Big Data, Big Knowledge: Big Data for Personalized Healthcare.

The idea that the purely phenomenological knowledge that we can extract by analyzing large amounts of data can be useful in healthcare seems to contradict the desire of VPH researchers to build detailed mechanistic models for individual patients. But in practice no model is ever entirely phenomenological or entirely mechanistic. We propose in this position paper that big data analytics can be successfully combined with VPH technologies to produce robust and effective in silico medicine solutions. In order to do this, big data technologies must be further developed to cope with some specific requirements that emerge from this application. Such requirements are: working with sensitive data; analytics of complex and heterogeneous data spaces, including nontextual information; distributed data management under security and performance constraints; specialized analytics to integrate bioinformatics and systems biology information with clinical observations at tissue, organ and organisms scales; and specialized analytics to define the "physiological envelope" during the daily life of each patient. These domain-specific requirements suggest a need for targeted funding, in which big data technologies for in silico medicine becomes the research priority

In silico assessment of biomedical products: the conundrum of rare but not so rare events in two case studies

In silico clinical trials, defined as “The use of individualized computer simulation in the development or regulatory evaluation of a medicinal product, medical device, or medical intervention,” have been proposed as a possible strategy to reduce the regulatory costs of innovation and the time to market for biomedical products. We review some of the the literature on this topic, focusing in particular on those applications where the current practice is recognized as inadequate, as for example, the detection of unexpected severe adverse events too rare to be detected in a clinical trial, but still likely enough to be of concern. We then describe with more details two case studies, two successful applications of in silico clinical trial approaches, one relative to the University of Virginia/Padova simulator that the Food and Drug Administration has accepted as possible replacement for animal testing in the preclinical assessment of artificial pancreas technologies, and the second, an investigation of the probability of cardiac lead fracture, where a Bayesian network was used to combine in vivo and in silico observations, suggesting a whole new strategy of in silico-augmented clinical trials, to be used to increase the numerosity where recruitment is impossible, or to explore patients’ phenotypes that are unlikely to appear in the trial cohort, but are still frequent enough to be of concern

A dual flow bioreactor with controlled mechanical stimulation for cartilage tissue engineering

In cartilage tissue engineering bioreactors can create a controlled environment to study chondrocyte behavior under mechanical stimulation or produce chondrogenic grafts of clinically relevant size. Here we present a novel bioreactor, which combines mechanical stimulation with a two compartment system through which nutrients can be supplied solely by diffusion from opposite sides of a tissue engineered construct. This design is based on the hypothesis that creating gradients of nutrients, growth factors and growth factor antagonists can aid in the generation of zonal tissue engineered cartilage. Computational modeling predicted that the design facilitates the creation of a biologically relevant glucose gradient. This was confirmed by quantitative glucose measurements in cartilage explants. In this system it is not only possible to create gradients of nutrients, but also of anabolic or catabolic factors. Therefore, the bioreactor design allows control over nutrient supply and mechanical stimulation useful for in vitro generation of cartilage constructs that can be used for the resurfacing of articulated joints or as a model for studying OA disease progression

A Systems Approach to Understanding Bone Cell Interactions in Health and Disease

Bone is an important organ performing three essential physiological functions: mechanical support, mineral homeostasis (such as calcium and phosphate) and support of haematopoiesis. In fact, bone diseases in the elderly are associated with high morbidity and increased mortality. Osteoporosis and related skeletal complications are amongst the most important diseases impacting both the quality of life of our aging population and contributing costs to our health care system

A multiscale framework based on the physiome markup languages for exploring the initiation of osteoarthritis at the bone-cartilage interface

The initiation of osteoarthritis (OA) has been linked to the onset and progression of pathologic mechanisms at the cartilage-bone interface. Most importantly, this degenerative disease involves cross-talk between the cartilage and subchondral bone environments, so an informative model should contain the complete complex. In order to evaluate this process, we have developed a multiscale model using the open-source ontologies developed for the Physiome Project with cartilage and bone descriptions at the cellular, micro, and macro levels. In this way, we can effectively model the influence of whole body loadings at the macro level and the influence of bone organization and architecture at the micro level, and have cell level processes that determine bone and cartilage remodeling. Cell information is then passed up the spatial scales to modify micro architecture and provide a macro spatial characterization of cartilage inflammation. We evaluate the framework by linking a common knee injury (anterior cruciate ligament deficiency) to proinflammatory mediators as a possible pathway to initiate OA. This framework provides a "virtual bone-cartilage" tool for evaluating hypotheses, treatment effects, and disease onset to inform and strengthen clinical studies