Biological information

management and analysis

as illustrated by malaria research

1. Problems

2. Managing data context

3. Managing and analyzing data

Factors in combating malaria

Scientific: biology, ecology, chemistry, etc.

EnvironmentalCultural/Sociological

Economic Political/Ethical

Scientific layers

Psychology/ Emergent properties of brain andSociology populations

Biology All complexity of element interactions(macromolecules, cells, brain, populations)

Chemistry Properties of "simple" element interactions

Physics Properties w/o inter-element interactions

The labyrinth of biological research

- Which direction to follow and in what way?

- What relevant information is available?

- How to keep a good record of the path?

- How to find useful collaborators?

- What do the results imply?

Researchers are drowning in the sea of information…

Problems with "physicalization" of biology

• Data richness

• Data sharing and integration

• Model-data correspondance

• Understanding bioresearch problems

• Understanding bioresearch constraints

• Tim Berners-Lee, James Hendler. Scientific publishing on the 'semantic web'. Nature Debates, April 2001.

• Jonathan Knight. Negative Results: Null and void. Nature, April 2003.

• Who'd want to work in a team (Editorial). Nature, July 2003.

• Declan Butler. Open-access row leads paper to shed authors. Nature, September 2003.

Information problems in the Nature journal

Information management needs of Anopheles GPH

• Inform scientific community (publications, database submitions, conferences…)

• Prevent loss of information (unpublished results, method details, …)

• Report to administration (progress, problems, …)

• Share and manage supplies (materials, equipment, …)

• Share informational resources (protocols, bibliography, …)

• Facilitate collaboration (share information, co-author documents, …)

IP, France IP, Korea

IP, SenegalIP, Madagascar

Columbia University, USA

(outside collaboration)

… … … …

Permanent, shared information (< 30%) :

Temporary, individual information (100%) :

Sources of Research Information: Status quo

Journals

NotebooksComputer Files

Databases

Permanent, shared information (100%):

Sources of Research Information: Ideal

Integrated Repositories of Structured Data

1. Problems

2. Managing data context

3. Managing and analyzing data

Researcher

AdvisorAdministration

Scientificcommunity

CollaboratorsResearch

group

Flow of research information: at present

Flow of research information: proposed

AdvisorAdministration

Scientificcommunity

CollaboratorsResearch

group

Researcher

Database

Unstructured information:

Research notes, Contracts, Project reports, Clinical trials documentation …

2 types of information

Structured information:

GenBank, Medline, Employee database, Invoice database, …

Forms

Documents

Methods of contributing written information

• Traditional documents - hard to search and manipulate

• Traditional forms - overly constraining, hard to create documents

• Structured documents (New!) - best of both worlds

Malaria surveys were carried out in two rural villages near the town of Ziniaré (35 km northeast of Ouagadougou) in a shrubby savanna of the Mossi plateau . An

intense P. falciparum transmission is detected …

Problems with forms

Project: Measurements of response to …

Experiment: Entomological Observations of …

The ability to resist Plasmodium falciparum malaria is an important adaptive trait of human populations living in …

The results of our comparative study show consistent interethnic differences in P. falciparum infection …

Method: Observations

Different response to P. …

Summary 1

• Biological function is based on infinity of interactions between basic elements

• Biologists are drowning in the complexity of information

• Need to understand biological problems and constraints before applying analytical approaches

• Need to resolve the problem of information storage and retrieval

Form constraints

1. Limited number of categories

2. Limited number of fields per category

3. Constrained field space

4. Limited editing (copy, move, delete, etc.)

5. No coherent document representation

6. Unable to represent complex hierarchical information

Database

iPad middle-layerserver

"3-tier" architecture of the iPad system

iPad EditoriPad Web Portal

iPad Demo

Major Benefits

Monetary savings:+ Less lost work+ Resource optimization

Time savings:+ Faster search+ Faster communication and formatting+ Less lost work

Increase in the quality and quantity of research:+ Useful perspectives+ Improved collaboration+ Improved project management+ More information given to the Institute community+ More information given to the scientific community (in the future)+ A tool to structure scientific data (in the near future)

Drawbacks

- Learning new software (very simple)

- Changing habits (will go away over time, gradual adoption)

Support for structured documents

1. WWW Consortium, industry analysts

2. General systems within the past year

(Microsoft, Arbortext, Altova, etc.)

3. Specific systems in the military

Evolution of information (Tim Berners-Lee)

First Consulting Group, "XML and Pharmaceutical Industry" (2003) :

"In order to be profitable and competitive as they serve our global healthcare needs, drug companies require information systems to help them work efficiently to deliver a high-quality product. With that in mind, momentum is growing to leverage XML technology in the content management and publishing systems, being used by the pharmaceutical industry throughout the drug development lifecycle."

* Interest from Aventis Pharma, Sopra Group, Genset

Gilbane Report, "XML for Content" (2003):

"So what's the biggest problem with XML content? Authoring it… The authoring tools are becoming more capable and people are starting to figure out that the ease of processing XML content can outweigh the pain of creating it, but there is still some way to go."

1. Problems

2. Managing data context

3. Managing and analyzing data

Summary 2

• Data context is important both for information management and for data interpretation

• iPad can be used to structure data context using XML markup

• Structuring data context is the precursor for better structuring of data.

3 Steps to "Paradise"

1. Agree on standard organizational categories

SB-UML

Gene Ontology Bioprocess ontology …

"Dynamic" ontologies

Bioprocessontology

3 Steps to "Paradise"

1. Agree on standard organizational categories- "Dynamic" ontologies, Gene Ontology, Bioprocess ontology, …, SB-UML.

2. Sort information into the ontological categories- Data mining algorythms, Electronic forms, Semantic markup.

<protein>p53</protein><interaction>activates</interaction><gene>CD95</gene>

Dynamic ontology

Entity Property Relation

BioStructure Process Data Method

Molecule MolecularComplex Organelle Organ Tissue Organism

name

alternative names

type

value

Data markup

X protein activates Y gene in A. gambiae salivary glands.

Molecule (name: X, type: protein)

Molecule (name: Y, type: gene)

Relation (name: activates, type: molecular interaction)

Entity (name: A. gambiae, alt. name: Anopheles gambiae, type: organism)

Entity (name: salivary glands, type: organ)

3 Steps to "Paradise"

1. Agree on standard organizational categories- "Dynamic" ontologies, Gene Ontology, Bioprocess ontology, …, SB-UML.

2. Sort information into the ontological categories- Data mining algorythms, Electronic forms, Semantic markup.

3. Develop search, visualization, and analysis tools- Blast, Bioprocess and molecular modeling, Concept network, …

Concept node

- Better global picture to see where to go

- Helpful info along the way

- Organized research process

- Better ways to share data

- Broader impact of results

- Modeling and simulation tools

Summary 1

• Biological function is based on infinity of interactions between basic elements

• Biologists are drowning in the complexity of information

• Need to understand biological problems and constraints before applying analytical approaches

• Need to resolve the problem of information storage and retrieval

Summary 2

• Data context is important both for information management and for data interpretation

• iPad can be used to structure data context using XML markup

• Structuring data context is the precursor for better structuring of data.

Summary 3

• 2 steps for structuring data: ontology + methods for data entry

• Simple "dynamic" ontologies can be used to derive standard "static" ontologies

• iPad-like system can be used to simplify structuring biological data

• Data analysis, modeling, and simulation tools need to be data-driven, generic, and easy to use.