COMPUTATIONAL MODELING AND SIMULATION AS ENABLERS FOR BIOLOGICAL DISCOVERY 193
users exhibit different dynamical patterns than those in the general population because of factors such
as rates of sexual contact with others (both inside and outside the individual’s own social group) and
different sexual practices of individuals in each group (e.g., use of condoms). Box 5.21 elaborates on this
notion in greater detail.
5.4.8 Evolution and Ecology5.4.8.1 Commonalities Between Evolution and Ecology
No two fields in biology encompass such a broad range of levels of biological organization as
ecology and evolutionary biology. Although the two fields ask different questions, they both contend
with factors of space and time, and share common theories about relationships between individuals,
populations, and communities. The two intertwined fields view these relationships in different ways.
Evolutionary biologists want to understand and quantify the effect of environment (e.g., natural selec-
tion) on individuals and populations; ecologists want to understanding the role of individuals and
populations in shaping their environment (ecological inheritance, niche construction).
The two fields encompass a diverse assemblage of topics with applications in resource manage-
ment, epidemiology, and global change. In these fields, data have been relatively difficult to collect in
ways that relate directly to mathematical or computational models, although this has been changing
over the past 10 years. Thus, both fields have relied heavily on theory to advance their insights. In fact,
ecology and evolution have been the substrate for the development of important mathematical con-
cepts. The quantitative study of biological inheritance and evolution provided the context for statistics,
probability theory, stochasticity, and dynamical systems theory.
Box 5.21
Social Heterogeneity in Epidemiology: An ExampleThe main focus for modeling social space (the space of social interactions) and disease is, of course, on AIDS and
other sexually transmitted infections. Simple models illustrated clearly that heterogeneities in contact rates can
substantially alter the predicted course of epidemics. This area has seen an explosion of research, both in data
analysis of contact structures and in graph-theoretic and other approaches to modeling. Models and data analysis are
most productive when combined, especially in allowing the observations to limit the universe of possible networks.The major computational challenge is how to deal with the complexity of networks, where concurrency of partner-
ships often means that closure to a few moments of the distribution is difficult. This problem is especially acute given
the sensitivity of obtaining data for STD networks, in that the nature of the network is generally only partially and
imperfectly known. The use of mathematical models for human immunodeficiency virus (HIV) transmission will be
especially important in assessing the impact of potential vaccines. Another major computational challenge—which
developed with the AIDS epidemic and is currently being applied to another pathogen, the bovine spongiform
encephalopathy agent—is to estimate the parameters of transmission models from disease incidence and other
demographic data.One hope for the future for both of these areas is network information embedded in viral genomes. A body of recent
work indicates exciting possibilities for estimating epidemiological parameters from the birth and death processes of
pathogen evolutionary trees. More generally, new mathematical and computational techniques will be needed to
understand the epidemiological implications of the rapidly accumulating data on pathogen sequences, especially in
the context of parasite genetic diversity and the host immunological response to it.SOURCE: Reprinted by permission from S.A. Levin, B. Grenfell, A. Hastings, and A.S. Perelson, “Mathematical and Computational
Challenges in Population Biology and Ecosystems Science,” Science 275(5298):334-343,1997. Copyright 1997 AAAS. (References omitted.)