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Topics in Interdisciplinary Biology and Biological Sciences (TiBBs)

Fall 2009 Syllabus Biology 503

Day, Time, Location- Wed. , 3-5:30pm, Castetter  Room 107


This course presents and discusses recent work in biological science that bridges scientific disciplines, integrates different approaches, and demonstrates the effectiveness of collaborative research. The three units in this course will be from Biology, Math and Theoretical Biology and Computer Sciences and Electrical and Computer Engineering.




Lead By



Course Overview


2, 3, 4

9/2/09, 9/9/09, 9/16/09

Human Macroecology

Professors Brown & Hamilton



Section Presentations


6, 7 ,9


9/28/09, 10/5/09, 10/19/09

Immune System, Ant Colonies, and Computational Biology

Professors Forrest & Moses

Week 8


Fall Break

No Class



Section Presentations




Modeling the Immune System and Viral Infections

Guest Lecturers LANL- Alan Perelson and SFI Cohorts

Week 13


Thanksgiving Holiday

No Class



Section Presentations




Course Wrap-Up


UNIT 1:Human Macroecology

Biology and Anthropology

James H. BrownMarcus J. Hamilton
James H. Brown
Marcus J. Hamilton


Human macroecology is the broad-scale study of statistical variation in the size, structure and diversity of human systems, and their dynamics over time and space. Human macroecology is founded on the principles of macroecology, which explores how the first principles of physics, chemistry, and biology influence patterns in the size, distribution, abundance, and diversity of species around the planet at various scales. Central to human macroecology is the recognition that the human species is, in many respects, simply another species, reliant on flows and stores of energy, information, and materials in order to meet the dual energy demands of biological metabolism (i.e., individual life history), and social metabolism (i.e., maintenance and growth of complex social networks and infrastructure). These flows and stores are constrained by fundamental physical laws, notably the first and second laws of thermodynamics. As a result, global patterns of human biocultural diversity show remarkable similarities to global distributions of biodiversity. Similarly, the size and structure of human systems display similar statistical properties to many other types of complex systems found in Nature.

In this section we will introduce the class to the conceptual basis of human macroecology, including the quantitative theory and statistical techniques used to study complex human systems and their interactions with ecosystems, from traditional hunter-gatherer societies to 21st century industrialized nation states. In particular, we will focus on the metabolic theory of ecology, and use both allometric and Horton-Strahler scaling theories to examine the size and structure of human systems. In consultation with the instructors, students will conduct projects using scaling theory and empirical data to examine any aspect of human macroecology they find to be of particular interest.



Suggested Topics for Presentations:



Essential Background Material

Brown, J. H. & Maurer, B. A. (1989) Macroecology: the division of food and space among species on continents Science 243, 1145-1150.

Smith, F. A., Lyons, S. K., Ernest, S. K. M. & Brown, J. H. (2008) Macroecology: more than the division of food and space among species on continents Progress in Physical Geography 32, 115-138.

Brown, J. H., et al. (2002) The fractal nature of Nature: power laws, ecological complexity and biodiversity Philosophical Transactions: Biological Sciences 357, 619-626.

Burnside, B., L. Bettencourt, O. Burger, M.J. Hamilton, M.E. Moses, and J.H. Brown. (In review) Human macroecology: Linking pattern to process in big-picture human ecology. Trends in Ecology and Evolution.

Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M. & West, G. B. (2004) Toward a metabolic theory of ecology Ecology 85, 1771-1789.

Week 2

Rodroguez-Iturbe, I. & Rinaldo, A. (1997). Fractal River Basins: Chance and Self-Organization. Cambridge; New York: Cambridge University Press, pp. 110-123.

Brown, J.H., G.B. West, and B.J. Enquist. 2000. Scaling in Biology: Patterns and Processes, Causes and Consequences. In J.H. Brown and G.B. West, eds. Scaling in Biology. Oxford University Press, Oxford, pp. 1-24.

Brown, J.H., J.F. Gillooly, G.B. West, and V.M. Savage. 2003. The Next Step in Macro-ecology: From General Empirical Patterns to Universal Ecological Laws. In T.M. Blackburn and K.J. Gaston, eds. Macroecology: Concepts and Consequences. Blackwell Science, pp. 408-423.

Peckham, S. and V. K. Gupta, 1999: A reformulation of Horton’s laws for large river networks in terms of statistical self-similarity, Water Resources Research 35, 2763-77.

Veitzer, S. A. & Gupta, V. K. (2000) Random self-similar river networks and derivations of generalized Horton laws in terms of statistical simple scaling Water Resources Research 36, 1033-1044.

Week 3


Week 4


UNIT 2: Immune System, Ant Colonies, and Computational Biology

Computer Sciences 


Ant colonies and immune systems use distributed information exchange to search their environments. Both are canonical distributed systems—there is no single ant or cell that tells the other ants or cells what their task is, or when or where they should do it. Yet both systems search dynamic, and potentially complex, landscapes effectively. We hypothesize that ant colonies, immune systems and other complex systems use common informational strategies to direct components to know what, when and where their tasks are and how to distinguish self from non-self. These strategies can be encoded in computer algorithms and systems to achieve distributed search and anomaly detection.


Suggested Topics for Presentations: Readings: Week 6 Introduction to Immune Systems and Ant Colonies

--Sompayrac, L.M. How the Immune System Works. Blackwell 2008. Chapters 1 and 6.

--Holldobler, B. and E. O. Wilson. “SuperOrganism. 2008. Chapters 1-3.

Reading for field trip on 10/3:

--Gordon, D. Ants at Work. 1999. Simon and Schuster. Introduction, & Chapters 1 & 2.

Field Trip 10/03- Ant Foraging

Week 7

Non-self recognition

-- Mokady, O. & L. W. Buss. 1996. “Transmission Genetics of Allorecognition in Hydractinia symbiolmgicarpus (Cnidaria: Hydrozoa)” Genetics 143: 823-827.

-- Heller, N.E., K.K. Ingram & D.M. Gordon. 2008. “Nest connectivity and colony structure in unicolonial Argentine ants” Insect. Soc. 55. 397 – 403.

Week 9

Distributed Search

--Joost B Beltman, Athanasius FM Mare´e and Rob J de Boer. 2007. “Spatial modelling of brief and long interactions between T cells and dendritic cells.” Immunology and Cell Biology.

-- Bonabeau, E., M. Dorigo & G. Theraulaz. 2000. “Inspiration for optimization from social insect behaviour. ” Nature 406: 39-42.

UNIT 3: Modeling the Immune System and Viral Infections

Mathematical and Theoretical Biology



Suggested Topics for Presentations:


Week 11

Week 12

Week 13