184 research outputs found
Efficient inference of parsimonious phenomenological models of cellular dynamics using S-systems and alternating regression
The nonlinearity of dynamics in systems biology makes it hard to infer them
from experimental data. Simple linear models are computationally efficient, but
cannot incorporate these important nonlinearities. An adaptive method based on
the S-system formalism, which is a sensible representation of nonlinear
mass-action kinetics typically found in cellular dynamics, maintains the
efficiency of linear regression. We combine this approach with adaptive model
selection to obtain efficient and parsimonious representations of cellular
dynamics. The approach is tested by inferring the dynamics of yeast glycolysis
from simulated data. With little computing time, it produces dynamical models
with high predictive power and with structural complexity adapted to the
difficulty of the inference problem.Comment: 14 pages, 2 figure
Phase Transitions and Criticality in the Collective Behavior of Animals -- Self-organization and biological function
Collective behaviors exhibited by animal groups, such as fish schools, bird
flocks, or insect swarms are fascinating examples of self-organization in
biology. Concepts and methods from statistical physics have been used to argue
theoretically about the potential consequences of collective effects in such
living systems. In particular, it has been proposed that such collective
systems should operate close to a phase transition, specifically a
(pseudo-)critical point, in order to optimize their capability for collective
computation. In this chapter, we will first review relevant phase transitions
exhibited by animal collectives, pointing out the difficulties of applying
concepts from statistical physics to biological systems. Then we will discuss
the current state of research on the "criticality hypothesis", including
methods for how to measure distance from criticality and specific functional
consequences for animal groups operating near a phase transition. We will
highlight the emerging view that de-emphasizes the optimality of being exactly
at a critical point and instead explores the potential benefits of living
systems being able to tune to an optimal distance from criticality. We will
close by laying out future challenges for studying collective behavior at the
interface of physics and biology.Comment: to appear in "Order, disorder, and criticality", vol. VII, World
Scientific Publishin
Nucleation at the DNA supercoiling transition
Twisting DNA under a constant applied force reveals a thermally activated
transition into a state with a supercoiled structure known as a plectoneme.
Using transition state theory, we predict the rate of this plectoneme
nucleation to be of order 10^4 Hz. We reconcile this with experiments that have
measured hopping rates of order 10 Hz by noting that the viscosity of the bead
used to manipulate the DNA limits the measured rate. We find that the intrinsic
bending caused by disorder in the base-pair sequence is important for
understanding the free energy barrier that governs the transition. Both
analytic and numerical methods are used in the calculations. We provide
extensive details on the numerical methods for simulating the elastic rod model
with and without disorder.Comment: 18 pages, 15 figure
Quantifying dynamical high-order interdependencies from the O-information: an application to neural spiking dynamics
We address the problem of efficiently and informatively quantifying how
multiplets of variables carry information about the future of the dynamical
system they belong to. In particular we want to identify groups of variables
carrying redundant or synergistic information, and track how the size and the
composition of these multiplets changes as the collective behavior of the
system evolves. In order to afford a parsimonious expansion of shared
information, and at the same time control for lagged interactions and common
effect, we develop a dynamical, conditioned version of the O-information, a
framework recently proposed to quantify high-order interdependencies via
multivariate extension of the mutual information. We thus obtain an expansion
of the transfer entropy in which synergistic and redundant effects are
separated. We apply this framework to a dataset of spiking neurons from a
monkey performing a perceptual discrimination task. The method identifies
synergistic multiplets that include neurons previously categorized as
containing little relevant information individually
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