Boolean Models Research Articles

The simulation of gene knock-out in scale-free random Boolean models of genetic networks

This paper describes the effects of perturbations, which simulate the knock-out of single genes, one at a time, in random Boolean models of genetic networks (RBN). The analysis concentrates on the probability distribution of so-called avalanches (defined in the text) in gene expression. The topology of the random Boolean networks considered here is of the scale-free type, with a power-law distribution of outgoing connectivities. The results for these scale-free random Boolean networks (SFRBN) are compared with those of classical RBNs, which had been previously analyzed, and with experimental data on S. cerevisiae. It is shown that, while both models approximate the main features of the distribution of experimental data, SFRBNs tend to overestimate the number of large avalanches.

Identifying dynamical modules from genetic regulatory systems: applications to the segment polarity network.

BackgroundIt is widely accepted that genetic regulatory systems are 'modular', in that the whole system is made up of smaller 'subsystems' corresponding to specific biological functions. Most attempts to identify modules in genetic regulatory systems have relied on the topology of the underlying network. However, it is the temporal activity (dynamics) of genes and proteins that corresponds to biological functions, and hence it is dynamics that we focus on here for identifying subsystems.ResultsUsing Boolean network models as an exemplar, we present a new technique to identify subsystems, based on their dynamical properties. The main part of the method depends only on the stable dynamics (attractors) of the system, thus requiring no prior knowledge of the underlying network. However, knowledge of the logical relationships between the network components can be used to describe how each subsystem is regulated. To demonstrate its applicability to genetic regulatory systems, we apply the method to a model of the Drosophila segment polarity network, providing a detailed breakdown of the system.ConclusionWe have designed a technique for decomposing any set of discrete-state, discrete-time attractors into subsystems. Having a suitable mathematical model also allows us to describe how each subsystem is regulated and how robust each subsystem is against perturbations. However, since the subsystems are found directly from the attractors, a mathematical model or underlying network topology is not necessarily required to identify them, potentially allowing the method to be applied directly to experimental expression data.

Orientational analysis of planar fibre systems observed as a Poisson shot‐noise process

We consider two-dimensional fibrous materials observed as a digital greyscale image. The problem addressed is to estimate the orientation distribution of unobservable thin fibres from a greyscale image modelled by a planar Poisson shot-noise process. The classical stereological approach is not straightforward, because the point intensities of thin fibres along sampling lines may not be observable. For such cases, Kärkkäinen et al. (2001) suggested the use of scaled variograms determined from grey values along sampling lines in several directions. Their method is based on the assumption that the proportion between the scaled variograms and point intensities in all directions of sampling lines is constant. This assumption is proved to be valid asymptotically for Boolean models and dead leaves models, under some regularity conditions. In this work, we derive the scaled variogram and its approximations for a planar Poisson shot-noise process using the modified Bessel function. In the case of reasonable high resolution of the observed image, the scaled variogram has an approximate functional relation to the point intensity, and in the case of high resolution the relation is proportional. As the obtained relations are approximative, they are tested on simulations. The existing orientation analysis method based on the proportional relation is further experimented on images with different resolutions. The new result, the asymptotic proportionality between the scaled variograms and the point intensities for a Poisson shot-noise process, completes the earlier results for the Boolean models and for the dead leaves models.

Robustness and state-space structure of Boolean gene regulatory models

Robustness to perturbation is an important characteristic of genetic regulatory systems, but the relationship between robustness and model dynamics has not been clearly quantified. We propose a method for quantifying both robustness and dynamics in terms of state-space structures, for Boolean models of genetic regulatory systems. By investigating existing models of the Drosophila melanogaster segment polarity network and the Saccharomyces cerevisiae cell-cycle network, we show that the structure of attractor basins can yield insight into the underlying decision making required of the system, and also the way in which the system maximises its robustness. In particular, gene networks implementing decisions based on a few genes have simple state-space structures, and their attractors are robust by virtue of their simplicity. Gene networks with decisions that involve many interacting genes have correspondingly more complicated state-space structures, and robustness cannot be achieved through the structure of the attractor basins, but is achieved by larger attractor basins that dominate the state space. These different types of robustness are demonstrated by the two models: the D. melanogaster segment polarity network is robust due to simple attractor basins that implement decisions based on spatial signals; the S. cerevisiae cell-cycle network has a complicated state-space structure, and is robust only due to a giant attractor basin that dominates the state space.

The correlation between architecture and mRNA abundance in the genetic regulatory network of Escherichia coli

BackgroundTwo aspects of genetic regulatory networks are the static architecture that describes the overall connectivity between the genes and the dynamics that describes the sequence of genes active at any one time as deduced from mRNA abundances. The nature of the relationship between these two aspects of these networks is a fundamental question. To address it, we have used the static architecture of the connectivity of the regulatory proteins of Escherichia coli to analyse their relationship to the abundance of the mRNAs encoding these proteins. In this we build on previous work which uses Boolean network models, but impose biological constraints that cannot be deduced from the mRNA abundances alone.ResultsFor a cell population of E. coli, we find that there is a strong and statistically significant linear dependence between the abundance of mRNA encoding a regulatory protein and the number of genes regulated by this protein. We use this result, together with the ratio of regulatory repressors to promoters, to simulate numerically a genetic regulatory network of a single cell. The resulting model exhibits similar correlations to that of E. coli.ConclusionThis analysis clarifies the relationship between the static architecture of a regulatory network and the consequences for the dynamics of its pattern of mRNA abundances. It also provides the constraints on the architecture required to construct a model network to simulate mRNA production.

Nested canalyzing, unate cascade, and polynomial functions

This paper focuses on the study of certain classes of Boolean functions that have appeared in several different contexts. Nested canalyzing functions have been studied recently in the context of Boolean network models of gene regulatory networks. In the same context, polynomial functions over finite fields have been used to develop network inference methods for gene regulatory networks. Finally, unate cascade functions have been studied in the design of logic circuits and binary decision diagrams. This paper shows that the class of nested canalyzing functions is equal to that of unate cascade functions. Furthermore, it provides a description of nested canalyzing functions as a certain type of Boolean polynomial function. Using the polynomial framework one can show that the class of nested canalyzing functions, or, equivalently, the class of unate cascade functions, forms an algebraic variety which makes their analysis amenable to the use of techniques from algebraic geometry and computational algebra. As a corollary of the functional equivalence derived here, a formula in the literature for the number of unate cascade functions provides such a formula for the number of nested canalyzing functions.

Testing the boolean hypothesis in the non‐convex case when a bounded grain can be assumed

AbstractSpatial independence of objects is a strong hypothesis when using boolean models. Methods to test it have then been developed, but only when the objects are convex. We propose here to replace this assumption by a bound assumption of the objects which can be more easily assumed when modeling spatial patterns in ecology and agricultural science. A test is then proposed, based on the length of the voids of the intersection between transect lines and a dilation of the original process related to the bound value. Its application is shown to several examples, together with its extension to an epidemiological case on orchards, where this problem comes from. Copyright © 2007 John Wiley & Sons, Ltd.

The Local Minima Problem in Hierarchical Classes Analysis: An Evaluation of a Simulated Annealing Algorithm and Various Multistart Procedures

Hierarchical classes models are quasi-order retaining Boolean decomposition models for N-way N-mode binary data. To fit these models to data, rationally started alternating least squares (or, equivalently, alternating least absolute deviations) algorithms have been proposed. Extensive simulation studies showed that these algorithms succeed quite well in recovering the underlying truth but frequently end in a local minimum. In this paper we evaluate whether or not this local minimum problem can be mitigated by means of two common strategies for avoiding local minima in combinatorial data analysis: simulated annealing (SA) and use of a multistart procedure. In particular, we propose a generic SA algorithm for hierarchical classes analysis and three different types of random starts. The effectiveness of the SA algorithm and the random starts is evaluated by reanalyzing data sets of previous simulation studies. The reported results support the use of the proposed SA algorithm in combination with a random multistart procedure, regardless of the properties of the data set under study.

Transfer principle in quantum set theory

AbstractIn 1981, Takeuti introduced quantum set theory as the quantum counterpart of Boolean valued models of set theory by constructing a model of set theory based on quantum logic represented by the lattice of closed subspaces in a Hilbert space and showed that appropriate quantum counterparts of ZFC axioms hold in the model. Here, Takeuti's formulation is extended to construct a model of set theory based on the logic represented by the lattice of projections in an arbitrary von Neumann algebra. A transfer principle is established that enables us to transfer theorems of ZFC to their quantum counterparts holding in the model. The set of real numbers in the model is shown to be in one-to-one correspondence with the set of self-adjoint operators affiliated with the von Neumann algebra generated by the logic. Despite the difficulty pointed out by Takeuti that equality axioms do not generally hold in quantum set theory, it is shown that equality axioms hold for any real numbers in the model. It is also shown that any observational proposition in quantum mechanics can be represented by a corresponding statement for real numbers in the model with the truth value consistent with the standard formulation of quantum mechanics, and that the equality relation between two real numbers in the model is equivalent with the notion of perfect correlation between corresponding observables (self-adjoint operators) in quantum mechanics. The paper is concluded with some remarks on the relevance to quantum set theory of the choice of the implication connective in quantum logic.

Canalization and symmetry in Boolean models for genetic regulatory networks

Canalization of genetic regulatory networks has been argued to be favoured by evolutionary processes due to the stability that it can confer to phenotype expression. We explore whether a significant amount of canalization and partial canalization can arise in purely random networks in the absence of evolutionary pressures. We use a mapping of the Boolean functions in the Kauffman N-K model for genetic regulatory networks onto a k-dimensional Ising hypercube (where k = K) to show that the functions can be divided into different classes strictly due to geometrical constraints. The classes can be counted and their properties determined using results from group theory and isomer chemistry. We demonstrate that partially canalizing functions completely dominate all possible Boolean functions, particularly for higher k. This indicates that partial canalization is extremely common, even in randomly chosen networks, and has implications for how much information can be obtained in experiments on native state genetic regulatory networks.

An Almost-Markov-Type Mixing Condition and Large Deviations for Boolean Models on the Line

We consider a not necessarily stationary one-dimensional Boolean model Ξ=∪ i≥1(Ξ i +X i ) defined by a Poisson process \(\Psi=\sum_{i\ge 1}\delta_{X_{i}}\) with bounded intensity function λ(t)≤λ 0 and a sequence of independent copies Ξ 1,Ξ 2,… of a random compact subset Ξ 0 of the real line ℝ1 whose diameter ‖Ξ 0‖ possesses a finite exponential moment \(\mathsf{E}\exp\{a\|\Xi_{0}\|\}\) . We first study the higher-order covariance functions \(\mathop{\mathsf{E}}\limits^{\frown}\xi(t_{1})\xi(t_{2})\cdots \xi(t_{k})\) of the {0,1}-valued stochastic process \(\xi(t)=\mathbf{1}_{\Xi^{c}(t)},\ t\in \mathbb{R}^{1}\) , and derive exponential estimates of them as well as of the mixed cumulants Cum k (ξ(t 1),ξ(t 2),…,ξ(t k )). From this, we derive Cramer-type large deviations relations and a Berry–Esseen bound for the distribution of empirical total length meas(Ξ∩[0,T]) of Ξ within [0,T] as T grows large. Second, we prove that the family of events {ξ(t)=1}={t ∉ Ξ}, t∈ℝ1, satisfies an almost-Markov-type mixing condition with an exponentially decaying mixing rate. In case of a stationary Boolean model, i.e. λ(t)≡λ 0, these properties enable us to show the existence and analyticity of the thermodynamic limit $$L(z)=\lim_{T\to \infty}\frac{1}{T}\log \mathsf{E}\exp\bigl\{z\mathop{\mathrm{meas}}\bigl(\Xi\cap [0,T]\bigr)\bigr\}\quad \hbox{for}\ |z|<\varepsilon(a,\lambda_{0}).$$

The surface pair correlation function for stationary Boolean models

The random surface measure of a stationary Boolean model with grains from the convex ring is considered. A sufficient condition and a necessary condition for the existence of the density of the second-order moment measure of are given and a representation of this density is derived. As applications, the surface pair correlation functions of a Boolean model with spheres and a Boolean model with randomly oriented right circular cylinders in ℝ3 are determined.

The surface pair correlation function for stationary Boolean models

The random surface measureof a stationary Boolean modelwith grains from the convex ring is considered. A sufficient condition and a necessary condition for the existence of the density of the second-order moment measure ofare given and a representation of this density is derived. As applications, the surface pair correlation functions of a Boolean model with spheres and a Boolean model with randomly oriented right circular cylinders in ℝ3are determined.

Studying genetic regulatory networks at the molecular level: Delayed reaction stochastic models

Current advances in molecular biology enable us to access the rapidly increasing body of genetic information. It is still challenging to model gene systems at the molecular level. Here, we propose two types of reaction kinetic models for constructing genetic networks. Time delays involved in transcription and translation are explicitly considered to explore the effects of delays, which may be significant in genetic networks featured with feedback loops. One type of model is based on delayed effective reactions, each reaction modeling a biochemical process like transcription without involving intermediate reactions. The other is based on delayed virtual reactions, each reaction being converted from a mathematical function to model a biochemical function like gene inhibition. The latter stochastic models are derived from the corresponding mean-field models. The former ones are composed of single gene expression modules. We thus design a model of gene expression. This model is verified by our simulations using a delayed stochastic simulation algorithm, which accurately reproduces the stochastic kinetics in a recent experimental study. Various simplified versions of the model are given and evaluated. We then use the two methods to study the genetic toggle switch and the repressilator. We define the “on” and “off” states of genes and extract a binary code from the stochastic time series. The binary code can be described by the corresponding Boolean network models in certain conditions. We discuss these conditions, suggesting a method to connect Boolean models, mean-field models, and stochastic chemical models.

Chord calculus and stochastic geometry

Chord calculus is a collection of integration procedures applied to to the combinatorial decompositions that give the solution of the Buffon-Sylvester problem for n needles in a plane or the similar problem in IR 3. It is a source of various integral geometry identities, some of which find their application in Stochastic geometry. In the present paper these applications are focused on random convex polygons and polyhedrons, where we define certain classes where rather simple tomography analysis is possible. The choice of these classes (the Independent Angles class and the Independent Orientations class) is due to the nature of the results of the Chord calculus. The last section points at an application of the convex polygons from the Independent Angles class to Boolean sets in the plane (Boolean models) whose probability distibutions are invariant with respect to the group of Euclidean motions of the plane.

Simulation study in Probabilistic Boolean Network models for genetic regulatory networks

Probabilistic Boolean Network (PBN) is widely used to model genetic regulatory networks. Evolution of the PBN is according to the transition probability matrix. Steady-state (long-run behaviour) analysis is a key aspect in studying the dynamics of genetic regulatory networks. In this paper, an efficient method to construct the sparse transition probability matrix is proposed, and the power method based on the sparse matrix-vector multiplication is applied to compute the steady-state probability distribution. Such methods provide a tool for us to study the sensitivity of the steady-state distribution to the influence of input genes, gene connections and Boolean networks. Simulation results based on a real network are given to illustrate the method and to demonstrate the steady-state analysis.

Logic and Computational Complexity for Boolean Information Retrieval

We study the complexity of query satisfiability and entailment for the Boolean information retrieval models WP and AWV using techniques from propositional logic and computational complexity. WP and AWV can be used to represent and query textual information under the Boolean model using the concept of attribute with values of type text, the concept of word, and word proximity constraints. Variations of WP and AWP are in use in most deployed digital libraries using the Boolean model, text extenders for relational database systems (e.g., Oracle 10g), search engines, and P2P systems for information retrieval and filtering

Visual setup of logical models of signaling and regulatory networks with ProMoT.

BackgroundThe analysis of biochemical networks using a logical (Boolean) description is an important approach in Systems Biology. Recently, new methods have been proposed to analyze large signaling and regulatory networks using this formalism. Even though there is a large number of tools to set up models describing biological networks using a biochemical (kinetic) formalism, however, they do not support logical models.ResultsHerein we present a flexible framework for setting up large logical models in a visual manner with the software tool ProMoT. An easily extendible library, ProMoT's inherent modularity and object-oriented concept as well as adaptive visualization techniques provide a versatile environment. Both the graphical and the textual description of the logical model can be exported to different formats.ConclusionNew features of ProMoT facilitate an efficient set-up of large Boolean models of biochemical interaction networks. The modeling environment is flexible; it can easily be adapted to specific requirements, and new extensions can be introduced. ProMoT is freely available from .

A recursive method for breaking complex logic loops in Boolean system models

A finite iterative method is developed for solving system fault trees containing logic loops. The procedure yields correct expressions for the top events as well as for all intermediate gates.

Analysis of gene regulatory network models with graded and binary transcriptional responses

The steep sigmoid framework developed by Plahte and Kjøglum [Plahte, E., Kjøglum, S., 2005. Analysis and generic properties of gene regulatory networks with graded response functions. Phys. D 201, 150–176, doi:10.1016/j.physd.2004.11.014] provides a uniform description of gene regulatory networks in which there may be both graded and binary transcriptional responses, as well as a method for analysing the models developed. Here we extend this framework. We show that there is a relation between the location of steady states and the feedback structure of a system, thus generalising existing results for Boolean type models. In addition, we justify underlying assumptions and generic features of the modelling framework in terms of biology and generalise the overall approach to take into account that each transcription factor only regulates one gene at a given threshold. By this assumption, the analysis of the models are greatly simplified.