109,531 research outputs found
Elucidation of molecular kinetic schemes from macroscopic traces using system identification
Overall cellular responses to biologically-relevant stimuli are mediated by networks of simpler lower-level processes. Although information about some of these processes can now be obtained by visualizing and recording events at the molecular level, this is still possible only in especially favorable cases. Therefore the development of methods to extract the dynamics and relationships between the different lower-level (microscopic) processes from the overall (macroscopic) response remains a crucial challenge in the understanding of many aspects of physiology. Here we have devised a hybrid computational-analytical method to accomplish this task, the SYStems-based MOLecular kinetic scheme Extractor (SYSMOLE). SYSMOLE utilizes system-identification input-output analysis to obtain a transfer function between the stimulus and the overall cellular response in the Laplace-transformed domain. It then derives a Markov-chain state molecular kinetic scheme uniquely associated with the transfer function by means of a classification procedure and an analytical step that imposes general biological constraints. We first tested SYSMOLE with synthetic data and evaluated its performance in terms of its rate of convergence to the correct molecular kinetic scheme and its robustness to noise. We then examined its performance on real experimental traces by analyzing macroscopic calcium-current traces elicited by membrane depolarization. SYSMOLE derived the correct, previously known molecular kinetic scheme describing the activation and inactivation of the underlying calcium channels and correctly identified the accepted mechanism of action of nifedipine, a calcium-channel blocker clinically used in patients with cardiovascular disease. Finally, we applied SYSMOLE to study the pharmacology of a new class of glutamate antipsychotic drugs and their crosstalk mechanism through a heteromeric complex of G protein-coupled receptors. Our results indicate that our methodology can be successfully applied to accurately derive molecular kinetic schemes from experimental macroscopic traces, and we anticipate that it may be useful in the study of a wide variety of biological systems
Control of stochastic and induced switching in biophysical networks
Noise caused by fluctuations at the molecular level is a fundamental part of
intracellular processes. While the response of biological systems to noise has
been studied extensively, there has been limited understanding of how to
exploit it to induce a desired cell state. Here we present a scalable,
quantitative method based on the Freidlin-Wentzell action to predict and
control noise-induced switching between different states in genetic networks
that, conveniently, can also control transitions between stable states in the
absence of noise. We apply this methodology to models of cell differentiation
and show how predicted manipulations of tunable factors can induce lineage
changes, and further utilize it to identify new candidate strategies for cancer
therapy in a cell death pathway model. This framework offers a systems approach
to identifying the key factors for rationally manipulating biophysical
dynamics, and should also find use in controlling other classes of noisy
complex networks.Comment: A ready-to-use code package implementing the method described here is
available from the authors upon reques
Collective behaviour without collective order in wild swarms of midges
Collective behaviour is a widespread phenomenon in biology, cutting through a
huge span of scales, from cell colonies up to bird flocks and fish schools. The
most prominent trait of collective behaviour is the emergence of global order:
individuals synchronize their states, giving the stunning impression that the
group behaves as one. In many biological systems, though, it is unclear whether
global order is present. A paradigmatic case is that of insect swarms, whose
erratic movements seem to suggest that group formation is a mere epiphenomenon
of the independent interaction of each individual with an external landmark. In
these cases, whether or not the group behaves truly collectively is debated.
Here, we experimentally study swarms of midges in the field and measure how
much the change of direction of one midge affects that of other individuals. We
discover that, despite the lack of collective order, swarms display very strong
correlations, totally incompatible with models of noninteracting particles. We
find that correlation increases sharply with the swarm's density, indicating
that the interaction between midges is based on a metric perception mechanism.
By means of numerical simulations we demonstrate that such growing correlation
is typical of a system close to an ordering transition. Our findings suggest
that correlation, rather than order, is the true hallmark of collective
behaviour in biological systems.Comment: The original version has been split into two parts. This first part
focuses on order vs. correlation. The second part, about finite-size scaling,
will be included in a separate paper. 15 pages, 6 figures, 1 table, 5 video
Brain complexity born out of criticality
In this essay we elaborate on recent evidence demonstrating the presence of a
second order phase transition in human brain dynamics and discuss its
consequences for theoretical approaches to brain function. We review early
evidence of criticality in brain dynamics at different spatial and temporal
scales, and we stress how it was necessary to unify concepts and analysis
techniques across scales to introduce the adequate order and control parameters
which define the transition. A discussion on the relation between structural
vs. dynamical complexity exposes future steps to understand the dynamics of the
connectome (structure) from which emerges the cognitome (function).Comment: In Proceedings of the 12th Granada Seminar "Physics, Computation, and
the Mind - Advances and Challenges at Interfaces-". (J. Marro, P. L. Garrido
& J. J. Torres, Eds.) American Institute of Physics (2012, in press
Measuring reproducibility of high-throughput experiments
Reproducibility is essential to reliable scientific discovery in
high-throughput experiments. In this work we propose a unified approach to
measure the reproducibility of findings identified from replicate experiments
and identify putative discoveries using reproducibility. Unlike the usual
scalar measures of reproducibility, our approach creates a curve, which
quantitatively assesses when the findings are no longer consistent across
replicates. Our curve is fitted by a copula mixture model, from which we derive
a quantitative reproducibility score, which we call the "irreproducible
discovery rate" (IDR) analogous to the FDR. This score can be computed at each
set of paired replicate ranks and permits the principled setting of thresholds
both for assessing reproducibility and combining replicates. Since our approach
permits an arbitrary scale for each replicate, it provides useful descriptive
measures in a wide variety of situations to be explored. We study the
performance of the algorithm using simulations and give a heuristic analysis of
its theoretical properties. We demonstrate the effectiveness of our method in a
ChIP-seq experiment.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS466 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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