252 research outputs found
Inference in Hidden Markov Models with Explicit State Duration Distributions
In this letter we borrow from the inference techniques developed for
unbounded state-cardinality (nonparametric) variants of the HMM and use them to
develop a tuning-parameter free, black-box inference procedure for
Explicit-state-duration hidden Markov models (EDHMM). EDHMMs are HMMs that have
latent states consisting of both discrete state-indicator and discrete
state-duration random variables. In contrast to the implicit geometric state
duration distribution possessed by the standard HMM, EDHMMs allow the direct
parameterisation and estimation of per-state duration distributions. As most
duration distributions are defined over the positive integers, truncation or
other approximations are usually required to perform EDHMM inference
Inferring Network Mechanisms: The Drosophila melanogaster Protein Interaction Network
Naturally occurring networks exhibit quantitative features revealing
underlying growth mechanisms. Numerous network mechanisms have recently been
proposed to reproduce specific properties such as degree distributions or
clustering coefficients. We present a method for inferring the mechanism most
accurately capturing a given network topology, exploiting discriminative tools
from machine learning. The Drosophila melanogaster protein network is
confidently and robustly (to noise and training data subsampling) classified as
a duplication-mutation-complementation network over preferential attachment,
small-world, and other duplication-mutation mechanisms. Systematic
classification, rather than statistical study of specific properties, provides
a discriminative approach to understand the design of complex networks.Comment: 19 pages, 5 figure
Optimal signal processing in small stochastic biochemical networks
We quantify the influence of the topology of a transcriptional regulatory
network on its ability to process environmental signals. By posing the problem
in terms of information theory, we may do this without specifying the function
performed by the network. Specifically, we study the maximum mutual information
between the input (chemical) signal and the output (genetic) response
attainable by the network in the context of an analytic model of particle
number fluctuations. We perform this analysis for all biochemical circuits,
including various feedback loops, that can be built out of 3 chemical species,
each under the control of one regulator. We find that a generic network,
constrained to low molecule numbers and reasonable response times, can
transduce more information than a simple binary switch and, in fact, manages to
achieve close to the optimal information transmission fidelity. These
high-information solutions are robust to tenfold changes in most of the
networks' biochemical parameters; moreover they are easier to achieve in
networks containing cycles with an odd number of negative regulators (overall
negative feedback) due to their decreased molecular noise (a result which we
derive analytically). Finally, we demonstrate that a single circuit can support
multiple high-information solutions. These findings suggest a potential
resolution of the "cross-talk" dilemma as well as the previously unexplained
observation that transcription factors which undergo proteolysis are more
likely to be auto-repressive.Comment: 41 pages 7 figures, 5 table
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