Bio Summer15

Bio
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Summer15
THE UNIVERSITY
OF SYDNEY
7-11 DEC 2015
WWW.AMSI.ORG.AU/BIS
AMSI RESEARCH
Special Session: Mathematical Biology
13:30-14:15
Edward Green,
FORMATION OF CELL AGGREGATES:
University of Adelaide
Abstract: The formation of cell aggregates - clumps, clusters or spheroids - is a common
phenomenon when cells are grown in vitro. They can arise either as the result of cell proliferation, or
attractions between cells, or some combination of the two. In this talk, I will present two mathematical
approaches to studying these processes: continuum and agent-based models. The first part of the talk is
motivated by liver tissue engineering, where aggregation is key to producing viable, functional tissue.
The formation of aggregates can be promoted by co-culturing hepatocytes (the main type of cell from
the liver) with stellate cells, and I will present a continuum model for the interactions of these cell types.
the second part of the talk focuses on agent-based modelling, where each individual cell is represented,
and stochastic effects can also be included. I will show how we can quantify the spatial distribution of the
cells using a pair correlation function, and how this can give us useful information about the mechanisms of
aggregate formation.
14:15-15:00 BIOLOGICAL NETWORKS:
Adelle Coster,
UNSW
Abstract: Biological systems are complex, with many interacting parts and
complex dynamics. Dynamical systems approaches attempt to reduce the
system to the main pathways that embody the main processes underlying
the dynamic behaviour. When you are satisfied you have your network of
interactions right, however, how do you determine the points at which other
influences affect your system? For instance, if a particular biochemical
component is knocked-down or removed, how and where does this affect the
dynamics? The particular system presented is the GLUT4 translocation system in cells. GLUT4, or glucose
transporter 4, is the main insulin-responsive glucose transporter in mammalian fat and muscle cells. These
membrane embedded proteins are dynamically cycled to and from the cell surface controlling the level of
glucose uptake by the cell. A dynamical system for the underlying processes has been developed utilising
multiple experiments and experimental modalities. The approach taken for the creation of the dynamical
system and the simultaneous optimisation to multiple experimental data sets is presented. Furthermore,
ways to optimally extend the system so that it simultaneously embodies the dynamics of the system both
under control conditions and when perturbed biochemically are explored. These extended models can
then be used to predict where and how the perturbations influence the translocation system.
Bio
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15:30-16:15
Mark Tanaka,
Summer15
THE UNIVERSITY
OF SYDNEY
7-11 DEC 2015
WWW.AMSI.ORG.AU/BIS
AMSI RESEARCH
EVOLUTION OF PATHOGENS:
UNSW
Abstract: By characterising genetic variation among bacterial or viral isolates we can form a picture of
how pathogens evolve. Mathematical models can be used to make inferences from such data. However,
the evolution of pathogens is ultimately a process that occurs within hosts and is influenced by dynamics
within and interactions with the host. Most models of pathogen evolution focus on either the within-host
or the between-host level. Here I describe steps towards bridging the two scales. First I present a model of
influenza virus evolution that incorporates within-host dynamics to obtain the between-host rate of
molecular substitution as a function of the mutation rate, the within-host reproduction number and other
factors. Next I discuss a model of viral evolution in which some hosts are immunocompromised, thereby
extending opportunities for within-host virus evolution which then affects between-host evolution. Finally,
I describe a model of Mycobacterium tuberculosis in which multi-drug resistance evolves within hosts and
spreads between hosts.
16:15-17:00 SEARCH FOR NEW DRUGS FOR MALARIA
David Khoury,
UNSW
Abstract: Malaria is responsible for more than half a million deaths
annually, the vast majority of which are in children under the age of 5,
in Africa. Although the mortality rate from malaria has been in decline
over the last 15 years, the emergence of drug resistant strains of the parasite in Southeast Asia is a cause
for great concern. If the same drug resistance were to spread or spontaneously emerge in Africa it would
be catastrophic. For this reason there is currently a large intentional effort to develop new therapies for
the treatment of malaria. Central to the development of new antimalarials is the question of how you
determine the efficacy of antimalarials. The most common method of measuring drug efficacy is by
measuring how quickly parasite concentrations decline in patients after treatment. This decline in parasite
concentration in a patient after treatment is called the parasite clearance curve. Drug resistance to our
most effective antimalarials was first detected as slower declines in parasite concentrations after
treatment. Surprisingly, for a feature of malaria treatment that is so central to most current drug research
the parasite clearance curve is poorly understood. In our work we have dissected the mechanisms that
underpin this curve using a combination of carefully designed experiments in mice, data from clinical
trials and mathematical modelling. Our work has established a clearer understanding of how to interpret
the parasite clearance curve, and has revealed some fundamental misunderstandings about how
antimalarials actually work to reduce parasite concentrations.