Levels and building blocks-toward a domain granularity framework for the life sciences

Abstract

Background: With the emergence of high-throughput technologies, Big Data and eScience, the use of online data repositories and the establishment of new data standards that require data to be computer-parsable become increasingly important. As a consequence, there is an increasing need for an integrated system of hierarchies of levels of different types of material entities that helps with organizing, structuring and integrating data from disparate sources to facilitate data exploration, data comparison and analysis. Theories of granularity provide such integrated systems.

Results: On the basis of formal approaches to theories of granularity authored by information scientists and ontology researchers, I discuss the shortcomings of some applications of the concept of levels and argue that the general theory of granularity proposed by Keet circumvents these problems. I introduce the concept of building blocks, which gives rise to a hierarchy of levels that can be formally characterized by Keet's theory. This hierarchy functions as an organizational backbone for integrating various other hierarchies that I briefly discuss, resulting in a domain granularity framework for the life sciences. I also discuss the consequences of this granularity framework for the structure of the top-level category of 'material entity' in Basic Formal Ontology.

Conclusions: The domain granularity framework suggested here is meant to provide the basis on which a more comprehensive information framework for the life sciences can be developed, which would provide the much needed conceptual framework for representing domains that cover multiple granularity levels. This framework can be used for intuitively structuring data in the life sciences, facilitating data exploration, and it can be employed for reasoning over different granularity levels across different hierarchies. It would provide a methodological basis for establishing comparability between data sets and for quantitatively measuring their degree of semantic similarity.

Keywords: Building block; Domain granularity framework; Granularity; Hierarchy; Knowledge management; Level; Ontology; SEMANTICS.

Fig. 1

Four different Types of Hierarchies. a A constitutive hierarchy of molecules, organelles, cells, and organs of a multi-cellular organism. It can be represented as an encaptic hierarchy of types, with every molecule being part of some organelle, every organelle part of some cell and every cell part of some organ. b The same set of entities as in A), organized in a cumulative constitutive hierarchy, which models the organization of biological material entities more accurately. Here, not every molecule that is part of an organism is also necessarily part of some organelle and not every cell necessarily part of some organ. c An aggregative hierarchy is based on mereological/meronymic inclusion that results from a part-whole relation (e.g., ecological hierarchies [15, 17]) or it is based on taxonomic inclusion [138] that results from a subsumption relation (e.g., Linnean taxonomy). In case of mereological inclusion, this hierarchy represents a mereological granularity tree and higher level entities consist of parts that are not physically connected, but only associated with each other. d In a cumulative aggregative hierarchy, as it is used in the hierarchical organization of military stuff, individuals with higher ranks, such as sergeants, lieutenants, and captains, appear in aggregates of higher order, so that squads consist of privates and sergeants, in the next level platoons of privates, sergeants, and lieutenants, and companies of privates, sergeants, lieutenants, and captains. (Figure modified from [58])

Fig. 2

Instance Granularity Tree and Type Granularity Tree based on bona fide Granular Partition for Constitutive and Cumulative Constitutive Hierarchies. a Compositional partitions of a constitutively and a cumulative-constitutively organized idealized multi-cellular organism into their constitutive bona fide object parts. Four corresponding partitions are shown. Left: into organs (f); cells (e); organelles (c, d); and molecules (a, b). Right: into organs with cells and extracellular molecules (i, j, g, h); cells with organelles and extracellular and cellular molecules (q, m, n, o, p, k, l); organelles and molecules (v, w, t, u, r, s); and molecules (x, y). b The four compositional partitions from A) represented as a bona fide instance granularity tree. Each partition constitutes a cut in the instance granularity tree (Cut I–IV) and thus an instance granularity level. Left: Instances of the same type of material entity do not belong to different cuts and thus are restricted to the same level of instance granularity. Right: Instances of the same type of material entity, for instance ‘molecule’, belong to different cuts and therefore to different levels of the respective instance granularity tree. The extension of the class ‘molecule’ thus transcends the boundaries between instance granularity levels. c Left: The bona fide instance granularity tree can be directly transformed into the corresponding type granularity tree—no sortation of any parts across the boundaries of granularity levels required, because the topology of the bona fide instance granularity tree is identical with the bona fide type granularity tree. Right: The bona fide instance granularity tree cannot be directly transformed into or mapped upon the corresponding type granularity tree. However, by following the simple and intuitive rule that a type must occupy the same granularity level as its finest grained instance (i.e., sortation-by-type [58]) and by applying the concept of granular representation (see further below), one can transfer the instance granularity tree into a corresponding type granularity tree. (Figure modified from [87])

Fig. 3

BFO’s Basic Granularity Framework. A bona fide partition from a multi-cellular organism to a molecule represents the center of BFO’s granularity framework and reflects direct subclasses of BFO’s ‘object’ for the biological domain. According to BFO, each level of the corresponding bona fide granularity tree must be modeled by its own domain reference ontology (i.e., a molecule ontology, a cell ontology, etc.). Within each such level-specific ontology, BFO’s top-level distinction of ‘object’, ‘fiat object part’, and ‘object aggregate’ indicates a basic fiat partition that orthogonally crosses the bona fide partition. The bona fide partition can therefore be understood as an integrating cross-granular backbone for the different ontologies of a given domain together with their implicit fiat partitions

Fig. 4

Compositional Building Block (CBB) Granular Perspective. The different building blocks are granulated according to the direct proper parthood granulation relation (the large dark arrows). The granulation is of the non-scale dependent single-relation-type granularity type (nrG [61]), and uses the combination of the granulation relation together with the common properties of all categories of the building block type as its granulation criterion. Due to the cumulative constitutive organization, finer-level building block entities can be considered to be parts associated with coarser-level building block entities, for instance, ECM being an associated part of a eukaryotic cell

Fig. 5

Set of Granular Perspectives within a given spatio-structural Frame of Reference. The figure shows all qualitative granular perspectives that the domain granularity framework for the life sciences distinguishes for any given spatio-structural frame of reference and thus any corresponding CBB granularity level (here, the set of perspectives for the eukaryotic cell level as an example). The large dark arrows indicate the granulation relation and the white boxes contain the granulated entity types. a = Region-Based Fiat Building Block Part Granularity Perspective; b = Region-Based Fiat Building Block Cluster Granularity Perspective; c = Region-Based Group of Building Block Level Objects Granularity Perspective; d = Region-Based Group of Fiat Building Block Level Entities Granularity Perspective (see also Table 1)

Fig. 6

Top-Level Subclasses of ‘material entity’ and ‘spatio-structural entity’. The labeled grey boxes represent classes. The class ‘spatio-structural entity’ is characterized in reference to causal unity via internal physical forces, ‘functional entity’ in reference to causal unity via bearing a specific function, and ‘historical/evolutionary entity’ in reference to causal unity via common historical/evolutionary origin. As a consequence of the perspective-dependence of bona fideness, these three classes are not disjoint. The functional and historical/evolutionary entities are further differentiated according to disjoint categories of bona fide units and fiat unit parts. Spatio-structural entities are further differentiated in correspondence with the granularity levels of the compositional building block granular perspective (see discussion in text), ranging from ‘atom level entity’ to ‘epithelially-delimited multi-cellular organism level entity’, but include not only the respective bona fide entities of that level, but also their corresponding object aggregate and fiat object part entities. Because bona fideness is not only perspective-dependent, but also granularity-dependent, each building block level has its own spatio-structural frame of reference and thus its own perspective. Due to the cumulative-constitutive organization of biological entities, entities from finer spatio-structural frames of reference (e.g., molecules) must be represented in coarser frames of reference (e.g., eukaryotic cell) as fiat portions of matter. These representations are covered through the ‘portion of matter entity’ class (see also Fig. 8)

Fig. 7

Top-Level Subclasses of ‘eukaryotic cell level entity’. Eukaryotic cell level entities are differentiated into a bona fide ‘eukaryotic cell level object’ and a ‘fiat eukaryotic cell level entity’ class, which are disjoint. The former is differentiated based on its underlying type of causal unity into ‘eukaryotic cell’, which is based on physical covering, and ‘bona fide cluster of eukaryotic cells’, which is based only on internal physical forces and not on physical covering. The fiat eukaryotic cell level entities are differentiated based on their self-connectedness into the disjoint subclasses ‘self-connected fiat eukaryotic cell entity’ and ‘scattered fiat eukaryotic cell entity’. See text for more details

Fig. 8

Top-Level Subclasses of ‘portion of matter entity’. The entities of each building block level, except for the coarsest level of epithelially-delimited multi-cellular organisms, can be represented as a respective portion of matter entity in coarser spatio-structural frames of reference. Therefore, ‘portion of matter entity’ is differentiated into building block level specific subclasses. Further differentiations are shown for the classes ‘portion of molecule entity’ and ‘portion of eukaryotic cell entity’, which are based on whether the entity is a self-connected portion of matter, for instance, a portion of ECM or a portion of connective tissue, or a group of scattered portions, for instance, the group of portions of muscle tissues in a human being

Fig. 9

Resolution-Based Representation (RBR) and Resolution-Based Countability Representation (RBCR) Granularity Perspective. The different levels of the RBR granular perspective are granulated according to the has coarser granular representation relation (the white broad arrows). The granulation is of the scale dependent grain-size-according-to-resolution granularity type (sgrG [61]). The two levels of each of the two RBCR granular perspectives, on the other hand, are granulated according to the has coarser non-countable granular representation relation and the has finer countable granular representation relation, respectively (dotted gray arrows). Their granulation is of the scale dependent grain-size-according-to-resolution granularity type (sgrG [61]). All three perspectives use the combination of the granulation relation together with the scale provided through the set of different spatio-structural frames of reference that are sequentially ordered through the associated CBB granular perspective (i.e., the building block levels hierarchy). As a consequence, the RBR granular perspective comprises six granularity levels, whereas the two RBCR granular perspectives each comprise only two granularity levels, because their granulation relation is not transitive (its domain and range differ)