PLENARY TALKS
Predicting
the unpredictable: the transmission of drug resistant HIV
1DEPARTMENT OF BIOMATHEMATICS, UCLA SCHOOL OF
MEDICINE, 10833, LE CONTE AVENUE, LOS ANGELES, CA 90095-1766
2HARVARD MEDICAL SCHOOL, BOSTON
3 UNIVERSITY OF CALIFORNIA, SAN FRANCISCO AND SAN
FRANCISCO GENERAL HOSPITAL, WARD 84, 995 POTRERO AVENUE, SAN FRANCISCO, 94110
4DEPARTAMENTO de MATEMATICAS, UAM-IZTAPALAPA AND
PIMAYC INSTITUTO MEXICANO del PETROLEO, EJE CENTRAL LAZARO CARDENAS 152, SAN
BARTOLO, ATEPEHUACAN, CP 07730, MEXICO DF.
We
use a mathematical model to understand, and to predict, the evolution of the
epidemic of drug resistant HIV in San Francisco from 1996 to 2005, due to the
use of combination antiretroviral therapies (ARV). We predict the evolution of
1,000 different drug resistant strains; each strain has a different fitness
(relative to a drug sensitive strain). We calculate that the current prevalence
of resistance is high, and predict it will continue to rise. In contrast, we
calculate that transmission of resistance is currently low, and predict it will
remain low. We determine that the epidemic of resistance is being fuelled
mainly by the conversion of drug sensitive cases to drug resistant cases, and
not by the transmission of resistant strains. We also determine that
transmission of resistant strains has not increased the overall number of new
HIV infections. Our results imply that – surprisingly - transmission of
resistant strains is, and will remain, a relatively minor public health
problem. We also calculate the overall impact that ARV will have on the HIV
epidemic in San Francisco; we quantified the epidemic-level impact of ARV on
reducing epidemic.
Integrative models of swimming organisms
Lisa
Fauci
DEPARTMENT
OF MATHEMATICS, TULANE UNIVERSITY, NEW ORLEANS, LA 70118
Problems in biological fluid dynamics typically involve the
interaction of an elastic structure with the surrounding fluid. Motile spermatozoa
in the reproductive tract and swimming leeches are examples of such
fluid-structure interactions. We will describe a computational approach, based
upon the immersed boundary method, that couples internal force-generating
mechanisms of organisms and cells with an external, viscous, incompressible
fluid.
We will present recent progress in the modeling of the internal axoneme mechanics of beating cilia and flagella, as well as 3d undulatory swimming of nematodes and leeches.
Miller, Michael I.
DIRECTOR, CENTER FOR IMAGING SCIENCE, JOHNS HOPKINS UNIVERSITY, CLARK HALL, BALTIMORE, MD 21218
Recent years have seen rapid advances in the mathematical specification of human anatomy. As first described in "Computational Anatomy: An Emerging Discipline", Grenander and Miller, Quarterly of Applied Mathematics, Vol.
56, 617-694, 1998, human anatomy is modelled as
a deformable template, an orbit under the group action of infinite dimensional
diffeomorphisms. In this talk we will describe recent advances in CA,
specifying a metric on the ensemble of images, and examine distances between
elements of the orbits, "Group Actions, Homeomorphisms, and Matching: A General Framework", Miller and
Younes, Int. J. Comp. Vision Vol. 41, 61-84, 2001. Numerous results will be shown comparing shapes through this
metric formulation of the deformable template, including results from disease
testing on the hippocampus, and cortical structural and functional mapping.
DEPARTMENT OF ECOLOGY, EVOLUTION AND MARINE BIOLOGY, UNIVERSITY OF
CALIFORNIA, SANTA BARBARA, CA 93106, USA
Few ecologists
regard simple population models (logistic, Lotka-Volterra, Nicholson-Bailey
etc.) as serious, quantitative, representations of reality. At the same time,
new statistical methods are increasingly used for fitting simple, nonlinear
models to time series and other data. I
shall review two contexts where simple mechanistic models can be derived from
reasonable biological assumptions, and their predictions tested. The first is the biomass dynamics of
interacting consumer and resource populations with continuous reproduction. Here, predictions from a simple model have
guided a long-running program on zooplankton population dynamics. The second
concerns populations with annual reproduction.
Here, simple theory reveals a scaling from which it emerges that a large
number of populations have cycle periods consistent with the predictions of
single-species or simple consumer-resource models. The conclusion is that simple, mechanistic models make testable,
quantitative predictions, whose
validity (or
otherwise) can guide empirical research.
DEPARTMENT OF COMPUTER SCIENCE AND APPLIED MATHEMATICS, WEIZMANN
INSTITUTE, REHOVOT, ISRAEL IL-76100
For many years, two theorists (Hanna Parnas of the Hebrew University and myself) and two experimental groups have been working together to understand the control of neurotransmitter release. Our models have progressed from phenomenological to molecular, and have been integrated with many experiments. This field is a "hot" one with many papers appearing in the last few years published by Nobel prize winners and others in major journals. Our ideas are not mainstream, and have often been brushed aside in a manner that we regard as unscientific. I will survey the whole story as something of a paradigm of contemporary science as it is.
Inference from the fossil
record: when was the last common ancestor
of extant primates?
Tavaré, Simon1, Soligo, Christophe2, Will, Olive3, Marshall, Charles4,
Martin, Robert5
1PROGRAM IN MOLECULAR AND COMPUTATIONAL BIOLOGY, UNIVERSITY OF SOUTHERN CALIFORNIA, 835 WEST 37TH STREET, SHS172, LOS ANGELES, CA 90089-1340
2NATURAL
HISTORY MUSEUM, LONDON
3UNIVERSITY
OF WASHINGTON
4HARVARD
5THE
FIELD MUSEUM
Abstract:
Divergence times estimated from molecular data often considerably pre-date the
earliest known fossil representatives of the groups studied. For the order
Primates, molecular data uniformly suggest a
divergence from other placental mammals some 90 million years ago (mya),
whereas the oldest known fossil primates are from 55 mya. The common ancestor of primates should be
earlier than the oldest known fossils, but
quantification is needed to
interpret possible discrepancies between the molecular and palaeontological
estimates. Here we present a Bayesian approach, complementary to our recent
Nature paper (vol 416, pp 726-730, 2002), for using the fossil record to
estimate the divergence time of a group of species. The method estimates the time of the
haplorhine-strepsirrhine split at about 80mya, but there is considerable
uncertainty in this estimate. The statistical approach is based on an
approximate Bayesian computation that is faster and simpler than Markov chain
Monte Carlo.