Mathematical modelling of signal sensing and transduction: revisiting classical mechanisms
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Date
28/11/2013Item status
Restricted AccessEmbargo end date
31/12/2100Author
Martins, Bruno Miguel Cardoso
Metadata
Abstract
The ability of cells to react to changes in their environment is critical to their
survival. Effective decision making strategies leading to the activation of specific
intracellular pathways hinge on cells sensing and processing extracellular
variation. We will only be able to understand and manipulate how cells make
decisions if
we
understand
the
“design”
of
the
mechanisms
that
enable them to
make such decisions, in terms of how they function, and in terms of their
limitations and architecture. In this thesis, using mathematical modelling, I
revisited classical signal sensing and transduction mechanisms in light of recent
developments in methodological approaches and data collection. I studied the
sensing characteristics of one of the simplest of sensors, the allosteric sensor, to
understand the
limits
and
effectiveness
of
its
“design”.
Using
the
classical
Monod-Wyman-Changeux model of allostery, I defined and evaluated six
engineering-inspired characteristics as a function of the parameters and
number of sensors. I found that specifying one characteristic strongly constrains
others and I determined the trade-offs that follow from these constraints. I also
calculated the probability distribution of the number of input molecules that
maximizes information transfer and, as a consequence, the number of
environmental states a given population of sensors can discriminate between.
Next, I proposed a new general model of phosphorylation cycles that can
account for experimental reports of ultrasensitivity occurring in regimes that
are
far
away
from
Goldbeter
and
Koshland’s zero-order saturation, the classical
ultrasensitivity-generating mechanism. The new model exhibits robust
ultrasensitivity
in
“anti-zero-order”
regimes.
The
degree
of
ultrasensitivity,
its
limits, and its dependence on the parameters of the system are analytically
tractable. The model can, additionally, explain in an intuitive way some puzzling
experimental observations. Finally, I addressed the problem of integrating
different types of signals from multiple sources, and performed some
preliminary exploration of how cells can
“learn”
to associate and dissociate
correlated signals in non-evolutionary time-scales. This work provides insights
into the function and robustness of signal sensing and transduction mechanisms
and as such is applicable to both the study of endogenous systems and the
design of synthetic ones.