Biophysicist (Rockville, Md.)最新文献

{"title":"The Joy of Markov Models—Channel Gating and Transport Cycling Made Easy","authors":"G. Zifarelli, P. Zuccolini, S. Bertelli, M. Pusch","doi":"10.35459/TBP.2019.000125","DOIUrl":"https://doi.org/10.35459/TBP.2019.000125","url":null,"abstract":"\u0000 The behavior of ion channels and transporters is often modeled using discrete state continuous-time Markov models. Such models are helpful for the interpretation of experimental data and can guide the design of experiments by testing specific predictions. Here, we describe a computational tool that allows us to create Markov models of chosen complexity and to calculate the predictions on a macroscopic scale, as well on a single-molecule scale. The program calculates steady-state properties (current, state probabilities, and cycle frequencies), deterministic macroscopic and stochastic time courses, gating currents, dwell-time histograms, and power spectra of channels and transporters. In addition, a visual simulation mode allows us to follow the time-dependent stochastic behavior of a single channel or transporter. After a basic introduction into the concept of Markov models, real-life examples are discussed, including a model of a simple K+ channel, a voltage-gated sodium channel, a 3-state ligand-gated channel, and an electrogenic uniporter. In this manner, the article has a modular architecture, progressing from basic to more advanced topics. This illustrates how the MarkovEditor program can serve students to explore Markov models at a basic level but is also suited for research scientists to test and develop models on the mechanisms of protein function.","PeriodicalId":72403,"journal":{"name":"Biophysicist (Rockville, Md.)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47482677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Flexible Laboratory Exercise Introducing Practical Aspects of Mean Squared Displacement","authors":"Alexander B. C. Mantilla, N. Kuwada","doi":"10.35459/TBP.2020.000157","DOIUrl":"https://doi.org/10.35459/TBP.2020.000157","url":null,"abstract":"\u0000 Mean squared displacement is a standard biophysical tool for characterizing the motion of particles in a thermally dominated environment, yet it is rarely formally introduced or discussed in undergraduate curriculum. Here, we provide a flexible and adaptable experimental or computational lab activity that provides a practical introduction to mean squared displacement and anomalous diffusion that includes optional experimental protocols and computational simulation techniques for data collection and discusses a variety of analysis techniques. This lab activity has been implemented both face-to-face and completely online and provides crucial experience in important research techniques, helping to bridge traditional undergraduate curriculum and modern biophysics research.","PeriodicalId":72403,"journal":{"name":"Biophysicist (Rockville, Md.)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47567057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantitative Fundamentals of Molecular and Cellular Engineering by K. Dane Wittrup, Bruce Tidor, Benjamin J. Hackel, and Casim A. Sarkar","authors":"D. Hammer","doi":"10.35459/TBP.2020.00161","DOIUrl":"https://doi.org/10.35459/TBP.2020.00161","url":null,"abstract":"For those of us who have been teaching molecular and cellular engineering, an important and significant new tool is now available. Wittrup et al. have written a very nice textbook that spans many of the important areas of this discipline and provides a substantial number of problems that should prove a significant aid to instructors. Cellular engineering, broadly defined, is the quantification and manipulation of cell behavior. The idea that one can design a system to behave as intended is endemic to engineering, and now that we have more knowledge about the parts of a cell and how they work, as well as sophisticated tools for genetic manipulation (such as mutation, clustered regularly interspaced short palindromic repeats [CRISPR] editing, transfections, and knock downs), we are at the point that we can manipulate cells to do what we wish. The goals are simple: inhibit cell function when it has gone awry, but more so, manipulate and enhance cell function when desired. A current successful example is chimeric antigen receptor T-lymphocyte therapy, but many other examples will be forthcoming, and we need to prepare quantitative scientists for the challenges of predicting, designing, and quantifying cell behavior.","PeriodicalId":72403,"journal":{"name":"Biophysicist (Rockville, Md.)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44280352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Curiosity-Based Biophysics Projects in a High School Setting with Graduate Student Mentorship","authors":"Cooper J. Galvin, Katherine N. Liu, A. Kennard, Sahil K. Tembulkar, Alexander Dunlap, Tao A. G. Large, Thao Pham, Derek J. Le, Aurora Alvarez-Buylla, Helen Nguyen, Ezequiel Ponce, Sophia Tran, Nikki Nguyen, Christina Ngo, Christina Tran, Gabriela Huynh, Patrick Allamandola, Z. Bryant","doi":"10.35459/TBP.2019.000136","DOIUrl":"https://doi.org/10.35459/TBP.2019.000136","url":null,"abstract":"Program in Biophysics, Stanford University, Stanford, CA 94305, USA Department of Bioengineering, Stanford University, Stanford, CA 94305, USA Department of Chemistry, Stanford University, Stanford, CA 94305, USA Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA Department of Mathematics, Stanford University, Stanford, CA 94305, USA Andrew P. Hill High School, San Jose, CA 95111, USA Program in Cancer Biology, Stanford University, Stanford, CA 94305, USA Department of Biology, Stanford University, Stanford, CA 94305, USA James Lick High School, San Jose, CA 95127, USA","PeriodicalId":72403,"journal":{"name":"Biophysicist (Rockville, Md.)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69814337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Practical Guide to Fluorescence Temporal and Spatial Correlation Spectroscopy","authors":"E. Pandzic, R. Whan","doi":"10.35459/TBP.2019.000143","DOIUrl":"https://doi.org/10.35459/TBP.2019.000143","url":null,"abstract":"\u0000 The aim of this article is to introduce the basic principles behind the widely used microscopy tool: fluorescence fluctuation correlation spectroscopy (FFCS). We present the fundamentals behind single spot acquisition (FCS) and its extension to spatiotemporal sampling, which is implemented through image correlation spectroscopy (ICS). The article is an educational guide that introduces theoretic concepts of FCS and some of the ICS techniques, followed by interactive exercises in MATLAB. There, the learner can simulate data time series and the application of various FFCS techniques, as well as learn how to measure diffusion coefficients, molecular flow, and concentration of particles. Additionally, each section is followed by a short exercise to reinforce learning concepts by simulating different scenarios, seek verification of outcomes, and make comparisons. Furthermore, we invite the learner throughout the article to consult the literature for different extensions of FFCS techniques that allow measurements of different physicochemical properties of materials. Upon completion of the modules, we anticipate the learner will gain a good understanding in the field of FFCS that will encourage further exploration and adoption of the FFCS tools in future research and educational practices.","PeriodicalId":72403,"journal":{"name":"Biophysicist (Rockville, Md.)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45156215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using Plant Cells of Nitellopsis obtusa for Biophysical Education","authors":"Vilmantas Pupkis, Rokas Buisas, Indre Lapeikaite, Vilma Kisnieriene","doi":"10.35459/TBP.2019.000130","DOIUrl":"https://doi.org/10.35459/TBP.2019.000130","url":null,"abstract":"\u0000 Using giant characeaen algae Nitellopsis obtusa in laboratory exercises is proposed to familiarize students with basic concepts of electrophysiology and provide some simple hands-on practice. The described concept experiments present extracellular registration of action potentials (APs) and investigation of cytoplasmic streaming properties. Students are expected to register the propagation velocity of APs (found to be 3.4 ± 1.5 cm/s in N. obtusa), as well as the velocity of cytoplasmic streaming (66.7 ± 9 μm/s). Proposed exercises also concern recovery dynamics of cytoplasmic streaming after a stimulation (recovery time constant τ = 3.7 ± 2.1 min) as well as investigation of an effect of various chemicals (e.g., KCl) on all selected parameters. The experiments endorse characeaen algae as a model system to be routinely explored in education of biophysics and bioelectrical phenomena of the cell.","PeriodicalId":72403,"journal":{"name":"Biophysicist (Rockville, Md.)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46907075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Project Symphony: A Biophysics Research Experience at a Primarily Undergraduate Institution","authors":"M. Muzzio, Sue Ellen Evangelista, Jacqueline Denver, Maria Lopez, Sunghee Lee","doi":"10.35459/TBP.2019.000135","DOIUrl":"https://doi.org/10.35459/TBP.2019.000135","url":null,"abstract":"Increased attention has been conferred upon interdisciplinary science, technology, engineering, and math (STEM) education to prepare students for deeper understanding to address complex challenges (1–3). Particularly at the undergraduate level, there is recognized value in providing opportunities for students to integrate knowledge across disciplinary boundaries (4–7). In addition to core technical knowledge, it is beneficial to confer behavioral skills that allow students to perform well with others through effective communication, time management, and teamwork (8). Undergraduate research experiences have been considered to be a powerful learning tool, engaging students and stimulating their enthusiasm, thereby improving academic performance and persistence in science and preparing students for advanced degrees and careers in STEM fields (9–17). This report, the culmination of more than a decade’s work with undergraduate students, presents practices demonstrating that early exposure to the interdisciplinary field of biophysics can be effectively introduced at a primarily undergraduate institution (PUI) level through a well-structured research plan involving undergraduates with different STEM majors. The management of this group, called ‘‘Project Symphony’’ (18), overcame the challenges of sustaining research activities at a PUI via the incorporation of 2 essential elements of success: (a) establishment of a cooperative learning variant whereby students work together to maximize individual learning and each other’s learning; and (b) promotion of an integrated understanding via interdisciplinary biophysics projects.","PeriodicalId":72403,"journal":{"name":"Biophysicist (Rockville, Md.)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44333276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning in a Molecular Modeling Course for Chemistry, Biochemistry, and Biophysics Students.","authors":"Jacob M Remington,&nbsp;Jonathon B Ferrell,&nbsp;Marlo Zorman,&nbsp;Adam Petrucci,&nbsp;Severin T Schneebeli,&nbsp;Jianing Li","doi":"10.35459/tbp.2019.000140","DOIUrl":"https://doi.org/10.35459/tbp.2019.000140","url":null,"abstract":"<p><p>Recent advances in computer hardware and software, particularly the availability of machine learning libraries, allow the introduction of data-based topics such as machine learning into the Biophysical curriculum for undergraduate and/or graduate levels. However, there are many practical challenges of teaching machine learning to advanced-level students in the biophysics majors, who often do not have a rich computational background. Aiming to overcome such challenges, we present an educational study, including the design of course topics, pedagogical tools, and assessments of student learning, to develop the new methodology to incorporate the basis of machine learning in an existing Biophysical elective course, and engage students in exercises to solve problems in an interdisciplinary field. In general, we observed that students had ample curiosity to learn and apply machine learning algorithms to predict molecular properties. Notably, feedback from the students suggests that care must be taken to ensure student preparations for understanding the data-driven concepts and fundamental coding aspects required for using machine learning algorithms. This work establishes a framework for future teaching approaches that unite machine learning and any existing course in the biophysical curriculum, while also pinpointing the critical challenges that educators and students will likely face.</p>","PeriodicalId":72403,"journal":{"name":"Biophysicist (Rockville, Md.)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8323870/pdf/nihms-1641230.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39274383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reflections on undergraduate research mentoring.","authors":"Nicholas B Whitticar,&nbsp;Craig S Nunemaker","doi":"10.35459/tbp.2019.000112","DOIUrl":"https://doi.org/10.35459/tbp.2019.000112","url":null,"abstract":"<p><p>Recruiting talented high school and college students to consider a career in the biomedical or biophysical sciences is important, yet often difficult. Encouraging students in regions like Appalachia adds additional challenges due to socioeconomic hurdles and misperceptions. This brief report contains the reflections of a research mentor engaging with students as a high school physics teacher, a principal investigator at research-intensive university, and as a principal investigator at a predominantly undergraduate-focused research university, as well as the viewpoint of a former undergraduate student in the mentor's lab. Different hurdles stand in the way of success at each level. A key issue at the high school level is engaging students in 'real science', the discovery of new knowledge and ideas. With undergraduate students at a larger research institution, a key issue is for the student to have opportunities to engage in meaningful scientific research. At a smaller and more rural research institution, especially in Appalachia, many students have socioeconomic concerns and misconceptions of what scientific careers entail. Regardless of background and environment, there are certain students who thrive on the scientific curiosity to discover new things. All they need is that opportunity to engage in meaningful scientific discovery to become interested in a scientific career.</p>","PeriodicalId":72403,"journal":{"name":"Biophysicist (Rockville, Md.)","volume":"1 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9137253/pdf/nihms-1774172.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10256440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stochastic Modelling of Reaction–Diffusion Processes by Radek Erban and S. Jonathan Chapman","authors":"P. Nelson","doi":"10.35459/tbp.2020.000155","DOIUrl":"https://doi.org/10.35459/tbp.2020.000155","url":null,"abstract":"Stochastic simulation has become an indispensable tool in the scientific toolkit. We were all told as students that molecules jostle one another incessantly because of thermal motion and that chemical reactions rely on the resulting chance encounters between molecules. However, most of us took chemistry and physics classes in which attention quickly shifted to vast collections of molecules, for which the inherent randomness washed out when viewing overall concentrations; we then formulated and solved deterministic rate equations. However, some key actors in cells appear in only a small number of copies (perhaps just one, for some genes). Moreover, experimental technique now allows routine study of single cells and even single molecules, so corresponding analytical tools that go beyond ensemble averaging are needed, just to extract the lessons that are latent in our datasets. Most of us also took classes in which chemical reactions were studied in imagined ‘‘well-stirred’’ conditions, and diffusion was studied separately in contexts where chemical reactions were not important. However, a vesicle of neurotransmitter must travel across a synapse while being degraded; a morphogen must bind and activate receptors while establishing a spatial gradient; and so on. This book’s title expresses the authors’ aim to establish a framework capable of handling biophysically relevant situations like these. Student interest in this topic is strong. My own students are at least implicitly aware that results from even the simplest stochastic simulation seem more ‘‘lifelike’’ than deterministic results, and they are always excited to see the gradual emergence of deterministic behavior as copy numbers get large. I realized some time ago that stochastic simulation belongs in any biophysics curriculum, starting from the very first introductory course and reappearing as appropriate at later stages. However, it was not so easy to find appropriate course materials. Erban and Chapman now give us a concise, elegant, and practical survey of numerical methods that are useful for such analyses, although at a level somewhat higher than first-year courses. An advanced undergraduate who is comfortable with ordinary differential equations and conditional probability and the associated mathemat-","PeriodicalId":72403,"journal":{"name":"Biophysicist (Rockville, Md.)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42279476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}