational switching based on assay-specific criteria and thresholds. |
| | Procedures | The explicit steps required to perform a measurement in a reproducible way. | Standardized workflows such as affinity purification, native PAGE, crosslinking assays, time-resolved FRET, super-resolution imaging protocols, cryo-EM grid preparation, interaction-profiling pipelines, and proximity-labeling steps. |
| 2.4 Data Acquisition | Protocols | Formal processes for gathering data under controlled or standardized conditions. | Controlled acquisition through imaging time-courses, cryo-EM data collection, proteomics runs, crosslinking mass-spec experiments, single-particle tracking, and multi-replicate measurements of dynamic assembly states. |
| | Sampling | Rules determining which subset of the domain is measured and how representative it is. | Rules for selecting complex types, subunits, temporal windows, cellular compartments, environmental conditions, or signaling states to ensure representative measurement of assembly dynamics and information flow. |
| 2.5 Data Character & Format | Data Types | The form raw evidence takes (time series, spectra, images, counts, qualitative records). | EM density maps, fluorescence trajectories, interaction matrices, proteomic composition tables, crosslinking spectra, single-molecule intensity traces, chromatin-contact networks, and phase-separation metrics. |
| | Resolution | The granularity or precision with which data is captured. | Structural resolution (Å), temporal resolution for signaling/assembly events (ms–s), spatial resolution of super-res imaging (tens of nm), mass-spec detection resolution, and sensitivity for detecting rare sub-complexes. |
| 2.6 Reliability & Calibration | Calibration | Adjustment procedures ensuring instruments produce accurate results. | Calibration of imaging intensity, EM magnification, mass-spec mass accuracy, FRET distance standards, crosslinking efficiency controls, interaction-mapping normalization, and correction for acquisition bias in dynamic measurements. |
| | Error Characterization | Identification and quantification of noise, uncertainty, bias, and measurement error. | Quantifying noise from fluorescence fluctuations, interaction false positives, mis-assigned complex composition, EM classification errors, crosslinking artifacts, phase-separation detection bias, and sampling variability in transient assemblies. |
| 3. Structural Layer | 3.1 Patterns & Regularities | Laws / Relations | Stable, repeatable patterns governing how observables behave across conditions. | Recurring principles include cooperative assembly, modular subunit organization, signal amplification–attenuation rules, conformational switching cycles, regulated assembly/disassembly, and fidelity constraints in information transfer. |
| | Invariants | Quantities or properties that remain constant under transformations (symmetries, conservation laws). | Conserved complex architectures, stable interaction motifs, persistent signaling topologies, reproducible stoichiometries, invariant domain interfaces, and consistent information-processing logic across organisms. |
| 3.2 Causal Architecture | Mechanisms | Underlying processes or structures that produce the observed regularities. | Mechanisms include hierarchical assembly, allosteric propagation through complexes, reaction-coupled conformational cycling, subunit exchange, scaffold-mediated signal integration, and multi-step decoding of biochemical inputs. |
| | Pathways | Organized sequences of interactions forming a causal chain or network. | Sequential processes such as signal → receptor complex activation → downstream effector recruitment → conformational relay → output response; or chromatin remodeler loading → nucleosome repositioning → transcriptional activation; or replisome progression → proofreading → ligation. |
| 3.3 Theoretical Vocabulary | Concepts | Core terms that encode the domain’s structure (force, gene, equilibrium, field). | Core terms include scaffolding, multivalency, allosteric networks, stoichiometry, condensates, signal fidelity, cooperativity, conformational ensembles, information throughput, and assembly dynamics. |
| | Classifications | Taxonomies, categories, or typologies that organize entities and relations. | Complex types (enzymatic machines, structural assemblies, signaling hubs), interaction categories (transient vs stable, high vs low affinity), structural states (active, inactive, intermediate), and information-flow motifs (feedforward, feedback, integration nodes). |
| 3.4 Formal Representations | Equations | Mathematical constructs expressing laws, relations, or mechanisms. | Binding/assembly equations (mass-action kinetics), cooperativity equations (Hill functions), thermodynamic stability equations (ΔG), signal-transduction rate laws, stochastic switching models, and kinetic proofreading equations. |
| | Models | Structured representations—mathematical, computational, or conceptual—used to predict and explain phenomena. | Multi-subunit assembly models, allosteric-network models, phase-separation models, kinetic proofreading frameworks, network-information models, 3D architecture models, and computational docking/MD models for complex dynamics. |
| 3.5 Idealized Structures | Simplified Models | Purposeful abstractions that capture essential dynamics while omitting irrelevant detail. | Representing complexes as rigid units, using two-state conformational models, collapsing multi-step signaling into single interactions, coarse-graining dynamic assemblies, or treating condensates as uniform liquid droplets. |
| | Limit Conditions | Regimes where specific models or approximations hold (classical vs. quantum, linear vs. nonlinear). | Approximations hold under moderate crowding, stable signaling environments, typical subunit ratios, and slow conformational cycling; they fail under rapid dynamics, extreme perturbations, heterogeneous compositions, or high-noise signaling contexts. |
| 3.6 Integrative Frameworks | Unifying Theories | Higher-order structures that connect disparate laws or mechanisms under a coherent whole. | Higher-order frameworks include modularity in molecular systems, information-theoretic models of signaling, emergent behavior from multivalent interactions, genome-wide coordination of molecular machines, and principles of hierarchical biological organization. |
| | Interdisciplinary Links | Points where the theory connects to adjacent sciences or larger explanatory systems. | Links to systems biology, structural biology, biophysics, computational biology, cell biology, and information theory through shared principles of signaling logic, assembly dynamics, and network-level information integration. |
| 4. Method Layer | 4.1 Inquiry Design | Experimental Design | Structured plans for manipulating variables to test causal claims. | Manipulating complex assembly, subunit composition, spatial localization, or signaling inputs via mutagenesis, targeted recruitment, optogenetic control of assembly, chemical perturbation, or forced dissociation of complexes. |
| | Observational Design | Systematic approaches for gathering non-manipulated data (surveys, field studies, natural experiments). | Monitoring native assembly dynamics, tracking conformational shifts, observing signaling cascades, imaging condensate formation, mapping interaction networks, and profiling complex stoichiometry without direct perturbation. |
| 4.2 Testing & Validation | Hypothesis Testing | Procedures for evaluating whether evidence supports or contradicts specific claims. | Testing claims about assembly requirements, information-flow pathways, allosteric propagation, stoichiometric necessity, or signaling fidelity through targeted perturbations, controlled binding assays, or disruption of candidate subunits. |
| | Replication | The requirement that results be independently reproducible under similar conditions. | Repeating imaging time-courses, interaction mapping, proteomics composition analyses, structural determinations (cryo-EM), and signaling-activity assays across multiple replicates/labs to ensure reproducibility. |
| 4.3 Inference & Evaluation | Statistical Inference | Rules for drawing conclusions from noisy or incomplete data. | Drawing conclusions from noisy imaging, interaction, proteomic, or structural data using probabilistic modeling, error propagation, statistical clustering of subunit states, and Bayesian inference for dynamic assembly states. |
| | Model Comparison | Criteria (fit, simplicity, predictive accuracy, robustness) used to evaluate competing models. | Comparing alternative information-flow models, assembly-pathway models, signaling dynamics models, and structural predictions based on predictive accuracy, fit to experimental data, robustness, and consistency across assays. |
| 4.4 Error Management | Error Analysis | Identification and quantification of random and systematic errors. | Quantifying noise and errors in fluorescence measurements, misassignment of subunits, crosslinking artifacts, EM classification errors, misidentified interactions, phase-separation detection noise, and temporal undersampling of dynamic events. |
| | Bias Control | Methods for minimizing subjective, instrumental, or procedural biases. | Reducing bias through randomized imaging order, validated antibodies/reagents, internal standards for proximity labeling, controlled crosslinking conditions, calibrated imaging settings, and consistent data-normalization pipelines. |
| 4.5 Adjudication & Revision | Peer Scrutiny | Collective evaluation of claims through critique, review, and debate. | Independent evaluation of structural claims, interaction networks, stoichiometric models, information-flow interpretations, and complex-dynamics analyses by other researchers for verification. |
| | Theory Revision | Procedures for modifying, replacing, or discarding models based on new evidence. | Updating models of assembly dynamics, signaling logic, allosteric propagation, or emergent properties of condensates when new evidence contradicts existing conceptual or structural frameworks. |
| 4.6 Integrity Conditions | Transparency | Requirements to disclose methods, data, assumptions, and limitations. | Full reporting of imaging parameters, perturbation conditions, structural processing pipelines, mass-spec filters, interaction-scoring thresholds, calibration steps, and assumptions used in modeling. |
| | Ethical Standards | Norms ensuring responsible conduct in experimentation, data handling, and publication. | Responsible reporting of interaction networks, honest representation of complex structures, appropriate handling of engineered assemblies, prevention of overinterpretation of noisy data, and adherence to research integrity norms. |