GSOC2011 Mocapy

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Mocapy++Biopython: from data to probabilistic models of biomolecules

Mocapy++ is a machine learning toolkit for training and using Bayesian networks. It has been used to develop probabilistic models of biomolecular structures. The goal of this project is to develop a Python interface to Mocapy++ and integrate it with Biopython. This will allow the training of a probabilistic model using data extracted from a database. The integration of Mocapy++ with Biopython will provide a strong support for the field of protein structure prediction, design and simulation.

Discovering the structure of biomolecules is one of the biggest problems in biology. Given an amino acid or base sequence, what is the three dimensional structure? One approach to biomolecular structure prediction is the construction of probabilistic models. A Bayesian network is a probabilistic model composed of a set of variables and their joint probability distribution, represented as a directed acyclic graph. A dynamic Bayesian network is a Bayesian network that represents sequences of variables. These sequences can be time-series or sequences of symbols, such as protein sequences. Directional statistics is concerned mainly with observations which are unit vectors in the plane or in three-dimensional space. The sample space is typically a circle or a sphere. There must be special directional methods which take into account the structure of the sample spaces. The union of graphical models and directional statistics allows the development of probabilistic models of biomolecular structures. Through the use of dynamic Bayesian networks with directional output it becomes possible to construct a joint probability distribution over sequence and structure. Biomolecular structures can be represented in a geometrically natural, continuous space. Mocapy++ is an open source toolkit for inference and learning using dynamic Bayesian networks that provides support for directional statistics. Mocapy++ is excellent for constructing probabilistic models of biomolecular structures; it has been used to develop models of protein and RNA structure in atomic detail. Mocapy++ is used in several high-impact publications, and will form the core of the molecular modeling package Phaistos, which will be released soon. The goal of this project is to develop a highly useful Python interface to Mocapy++, and to integrate that interface with the Biopython project. Through the Bio.PDB module, Biopython provides excellent functionality for data mining biomolecular structure databases. Integrating Mocapy++ and Biopython will allow training a probabilistic model using data extracted from a database. Integrating Mocapy++ with Biopython will create a powerful toolkit for researchers to quickly implement and test new ideas, try a variety of approaches and refine their methods. It will provide strong support for the field of biomolecular structure prediction, design, and simulation.


Work Plan

Gain understanding of SEM and directional statistics

  • Review the theory behind machine learning for bioinformatics, Markov chain Monte Carlo and dynamic Bayesian networks.
  • Build the theoretical background on the algorithms used in Mocapy++, such as parameter learning of Bayesian networks using Stochastic Expectation Maximization (SEM).


Study Mocapy++'s use cases

  • Read several papers and attempt to replicate part of the experiments described using Mocapy++.
  • Get a better understanding of biological sequence analysis done through probabilistic models of proteins and nucleic acids.


Work with Mocapy++

  • Understand Mocapy++'s internal architecture and algorithms by exploring its source code and running its test cases.
  • Research other applications of Mocapy++ in Bioinformatics.


Design Mocapy++'s Python interface

  • Explore the source code of Biopython to understand its design and implementation. The Mocapy++ interface to be included in Biopython must be made compatible with the methods of solving problems in Biopython.
  • Design a Python interface for Mocapy++, based on its data structures and algorithms. Examine Mocapy++'s use cases and existing test cases to provide guidance for the interface design.


Implement Python bindings

  • Implement test cases in Python using the new interface to Mocapy++.
  • Implement python bindings for the defined interface.


Explore Mocapy++'s applications

  • Develop example applications that involve data mining of biomolecular structure databases using Biopython.
  • Formulate probabilistic models using Python-Mocapy++. Apply the models to solve biological problems. Examples of problems that can be solved using dynamic Bayesian networks include deciding if a pair of sequences is evolutionarily related, finding sequences which are homologous to a known evolutionary family and predicting RNA secondary structure.


Timeline

Before April 25

  • Get involved with the Biopython project and community.
  • Learn the statistical methods used in Mocapy++ and relevant concepts in structural biology.


Community bonding period

April 25 - May 22 (4 weeks)

  • Familiarize myself with Mocapy++'s functionality and architecture.
  • Configure a development environment and explore the Mocapy++ and Bio.PDB source code more thoroughly. Document the code during exploration.
  • Study Mocapy++ and Bio.PDB test cases in order to understand the Mocapy++ interface requirements.
  • Compare options to create python bindings to C++ code (Boost Python, Cython, Swig). Perform the necessary assesments and gathering of requirements to determine the best library to create the Python bindings.
  • Try fixing bugs in Biopython in order to get familiar with the development cycle.


Begin of coding phase

May 23 - June 5 (2 weeks)

  • Design and implement test cases for the Mocapy++ Python interface. The tests must include data structures such as the Multi-Dimentional array; and statistic models such as the hidden Markov Models, a special case of dynamic Bayesian networks.
  • Identify functions and data structures which impose challenges for the creation of Python bindings.
  • Implement Python bindings for the core functions and data structures, especially the ones which are straightforward to wrap.


June 6 - June 19 (2 weeks)

  • Implement Python bindings for the remaining Mocapy++ functionality that composes its interface.
  • Run and improve the previously designed test cases. Make sure the newly designed interface meets the requirements of the use cases.
  • Do performance analysis (code profiling) in order to make sure the Python interface of Mocapy++ is fast enough to be usable.
  • Try other options to create Python bindings to C++ code, in case the implementation doesn't meet the speed requirements.


June 20 - July 3 (2 weeks)

  • Integrate the Mocapy++ Python interface with Biopython.
  • Implement integration tests for Mocapy++ Biopython.
  • Test the operation of each module of the modified source code.


June 4 - July 10 (1 week)

  • Make further changes in the code to improve robustness and functionality.
  • Gather, organize, and improve the documentation written during the previous weeks.


Mid-term evaluations (July 11)

July 11 - July 24 (2 weeks)

  • Design probabilistic models for biological problems. Solve them using Mocapy++ Biopython.
  • Offer the code to the community for testing and suggestions for further functionality.


July 25 - August 7 (2 weeks)

  • Create applications which use the data mining provided by Bio.PDB together with the machine learning features of Mocapy++.
  • Profile the code and work on speed and robustness improvements.


August 8 - August 14 (1 week)

  • Perform last review and improvements on code and documentation.


August 15 - August 21 (1 week)

  • A buffer for any unpredictable delay.


Firm pencils down (August 22)


External References:

Mocapy++Biopython -Box of ideas

Mocapy++ Bindings Prototype

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