tailieunhanh - Báo cáo hóa học: " Detecting controlling nodes of boolean regulatory networks"

Tuyển tập các báo cáo nghiên cứu về hóa học được đăng trên tạp chí sinh học đề tài :Detecting controlling nodes of boolean regulatory networks | Schober et al. EURASIP Journal on Bioinformatics and Systems Biology 2011 2011 6 http content 2011 1 6 s EURASIP Journal on Bioinformatics and Systems Biology a SpringerOpen Journal RESEARCH Open Access Detecting controlling nodes of boolean regulatory networks 1 1 12 1 Steffen Schober David Kracht Reinhard Heckel and Martin Bossert Abstract Boolean models of regulatory networks are assumed to be tolerant to perturbations. That qualitatively implies that each function can only depend on a few nodes. Biologically motivated constraints further show that functions found in Boolean regulatory networks belong to certain classes of functions for example the unate functions. It turns out that these classes have specific properties in the Fourier domain. That motivates us to study the problem of detecting controlling nodes in classes of Boolean networks using spectral techniques. We consider networks with unbalanced functions and functions of an average sensitivity less than 3k where k is the number of controlling variables for a function. Further we consider the class of 1-low networks which include unate networks linear threshold networks and networks with nested canalyzing functions. We show that the application of spectral learning algorithms leads to both better time and sample complexity for the detection of controlling nodes compared with algorithms based on exhaustive search. For a particular algorithm we state analytical upper bounds on the number of samples needed to find the controlling nodes of the Boolean functions. Further improved algorithms for detecting controlling nodes in large-scale unate networks are given and numerically studied. 1 Introduction The reconstruction of genetic regulatory networks using possibly noisy expression data is a contemporary problem in systems biology. Modern measurement methods for example the so-called microarrays allow measuring the expression levels of thousands of genes under particular conditions. A

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