Modeling complex gene interacting systems as Boolean networks lead to a significant simplification of computational investigation. This can be achieved by discretization of the expression level to ON or OFF states and classifying the interactions to inhibitory and activating. In this respect, Boolean functions are responsible for the evolution of the binary elements of the Boolean networks. In this thesis, we investigate the mostly used Boolean functions in modeling gene regulatory networks. Moreover, we introduce a new type of function with strong inhibitory namely the veto function. Our computational and analytic studies on the verity of the networks capable of constructing the same State Transition Graph lead to define a new concept namely the “degeneracy” of Boolean functions. We further derive analytically the sensitivity of the Boolean functions to perturbations. It turns out that the veto function forms the most robust dynamics. Furthermore, we verify the applicability of veto function to model the yeast cell cycle networks. In particular, we show that in an intracellular signal transduction network [Helikar et al, PNAS (2008)], the functions with veto are over-represented by a factor exceeding the over-representation of threshold functions and canalyzing functions in the same system. The statistics of the connections of the functional networks are studied in detail. Finally, we look at a different scale of biological phenomena using a binary model. We propose a simple correlation-based model to describe the pattern formation of Fly eye. Specifically, we model two different procedures of Fly eye formation, and provide a generic approach for Fly eye simulation.

LD2668 .T4 1966 A677 / Master of Science

Call number: LD2668 .T4 1984 T33 / Master of Science

New tools are available for reconstructing the attractor basins of discrete dynamical networks where state-space is linked according the network's dynamics. In this thesis the computer software "Discrete Dynamics Lab" is applied to examine simple networks ranging from cellular automata (CA) to random Boolean networks (RBN), that have been widely applied as idealised models of physical and biological systems, to search for general principles underlying their dynamics. The algorithms and methods for generating pre-images for both CA and RBN, and reconstructing and representing attractor basins are described, and also considered in the mathematical context of random directed graphs. RBN and CA provide contrasting notions of self-organisation. RBN provide models of hierarchical categorisation in biology, for example memory in neural and genomic networks. CA provide models at the lower level of emergent complex pattern. New measures and results are presented on CA attractor basins and how they relate to measures on local dynamics and the Z parameter, characterising ordered to "complex" to chaotic behaviour. A method is described for classifying CA rules by an entropy-variance measure which allows glider rules and related complex rules to be found automatically giving a virtually unlimited sample for further study. The dynamics of RBN and intermediate network architectures are examined in the context of memory, where categorisation occurs at the roots of subtrees as well as at attractors. Learning algorithms are proposed for "sculpting" the basin of attraction field. RBN are proposed as a possible neural network model, and also discussed as a model of genomic regulatory networks, where cell types have been explained as attractors

No description available.

A wide range of problems can be formalized as a set of constraints that need to be satisfied. In fact, such a model is called a constraint satisfaction problem (CSP). Another way to represent a problem is to express it as a formula in propositional logic, or, in other words, a Boolean satisfiability problem (SAT). In the quest to find efficient algorithms for solving instances of CSP and SAT specialised software has been developed. It is, however, not clear when should we choose a SAT-solver over a constraint solver (and vice versa). CSP-solvers are known for their domain-specific reasoning, whereas SAT-solvers are considered to be remarkably fast on Boolean instances. In this thesis we tackle these issues by investigating the connections between CSP and SAT. In order to answer the question why SAT-solvers are so efficient on certain classes of CSP instances, we first present the various ways one can encode a CSP instance into SAT. Next, we show that with some encodings SAT-solvers simulate the effects of enforcing a form of local consistency, called k-consistency, in expected polynomial-time. Thus SAT-solvers are able to solve CSP instances of bounded-width structure efficiently in contrast to conventional constraint solvers. By considering the various ways one can encode CSP domains into SAT, we give theoretical reasons for choosing a particular SAT encoding for several important classes of CSP instances. In particular, we show that with this encoding many problem instances that can be solved in polynomial-time will still be easily solvable once they are translated into SAT. Furthermore, we show that this is not true for several other encodings. Finally, we compare the various ways one can use a SAT-solver to solve the classical problem of the pigeonhole principle. We perform both theoretical and empirical comparison of the various encodings. We conclude that none of the known encodings for the classical representation of the problem will result in an efficiently-solvable SAT instance. Thus in this case constraint solvers are a much better choice.

by So Kwok Yu, Andy. / Bibliography: leaves 64-65 / Thesis (M.Ph.)--Chinese University of Hong Kong, 1985

The field of property testing has been studied for decades, and Boolean functions are among the most classical subjects to study in this area. In this thesis we consider the property testing of Boolean functions: distinguishing whether an unknown Boolean function has some certain property (or equivalently, belongs to a certain class of functions), or is far from having this property. We study this problem under both the standard setting, where the distance between functions is measured with respect to the uniform distribution, as well as the distribution-free setting, where the distance is measured with respect to a fixed but unknown distribution. We obtain both new upper bounds and lower bounds for the query complexity of testing various properties of Boolean functions: - Under the standard model of property testing, we prove a lower bound of \Omega(n^{1/3}) for the query complexity of any adaptive algorithm that tests whether an n-variable Boolean function is monotone, improving the previous best lower bound of \Omega(n^{1/4}) by Belov and Blais in 2015. We also prove a lower bound of \Omega(n^{2/3}) for adaptive algorithms, and a lower bound of \Omega(n) for non-adaptive algorithms with one-sided errors that test unateness, a natural generalization of monotonicity. The latter lower bound matches the previous upper bound proved by Chakrabarty and Seshadhri in 2016, up to poly-logarithmic factors of n. - We also study the distribution-free testing of k-juntas, where a function is a k-junta if it depends on at most k out of its n input variables. The standard property testing of k-juntas under the uniform distribution has been well understood: it has been shown that, for adaptive testing of k-juntas the optimal query complexity is \Theta(k); and for non-adaptive testing of k-juntas it is \Theta(k^{3/2}). Both bounds are tight up to poly-logarithmic factors of k. However, this problem is far from clear under the more general setting of distribution-free testing. Previous results only imply an O(2^k)-query algorithm for distribution-free testing of k-juntas, and besides lower bounds under the uniform distribution setting that naturally extend to this more general setting, no other results were known from the lower bound side. We significantly improve these results with an O(k^2)-query adaptive distribution-free tester for k-juntas, as well as an exponential lower bound of \Omega(2^{k/3}) for the query complexity of non-adaptive distribution-free testers for this problem. These results illustrate the hardness of distribution-free testing and also the significant role of adaptivity under this setting. - In the end we also study distribution-free testing of other basic Boolean functions. Under the distribution-free setting, a lower bound of \Omega(n^{1/5}) was proved for testing of conjunctions, decision lists, and linear threshold functions by Glasner and Servedio in 2009, and an O(n^{1/3})-query algorithm for testing monotone conjunctions was shown by Dolev and Ron in 2011. Building on techniques developed in these two papers, we improve these lower bounds to \Omega(n^{1/3}), and specifically for the class of conjunctions we present an adaptive algorithm with query complexity O(n^{1/3}). Our lower and upper bounds are tight for testing conjunctions, up to poly-logarithmic factors of n.

A Boolean matrix is a matrix with elements from the Boolean semiring ({0, 1}, +, x), where the addition and multiplication are as usual with the exception that 1 + 1 = 1. In this thesis we study eight classes of monoids whose elements are Boolean matrices. Green's relations are five equivalence relations and three pre-orders which are defined on an arbitrary monoid M and describe much of its structure. In the monoids we consider the equivalence relations are uninteresting - and in most cases completely trivial - but the pre-orders are not and play a vital part in understanding the structure of the monoids. Each of the three pre-orders in each of the eight classes of monoids can be viewed as a computational decision problem: given two elements of the monoid, are they related by the pre-order? The main focus of this thesis is determining the computational complexity of each of these twenty-four decision problems, which we successfully do for all but one.

In this report, asymptotic upper and lower bounds are given for the minimum number of gates required to compute a function which is only partially specified and for which we allow a certain amount of error. The upper and lower bounds match. Hence, the behavior of these minimum circuit sizes is completely (asymptotically) determined.