BioSpice: Towards a Network Bioinformatics

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Bio/Spice Towards a Network Bioinformatics

• NIH, July 2001
• Adam Arkin
• Howard Hughes Medical Institute
• Departments of Bioengineering and Chemistry
• University of California
• Physical Biosciences Division
• Lawrence Berkeley National Laboratory
• Berkeley, CA 94720
• Aparkin_at_lbl.gov
• http//genomics.lbl.gov

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Can Molecular Biology Become Cellular
Engineering?Prediction, Control and Design

• Funding ONR, DOE, DARPA, NIH

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Genome projects are providing parts lists for the
genetic and protein components of the cellular
circuitry. Bioinformatics analysis of this data
provides protein function and sometimes structure
by homology, partial identification of regulatory
sites on the DNA and functional RNAs. Partial
networks can be constructed by homology to known
biochemical networks. Genetic defects that lead
to disease can also be identified at this level.
Evolutionary relationships among organisms can
also be calculated from this data.
Structural biology provides experimental data on
the 3-dimensional structure of biomolecules and
computational approaches to predicting structure
from sequence and for predicting biomolecular
recognition. Both static and dynamic models of
biomolecular interactions are the basis for
rational drug design and automated biochemical
reaction network prediction. Biochemical studies
also provide much of this information as well as
quantification of the kinetics and thermodynamics
of the interactions.
Ultimately, integration of genomic data and
genome derived data such as that from gene chips,
structural and molecular dynamic data, network
functional analyses and data, will lead to a
quantitative understanding of differential
developmental processes and finally a full
tracing of the molecular basis of development
from fertilized egg to adult organism
Adult 1.5 mm long 1000 cells
Biochemical and genetic network analysis
integrates data from all the steps above to
provide a prediction of cellular system function.
Such analyses provide insight into how cells
process and act upon complex external and
internal signals. These are the fundamental
control mechanisms that 1) lead to partial
penetrance of genotype and maintenance of
population heterogeneity, 2) determine
reliability of cellular function and the
propensity for disease given partial failure of a
network component, 3) govern adaptation of
pathogens to pharmaceutical attack, the stages of
facultative infection and dynamical diseases, and
4) may provide the basis for reversal of
development defects and early detection of
cellular control failure.
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Complex Behaviors of Cellular Systems
Human neutrophil tracking a Staphylococcus.
Myxococcus xanthus colony undergoing traveling
wave self-organization on its way to sporulation.
Single cells in the wave
Drosophila melanogaster embryo developing
Photos from everyone but me
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Another Cytokine
Response cytokine
Primary chemoattractant
gt25 signals Inhomogenous environment Non-simple
geometrical space
Site of infection
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Goals of Network Biology Approach

• From the elementary interactions among the
  participating models, explain the complex
  behavior of a cellular function.
• The Alliance for Cellular Signaling has
  identified over 600 molecules involved in
  G-protein coupled signal transduction.
• By comparing networks from many organisms,
  deducing the engineering principles by which cell
  perform particular functions and deal with
  uncertainty in their environment.

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These networks become quite large and complex
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Tucker, Gera, and Uetz (2001)
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Genetic Engineering and Measurement
Methods for manipulating DNA have become better
and better (Methods for design proteins, etc, are
still not so good) Methods for measuring
cellular components exploding! (Still needs lots
of improvement)
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Goals
From Genome Sequence (and other data) Reverse
Engineer Cellular Network Predict Cellular
Function Diagnose Failures (Disease) Design
Control (Disease Treatments) Forward Engineer
New Function Use discovered control laws for
biomimetic systems
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What would success look like?

• Very rapid deduction of new cellular function
  from well-controlled experiments
• Rapid prediction of controllable aspects of cell
  function and design of control protocols
• Robust forward design of novel function and
  systems
• Need for a rapid manufacture protocol
• Identification of novel computational and control
  algorithms that can be abstracted into machinery.

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Building a Rational Engineering Tool for
Biosystems

• SPICE for Cells?

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Analysis and engineering of cellular circuitry
Courtesy of IBM
From Wasserman Lab, Loyola
Asynchronous Digital Telephone Switching
Circuit Full knowledge of parts list Full
knowledge of device physics Full knowledge of
interactions No one fully understands how this
circuit works!! Its just too complicated. Designe
d and prototyped on a computer (SPICE
analysis) Experimental implementation fault
tested on computer
Asynchronous Analog Biological Switching
Circuit Partial knowledge of parts list Partial
knowledge of device physics Partial knowledge
of interactions No one fully understands how
this circuit works!! Its just too
complicated. We need a SPICE-like analysis for
biological systems
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SPICE Simulation Program for Integrated Circuit
Evaluation
Parts database
From subcircuit database
Integrated circuit database
Automated fault diagnosis
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Tools for multilevel analysis
Cellular networks
Physical properties
Finding Parts
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Design Philosophy and Goals

• Weakly-coupled architecture
• Provides application framework for extensibility
• Highly configurable to non-programmers
• Modular, object-oriented simulation and model
  analysis
• Multiple-layers of simulation, analogous to SPICE
• Full database and knowledge environment
• Realms of current development GUI,
  middleware/kernels, and database

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System Architecture
Database
GUI
Local DB
Reflection of remote DBs
Remote DBs
Database access layer
component 1
component 2
Component manager
component 3
GUI component server
component n
Analysis Kernels
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BIO/SPICE Databasing, simulation and analysis
Bio/Spice A Web-Servable, Biologist-Friendly,
database, analysis and simulation interface was
developed into a true beta product. Interfaces
to ReactDB, MechDB, and ParamDB. With Kernel,
performs basic flux-balance analysis,
stochastic and deterministic kinetics, Scientific
Visualization of results. Notebook/Kernel
design optimized for distributed computing.
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GUI must represent biological models at different
levels of abstraction.
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Database

• Relational, open source
• Local database NCBI / BIND schemas
  modifications
• Reflections of useful remote databases
• API allows common database use among lab tools

Also tracks Data provenance Data type
hypothetical, computed, measured Quality
measures Edited/community Authorities
submission, revision
Local DB
Reflection of remote DBs
Remote DBs
Database access layer
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Knowledge representation for data classification
and analysis
Aid to user in decision making. Allows for data
fusion.
Data Ontology
Analysis Ontology
Mathematical Ontology
Differential, Algebraic, Stochastic
Motion , Shape Change, Transport, Transformation
Cellular Ontology
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Leaves of the ontologies Cellular
Gene expression
Transcription
Translation
Initiation
RBS Binding
Elongation
Termination
Forms a hierarchy for modeling and data
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Levels of Abstraction
Physical Mathematical Conceptual Molecular
Mechanics Time-scale separation Phenomenal
Models ab initio Ensemble averaging Boolean
Approximations Semiempirical Large system
limits Modularization (bioinformatic) Global/Lo
cal stability Molecular Dynamics Chemical Master
Equation Langevin Equations Deterministic
Kinetics Reaction-Diffusion Discrete
Mechanical Continuum Mechanical Statistical/Therm
odynamic
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Analysis kernel

• Configuration XML
• Client/Server registry model

Mathematica dispatcher
MATLAB dispatcher
Component manager
Bio/Spice simulator
component n
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Automated Analysis/Target Hypothesis
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Specific Hypothesis Testing
Perturbation Sequence Design
Experimental Replication
Gene Expression
Significant Effect Detection
Protein Expression
Network Deduction
Metabolite Expression
Data Generation
Raw Data Storage
Data Filtering and Mining
Statistical Data Modeling/QC
Network Analytical Suite
Network Construction
Cellular Physiologic Imaging
Network Simulation Suite
Bioinformatic Tool Integration
Biological Sub-model Production
Phenotype Catalog
Data Linkage to Knowledge Base
Knowledge Base Population
Literature Database Annotation
Stage I
Stage II
Stage III
Stage IV
Stage V
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Conclusions
It is time to move cell biology into a true
engineering discipline To do this we will need
to develop a sytems theory of cell
phenomena Physical models of cellular
processes Precise measurements of many
variables in single cells Abstractions of
processes derived from physical models Theories
of how subprocesses communicate Theories of
network decomposition These circuits are not
like electronic (or electrical) circuits but they
Achieve pretty amazing engineering
feats. Knowledge representation is perhaps the
central challenge Open-source/freeware software
development necessary.