Relational grounding facilitates development of scientifically useful multiscale models

We review grounding issues that influence the scientific usefulness of any biomedical multiscale model (MSM). Groundings are the collection of units, dimensions, and/or objects to which a variable or model constituent refers. To date, models that primarily use continuous mathematics rely heavily on absolute grounding, whereas those that primarily use discrete software paradigms (e.g., object-oriented, agent-based, actor) typically employ relational grounding. We review grounding issues and identify strategies to address them. We maintain that grounding issues should be addressed at the start of any MSM project and should be reevaluated throughout the model development process. We make the following points. Grounding decisions influence model flexibility, adaptability, and thus reusability. Grounding choices should be influenced by measures, uncertainty, system information, and the nature of available validation data. Absolute grounding complicates the process of combining models to form larger models unless all are grounded absolutely. Relational grounding facilitates referent knowledge embodiment within computational mechanisms but requires separate model-to-referent mappings. Absolute grounding can simplify integration by forcing common units and, hence, a common integration target, but context change may require model reengineering. Relational grounding enables synthesis of large, composite (multi-module) models that can be robust to context changes. Because biological components have varying degrees of autonomy, corresponding components in MSMs need to do the same. Relational grounding facilitates achieving such autonomy. Biomimetic analogues designed to facilitate translational research and development must have long lifecycles. Exploring mechanisms of normal-to-disease transition requires model components that are grounded relationally. Multi-paradigm modeling requires both hyperspatial and relational grounding

At the Biological Modeling and Simulation Frontier

We provide a rationale for and describe examples of synthetic modeling and simulation (M&S) of biological systems. We explain how synthetic methods are distinct from familiar inductive methods. Synthetic M&S is a means to better understand the mechanisms that generate normal and disease-related phenomena observed in research, and how compounds of interest interact with them to alter phenomena. An objective is to build better, working hypotheses of plausible mechanisms. A synthetic model is an extant hypothesis: execution produces an observable mechanism and phenomena. Mobile objects representing compounds carry information enabling components to distinguish between them and react accordingly when different compounds are studied simultaneously. We argue that the familiar inductive approaches contribute to the general inefficiencies being experienced by pharmaceutical R&D, and that use of synthetic approaches accelerates and improves R&D decision-making and thus the drug development process. A reason is that synthetic models encourage and facilitate abductive scientific reasoning, a primary means of knowledge creation and creative cognition. When synthetic models are executed, we observe different aspects of knowledge in action from different perspectives. These models can be tuned to reflect differences in experimental conditions and individuals, making translational research more concrete while moving us closer to personalized medicine

Using ontology and semantic web services to support modeling in systems biology

This thesis addresses the problem of collaboration among experimental biologists and modelers in the study of systems biology by using ontology and Semantic Web Services techniques. Modeling in systems biology is concerned with using experimental information and mathematical methods to build quantitative models across different biological scales. This requires interoperation among various knowledge sources and services. Ontology and Semantic Web Services potentially provide an infrastructure to meet this requirement. In our study, we propose an ontology-centered framework within the Semantic Web infrastructure that aims at standardizing various areas of knowledge involved in the biological modeling processes. In this framework, first we specify an ontology-based meta-model for building biological models. This meta-model supports using shared biological ontologies to annotate biological entities in the models, allows semantic queries and automatic discoveries, enables easy model reuse and composition, and serves as a basis to embed external knowledge. We also develop means of transforming biological data sources and data analysis methods into Web Services. These Web Services can then be composed together to perform parameterization in biological modeling. The knowledge of decision-making and workflow of parameterization processes are then recorded by the semantic descriptions of these Web Services, and embedded in model instances built on our proposed meta-model. We use three cases of biological modeling to evaluate our framework. By examining our ontology-centered framework in practice, we conclude that by using ontology to represent biological models and using Semantic Web Services to standardize knowledge components in modeling processes, greater capabilities of knowledge sharing, reuse and collaboration can be achieved. We also conclude that ontology-based biological models with formal semantics are essential to standardize knowledge in compliance with the Semantic Web vision