Harmonizing semantic annotations for computational models in biology.

Life science researchers use computational models to articulate and test hypotheses about the behavior of biological systems. Semantic annotation is a critical component for enhancing the interoperability and reusability of such models as well as for the integration of the data needed for model parameterization and validation. Encoded as machine-readable links to knowledge resource terms, semantic annotations describe the computational or biological meaning of what models and data represent. These annotations help researchers find and repurpose models, accelerate model composition and enable knowledge integration across model repositories and experimental data stores. However, realizing the potential benefits of semantic annotation requires the development of model annotation standards that adhere to a community-based annotation protocol. Without such standards, tool developers must account for a variety of annotation formats and approaches, a situation that can become prohibitively cumbersome and which can defeat the purpose of linking model elements to controlled knowledge resource terms. Currently, no consensus protocol for semantic annotation exists among the larger biological modeling community. Here, we report on the landscape of current annotation practices among the COmputational Modeling in BIology NEtwork community and provide a set of recommendations for building a consensus approach to semantic annotation.

SemGen: a tool for semantics-based annotation and composition of biosimulation models.

SUMMARY: As the number and complexity of biosimulation models grows, so do demands for tools that can help users understand models and compose more comprehensive and accurate systems from existing models. SemGen is a tool for semantics-based annotation and composition of biosimulation models designed to address this demand. A key SemGen capability is to decompose and then integrate models across existing model exchange formats including SBML and CellML. To support this capability, we use semantic annotations to explicitly capture the underlying biological and physical meanings of the entities and processes that are modeled. SemGen leverages annotations to expose a model's biological and computational architecture and to help automate model composition. AVAILABILITY AND IMPLEMENTATION: SemGen is freely available at https://github.com/SemBioProcess/SemGen. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Multiple ontologies in action: composite annotations for biosimulation models.

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.

Qualitative causal analyses of biosimulation models.

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.

BaroFuse, a novel pressure-driven, adjustable-throughput perfusion system for tissue maintenance and assessment.

OBJECTIVES: Microfluidic perfusion systems are used for assessing cell and tissue function while assuring cellular viability. Low perfusate flow rates, desired both for conserving reagents and for extending the number of channels and duration of experiments, conventionally depend on peristaltic pumps to maintain flow yet such pumps are unwieldy and scale poorly for high-throughput applications requiring 16 or more channels. The goal of the study was to develop a scalable multichannel microfluidics system capable of maintaining and assessing kinetic responses of small amounts of tissue to drugs or changes in test conditions. METHODS: Here we describe the BaroFuse, a novel, multichannel microfluidics device fabricated using 3D-printing technology that uses gas pressure to drive large numbers of parallel perfusion experiments. The system is versatile with respect to endpoints due to the translucence of the walls of the perifusion chambers, enabling optical methods for interrogating the tissue status. The system was validated by the incorporation of an oxygen detection system that enabled continuous measurement of oxygen consumption rate (OCR). RESULTS: Stable and low flow rates (1-20 µL/min/channel) were finely controlled by a single pressure regulator (0.5-2 psi). Control of flow in 0.2 µL/min increments was achieved. Low flow rates allowed for changes in OCR in response to glucose to be well resolved with very small numbers of islets (1-10 islets/channel). Effects of acetaminophen on OCR by precision-cut liver slices of were dose dependent and similar to previously published values that used more tissue and peristaltic-pump driven flow. CONCLUSIONS: The very low flow rates and simplicity of design and operation of the BaroFuse device allow for the efficient generation of large number of kinetic profiles in OCR and other endpoints lasting from hours to days. The use of flow enhances the ability to make measurements on primary tissue where some elements of native three-dimensional structure are preserved. We offer the BaroFuse as a powerful tool for physiological studies and for pharmaceutical assessment of drug effects as well as personalized medicine.

Regulation of ATP/ADP in pancreatic islets.

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.

Semantics-Based Composition of Integrated Cardiomyocyte Models Motivated by Real-World Use Cases.

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.

Quantification of Low-Level Drug Effects Using Real-Time, in vitro Measurement of Oxygen Consumption Rate.

There is a general need to detect toxic effects of drugs during preclinical 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 suprapharmacologic 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 h in liver slices in a continuous flow chamber with the ability to resolve changes as small as 0.3%/h. 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 suprapharmacologic levels.

Non-invasive imaging of beta cell mass: a quantitative analysis.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Dynamic perifusion to maintain and assess isolated pancreatic islets.

Advances in human islet transplant techniques are hampered by the inability to assess the quality of isolated islets. A flow culture system was developed to perifuse isolated pancreatic islets or cultured beta-cell lines in order to continuously and noninvasively assess cell function and viability with high kinetic resolution. Continuous perifusion of large amounts of islet tissue as isolated from human pancreata enables the use of noninvasive measurement technologies not previously applied to islets. To compare dynamic perifusion of tissue at high density with conventional static cultures, we measured glucose-stimulated insulin secretion and O2 consumption of large amounts of INS-1 cells (45-65 x 10(6)) to confirm that perifused cells were adequately supplied with oxygen and nutrients and remained functionally responsive. Isolated human and monkey islets that were perifused for 18 h showed robust biphasic insulin secretion in response to a step increase in glucose, demonstrating the ability to maintain islets and the high kinetic resolution of the system. As an example of the system's ability to resolve multiple indicator dilution experiments, the retention of [3H]-glibenclamide was kinetically distinguished from that of an extracellular marker. In summary, the perifusion system is able to maintain healthy cells, assess insulin secretion and metabolite fluxes such as oxygen consumption and lactate production, and characterize the kinetics of the interaction between radiopharmaceuticals and islet cells. The ability to systematically assess the metabolic and functional viability of islets will facilitate the optimization of islet isolation procedures, islet transplantation studies, and islet storage methodologies.

Evolution of a Foundational Model of Physiology: symbolic representation for functional bioinformatics.

We describe the need for a Foundational Model of Physiology (FMP) as a reference ontology for "functional bioinformatics". The FMP is intended to support symbolic lookup, logical inference and mathematical analysis by integrating descriptive, qualitative and quantitative functional knowledge. The FMP will serve as a symbolic representation of biological functions initially pertaining to human physiology and ultimately extensible to other species. We describe the evolving architecture of the FMP, which is based on the ontological principles of the BioD biological description language and the Foundational Model of Anatomy (FMA).

Representing physiological processes and their participants with PhysioMaps.

BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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.

Ontology of physics for biology: representing physical dependencies as a basis for biological processes.

BACKGROUND: 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). RESULTS: 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 entities 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. CONCLUSIONS: 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.

Physical properties of biological entities: an introduction to the ontology of physics for biology.

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.

Integrating systems biology models and biomedical ontologies.

BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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.

Advances in semantic representation for multiscale biosimulation: a case study in merging models.

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).

Composite annotations: requirements for mapping multiscale data and models to biomedical ontologies.

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.

Integration of multi-scale biosimulation models via light-weight semantics.

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.

Bridging biological ontologies and biosimulation: the ontology of physics for biology.

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.