## BioBayes

** Category** Cross-Omics>Pathway Analysis/Tools

** Abstract** BioBayes is a software package for Bayesian Inference in Systems Biology.

BioBayes supports standard definitions of mathematical models, and provides a framework for applying methods of Bayesian inference to ordinary differential equation (ODE) models of biochemical systems.

In addition to implementations of general inference and model comparison methods, BioBayes provides an infrastructure for plugging- in user specific methods using standard interfaces, thus enabling fine tailoring of the tool to the user’s specific requirements if needed.

The main benefit of adopting the Bayesian approach to model inference is the consistent propagation of uncertainty through all the stages of analysis and the formal way in which prior knowledge can be included in the modeling process.

This approach allows one to consider noisy observations as a source of data for learning full distributions of beliefs rather than restricting oneself to the most plausible explanation of some phenomenon.

So, instead of making future predictions based on one’s best guess, the Bayesian approach considers all probable outcomes.

BioBayes features/capabilities include:

Model Parameter Inference using Metropolis-Hastings Sampler -- The Metropolis-Hastings sampler is included in the default package distributed with BioBayes.

It utilizes Markov Chain Monte Carlo (MCMC) methods and enables model parameter inference from experimental data for simpler models of biochemical systems.

Users can define the desired prior distributions for model parameters, run this sampler to infer parameter posteriors using one or more experimental datasets, and progress can be monitored via the results pane.

The manufacturer optimizes the proposal distribution of the Metropolis- Hastings sampler for more effective convergence of Markov Chains by scaling the proposal variance proportionally to the local acceptance ratio and also by adjusting the proposal covariance matrix to a local approximation of the posterior distribution as described by Gelman et al. (1995).

This implementation of the Metropolis-Hastings sampler allows users to run several chains at the same time to monitor the convergence of the sampler to the true posterior distribution by comparing within-chain variance of the sample to between-chain variance as proposed by Gelman et al. (1995).

Population-based MCMC -- There is also a population-based MCMC sampler (Jasra et al., 2007) available that can be applied to more complex problems when straightforward Metropolis-Hastings fails to converge, e.g. when using nonlinear oscillator models.

This sampler runs several Markov chains in parallel using a tempered sequence of distributions as their targets.

Moves between different chains in such a sequence of distributions help the sampler to overcome energy barriers and therefore sample more efficiently from multi-modal posterior distributions.

The number of steps in such a sequence can be adjusted by the user.

The convergence of this sampler to the true posterior distribution is judged by using the ˆR statistic over several population-based MCMC samplers run simultaneously.

Model Ranking -- Model ranking using the methods of Bayesian inference can be performed for consistent hypotheses testing (see Vyshemirsky and Girolami, 2008).

To compare two alternative models by the weight of evidence supporting them, one needs to compute a value called the Bayes factor (which is a ratio of the 'marginal likelihoods' for these two alternative models).

In the case of nontrivial models these marginal likelihoods cannot be evaluated precisely, and have to be estimated using Monte Carlo integration procedures.

The manufacturer includes such estimators with BioBayes.

Annealing-Melting Integration -- Annealing-melting integration can be used to compute marginal likelihoods, the quantity used for evidence- based ranking of alternative models.

This algorithm is based on the Population-based MCMC sampler described above.

The samples from the tempered sequence of target distributions are used to estimate the marginal likelihoods with thermodynamic integrals.

Several population-based MCMC samplers are run simultaneously to evaluate their convergence to the true posterior distribution and at the same time the standard deviation of the final estimate is computed using this set of simultaneous samplers.

BioBayes has been updated recently to version 1.2 --

This version includes major bug fixes and the manufacturer strongly recommends all users to update as soon as possible.

The major updates are:

1) Three (3) major crashes have been fixed; the stability of the software is significantly improved.

2) The core of BioBayes has been updated to the latest version of the Eclipse platform (3.4.1) providing significant improvement in user interface compatibility.

3) Problems with importing existing projects from nonstandard locations have been resolved.

4) Major Update: Annealed Importance Sampling algorithm has been corrected.

5) The software updates system has been significantly improved, BioBayes will now fetch new updates automatically.

6) Several optimizations and interface improvements.

The 'BioBayes Download' page contains the following downloadable files --

- a) Windows 32bit version;
- b) Linux x86 version;
- c) Mac OS X (Intel only) version;
- d) Extensive PDF Tutorial;
- e) Models and datasets for the Tutorial;
- f) XML Schema for dataset descriptions;
- g) XML Schema for task definition; and
- h) MATLAB function(s) to convert matrices into BioBayes datasets.

*System Requirements*

Windows 32bit, Linux x86, Mac OS X (Intel only)

*Manufacturer*

- Inference Research Group
- Department of Computing Science
- University of Glasgow, G12 8QQ
- UK

** Manufacturer Web Site**
BioBayes

** Price** Contact manufacturer.

** G6G Abstract Number** 20362

** G6G Manufacturer Number** 104011