19,947 research outputs found
A Stochastic Approach to Shortcut Bridging in Programmable Matter
In a self-organizing particle system, an abstraction of programmable matter,
simple computational elements called particles with limited memory and
communication self-organize to solve system-wide problems of movement,
coordination, and configuration. In this paper, we consider a stochastic,
distributed, local, asynchronous algorithm for "shortcut bridging", in which
particles self-assemble bridges over gaps that simultaneously balance
minimizing the length and cost of the bridge. Army ants of the genus Eciton
have been observed exhibiting a similar behavior in their foraging trails,
dynamically adjusting their bridges to satisfy an efficiency trade-off using
local interactions. Using techniques from Markov chain analysis, we rigorously
analyze our algorithm, show it achieves a near-optimal balance between the
competing factors of path length and bridge cost, and prove that it exhibits a
dependence on the angle of the gap being "shortcut" similar to that of the ant
bridges. We also present simulation results that qualitatively compare our
algorithm with the army ant bridging behavior. Our work gives a plausible
explanation of how convergence to globally optimal configurations can be
achieved via local interactions by simple organisms (e.g., ants) with some
limited computational power and access to random bits. The proposed algorithm
also demonstrates the robustness of the stochastic approach to algorithms for
programmable matter, as it is a surprisingly simple extension of our previous
stochastic algorithm for compression.Comment: Published in Proc. of DNA23: DNA Computing and Molecular Programming
- 23rd International Conference, 2017. An updated journal version will appear
in the DNA23 Special Issue of Natural Computin
Lumpability Abstractions of Rule-based Systems
The induction of a signaling pathway is characterized by transient complex
formation and mutual posttranslational modification of proteins. To faithfully
capture this combinatorial process in a mathematical model is an important
challenge in systems biology. Exploiting the limited context on which most
binding and modification events are conditioned, attempts have been made to
reduce the combinatorial complexity by quotienting the reachable set of
molecular species, into species aggregates while preserving the deterministic
semantics of the thermodynamic limit. Recently we proposed a quotienting that
also preserves the stochastic semantics and that is complete in the sense that
the semantics of individual species can be recovered from the aggregate
semantics. In this paper we prove that this quotienting yields a sufficient
condition for weak lumpability and that it gives rise to a backward Markov
bisimulation between the original and aggregated transition system. We
illustrate the framework on a case study of the EGF/insulin receptor crosstalk.Comment: In Proceedings MeCBIC 2010, arXiv:1011.005
Joining Forces of Bayesian and Frequentist Methodology: A Study for Inference in the Presence of Non-Identifiability
Increasingly complex applications involve large datasets in combination with
non-linear and high dimensional mathematical models. In this context,
statistical inference is a challenging issue that calls for pragmatic
approaches that take advantage of both Bayesian and frequentist methods. The
elegance of Bayesian methodology is founded in the propagation of information
content provided by experimental data and prior assumptions to the posterior
probability distribution of model predictions. However, for complex
applications experimental data and prior assumptions potentially constrain the
posterior probability distribution insufficiently. In these situations Bayesian
Markov chain Monte Carlo sampling can be infeasible. From a frequentist point
of view insufficient experimental data and prior assumptions can be interpreted
as non-identifiability. The profile likelihood approach offers to detect and to
resolve non-identifiability by experimental design iteratively. Therefore, it
allows one to better constrain the posterior probability distribution until
Markov chain Monte Carlo sampling can be used securely. Using an application
from cell biology we compare both methods and show that a successive
application of both methods facilitates a realistic assessment of uncertainty
in model predictions.Comment: Article to appear in Phil. Trans. Roy. Soc.
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