A brief introduction to the model microswimmer {\it Chlamydomonas reinhardtii}

The unicellular biflagellate green alga {\it Chlamydomonas reinhardtii} has been an important model system in biology for decades, and in recent years it has started to attract growing attention also within the biophysics community. Here we provide a concise review of some of the aspects of {\it Chlamydomonas} biology and biophysics most immediately relevant to physicists that might be interested in starting to work with this versatile microorganism.Comment: 16 pages, 7 figures. To be published as part of EPJ S

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

Biophysics: Concepts and Fields

There is an idiom that “biophysicist is who discusses about biology when meets physicist, talks about physics when meets biologist and says joke when meets another biophysicist”. This idiom points to multidisciplinary nature of biophysics but what really is the biophysics? And who is the biophysicist?  Biophysics was  defined as: "that branch of knowledge that applies the principles of physics and chemistry and the methods of mathematical analysis and computer modeling to understand how the mechanisms of biological systems work” in homepage of Biophysical Society[1]. Biophysics may be thought of as the central circle in a two-dimensional array of overlapping circles, which include physics, chemistry, physiology, and general biology.[2]Two wings of Biophysics are Biology and physics. Organisms are made of biomaterials, which can be studied by physical laws, since physical principles and laws hold from microscopic level to macroscopic level. Biophysicist selects a part of biological problems that are pliable to interpret by physical principles and then formulate hypotheses that can be tested by experiment2. Historically, bioluminescence can be considered among the earliest biophysical phenomena. The modern biophysics appeared by discovering of molecular structure of myoglobin and deoxyribonucleic acid (DNA). There is no doubt that Biophysics as a multidisciplinary science covers wide spectrum of subjects as follows: Instrumental biophysics, Radiation Biophysics and radiobiology, Structural biology, Physiological biophysics, bio-cybernetics, Membrane Biophysics, Molecular biophysics, Bioenergetics, Mathematical and theoretical biophysics and Biophysical Chemistry.And the final question, do you still believe the above idiom about the biophysicist?            [1] http://www.biophysics.org/[2] http://www.britannica.com

<cellular biophysics- a study of the structure and function of living cells< progress report, period ending jul. 1, 1964

Cellular biophysics - ionizing radiation effect on genetics, cell mutation, and mutagenic nature of tritium deca

Report of travel grants to the international biophysics meeting, paris, june 22-27, 1964

Travel grants for international biophysics meeting, and membership list of international organization for pure and applied biophysic

Predicting Secondary Structures, Contact Numbers, and Residue-wise Contact Orders of Native Protein Structure from Amino Acid Sequence by Critical Random Networks

Prediction of one-dimensional protein structures such as secondary structures and contact numbers is useful for the three-dimensional structure prediction and important for the understanding of sequence-structure relationship. Here we present a new machine-learning method, critical random networks (CRNs), for predicting one-dimensional structures, and apply it, with position-specific scoring matrices, to the prediction of secondary structures (SS), contact numbers (CN), and residue-wise contact orders (RWCO). The present method achieves, on average, $Q_3$ accuracy of 77.8% for SS, correlation coefficients of 0.726 and 0.601 for CN and RWCO, respectively. The accuracy of the SS prediction is comparable to other state-of-the-art methods, and that of the CN prediction is a significant improvement over previous methods. We give a detailed formulation of critical random networks-based prediction scheme, and examine the context-dependence of prediction accuracies. In order to study the nonlinear and multi-body effects, we compare the CRNs-based method with a purely linear method based on position-specific scoring matrices. Although not superior to the CRNs-based method, the surprisingly good accuracy achieved by the linear method highlights the difficulty in extracting structural features of higher order from amino acid sequence beyond that provided by the position-specific scoring matrices.Comment: 20 pages, 1 figure, 5 tables; minor revision; accepted for publication in BIOPHYSIC

Partial differential equations in medical biophysics

A number of examples of collaborative research are outlined which show how mathematicians and medical biophysicists have contributed to a wider understanding of some problems in applied physiology