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Related Concept Videos

Probability in Statistics01:14

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...
Probability Laws01:49

Probability Laws

Overview
Propagation of Uncertainty from Random Error00:59

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...
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
Binomial Probability Distribution01:15

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 Distributions01:32

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...

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Related Experiment Videos

Sampling-rate-dependent probabilistic Boolean networks.

Golnaz Vahedi1, Babak Faryabi, Jean-Francois Chamberland

  • 1Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA. golnaz@tamu.edu

Journal of Theoretical Biology
|September 1, 2009
PubMed
Summary
This summary is machine-generated.

External control of genetic networks can prevent disease states. This study introduces a sampling-rate-dependent probabilistic Boolean network to improve intervention strategies by accounting for system observation intervals.

Related Experiment Videos

Area of Science:

  • Systems Biology
  • Computational Biology
  • Network Medicine

Background:

  • External control of genetic regulatory networks aims to prevent undesirable disease states.
  • Current intervention methods for probabilistic Boolean networks (PBNs) rely on continuous system observation, which is often impractical.
  • Real-world applications typically involve discrete-time sampling and intervention decisions at specific intervals.

Purpose of the Study:

  • To propose a novel framework for external control of genetic regulatory networks that accounts for system sampling rates.
  • To extend the existing probabilistic Boolean network model to incorporate sampling-rate dependency.

Main Methods:

  • Development of the sampling-rate-dependent probabilistic Boolean network (SRD-PBN) model.
  • Analysis of how discrete time sampling affects intervention policy implementation in genetic networks.

Main Results:

  • The proposed SRD-PBN framework effectively captures the impact of system sampling rates on control strategies.
  • This model provides a more realistic approach for designing interventions in biological systems with intermittent observations.

Conclusions:

  • The SRD-PBN offers a significant advancement for the external control of genetic regulatory networks, particularly in medical applications.
  • This framework enables more practical and effective intervention policies by considering the inherent limitations of discrete sampling.