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.

Response classification based on a minimal model of glioblastoma growth is prognostic for clinical outcomes and distinguishes progression from pseudoprogression.

Glioblastoma multiforme is the most aggressive type of primary brain tumor. Glioblastoma growth dynamics vary widely across patients, making it difficult to accurately gauge their response to treatment. We developed a model-based metric of therapy response called Days Gained that accounts for this heterogeneity.

Discriminating survival outcomes in patients with glioblastoma using a simulation-based, patient-specific response metric.

Accurate clinical assessment of a patient's response to treatment for glioblastoma multiforme (GBM), the most malignant type of primary brain tumor, is undermined by the wide patient-to-patient variability in GBM dynamics and responsiveness to therapy. Using computational models that account for the unique geometry and kinetics of individual patients' tumors, we developed a method for assessing treatment response that discriminates progression-free and overall survival following therapy for GBM. Applying these models as untreated virtual controls, we generate a patient-specific "Days Gained" response metric that estimates the number of days a therapy delayed imageable tumor progression.

Current progress in patient-specific modeling.

We present a survey of recent advancements in the emerging field of patient-specific modeling (PSM). Researchers in this field are currently simulating a wide variety of tissue and organ dynamics to address challenges in various clinical domains. The majority of this research employs three-dimensional, image-based modeling techniques.

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.

Subject-specific model estimation of cardiac output and blood volume during hemorrhage.

We have developed a novel method for estimating subject-specific hemodynamics during hemorrhage. First, a mathematical model representing a closed-loop circulation and baroreceptor feedback system was parameterized to match the baseline physiology of individual experimental subjects by fitting model results to 1 min of pre-injury data. This automated parameterization process matched pre-injury measurements within 1.

The role of oxidative stress in the pathophysiology of cerebrovascular lesions in Alzheimer's disease.

Alzheimer's disease (AD) and stroke are two leading causes of age-associated dementia. A rapidly growing body of evidence indicates that increased oxidative stress from reactive oxygen radicals is associated with the aging process and age-related degenerative disorders such as atherosclerosis, ischemia/reperfusion, arthritis, stroke, and neurodegenerative diseases. New evidence has also indicated that vascular lesions are a key factor in the development of AD.