4,644 research outputs found
Limits of feedback control in bacterial chemotaxis
Inputs to signaling pathways can have complex statistics that depend on the
environment and on the behavioral response to previous stimuli. Such behavioral
feedback is particularly important in navigation. Successful navigation relies
on proper coupling between sensors, which gather information during motion, and
actuators, which control behavior. Because reorientation conditions future
inputs, behavioral feedback can place sensors and actuators in an operational
regime different from the resting state. How then can organisms maintain proper
information transfer through the pathway while navigating diverse environments?
In bacterial chemotaxis, robust performance is often attributed to the zero
integral feedback control of the sensor, which guarantees that activity returns
to resting state when the input remains constant. While this property provides
sensitivity over a wide range of signal intensities, it remains unclear how
other parameters affect chemotactic performance, especially when considering
that the swimming behavior of the cell determines the input signal. Using
analytical models and simulations that incorporate recent experimental
evidences about behavioral feedback and flagellar motor adaptation we identify
an operational regime of the pathway that maximizes drift velocity for various
environments and sensor adaptation rates. This optimal regime is outside the
dynamic range of the motor response, but maximizes the contrast between run
duration up and down gradients. In steep gradients, the feedback from
chemotactic drift can push the system through a bifurcation. This creates a
non-chemotactic state that traps cells unless the motor is allowed to adapt.
Although motor adaptation helps, we find that as the strength of the feedback
increases individual phenotypes cannot maintain the optimal operational regime
in all environments, suggesting that diversity could be beneficial.Comment: Corrected one typo. First two authors contributed equally. Notably,
there were various typos in the values of the parameters in the model of
motor adaptation. The results remain unchange
Multiple verification in computational modeling of bone pathologies
We introduce a model checking approach to diagnose the emerging of bone
pathologies. The implementation of a new model of bone remodeling in PRISM has
led to an interesting characterization of osteoporosis as a defective bone
remodeling dynamics with respect to other bone pathologies. Our approach allows
to derive three types of model checking-based diagnostic estimators. The first
diagnostic measure focuses on the level of bone mineral density, which is
currently used in medical practice. In addition, we have introduced a novel
diagnostic estimator which uses the full patient clinical record, here
simulated using the modeling framework. This estimator detects rapid (months)
negative changes in bone mineral density. Independently of the actual bone
mineral density, when the decrease occurs rapidly it is important to alarm the
patient and monitor him/her more closely to detect insurgence of other bone
co-morbidities. A third estimator takes into account the variance of the bone
density, which could address the investigation of metabolic syndromes, diabetes
and cancer. Our implementation could make use of different logical combinations
of these statistical estimators and could incorporate other biomarkers for
other systemic co-morbidities (for example diabetes and thalassemia). We are
delighted to report that the combination of stochastic modeling with formal
methods motivate new diagnostic framework for complex pathologies. In
particular our approach takes into consideration important properties of
biosystems such as multiscale and self-adaptiveness. The multi-diagnosis could
be further expanded, inching towards the complexity of human diseases. Finally,
we briefly introduce self-adaptiveness in formal methods which is a key
property in the regulative mechanisms of biological systems and well known in
other mathematical and engineering areas.Comment: In Proceedings CompMod 2011, arXiv:1109.104
The role of body wall muscles in C. elegans locomotion
Over the past four decades, one of the simplest nervous systems across the animal kingdom, that of the nematode
worm C. elegans, has drawn increasing attention. This system is the subject of an intensive concerted effort to
understand the behaviour of an entire living animal, from the bottom up and the top down. C. elegans locomotion,
in particular, has been the subject of a number of models, but there is as yet no general agreement about the key
(rhythm generating) elements. In this paper we investigate the role of one component of the locomotion subsystem,
namely the body wall muscles, with a focus on the role of inter-muscular gap junctions. We construct a detailed
electrophysiological model which suggests that these muscles function, to a first approximation, as mere actuators
and have no obvious rhythm generating role. Furthermore, we show that within our model inter-muscular coupling
is too weak to have a significant electrical effect. These results rule out muscles as key generators of locomotion,
pointing instead to neural activity patterns. More specifically, the results imply that the reduced locomotion velocity
observed in unc-9 mutants is likely to be due to reduced neuronal rather than inter-muscular coupling
Ratiometric control for differentiation of cell populations endowed with synthetic toggle switches
We consider the problem of regulating by means of external control inputs the
ratio of two cell populations. Specifically, we assume that these two cellular
populations are composed of cells belonging to the same strain which embeds
some bistable memory mechanism, e.g. a genetic toggle switch, allowing them to
switch role from one population to another in response to some inputs. We
present three control strategies to regulate the populations' ratio to
arbitrary desired values which take also into account realistic physical and
technological constraints occurring in experimental microfluidic platforms. The
designed controllers are then validated in-silico using stochastic agent-based
simulations.Comment: Accepted to CDC'201
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