Fall 2006 BIO 503Topics in Interdisciplinary Biology and Biological Sciences (TIBBS) Listed as: Fall 2006 BIO 503 Wed 3-5:30, Rm 258 (Also taught with: CS/591C, Stat/579.04, Math/579.02, Anth 560) In this course we will present and discuss recent work in biological science that bridges scientific disciplines, integrates different approaches, and demonstrates the effectiveness of collaborative research. Instructors teaching the multiple units in this course will be from Biology: Blair Wolf (UNM), Jim Brown (UNM), Geoffrey B. West (Santa Fe Institute); Math and Statistics: Edward Bedrick (UNM); and from Computer Sciences: Terran Lane (UNM). BiologyBlair Wolf, Unit 1 The unit will focus on the dynamics and movements of energy and materials in the biosphere at multiple scales and how they are traced, recorded and modeled. We will introduce the use of elemental tracers (with a focus on natural abundance stable isotopes of carbon and nitrogen) to indicate, integrate, record, traces and model biological processes at scales that vary from cellular to global. We will also introduce the application of models to examining these same processes. The format will combine readings from the literature with discussions and lectures.
Week 2 O'Brien et al. (2000) Week 3 Norris et al. (2004) Week 4 Cerling et al. (1997)
Ehlering & Monson (1993)
Week 2 (
Jim Brown and Geoffrey B. West, Unit 3 This unit will provide an interdisciplinary exploration of scaling. We will start with a first segment designed to provide general background on scaling from the perspective of physics and mathematics: What is scaling? Why is it important? What is the history and current state of the subject? How are scaling relations described mathematically and interpreted scientifically? The second part will focus in depth on West, Brown, and Enquist’s theory for the quarter-power allometric scaling of metabolic rate and other biological attributes. We will examine the fractal model for scaling of resource distributing vascular networks, the more general model that incorporates both body size and temperature to explain the scaling of metabolic rate, and the extension of this framework to address biological scaling at levels from molecules and cells to ecology and evolution. The last part will be more speculative and free-form. It will address implications of scaling for human social, economic, and technological systems. Possible examples include scaling of and possible parallels between brains, computers, cities, businesses, economies, and the Internet. Week 1 Schmidt-Nielson (1984) Week 3 Scaling Homework Gillooly, Brown, West, Savage & Charnov(2001)
Math and StatsEd Bedrick, Unit 4 The effect of spontaneous abortion on the dairy industry is substantial, costing the industry on the order of $200 million per year in California alone. We analyze data from a cohort study of nine dairy herds in Central California. A key feature of the analysis is the observation that only a relatively small proportion of cows will abort (around 10-15%), so that it is inappropriate to analyze the time-to-abortion (TTA) data as if it were standard censored survival data, with cows that fail to abort by the end of the study treated as censored observations. We thus broaden the scope to consider the analysis of fetal lifetime distribution (FLD) data for the cows, with the dual goals of characterizing the effects of various risk factors on (i) the likelihood of abortion and, conditional on abortion status, on (ii) the risk of early versus late abortion. A single model is developed to accomplish both goals with two sets of specific herd effects modeled as random effects. Because a multimodal fetal hazard function is expected for the TTA data, both a parametric mixture model and a nonparametric model are developed. Furthermore, the two sets of analyses are linked because of anticipated dependence between the random herd effects. All modeling and inferences are accomplished using modern Bayesian methods. This module will include a discussion of the basic statistical methods used in the research, and the biological motivation that led to the ultimate statistical model. Please follow this link to Dr. Bedrick's course material for this Unit Computer ScienceTerran Lane, Unit 2 Life, Mind, and the Pursuit of Good Books: Network Modeling and Inference Across Disciplines…. will examine network models of large, complex, multi-element systems. For systems, we’ll examine some subset of genetic regulatory networks: the internet; and social graphs. The important commonality among these is the underlying, mathematical and computational formalisms that we’ll use to abstract and represent their structures and behaviors. We will survey some of the different mathematical formalizations that have been applied to represent characterize; simulate; and predict such systems, Boolean models, or others, depending on the whims of the class and instructor.
Week 1 De Jong (2002) Week 2 Charniak (1991) Week 3 Friston et al. (2003)
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PIBBS - MSC 03 2020- 1 University of New Mexico - Albuquerque NM 87131 - USA - (505) 277-9337 |
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