Physical Models of Biophysical Systems
1st Edition
Book
Hardback
500 pages
978-3-527-41129-0 (ISBN)
Description
Advances in biology and medicine are increasingly dependent upon statistical physics, dynamics, network and systems sciences, and computational and mathematical modeling. At the interface between the physical and life sciences, you have genomic sequencing, x-ray and magnetic resonance imaging, electron microscopes, CAT scan technology, computer-based drug discovery methods, etc. And at the interface of physics, biology, and mathematics, researchers attempt to explain the structures and properties of biochemical networks in cells, to model the dynamical processes of cancer cells, etc. Conversely, understanding the physical foundations of the field will be of ever greater importance to scientists. The interdisciplinary field of Biological Physics is enjoying a sharp upswing. Physical-science students cannot afford to be ignorant of the big events at the interface of life- and physical science. They need to learn topics that they can see as relevant to their future employment. Reports in the past 15 years from the US National Research Council have emphasized the importance of building a strong foundation in mathematics and the physical and information sciences to prepare biophysics, biomedical and biochemistry students for research that is increasingly interdisciplinary in character. The teachers now approach the teaching of biophysics very differently and have to target students from many backgrounds. They themselves come from varied backgrounds such as physics, engineering, chemistry, biology, material sciences, statistics, mathematics, medicine, neuroscience, etc. Many Universities are creating multidisciplinary centers to work at the interface between physical and life sciences. These institutes are hiring new faculty, who in turn want to teach their subject. They need suitable texts. A decade ago, the options were limited. There were mainstream texts that were old or out of print. There were brilliant newer books, but they were short and/or specialized, and there were various books that Physics professors would not have felt comfortable teaching. The author has created such a course at the University of Pennsylvania and taught it four times. It has proved very popular with students majoring in Physics, Biology, Biophysics, Biochemistry, Materials Science, Chemical and Bio-Engineering. The author proposes to create a complete book based on this course in about two years. The core foundational material for this subject can be taught successfully at the undergraduate, graduate and upper level.
This self-contained accessible textbook will help students better understand the physical principles and models of biological systems, improve their lab practice, and prepare them to invent new techniques (or adapt old ones). It avoids lengthy abstract discussions and goes through specific problems, with explanation of why these particular steps are taken to explain key experimental results. The requirements are a minimum of one year of university physics, and corresponding math. No background in computer programming is assumed. The book will include a series of ?Your Turn" questions, ?Signpost" sections at the start and end of each chapter discussing the plotline, boxed special topics written by expert consultants, a global symbol list, a compendium of useful numerical values, and literature citations for the Track II material (sections and problems for the more mathematically-mature audience); as well as an online collection of video micrographs and animations, a collection of real experimental datasets to accompany homework problems, and instructor guide.
This self-contained accessible textbook will help students better understand the physical principles and models of biological systems, improve their lab practice, and prepare them to invent new techniques (or adapt old ones). It avoids lengthy abstract discussions and goes through specific problems, with explanation of why these particular steps are taken to explain key experimental results. The requirements are a minimum of one year of university physics, and corresponding math. No background in computer programming is assumed. The book will include a series of ?Your Turn" questions, ?Signpost" sections at the start and end of each chapter discussing the plotline, boxed special topics written by expert consultants, a global symbol list, a compendium of useful numerical values, and literature citations for the Track II material (sections and problems for the more mathematically-mature audience); as well as an online collection of video micrographs and animations, a collection of real experimental datasets to accompany homework problems, and instructor guide.
More details
Person
Prof. Philip Nelson is the Director of the University of Pennsylvania's Biophysics Major Program. He has taught physics courses at Boston University and University of Pennsylvania for 24 years. In 2009 he was awarded the Emily Gray Prize of the Biophysical Society, \for far reaching and signicant contributions to the teaching of biophysics, developing innovative educational materials, and fostering an environment exceptionally conducive to education in Biological Physics. In 2001, he was awarded the Penn's Ira Abrams Award, the School of Arts and Sciences' highest distinguished teaching honor, mostly for his work creating the Biological Physics course, and the reception it got from the students. He holds a PhD in Physics from Harvard University (1983). He is the author of the textbook ?Biological Physics: Energy, Information, Life ?(2003), and author of some 80 refereed publications, including in Science, PNAS, Biophysical Journal, Nature Nanotechnology, and Physical Review Letters.
Content
Prolog: A breakthrough on HIV
I Tools and concepts
Chapter 1 Physics and Biology
Key themes of the entire book: Living organisms at every level of organization obtain information by using physical means, and must use it appropriately.
1.1 First signpost
1.2 The intersection
Chapter 2 Meet your assistant
Biological question: Why did the first antiretroviral drugs succeed briefly, then fail?
Physical idea: A physical model, combined with an appropriate clinical trial, established a surprising feature of HIV infection
Payoffs: Familiarize student with computer graphing and a simple kinetic model, in the gripping historical context of a real breakthrough.
2.1 Signpost
2.2 What computers can and cannot do for you
2.3 Model the course of HIV infection
2.4 Perils of blind fitting
Chapter 3 Dimensional analysis
Biological question: How can I make quantitative estimates without solving any equations?
Physical idea: Dimensional analysis sometimes constrains the form of our answers so much that this becomes possible.
Payoffs: A tool for catching errors, for organizing and classifying quantities, and for making quantitative estimates without working too hard.
3.1 Signpost
3.2 Basics
3.3 Dimensionless quantities
3.4 About graphs
3.5 About angles
3.6 On the sizes of atoms
Chapter 4 Discrete randomness
Biological question: If a medical test is \95% accurate," and it comes out positive, are you necessarily sick?
Physical idea: Conditional probability as a framework for understanding randomness.
Establish some iconic examples, which will arise many times in later chapters. An introductory text should take a practical approach to probability, with lots of real-world examples, rather than the axiomatic approach. Case studies: Interpreting medical tests; crib death and Prosecutor's Fallacy; cancer clusters.
4.1 Signpost
4.2 Avatars of randomness
4.3 Probability distribution of a random system
4.4 Conditional probability
4.5 Expectations and moments
4.6 Correlation and covariance
Chapter 5 Some useful discrete distributions
Biological question: How do bacteria become resistant to a drug or virus that they've never encountered?
Physical idea: The Luria-Delbrück experiment tested a model by checking a statistical prediction.
Binomial and Poisson recur endlessly in biological physics applications. Short case studies: Infer number of fluorescent molecules in a cell; Katz/Miledi determination of single ion channel conductance. Detailed case study: Luria-Delbrück experiment, which has applications to drug-resistance in bacteria, a very topical concern in medicine. Concept: sometimes a model's prediction is probabilistic in character.
Concept: long-tail distributions.
5.1 Signpost
5.2 Binomial distribution
5.3 Poisson distribution
5.4 Jackpot distributions in bacterial genetics
Chapter 6 Continuous distributions
Biological question: How can I say objectively which model is more successful?
Physical idea: Maximum likelihood analysis as the basis for model selection and parameter determination.
Gaussian and power-law distributions also recur frequently. Case studies: FIONA and other superresolution imaging via probability of photon arrivals. Concept: Parameter estimation via maximum likelihood; leastsquares fitting is a special case. Concept: The assumptions behind least-squares are often not satisfied, but sometimes we can easily find a correct alternative.
6.1 Signpost
6.2 Probability density function
6.3 The Gaussian distribution
6.4 More on power-law distributions
6.5 Model selection and parameter estimation by maximum likelihood
6.6 Example: Parameter estimation for Bernoulli trials
6.7 Applications
Chapter 7 Poisson processes
Biological question: How do you detect an invisible step in a molecular motor cycle?
Physical idea: The waiting-time distribution tells us about the mechanism.
This class of random processes arises everywhere in biological physics, from the absorption of ph
I Tools and concepts
Chapter 1 Physics and Biology
Key themes of the entire book: Living organisms at every level of organization obtain information by using physical means, and must use it appropriately.
1.1 First signpost
1.2 The intersection
Chapter 2 Meet your assistant
Biological question: Why did the first antiretroviral drugs succeed briefly, then fail?
Physical idea: A physical model, combined with an appropriate clinical trial, established a surprising feature of HIV infection
Payoffs: Familiarize student with computer graphing and a simple kinetic model, in the gripping historical context of a real breakthrough.
2.1 Signpost
2.2 What computers can and cannot do for you
2.3 Model the course of HIV infection
2.4 Perils of blind fitting
Chapter 3 Dimensional analysis
Biological question: How can I make quantitative estimates without solving any equations?
Physical idea: Dimensional analysis sometimes constrains the form of our answers so much that this becomes possible.
Payoffs: A tool for catching errors, for organizing and classifying quantities, and for making quantitative estimates without working too hard.
3.1 Signpost
3.2 Basics
3.3 Dimensionless quantities
3.4 About graphs
3.5 About angles
3.6 On the sizes of atoms
Chapter 4 Discrete randomness
Biological question: If a medical test is \95% accurate," and it comes out positive, are you necessarily sick?
Physical idea: Conditional probability as a framework for understanding randomness.
Establish some iconic examples, which will arise many times in later chapters. An introductory text should take a practical approach to probability, with lots of real-world examples, rather than the axiomatic approach. Case studies: Interpreting medical tests; crib death and Prosecutor's Fallacy; cancer clusters.
4.1 Signpost
4.2 Avatars of randomness
4.3 Probability distribution of a random system
4.4 Conditional probability
4.5 Expectations and moments
4.6 Correlation and covariance
Chapter 5 Some useful discrete distributions
Biological question: How do bacteria become resistant to a drug or virus that they've never encountered?
Physical idea: The Luria-Delbrück experiment tested a model by checking a statistical prediction.
Binomial and Poisson recur endlessly in biological physics applications. Short case studies: Infer number of fluorescent molecules in a cell; Katz/Miledi determination of single ion channel conductance. Detailed case study: Luria-Delbrück experiment, which has applications to drug-resistance in bacteria, a very topical concern in medicine. Concept: sometimes a model's prediction is probabilistic in character.
Concept: long-tail distributions.
5.1 Signpost
5.2 Binomial distribution
5.3 Poisson distribution
5.4 Jackpot distributions in bacterial genetics
Chapter 6 Continuous distributions
Biological question: How can I say objectively which model is more successful?
Physical idea: Maximum likelihood analysis as the basis for model selection and parameter determination.
Gaussian and power-law distributions also recur frequently. Case studies: FIONA and other superresolution imaging via probability of photon arrivals. Concept: Parameter estimation via maximum likelihood; leastsquares fitting is a special case. Concept: The assumptions behind least-squares are often not satisfied, but sometimes we can easily find a correct alternative.
6.1 Signpost
6.2 Probability density function
6.3 The Gaussian distribution
6.4 More on power-law distributions
6.5 Model selection and parameter estimation by maximum likelihood
6.6 Example: Parameter estimation for Bernoulli trials
6.7 Applications
Chapter 7 Poisson processes
Biological question: How do you detect an invisible step in a molecular motor cycle?
Physical idea: The waiting-time distribution tells us about the mechanism.
This class of random processes arises everywhere in biological physics, from the absorption of ph