25,857 research outputs found
The dawn of mathematical biology
In this paper I describe the early development of the so-called mathematical
biophysics, as conceived by Nicolas Rashevsky back in the 1920's, as well as
his latter idealization of a "relational biology". I also underline that the
creation of the journal "The Bulletin of Mathematical Biophysics" was
instrumental in legitimating the efforts of Rashevsky and his students, and I
finally argue that his pioneering efforts, while still largely unacknowledged,
were vital for the development of important scientific contributions, most
notably the McCulloch-Pitts model of neural networks.Comment: 9 pages, without figure
Recommended from our members
Mathematical Biology
Mathematical biology is a fast growing field of research, which on one hand side faces challenges resulting from the enormous amount of data provided by experimentalists in the recent years, on the other hand new mathematical methods may have to be developed to meet the demand for explanation and prediction on how specific biological systems function
Recommended from our members
Mathematical Biology
This years meeting on Mathematical Biology focussed on the mathematical modeling and analysis of some specific bio-medical questions, where quite detailed experimental findings are available. A main aim for this decision was to further deepen the exchange between the fields, on the long run in a similar manner as known e.g. from mathematics and physics. Talks by mathematicians and talks by experimentalists on related scientific questions were put back to back, wherever possible
For principled model fitting in mathematical biology
The mathematical models used to capture features of complex, biological
systems are typically non-linear, meaning that there are no generally valid
simple relationships between their outputs and the data that might be used to
validate them. This invalidates the assumptions behind standard statistical
methods such as linear regression, and often the methods used to parameterise
biological models from data are ad hoc. In this perspective, I will argue for
an approach to model fitting in mathematical biology that incorporates modern
statistical methodology without losing the insights gained through non-linear
dynamic models, and will call such an approach principled model fitting.
Principled model fitting therefore involves defining likelihoods of observing
real data on the basis of models that capture key biological mechanisms.Comment: 7 pages, 3 figures. To appear in Journal of Mathematical Biology. The
final publication is available at Springer via
http://dx.doi.org/10.1007/s00285-014-0787-
Bulletin of Mathematical Biology - facts, figures and comparisons
The Society for Mathematical Biology (SMB) owns the Bulletin of Mathematical Biology (BMB). This is an international journal devoted to the interface of mathematics and biology. At the 2003 SMB annual meeting in Dundee the Society asked the editor of the BMB to produce an analysis of impact factor, subject matter of papers, submission rates etc. Other members of the society were interested in the handling times of articles and wanted comparisons with other (appropriate) journals. In this article we present a brief history of the journal and report on how the journal impact factor has grown substantially in the last few years. We also present an analysis of subject areas of published papers over the past two years. We finally present data on times from receipt of paper to acceptance, acceptance to print (and to online publication) and compare these data with some other journals
Artificial in its own right
Artificial Cells, , Artificial Ecologies, Artificial Intelligence, Bio-Inspired Hardware Systems, Computational Autopoiesis, Computational Biology, Computational Embryology, Computational Evolution, Morphogenesis, Cyborgization, Digital Evolution, Evolvable Hardware, Cyborgs, Mathematical Biology, Nanotechnology, Posthuman, Transhuman
Paying Our Dues: The Role of Professional Societies in the Evolution of Mathematical Biology Education.
Mathematical biology education provides key foundational underpinnings for the scholarly work of mathematical biology. Professional societies support such education efforts via funding, public speaking opportunities, Web presence, publishing, workshops, prizes, opportunities to discuss curriculum design, and support of mentorship and other means of sustained communication among communities of scholars. Such programs have been critical to the broad expansion of the range and visibility of research and educational activities in mathematical biology. We review these efforts, past and present, across multiple societies-the Society for Mathematical Biology (SMB), the Symposium on Biomathematics and Ecology Education and Research (BEER), the Mathematical Association of America (MAA), and the Society for Industrial and Applied Mathematics (SIAM). We then proceed to suggest ways that professional societies can serve as advocates and community builders for mathematical biologists at all levels, noting that education continues throughout a career and also emphasizing the value of educating new generations of students. Our suggestions include collecting and disseminating data related to biomath education; developing and maintaining mentoring systems and research communities; and providing incentives and visibility for educational efforts within mathematical biology
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