Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Probability Laws

Probability Laws

Binomial Probability Distribution

Binomial Probability Distribution

A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
There are only two possible outcomes,...

Probability Distributions

Probability Distributions

The probability of a random variable x is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson probability...

Probability in Statistics

Probability in Statistics

Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...

Propagation of Uncertainty from Random Error

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...

Poisson Probability Distribution

Poisson Probability Distribution

A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Distribution and enumeration of attractors in probabilistic Boolean networks.

IET systems biology·2009

Same author

Optimal control policy for probabilistic Boolean networks with hard constraints.

IET systems biology·2009

Same author

A transcriptional partner for MAD proteins in TGF-beta signalling.

Nature·1996

Same author

A hierarchical network of interreceptor interactions determines signal transduction by Neu differentiation factor/neuregulin and epidermal growth factor.

Molecular and cellular biology·1996

Same author

p53 levels, functional domains, and DNA damage determine the extent of the apoptotic response of tumor cells.

Genes & development·1996

Same author

A comprehensive large-insert yeast artificial chromosome library for physical mapping of the mouse genome.

Mammalian genome : official journal of the International Mammalian Genome Society·1996

Search research articles

Related Experiment Videos

Generating probabilistic Boolean networks from a prescribed transition probability matrix.

W-K Ching¹, X Chen, N-K Tsing

¹The University of Hong Kong, Advanced Modeling and Applied Computing Laboratory, Department of Mathematics, Hong Kong. wkc@maths.hku.hk

IET Systems Biology

|December 2, 2009

Summary

This study introduces efficient algorithms for building probabilistic Boolean networks (PBNs) from their transition probability matrices. These methods address the inverse problem of network inference using steady-state data, crucial for analyzing gene regulatory networks.

Related Experiment Videos

Area of Science:

Computational Biology
Systems Biology
Bioinformatics

Background:

Probabilistic Boolean networks (PBNs) are widely used for modeling complex genetic regulatory networks.
PBNs can be represented as Markov chain processes defined by a transition probability matrix.
Microarray data is often collected under steady-state conditions, making steady-state analysis important.

Purpose of the Study:

To develop efficient algorithms for constructing PBNs when the transition probability matrix is provided.
To analyze the computational complexity of these newly developed algorithms.
To address the inverse problem of network inference using steady-state data.

Main Methods:

Algorithm development for PBN construction from transition probability matrices.
Complexity analysis of the proposed algorithms.
Application to network inference problems using steady-state data.

Main Results:

Efficient algorithms for constructing PBNs from transition probability matrices have been successfully developed.
The computational complexity of these algorithms has been thoroughly analyzed.
The study provides a valuable approach for network inference from steady-state microarray data.

Conclusions:

The proposed algorithms offer an efficient solution for constructing PBNs given their transition probability matrices.
This work contributes to solving the inverse problem in network inference, particularly relevant for steady-state gene expression data.
The findings are significant for advancing the understanding and modeling of genetic regulatory networks.