21,080 research outputs found
AND-NOT logic framework for steady state analysis of Boolean network models
Finite dynamical systems (e.g. Boolean networks and logical models) have been
used in modeling biological systems to focus attention on the qualitative
features of the system, such as the wiring diagram. Since the analysis of such
systems is hard, it is necessary to focus on subclasses that have the
properties of being general enough for modeling and simple enough for
theoretical analysis. In this paper we propose the class of AND-NOT networks
for modeling biological systems and show that it provides several advantages.
Some of the advantages include: Any finite dynamical system can be written as
an AND-NOT network with similar dynamical properties. There is a one-to-one
correspondence between AND-NOT networks, their wiring diagrams, and their
dynamics. Results about AND-NOT networks can be stated at the wiring diagram
level without losing any information. Results about AND-NOT networks are
applicable to any Boolean network. We apply our results to a Boolean model of
Th-cell differentiation
Inferring context-sensitive probablistic boolean networks from gene expression data under multi-biological conditions
In recent years biological microarrays have emerged as a high-throughput data acquisition technology in bioinformatics. In conjunction with this, there is an increasing need to develop frameworks for the formal analysis of biological pathways. A modeling approach defined as Probabilistic Boolean Networks (PBNs) was proposed for inferring genetic regulatory networks [1]. This technology, an extension of Boolean Networks [2], is able to capture the time-varying dependencies with deterministic probabilities for a series of sets of predictor functions
Boolean Networks as Predictive Models of Emergent Biological Behaviors
Interacting biological systems at all organizational levels display emergent
behavior. Modeling these systems is made challenging by the number and variety
of biological components and interactions (from molecules in gene regulatory
networks to species in ecological networks) and the often-incomplete state of
system knowledge (e.g., the unknown values of kinetic parameters for
biochemical reactions). Boolean networks have emerged as a powerful tool for
modeling these systems. We provide a methodological overview of Boolean network
models of biological systems. After a brief introduction, we describe the
process of building, analyzing, and validating a Boolean model. We then present
the use of the model to make predictions about the system's response to
perturbations and about how to control (or at least influence) its behavior. We
emphasize the interplay between structural and dynamical properties of Boolean
networks and illustrate them in three case studies from disparate levels of
biological organization.Comment: Review, to appear in the Cambridge Elements serie
Therapeutic target discovery using Boolean network attractors: improvements of kali
In a previous article, an algorithm for identifying therapeutic targets in
Boolean networks modeling pathological mechanisms was introduced. In the
present article, the improvements made on this algorithm, named kali, are
described. These improvements are i) the possibility to work on asynchronous
Boolean networks, ii) a finer assessment of therapeutic targets and iii) the
possibility to use multivalued logic. kali assumes that the attractors of a
dynamical system, such as a Boolean network, are associated with the phenotypes
of the modeled biological system. Given a logic-based model of pathological
mechanisms, kali searches for therapeutic targets able to reduce the
reachability of the attractors associated with pathological phenotypes, thus
reducing their likeliness. kali is illustrated on an example network and used
on a biological case study. The case study is a published logic-based model of
bladder tumorigenesis from which kali returns consistent results. However, like
any computational tool, kali can predict but can not replace human expertise:
it is a supporting tool for coping with the complexity of biological systems in
the field of drug discovery
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