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A Framework for Integrated Biology

CI KB
by

S Mishra

on 13 March 2014

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Transcript of A Framework for Integrated Biology


An approach to address problems in biology that relies on the efficient, systematic and large scale use of high throughput, high resolution measurement
technologies
to generate data required for drawing a comprehensive picture of the structure and function of a
system
under normal, pathological or perturbation states
What Is Integrated Biology?

Case Study - Cell Interaction Knowledgebase of
Immune Cell Functions and their Interactions


Santosh K. Mishra

A presentation for
May 13, 2013
Understand the value you want from your information
Need to know what value one wants to add to their business

Make sure everybody is at the table
It's imperative that everyone works together to create and implement a strategy - it's no use for IT to go down one path while users are heading in different directions. Collaboration should be managed through a cross-functional team, so that everyone's information needs are understood and addressed appropriately

Store with access in mind
fast access to information provides a competitive advantage as well as a happy workforce. It is crucially important to make document retrieval easy for your staff - so implement a clear system which allows your records to be requested and retrieved quickly.

Watch out for hidden costs
The cost of managing information can be considerable but is often hidden, so consider expert help before implementing a programme to help provide the most cost-effective solution.

Implement policies and culture from the top
To maximize value, it needs to come from the top - it's about establishing a enterprise-wide culture of respect and protection for information.
Key Considerations to Realize Value from Information
Choice of a System
Cell Interaction Knowledge Base



Dr. Subhra K. BISWAS
Dr. Andrew GORYACHEV
Dr. Kumar Selvarajoo
Dr.Alexandra POKHILKO
Su CHEN
Arunkumar CHINNASWAMY
Kevin LAM
Hong Sang LOW
Da Jun TOH
Choon Kong YAP
Wan Shi ANG
The Cell Interaction TEAM
6th Annual International Conference on Systems Biology, Boston MA
Publications
38th Annual Meeting of Society of Leukocytes Biology, Oxford, UK
Examples of Rules
Conclusions
The overall goal is to create a centralized repository equipped with analysis tools that will generate cell interaction networks on demand according to user-defined specifications
Goal
International Cytokine Society Conference.
Seoul, S. Korea
Protein expression
Abundance/Degradation
Post translational Mod
P – P interactions
Location
Metabolic pathways
mRNA / RNA Abundance
DNA-Protein Interactions
Genetic Variations
Spontaneous mutations
Chemical mutagenesis
Phenotypic screens
RNA interference
Technologies
Affected
Controls
Clinical Samples
IS
Large Scale Integration
Map Editor Tool
Changes phenotype to
IL10
M2c
Changes phenotype to
M1
TGFb
IL10
M0
IL12
LPS
M0
Using this conceptual model, individual cell interactions can be represented as logical rules, for example, a proliferation rule for the naïve T0 lymphocytes can be described as follows: “If IL2 is present then proliferate and express in the extracellular environment IL2 and express on the surface IL2R”

Using macrophages as our test case, we performed characterization of several phenotypes according to tissue specificity and activation scenarios

Simple yet efficient data mining tool that allows the user to quickly survey differential (protein) expression of molecular markers between several activation phenotypes
Beyond Integration – A Rule Based Approach to Model Cell Interaction

CYTOKINE
CHEMOKINE
Signaling
Inducing and interacting pathways
Stimuli (inducing/inhibiting)
Negative regulators

Link with Cell Maps
Cell Maps
Pathology
Correlation with pathology (human & animals)
Experimental disease models
Associated chemokine/cytokine profiles (gene expression, proteomic data)
Cytokine/chemokine-based therapeutic approaches (experimental or preclinical)
Function
Physiological functions
Knockout phenotypes
Knockout technology used; curated characteristic and figures from literature

Link with Cell Maps
Genomics
Gene sequence, exon and intron structures, and location represented by an interactive Map.
Gene homology
Promoter sequence and TF binding sites
Other information: SNPs, CpG islands etc
Cell Types
Proteomics
Structure (primary, secondary and tertiary) 3D models, post- transcriptional and translational modification, half-life, isoforms.
Physico-chemical properties (MW, pI, KD, EC50, etc.)
Small molecule agonists / antagonists
Bioassays
Gene expression
Gene expression profile of individual cytokines across all available public and private data sets
Closely correlated genes
Mapping of gene expression profiles onto pathway maps
Information Type –
Molecules
Interaction ID
Representation of Cell Interactions in DB
TLR/IL-1 receptor pathway
JAK/STAT pathway
Chemokine receptor signaling
Query:
CELL MAPS
Modeling
Cytokines /
Chemokines
Cell Types
Information Type –
Cell Maps
A Rule Based Approach to Model Cell Interaction
[ Naïve M / M0 ]:

IF
LPS
THEN
changes phenotype to M1
AND
secretes extracellular IL-12
AND
express on the surface MHCII
[ Naïve T cell T0 ]:
IF

IL2

THEN

proliferate

AND

express
extracellular IL2

AND

express on the surface IL2R
Cell Maps
Cell Phenotype Map
Cell Interaction Map
Pathway Map
CELL TYPE
Modeling
TLR4 signaling model
Simulation of cytokine /chemokine expression profile
Cytokines/chemokines
Pathology
Gene expression profiles
Signaling pathways
Functional repertoire
Cytokine/chemokine
Metabolic products
Receptors
Physiological functions
Distribution
Functional phenotypes
Polarization/Tissue/Pathology
Phenotypic markers
Origin/Differentiation
Information Type –
Cell Types
NGS
MicroArrays
Sanger
Protein MicroArray
Mass Spectrometry
Tandem Affinity Purification
Yiest Two Hybrid
FRET
Metabolites
Biomarker Discovery
Toxicity/Efficacy of Drugs
Better understanding of Pathophysiology of disease
NMR
GC-MS
LC-MS
CE-MS
FTIR
LM
EM
STM
The Evolution Of High Resolution Biology
Outline
Integrated Biology
Choice of a System
Framework
A Rule Based Approach
Conclusions
Large repertoire of molecular
and cellular players
Complex dynamic behavior
Has a wealth of intricate intra- and inter- cellular interactions
Domain knowledge expertise
Easily accessible experts in laboratories who would collaborate for "wet-dry" interactions
High demand – Useful as a research and educational tool
Data Rich – Amenable to modeling
Capable of addressing biologically relevant and important questions
Response is highly context dependent
Aberration in molecular state or interaction that causes pathological or clinical conditions
High medical significance
Must provide understanding of Biological and Disease processes - Mechanism of Action
Translatable - linkages to Clinical Data
Cancer
Immunology
Metabolic Disorder
Neurology
Metabolic Syndrome is a very large system with many complexities
Neurology – Do Not have as much data
Immunology – In house expertise trained Immunologists in the team
Immunology – High Demand –
Many PIs with Immunology Labs
Neurology
Metabolic Disorder
Cancer
Complexity of Immune Cell Interactions
Origin/differentiation
Functions
Phenotype
Cytokines/chemokines
Signaling pathways
Transcriptome
Pathology
Genomics
Proteomics
Knockout phenotypes
Pathology
Signaling
Information is scattered over many heterogeneous data sources, making its use and extracting knowledge, laborious, time-consuming and difficult

No single interactive platform with analysis tools that provides comprehensive information on immune cell types, their interacting molecules and the conditions of their interactions

Gap exists between front-line research and established textbook concepts - traditional methods cannot keep up with the fast-evolving progress
According to WHO infectious diseases and cancer take a combined toll of 22 million people annually worldwide

Innate immune cells and their interacting cytokines and chemokines play a fundamental role in the initiation of every immune response against infections, autoimmune diseases and malignancies (cancer)

Innate immune cells and their interacting cytokines/chemokines have emerged as potential targets in the multi-billion dollar market for the pharmaceutical industry
Value
Demand
It must be capable of providing the most comprehensive, up-to-date information pertaining to the specific conditions, cell types and signaling molecules in innate immunity
In Silico Biology
2007; 7(6): 0049
Talks presented at Keystone Symposium
Innate Immunity
NFkB
International symposium on translational research: apoptosis and cancer, Kerala, India
Los Alamos National Laboratory, USA
Framework
Comprehensive and Structured Storage
of Information

Immune Cell Types (e.g. Macrophages, DC, etc.)
Soluble Mediators (Chemokines, Cytokines, Growth Factors)
Physiological and Pathogical Signalling Conditions
Translating Research into Health
Marketing
Research
Discovery
Development
Analysis
Standard Software Systems
Modeling Tools
Simulations Environment
Inference Engines
Literature Mining Tools
Workflows
Presentation
(User Experience)

GUI
Graphical Editors
Movies
Audio
User Feedback
CELL-INTERACTION KNOWLEDGEBASE
CONDITIONS

Constitutive
Inducible stimuli
Diseases states
.
.

MOLECULES


Chemokines/ Cytokines/
Growth factors
.

CELL TYPES

Monocytes
Macrophages
Dendritic Cells
.
.
Conceptual Data Organization
A Web-based Framwork for Integrated Biology
IL-10 cell interaction maps
IL-12 cell interaction maps
and others
Cell interaction maps
Macrophage polarization phenotypes
Tissue macrophage phenotypes
Pathological macrophage phenotypes
Cell Phenotype Maps
Molecular Pathway Maps
Response Vocabulary
Sample Controlled Vocabularies and Ontology
Stimuli
Conditions
Responses
A
B
C
X
Y
Z
Logic of Cell Interaction
Interaction ID
Definition of Phenotype through Cell Phenotype Map
Cell Interaction Map
Provided the Biomedical community with:
A Framework and its implementation for Integrated Biology
An intense informatics-driven comprehensive knowledebase capable of managing and analyzing cell signaling in innate immunity
An integrated system to store, manage, analyze, and visualize biological data
Benefits
An easily accessible and continuously curated and updated knowledgebase
Substantial time savings for retrieving integrated information through this interactive platform
A valuable tool for investigative purposes in immunology (e.g. knowledge warehousing, intelligent data mining, support for hypothesis generation)
A one-stop resource for immunology
Cell Interactions
Highlight
Hide
Remove
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Find elsewhere…
I:\OLDLAPTOP\CCI_DB\Cell_Knowledgebase_Working_Copy\index.htm
Outline
Integrated Biology
Choice of a System
Framework
A Rule Based Approach
Conclusions
Outline
Integrated Biology
Choice of a System
Framework
A Rule Based Approach
Conclusions
Outline
Integrated Biology
Choice of a System
Framework
A Rule Based Approach
Conclusions
Outline
Integrated Biology
Choice of a System
Framework
Research
Conclusions
Content
Output Example - Using Rules
Individuals play and win a game,
but teams win championships
T
ogether
E
veryone
A
chieves
M
ore

Understanding the dynamic outcome of molecular interactions between biological molecules (e.g. DNA, RNA, proteins, lipids, and metabolites) at the pathway, organelle, cell, organ and organism

Founded on the synergistic interplay between biological, technological and computational sciences
Observations

Defective expression of pro-inflammatory cytokines TNFa and IL-1b

Delayed Activation of NF-κB after Lipid A Stimulation in MyD88 KO Macrophages

Higher expression of IFN-inducible chemokines like IP-10

Question

Why MyD88-independent pathway shows delayed activation ?
Conclusion

Computational simulation suggested existence
of multiple “unknown intermediates” to explain the time-delay in MyD88 independent pathway
Kawai et al (2001) J. Immunol.
Kawai et al (1999) Immunity
Akira S et al Nature, July 2004, 499
Late phase
(>120 min)
Early phase
(30-60min)
Using the KB and Cellware
Aberration of Regulation in LTR4 Pathway
Simulation of MyD88 KO Phenotype
Research Questions
What is the molecular mechanism for the regulation of specific phenotype associated genes in macrophages? How are these genes regulated across different phenotypes?
How do different proinflammatory stimuli (LPS, TNF, BAFF) regulate NF-kappaB family members ?
How is the expression of selected LPS-induced cytokine/chemokine genes regulated by different NF-kB family members ?
THANK YOU
Conclusion

Computational simulation suggested existence
of multiple “unknown intermediates” to explain the time-delay in MyD88 independent pathway
Using the KB and Cellware
Simulation of MyD88 KO Phenotype
Full transcript