Creating Metabolic Network Models using Text Mining and Expert Knowledge J.A. Dickerson, D. Berleant, Z. Cox, W. Qi, and E. Wurtele Iowa State University

Creating Metabolic Network

Models using Text Miningand Expert Knowledge

J.A. Dickerson, D. Berleant, Z. Cox,W. Qi, and E. WurteleIowa State University

22 April 2003 © 2003, SNU BioIntelligence Lab, http://bi.snu.ac.kr/ 2

Outline

• Introduction to the Gene Expression Toolkit

• Graphical Network Models• PathBinder• Fuzzy Cognitive Map (FCM)

Modeling Tool• Example• Conclusions and Future Work


Gene Expression Toolkit

• PathBinder: Automatic document processing system that automates the process of pulling relationships from publications.

• ChipView: Explanatory models synthesized by clustering techniques together with a genetic algorithm-based data mining tool

• FCModeler: Predictive models summarize known regulatory relationships in fuzzy cognitive maps (FCMs).


Gene Expression Toolkit Components


Metabolic Networks• Metabolic networks form the basis for the net

accumulation of biomolecules in organisms.• Regulatory networks modulate the action of

metabolic networks, leading to physiological and morphological changes.

• Modeling tool represents the interactions within and between these networks– Nodes represent specific biochemicals such as proteins, RNA, and small molecules, or stimuli, such as light, heat, or nutrients.– Links- interactions between nodes


Link Types• Conversion link (black arrow), a node is converted into

another node, and used up in the process.• Regulatory link (green and red arrows), node

activates or deactivates another node, not used up.• Catalytic link (blue arrows) an enzyme that enables a

chemical conversion and not used up in the process.


What is PathBinder?

• PathBinder extracts node-node interactions from MEDLINE

• The processing unit is single sentences: abstracts are parsed into individual sentences, which in turn are searched for the presence of a pair of node names and an interaction-related Verb.


Features of PathBinder

• Users can select one or more protein name from a list of protein names.

• The query can contain one or more “starting terms,”

and use words like “And” and “Or” to connect them.


Program Operation

• Processing steps– user input– document pre-

processing– find synonyms– synonym extraction– document retrieval– sentences extraction– multi- display


Format of synonym list & sentence index


FCModeler Goals• Capture the intuitions of biologists and provide a

modeling framework for assessing large amounts of information

• Goals of this work:– Model the data matrix as a fuzzy cognitive map and

explore ways to combine the information from different FCMs.

– Locate and visualize closely coupled subgraphs or signal transduction modules.

– Develop simulation tools for modeling intervention in the network (e.g. what happens when a node is shut off) and search for critical paths and control points in the network.


Fuzzy Cognitive Maps• Fuzzy Cognitive Maps to show

interactions between different variables– Fuzzy signed digraphs represent causal

flow between objects or concepts– Constructed using expert knowledge or

neural learning


Simple Fuzzy Cognitive Maps

• Graph edges are {-1,0,1}. Used when the direction of causality is agreed on, but not its degree

• Concepts either occur or do not occur

• Can test out hypotheses• Concepts are usually summed then

thresholded to get the next state


Nested FCMs• If more information is known about

the links between concepts, then more detailed functional links can combine information– Differential equations– Fuzzy approximators


FCM Combination• Can combine FCMs additively


Gibberellin Example

• Create metabolic map of system using expert knowledge

• PathBinder literature search for new relationships

• Add relationships to metabolic map• Check implications of the additions


Initial Metabolic Network


PathBinder Results• Query: Find sentences containing (either

gibberellin, gibberellins, or GA) AND (either SPY, SPY-4, SPY-5, or SPY-7)

• Sentence:”Here we describe detailed studies of the effects of two of these suppressors, spy-7 and gar2-1, on several different GA-responsive growth processes (seed germination, vegetative growth, stem elongation, chlorophyll accumulation, and flowering) and on the in plant amounts of active and inactive GA species.”

• Source:UI - 99214450 Peng J, Richards DE, Moritz T, Cano-Delgado A, Harberd NP, Plant Physiol 1999 Apr;119(4):1199-208.


Updated Metabolic Network


Conclusions

• Integration of FCModeler with PathBinder allows biologists to gather and combine information from– Literature databases– Their expert knowledge– Public databases of mRNA results

• This gives a more complete picture of the metabolism

Documents

Creating Metabolic Network Models using Text Mining and Expert Knowledge J.A. Dickerson, D. Berleant, Z. Cox, W. Qi, and E. Wurtele Iowa State University