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Finding the control policies for intervention for large PBNs is a serious computational challenge. We study the effects of reduction mappings on the policy design. Our results suggest that for a specific class of network models, one can use the control policy designed on the reduced net work to approximate the control policy for the original model. The(More)
Probabilistic Boolean networks (PBNs) represent a class of nonlinear models of genetic regulatory networks incorporating the indeterminacy owing to latent variables external to the model that have biological interaction with genes in the model. Besides being used to model biological phenomena, such as cellular state dynamics and the switch-like behavior of(More)
Constructing network models of genomic regulation from data can help to better understand the manner in which genes interact in an integrative and holistic way within a given genome. One of the major impediments for the practical application of such models is their structural and computational complexity. Thus, it is sometimes necessary to construct(More)
The long-run characteristics of a dynamical system are critical and their determination is a primary aspect of system analysis. In the other direction, system synthesis involves constructing a network possessing a given set of properties. This constitutes the inverse problem. This paper addresses the long-run inverse problem pertaining to Boolean networks(More)
A serious obstacle in applying computational models of genomic regulation is their complexity. Thus, there is a need for size reducing mappings that preserve biologically meaningful properties of the models. There are several available reduction mappings for the PBN model that are capable of preserving important structural or dynamical properties of the(More)
This work presents a computational material model of flexible woven fabric for finite element impact analysis and simulation. The model is implemented in the nonlinear dynamic explicit finite element code LSDYNA. The material model derivation utilizes the micro-mechanical approach and the homogenization technique usually used in composite material models.(More)
Inferring Boolean networks from non-temporal data suffers from a lack of directionality of the prediction. As a result, inferred networks often possess spurious non-singleton attractor cycles inconsistent with the assumption that the data represent the steady-state of the real genomic regulatory system. Bidirectional gene relationships can be responsible(More)
Owing to computational complexity, it is sometimes necessary to reduce the size of a gene regulatory network. This paper proposes a strategy to reduce the size of a probabilistic Boolean network (PBN) while preserving its dynamical structure, a crucial requirement for the development of intervention strategies based on control theory. In particular, we(More)
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