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Physics in Biology and Medicine, Third Edition covers topics in physics as they apply to the life sciences, specifically medicine, physiology, nursing, and other. 5 Elasticity and'Strength of Materials. longitudinal Stretch and Compression. A Spring. Bone Fracture: Energy Considerations. 60 . PDF | On Sep 1, , Ivan A Dotsinsky and others published Handbook of Physics in Medicine and Biology.
The two worked on computer-assisted interpretation of laboratory test results for several years.
Systems Biology and the Future of Medicine
Hoping to make the pre-med physics course more interesting, Prof. Hobbie, between and , audited all the courses medical students take in their first two years at the University of Minnesota.
He was amazed by the amount of physics he found in these courses that had seldom been discussed in the general physics course.
A number of articles he wrote for the American Journal of Physics presaged the current content recommendations for pre-med and biological physics courses. Hobbie also found a great discrepancy between the physics in papers in the biological research literature and the level of understanding of most biology majors or pre-med students who have taken a year of physics.
It was clear that an intermediate level physics course would help these students.
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Hobbie developed such a course in , which led to the publication of Intermediate Physics for Medicine and Biology in Bradley J. All of the biological networks relevant to disease manifest these properties, as well, which gives us a starting point from which to begin to identify those subnetworks or modules that are responsible for a specific pathobiological process or a specific disease. Armed with this knowledge, one can intuitively surmise that mutations in hub genes or proteins are more likely to yield disease than those in less connected, peripheral genes or proteins in the network.
However, from a systems perspective, only genes or proteins that are peripherally located in the molecular network are likely to account for complex disease in adults owing to the fact that hubs are more likely to correspond to essential genes, loss of the function of which can lead to embryonic lethality 28 , 4.
While not typically hubs, disease genes and proteins do cluster in the same network neighborhood, as shown by Goh and colleagues who reported a fold increase in the number of physical interactions observed between gene products associated with the same disease than would be expected by chance In addition, genes linked to diseases with similar pathophenotypes have a higher likelihood of interacting with each other than those not linked to the pathophenotype 29 , Taken together, these observations support the notion that disease-related components of a network are likely to comprise a subnetwork or disease module.
A disease module is defined as a group of network components that contribute to a cellular or organismic phenotype the disruption of which leads to a particular pathophenotype.
Most precisely, a disease module represents a subnetwork in the overall molecular network that reflects a unique set of interactions, either proximate or remote, that contribute to an abnormal phenotype when one or more of its components is are dysfunctional. Importantly, a specific gene, protein, or metabolite can participate in several disease modules, indicating that disease modules themselves can overlap within the global network, and consistent with the clustering of disease determinants described above Disease modules can be identified either using bioinformatics or experimental methodologies Bioinformatics-based approaches comprise two broad categories that exploit knowledge of relevant molecular networks or of functional and structural similarities among elements of the global network.
In subsequent steps, the network is systematically reduced using genetic and biochemical data to identify the functional modules i.
In order to identify the most relevant pathways within each module, algorithms that incorporate flow- or diffusion- based pathway prioritization are used that assign to each path within the module a quantitative value reflecting its topological and functional proximity to the validated disease components. Prior work indicates that the highest ranked pathways not only show a statistically significant enrichment in disease-related components, but also are of potential relevance to the underlying causative mechanisms of the disease Owing to the current limited knowledge of the human interactome, the molecular network- based approach may fail to identify genes whose interactions with the genes involved in the disease modules have not yet been validated.
This functional and structural similarity search-based approach once again begins with the list of genes known to be involved in the pathophenotype of interest. An iterative process next ensues in which the new candidates are functionally characterized and validated until available evidence fails to support further expansion of the disease module. Accordingly, the functional and structural similarity search-based approach proceeds according to the following algorithm.
The initial seed components identified above are clustered into nascent modules using publically available bioinformatics resources as in the molecular network-based strategy, viz.
The nascent disease module is next expanded by deriving a set of candidate genes with which to augment the disease module using information in the bioinformatics resources, relying especially on GO, KEGG, and GRAIL patterns of tissue-specific co-expression, and protein-protein interaction datasets. These bioinformatics approaches or variants thereof have been used to identify disease modules for a wide range of disorders, including Alzheimer disease 46 , Parkinson disease 47 , type 2 diabetes mellitus 48 , asthma 49 , cardiovascular diseases 50 , and a variety of malignancies 51 , What often limits the successful mapping of a disease module, however, is the limited coverage of available cellular maps from bioinformatics resources.
Under these circumstances, brute-force experimentation designed to identify the universe of interaction partners is required and has also been used successfully for several diseases, including spinocerebellar ataxia 53 , Huntington disease 54 , and schizophrenia The Disease-ome and its Identification A natural extension of developing a systems-based approach to the identification of disease modules is the growing recognition that these modules are often interdependent see Figure 1.
Systematic mapping of overlapping disease modules and their pathophenotypes leads to the construction of the disease-ome, or a network of disease nodes linked to one another by their common molecular underpinnings. There are at least three different representations of the disease-ome that have gained traction of late, each of which reflects shared mechanisms or shared intermediate pathophenotypes between the incorporated diseases.
The first, the shared gene formalism, recognizes that diseases which share a gene or genes likely have a common genetic basis.
This concept has been developed by Goh and colleagues 28 , who utilized the Online Mendelian Inheritance in Man OMIM database to build a disease network the links within which reflect genes shared between diseases. View on ScienceDirect. Paul Davidovits. Paperback ISBN: Academic Press.
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Intermediate Physics for Medicine and Biology
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Institutional Subscription. Instructor Ancillary Support Materials. Free Shipping Free global shipping No minimum order. Provides practical techniques for applying knowledge of physics to the study of living systems Presents material in a straight forward manner requiring very little background in physics or biology Includes many figures, examples and illustrative problems and appendices which provide convenient access to the most important concepts of mechanics, electricity, and optics in the body.
English Copyright:Sumpter, J.
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Department of Energy. Nanotube-based Membrane Systems, Lane A. You are connected as. Taken together, these observations support the notion that disease-related components of a network are likely to comprise a subnetwork or disease module.
With this background and rationale, we propose a redefinition of all human disease using a combination of approaches to identify systems-based pathobiological mechanisms that render one susceptible to preclinical and overt pathophenotypes.
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