Methods and systems for creating and using comprehensive and data-driven simulations of biological systems for pharmacological and industrial applications

• US 20040088116A1
• Filed: 05/14/2003
• Published: 05/06/2004
• Est. Priority Date: 11/04/2002
• Status: Abandoned Application

• Current Assignee: Gene Network Science Incorporated
• Original Assignee: Gene Network Sciences, Inc
• Inventors: Hill, Colin, Khalil, Iya
• Application Number:
  US10/439,288
• Publication Number:
  US 20040088116A1
• Time in Patent Office: Days
• US Class Current: 702/20
• CPC Class Codes: G16B 20/00 ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
• CPC Class Codes: G16B 40/00 ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• CPC Class Codes: G16B 40/10 Signal processing, e.g. from mass spectrometry [MS] or from PCR
• CPC Class Codes: G16B 5/00 ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
• CPC Class Codes: G16B 5/20 Probabilistic models
• CPC Class Codes: G16B 50/00 ICT programming tools or database systems specially adapted for bioinformatics
• CPC Class Codes: G16B 50/20 Heterogeneous data integration
• CPC Class Codes: Y02A 90/10 Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

Presented herein are techniques and methodologies for creating large-scale data-driven models of biological systems and exemplary applications thereof including drug discovery and industrial applications. Exemplary embodiments include creating a core skeletal simulation (scaleable to any size) from known biological information, collecting quantitative and qualitative experimental data to constrain the simulation, creating a probable reactions database, integrating the core skeletal simulation, the database of probable reactions, and static and dynamical time course measurements to generate an ensemble of biological network structures and their corresponding molecular concentration profiles and phenotypic outcomes that approximate output of the original biological network used for prediction, and finally experimentally validating and iteratively refining the model. The invention further describes methods of taking conventional small-scale models and extending them to comprehensive large-scale models of biological systems. The methods presented further describe ways to create models of arbitrary size and complexity and various ways to incorporate data to account for missing biological information.

Claims

1. A method of creating a scalable simulation of a biological system, comprising:

extracting data from the vast body of public domain information;

rating the biological information to determine the quality of the data;

representing the information in a concise and scalable manner that is automatically translatable to a mathematical representation or set of instructions readable by a human or computer; and

integrating diverse data types to account for missing biological information and to infer the functionality of unknown biological components and their interactions.

2. The method of claim 1 where the information is represented via a concise and scaleable language for representing and simulating biological interactions.

3. The method of claim 2, where the language comprises the Diagrammatic Cell Language.

4. The method of claim 1, where said unkown biological components comprise at least one of genes, proteins, and metabolites.

5. The method of claim 1 where integrating diverse data types includes utilizing data mining tools.