Negative local feedbacks in Boolean networks
نویسندگان
چکیده
منابع مشابه
Negative local feedbacks in Boolean networks
We study the asymptotic dynamical properties of Boolean networks without local negative cycle. While the properties of Boolean networks without local cycle or without local positive cycle are rather well understood, recent literature raises the following two questions about networks without local negative cycle. Do they have at least one fixed point? Should all their attractors be fixed points?...
متن کاملLocal negative circuits and fixed points in Boolean networks
To each Boolean function F : {0, 1} → {0, 1} and each point x ∈ {0, 1}, we associate the signed directed graph GF (x) of order n that contains a positive (resp. negative) arc from j to i if the discrete analogue of (∂fi/∂xj)(x) is positive (resp. negative). We then focus on the following open problem: Is the absence of a negative circuit in GF (x) for all x ∈ {0, 1} a sufficient condition for F...
متن کاملLocal negative circuits and fixed points in non-expansive Boolean networks
Given a Boolean function F : {0, 1} → {0, 1}, and a point x in {0, 1}, we represent the discrete Jacobian matrix of F at point x by a signed directed graph GF (x). We then focus on the following open problem: Is the absence of a negative circuit in GF (x) for every x in {0, 1} n a sufficient condition for F to have at least one fixed point? As result, we give a positive answer to this question ...
متن کاملLocal negative circuits and cyclic attractors in Boolean networks with at most five components
We consider the following question on the relationship between the asymptotic behaviours of Boolean networks and their regulatory structures: does the presence of a cyclic attractor imply the existence of a local negative circuit in the regulatory graph? When the number of model components n verifies n ≥ 6, the answer is known to be negative. We show that the question can be translated into a B...
متن کاملRepresenting local structure in Bayesian networks by Boolean functions
A number of studies on learning Bayesian networks have emphasized the importance of exploiting regularities in conditional probability distributions, i.e., local structure. In this paper, we encode local structures as linear combinations of Boolean functions. By using Lasso, we can simultaneously estimate the structure and parameters of the networks from limited data. We demonstrate that the me...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Discrete Applied Mathematics
سال: 2017
ISSN: 0166-218X
DOI: 10.1016/j.dam.2017.01.001