[Show abstract][Hide abstract]ABSTRACT: We describe an approach for performing qualitative , systems-level causal analyses on biosimulation models that leverages semantics-based modeling formats, formal ontology, and automated inference. The approach allows users to quickly investigate how a qualitative perturbation to an element within a model's network (an increment or decrement) propagates throughout the modeled system. To support such analyses, we must interpret and annotate the semantics of the models, including both the physical properties modeled and the dependencies that relate them. We build from prior work understanding the semantics of biological properties, but here, we focus on the semantics for dependencies, which provide the critical knowledge necessary for causal analysis of biosimulation models. We describe augmentations to the Ontology of Physics for Biology, via OWL axioms and SWRL rules, and demonstrate that a reasoner can then infer how an annotated model's physical properties influence each other in a qualitative sense. Our goal is to provide researchers with a tool that helps bring the systems-level network dynamics of biosimulation models into perspective, thus facilitating model development, testing, and application.
[Show abstract][Hide abstract]ABSTRACT: Semantics-based model composition is an approach for generating complex biosimulation models from existing components that relies on capturing the biological meaning of model elements in a machine-readable fashion. This approach allows the user to work at the biological rather than computational level of abstraction and helps minimize the amount of manual effort required for model composition. To support this compositional approach, we have developed the SemGen software, and here report on SemGen's semantics-based merging capabilities using real-world modeling use cases. We successfully reproduced a large, manually-encoded, multi-model merge: the "Pandit-Hinch-Niederer" (PHN) cardiomyocyte excitation-contraction model, previously developed using CellML. We describe our approach for annotating the three component models used in the PHN composition and for merging them at the biological level of abstraction within SemGen. We demonstrate that we were able to reproduce the original PHN model results in a semi-automated, semantics-based fashion and also rapidly generate a second, novel cardiomyocyte model composed using an alternative, independently-developed tension generation component. We discuss the time-saving features of our compositional approach in the context of these merging exercises, the limitations we encountered, and potential solutions for enhancing the approach.
[Show abstract][Hide abstract]ABSTRACT: There is a general need to detect toxic effects of drugs during pre-clinical screening. We propose that increased sensitivity of xenobiotics toxicity combined with improved in vitro physiological recapitulation will more accurately assess potentially toxic perturbations of cellular biochemistry that are near in vivo pharmacological exposure levels. Importantly, measurement of such cytopathologies avoids activating mechanisms mediating toxicity at supra-pharmacologic levels not relevant to in vivo effects. We present a sensitive method to measure changes in oxygen consumption rate (OCR), a well-established parameter reflecting a potential hazard, in response to exposure to pharmacologic levels of drugs using a flow culture system and state of the art oxygen sensing system. We tested metformin and acetaminophen on rat liver slices to illustrate the method. The features of the method include continuous and very stable measurement of OCR over the course of 48 hours in liver slices in a continuous flow chamber with the ability to resolve changes as small as 0.3%/hour. Kinetic modeling of metformin inhibition of OCR over a wide range of concentrations revealed both a slow and fast mechanism, where the fast mechanism activated only at concentrations above 0.6 mM. For both drugs, small amounts of inhibition were reversible, but higher decrements were irreversible. Overall the study highlights the advantages of measuring low-level toxicity so as to avoid the common extrapolations made about drug toxicity based on effects of drugs tested at supra-pharmacologic levels.
[Show abstract][Hide abstract]ABSTRACT: Overview Biological researchers increasingly rely on computational models to integrate biological systems knowledge, test hypotheses , and forecast system behavior. The expanding size of these models requires solutions for managing their complexity. Modularity, a time-tested design principle for managing complexity, can be applied within the biological modeling field to parallelize work, automate composition, and promote effective model sharing. As modelers of complex biological systems, we aim to apply modular production to accelerate our efforts and have therefore investigated several currently available approaches for modular modeling. We argue that some traditional features of modularity, in particular the isolation of a module's contents from the rest of the system, can impede model sharing and composition when applied within the context of biological simulation. Alternative approaches that can automatically interface model components based on the biological meaning of their contents (their semantics) avoid these limitations. Our conclusions have strategic implications for the design of systems biology, synthetic biology, and integrated physiological mod-eling technologies, as well as community-level model curation efforts.
Full-text Article · Oct 2014 · PLoS Computational Biology
[Show abstract][Hide abstract]ABSTRACT: In prior work, we presented the Ontology of Physics for Biology (OPB) as a computational ontology for use in the annotation and representations of biophysical knowledge encoded in repositories of physics-based biosimulation models. We introduced OPB:Physical entity and OPB:Physical property classes that extend available spatiotemporal representations of physical entities and processes to explicitly represent the thermodynamics and dynamics of physiological processes. Our utilitarian, long-term aim is to develop computational tools for creating and querying formalized physiological knowledge for use by multiscale "physiome" projects such as the EU's Virtual Physiological Human (VPH) and NIH's Virtual Physiological Rat (VPR).
Here we describe the OPB:Physical dependency taxonomy of classes that represent of the laws of classical physics that are the "rules" by which physical properties of physical enities change during occurrences of physical processes. For example, the fluid analog of Ohm's law (as for electric currents) is used to describe how a blood flow rate depends on a blood pressure gradient. Hooke's law (as in elastic deformations of springs) is used to describe how an increase in vascular volume increases blood pressure. We classify such dependencies according to the flow, transformation, and storage of thermodynamic energy that occurs during processes governed by the dependencies.
We have developed the OPB and annotation methods to represent the meaning---the biophysical semantics---of the mathematical statements of physiological analysis and the biophysical content of models and datasets. Here we describe and discuss our approach to an ontological representation of physical laws (as dependencies) and properties as encoded for the mathematical analysis of biophysical processes.
Full-text Article · Dec 2013 · Journal of Biomedical Semantics
[Show abstract][Hide abstract]ABSTRACT: As the number and size of biological knowledge resources for physiology grows, researchers need improved tools for searching and integrating knowledge and physiological models. Unfortunately, current resources-databases, simulation models, and knowledge bases, for example-are only occasionally and idiosyncratically explicit about the semantics of the biological entities and processes that they describe.
We present a formal approach, based on the semantics of biophysics as represented in the Ontology of Physics for Biology, that divides physiological knowledge into three partitions: structural knowledge, process knowledge and biophysical knowledge. We then computationally integrate these partitions across multiple structural and biophysical domains as computable ontologies by which such knowledge can be archived, reused, and displayed. Our key result is the semi-automatic parsing of biosimulation model code into PhysioMaps that can be displayed and interrogated for qualitative responses to hypothetical perturbations.
Strong, explicit semantics of biophysics can provide a formal, computational basis for integrating physiological knowledge in a manner that supports visualization of the physiological content of biosimulation models across spatial scales and biophysical domains.
Full-text Article · Apr 2013 · Journal of Biomedical Semantics
[Show abstract][Hide abstract]ABSTRACT: Our long-term goal is to provide guidance for semantic annotation of biosimulation models and data, allowing for improved multi-scale modeling, modular model construction, and integration of models across repositories. In this paper, we focus on the task of searching and retrieving candidate models for merging over multiple biosimulation model repositories. In particular, we describe our semantic integration of the CellML model repository, the Reactome database, and the BioModels database. We introduce BioSimConnector, a tool that uses this integrated knowledge base to search for connections across models from all three repositories. Researchers can then use these connections to better identify candidate models for merging into larger, multi-scale models for the Physiome or the Virtual Physiological Human.
[Show abstract][Hide abstract]ABSTRACT: As the number and size of biological knowledge resources for physiology grows, we need improved tools for searching and integrating knowledge and physiological models. Unfor- tunately, current resources are not always explicit about the biological semantics that they describe. Here, we present a formal approach that divides physiological knowledge into three partitions: structural knowledge, process knowledge and biophysical knowledge. We show how these are inter- related and present a PhysioMap, as a means to connect all three views.
[Show abstract][Hide abstract]ABSTRACT: As biomedical investigators strive to integrate data and analyses across spatiotemporal scales and biomedical domains, they have recognized the benefits of formalizing languages and terminologies via computational ontologies. Although ontologies for biological entities-molecules, cells, organs-are well-established, there are no principled ontologies of physical properties-energies, volumes, flow rates-of those entities. In this paper, we introduce the Ontology of Physics for Biology (OPB), a reference ontology of classical physics designed for annotating biophysical content of growing repositories of biomedical datasets and analytical models. The OPB's semantic framework, traceable to James Clerk Maxwell, encompasses modern theories of system dynamics and thermodynamics, and is implemented as a computational ontology that references available upper ontologies. In this paper we focus on the OPB classes that are designed for annotating physical properties encoded in biomedical datasets and computational models, and we discuss how the OPB framework will facilitate biomedical knowledge integration.
[Show abstract][Hide abstract]ABSTRACT: Systems biology is an approach to biology that emphasizes the structure and dynamic behavior of biological systems and the interactions that occur within them. To succeed, systems biology crucially depends on the accessibility and integration of data across domains and levels of granularity. Biomedical ontologies were developed to facilitate such an integration of data and are often used to annotate biosimulation models in systems biology.
We provide a framework to integrate representations of in silico systems biology with those of in vivo biology as described by biomedical ontologies and demonstrate this framework using the Systems Biology Markup Language. We developed the SBML Harvester software that automatically converts annotated SBML models into OWL and we apply our software to those biosimulation models that are contained in the BioModels Database. We utilize the resulting knowledge base for complex biological queries that can bridge levels of granularity, verify models based on the biological phenomenon they represent and provide a means to establish a basic qualitative layer on which to express the semantics of biosimulation models.
We establish an information flow between biomedical ontologies and biosimulation models and we demonstrate that the integration of annotated biosimulation models and biomedical ontologies enables the verification of models as well as expressive queries. Establishing a bi-directional information flow between systems biology and biomedical ontologies has the potential to enable large-scale analyses of biological systems that span levels of granularity from molecules to organisms.
Full-text Article · Aug 2011 · BMC Systems Biology
[Show abstract][Hide abstract]ABSTRACT: There now exists a rich set of ontologies that provide detailed semantics for biological entities of interest. However, there is not (nor should there be) a single source ontology that provides all the necessary semantics for describing biological phenomena. In the domain of physiological biosimulation models, researchers use annotations to convey semantics, and many of these annotations require the use of multiple reference ontologies. Therefore, we have developed the idea of composite annotations that access multiple ontologies to capture the physics-based meaning of model variables. These composite annotations provide the semantic expressivity needed to disambiguate the often-complex features of biosimulation models, and can be used to assist with model merging and interoperability. In this paper, we demonstrate the utility of composite annotations for model merging by describing their use within SemGen, our semantics-based model composition software. More broadly, if orthogonal reference ontologies are to meet their full potential, users need tools and methods to connect and link these ontologies. Our composite annotations and the SemGen tool provide one mechanism for leveraging multiple reference ontologies.
Full-text Article · Feb 2011 · Journal of Biomedical Informatics
[Show abstract][Hide abstract]ABSTRACT: Current methods for annotating biomedical data resources rely on simple mappings between data elements and the contents of a variety of biomedical ontologies and controlled vocabularies. Here we point out that such simple mappings are inadequate for large-scale multiscale, multidomain integrative "virtual human" projects. For such integrative challenges, we describe a "composite annotation" schema that is simple yet sufficiently extensible for mapping the biomedical content of a variety of data sources and biosimulation models to available biomedical ontologies.
Article · Sep 2009 · Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference
[Show abstract][Hide abstract]ABSTRACT: As a case-study of biosimulation model integration, we describe our experiences applying the SemSim methodology to integrate independently-developed, multiscale models of cardiac circulation. In particular, we have integrated the CircAdapt model (written by T. Arts for MATLAB) of an adapting vascular segment with a cardiovascular system model (written by M. Neal for JSim). We report on three results from the model integration experience. First, models should be explicit about simulations that occur on different time scales. Second, data structures and naming conventions used to represent model variables may not translate across simulation languages. Finally, identifying the dependencies among model variables is a non-trivial task. We claim that these challenges will appear whenever researchers attempt to integrate models from others, especially when those models are written in a procedural style (using MATLAB, Fortran, etc.) rather than a declarative format (as supported by languages like SBML, CellML or JSim's MML).
Article · Feb 2009 · Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
[Show abstract][Hide abstract]ABSTRACT: We introduce and define the Ontology of Physics for Biology (OPB), a reference ontology of physical principles that bridges the gap between bioinformat-ics modeling of biological structures and the bio-simulation modeling of biological processes. Where-as modeling anatomical entities is relatively well-studied, representing the physics-based semantics of biosimulation and biological processes remains an open research challenge. The OPB bridges this semantic gap-linking the semantics of biosimulation mathematics to structural bio-ontologies. Our design of the OPB is driven both by theory and pragmatics: we have applied systems dynamics theory to build an ontology with pragmatic use for annotating biosimulation models.
[Show abstract][Hide abstract]ABSTRACT: Currently, biosimulation researchers use a variety of computational environments and languages to model biological processes. Ideally, researchers should be able to semiautomatically merge models to more effectively build larger, multi-scale models. However, current modeling methods do not capture the underlying semantics of these models sufficiently to support this type of model construction. In this paper, we both propose a general approach to solve this problem, and we provide a specific example that demonstrates the benefits of our methodology. In particular, we describe three biosimulation models: (1) a cardio-vascular fluid dynamics model, (2) a model of heart rate regulation via baroreceptor control, and (3) a sub-cellular-level model of the arteriolar smooth muscle. Within a light-weight ontological framework, we leverage reference ontologies to match concepts across models. The light-weight ontology then helps us combine our three models into a merged model that can answer questions beyond the scope of any single model.
Full-text Article · Feb 2008 · Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
[Show abstract][Hide abstract]ABSTRACT: We introduce Chalkboard, a prototype tool for representing and displaying cell-signaling pathway knowledge, for carrying out simple qualitative reasoning over these pathways, and for generating quantitative biosimulation code. The design of Chalkboard has been driven by the need to quickly model and visualize alternative hypotheses about uncertain pathway knowledge. Chalkboard allows the biologists to test in silico the implications of various hypotheses. To fulfill this need, chalkboard includes (1) a rich ontology of pathway entities and interactions, which is ultimately informed by the basic chemistry and physics among molecules, and (2) a form of qualitative reasoning that computes causal chains and feedback loops within the network of entities and reactions. We demonstrate Chalkboard's capabilities in the domain of APP proteolysis, a pathway that plays a key role in the pathogenesis of Alzheimer's disease. In this pathway (as is common), information is incomplete and parts of the pathways are conjectural, rather than experimentally verified. With Chalkboard, we can carry out in silico perturbation experiments and explore the consequences of different conjectural connections and relationships in the network. We believe that pathway reasoning capabilities and in silico experiments will become a critical component of the hypothesis generation phase of modern biological research.
Article · Feb 2007 · Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
[Show abstract][Hide abstract]ABSTRACT: Dynamic simulation models of physiology are often represented as a set of mathematical equations. Such models are very useful for studying and understanding the dynamic behavior of physiological variables. However, the sheer number of equations and variables can make these models unwieldy, difficult to under-stand, and challenging to maintain. We describe a symbolic, ontologically-guided methodology for representing a physiological model of the circulation. We created an ontology describing the types of equations in the model as well as the anatomic components and how they are connected to form a circulatory loop. The ontology provided an explicit representation of the model, both its mathematical and anatomic content, abstracting and hiding much of the mathematical complexity. The ontology also provided a framework to construct a graphical representation of the model, providing a simpler visualization than the large set of mathematical equations. Our approach may help model builders to maintain, debug, and extend simulation models.
[Show abstract][Hide abstract]ABSTRACT: The integration of biomedical terminologies is indispensable to the process of information integration. When terminologies are linked merely through the alignment of their leaf terms, however, differences in context and ontological structure are ignored. Making use of the SNAP and SPAN ontologies, we show how three reference domain ontologies can be integrated at a higher level, through what we shall call the OBR framework (for: Ontology of Biomedical Reality). OBR is designed to facilitate inference across the boundaries of domain ontologies in anatomy, physiology and pathology.
[Show abstract][Hide abstract]ABSTRACT: Currently there are major efforts to develop strategies for the in vivo imaging of pancreatic beta cell mass as a clinical and investigational tool for detecting and tracking the loss of beta cells that underlies the progression of Type I diabetes. However, beta cells constitute only about 1% of pancreatic mass and are distributed throughout the pancreas within tiny islets of Langerhans that are each less than the spatial resolution of non-invasive imaging technologies.
To estimate the requisite binding characteristics of a candidate beta cell imaging agent, calculations of the beta cell contribution to a positron emission tomography signal were made using simple equations. These were based on the relative population of beta cells and non-beta cells within the pancreas and surrounding tissue and an equation describing equilibrium ligand binding.
The calculations show that two criteria must be met: (1) The low-volume fraction of beta cells within the exocrine pancreas (about 1:100) requires that beta cells retain labeled imaging agents at least 1,000-fold more strongly than exocrine cells. (2) Agents that label cell surface receptors, even if beta cell-specific, must do so at a high enough level so that the imaging signal arising from unbound label retained in extracellular spaces must not overwhelm signals from labeled beta cells.
The limits developed here can serve as criteria for identifying candidate imaging agents for the in vivo imaging of beta cell mass, thereby avoiding expensive preclinical development using compounds that have no chance of success.
Full-text Article · Nov 2004 · Diabetes Technology & Therapeutics
[Show abstract][Hide abstract]ABSTRACT: ATP and ADP levels are critical regulators of glucose-stimulated insulin secretion. In many aerobic cell types, the phosphorylation potential (ATP/ADP/P(i)) is controlled by sensing mechanisms inherent in mitochondrial metabolism that feed back and induce compensatory changes in electron transport. To determine whether such regulation may contribute to stimulus-secretion coupling in islet cells, we used a recently developed flow culture system to continuously and noninvasively measure cytochrome c redox state and oxygen consumption as indexes of electron transport in perifused isolated rat islets. Increasing substrate availability by increasing glucose increased cytochrome c reduction and oxygen consumption, whereas increasing metabolic demand with glibenclamide increased oxygen consumption but not cytochrome c reduction. The data were analyzed using a kinetic model of the dual control of electron transport and oxygen consumption by substrate availability and energy demand, and ATP/ADP/P(i) was estimated as a function of time. ATP/ADP/P(i) increased in response to glucose and decreased in response to glibenclamide, consistent with what is known about the effects of these agents on energy state. Therefore, a simple model representing the hypothesized role of mitochondrial coupling in governing phosphorylation potential correctly predicted the directional changes in ATP/ADP/P(i). Thus, the data support the notion that mitochondrial-coupling mechanisms, by virtue of their role in establishing ATP and ADP levels, may play a role in mediating nutrient-stimulated insulin secretion. Our results also offer a new method for continuous noninvasive measures of islet cell phosphorylation potential, a critical metabolic variable that controls insulin secretion by ATP-sensitive K(+)-dependent and -independent mechanisms.