توضیحات
ABSTRACT
To randomly generate a network of dynamically interacting agents is very convenient for modeling systems where the underlying structure or performance is not yet explicitly recognized. The RBN model consists of n nodes, each of which have k randomly chosen input links, that determine its value σi(t) at time step t from the set Σ = {0,1} where i = 1, . . . , n. At time t + 1 the state σi(t + 1) is completely determined by a randomly chosen Boolean function fi : Σk → Σ from its k inputs at a previous time step, i.e. σi(t + 1) = fi(σi1(t), . . . , σik(t)). These functions can be computed via a lookup table –one for each function. There have been several critiques for both, the Boolean idealization (Yingjun et al., 2007; Wittmann et al., 2009), and the temporal discretization (Bagley and Glass, 1996; Kappler et al., 2003) of RBNs. On the other hand, methods that justify the Boolean case have been proven effective, e.g., in Shmulevich and Zhang (2002); Karlebach (2013). Multi-state approaches (Wuensche, 1998; Sole et al. ´ , 2000; Wittmann and Theis, 2011) emerged from the consideration that in some natural systems, the discreteness of the actions performed at individual levels is well defined and thus, the dynamic assumption from a discrete model is fairly plausible; nevertheless, transitions from one state to another may not be sufficiently sharp for being represented by Boolean variables. Multi- valued variables that range over multiple (not only binary) states enhance the capability of the model to accurately portray gradual changes on the dynamic individual behavior of the system.
INTRODUCTION
No model can be considered effective if fundamentally it is more complicated than what it’s trying to represent. However, extreme simplification may potentially overlook important non-primary features, or even neglect the possibility to represent ambiguous or unclear observations. Therefore, achieving a balance between parsimonious and detailed models is of utmost importance for science and engineering. Over the past few decades, random Boolean networks (RBNs) (Kauffman, 1969) have become popular models for genetic regulatory networks. This popularity is associated with the fact that RBNs are very general models. No functionality or structure is particularly assumed when constructing them. However, the Boolean idealization has been constantly criticized based on the assumption that constraining the variables of the model to have only two possible values (0 and 1) entails a loss of dynamical information in the analysis of real gene expression data.
Year: 2013
Publisher : Fourteenth International Conference on the Synthesis and Simulation of Living Systems
By : Octavio B. Zapata and Carlos Gershenson
File Information: English Language/ 2 Page / size: 543 KB
Download: click
سال : 2013
ناشر : Fourteenth International Conference on the Synthesis and Simulation of Living Systems
کاری از : Octavio B. Zapata and Carlos Gershenson
اطلاعات فایل : زبان انگلیسی / 2 صفحه / حجم : KB 543
لینک دانلود : روی همین لینک کلیک کنید
نقد و بررسیها
هنوز بررسیای ثبت نشده است.