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@lbl.gov
http://genomics.lbl.gov
Can Molecular Biology Become Cellular Engineering?
Prediction, Control and Design
Funding: ONR, DOE, DARPA, NIH
Adult
1.5 mm long
~1000 cells
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.
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.
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
Single cells in the
wave
Human neutrophil tracking a
Staphylococcus.
Drosophila
melanogaster
embryo
developing
Myxococcus
xanthus
colony
undergoing traveling
wave self
-
organization on its
way to sporulation.
Complex Behaviors of Cellular Systems
Photos from everyone but me
>25 signals
Inhomogenous environment
Non
-
simple geometrical space
Site of infection
Primary chemoattractant
Response cytokine
Another
Cytokine
Actin
PIPKg
PIPK
P10
PIP4,5
PIP3,4,5
Rac
Goals of “Network Biology Approach”
SHiP
Plx
or
or
or
1.
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.
2.
By comparing networks from
many organisms, deducing the
engineering principles by which
cell perform particular functions
and deal with uncertainty in their
environment.
These networks become quite large and complex
Tucker, Gera, and Uetz (2001)
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)
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
What would success look like?
1.
Very rapid deduction of new cellular function from
well
-
controlled experiments
2.
Rapid prediction of controllable aspects of cell function
and design of control protocols
3.
Robust forward design of novel function and systems
1.
Need for a rapid manufacture protocol
4.
Identification of novel computational and control
algorithms that can be abstracted into machinery.
Building a Rational Engineering Tool for
Biosystems
SPICE for Cells?
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.
Designed 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
SPICE: Simulation Program for Integrated Circuit Evaluation
Parts
database
From
subcircuit
database
Integrated
circuit
database
Automated
fault
diagnosis
Genome Sequence
Genes/Regulatory Sequence
Proteins/RNAs
Other Chemical Species
Biochemical Pathways/Dynamics
Cytomechanical/Spatial Processes
Cell Development/Signaling
Tissue Physiology/Development
Organism Behavior
Tools for “multilevel” analysis
Finding Parts
Physical properties
Cellular networks
Assembled Genomes
Polymorphisms
ORF Identification
DNA Regulatory ID
RNA Gene ID
mRNA Regulation
mRNA Splicing
RNA 2
°
Struct
Protein Sequence ID
Homology Modeling
RNA 3
°
Struct
Protein 3
°
Struct
Protein Function ID
RNA Function ID
Molecular Interaction
Prediction
Chromatin Structure
Macromolecular
Dynamics
Biochemical and Genetic Network Prediction
Metabolic/Biosynthetic
Analysis & Engineering
Signal Transduction
Analysis
Gene expression/network
Analysis
Cytomechanical
Analysis
Morphogenesis &
Development
Homeostasis
Cell
-
Cell
Interactions
Tissue Mechanics
Cell Behavior &
Engineering
Organismal Behavior
Epidemiological/Ecological
Models
Cancer
Dynamics
Multi
-
organism function: e.g.
Infectious disease
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
System Architecture
Local DB
GUI
Database access layer
Database
Reflection of
remote DBs
Remote DBs
GUI component server
Analysis Kernels
Component
manager
component 1
component 2
component 3
component n
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.
GUI must represent biological models at different levels of abstraction.
Database
Local DB
Remote DBs
Database
access layer
•
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
Reflection of
remote DBs
Knowledge representation for data classification and analysis
Data Ontology
Analysis Ontology
Mathematical Ontology
Cellular Ontology
Aid to user in decision making.
Allows for data fusion.
Motion
, Shape Change, Transport, Transformation
Differential, Algebraic, Stochastic
Leaves of the ontologies: Cellular
Gene expression
Transcription
Translation
Initiation
RBS Binding
Forms a hierarchy for modeling and data
Elongation
Termination
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/Local stability
Molecular Dynamics
Chemical Master Equation
Langevin Equations
Deterministic Kinetics
Reaction
-
Diffusion
Discrete Mechanical
Continuum Mechanical
Statistical/Thermodynamic
Analysis kernel
Component
manager
Mathematica dispatcher
MATLAB dispatcher
Bio/Spice simulator
component n
•
Configuration XML
•
Client/Server registry model
Automated Analysis/Target Hypothesis
Data
Generation
Raw Data
Storage
Data Filtering
and Mining
Data Linkage
to
Knowledge
Base
Knowledge
Base
Population
Gene
Expression
Protein
Expression
Metabolite
Expression
Cellular
Physiologic
Imaging
Literature
Database
Annotation
Network
Construction
Network
Deduction
Statistical
Data
Modeling/QC
“Significant”
Effect
Detection
Phenotype
Catalog
Biological
Sub
-
model
Production
Network
Analytical
Suite
Network
Simulation
Suite
Bioinformatic
Tool
Integration
Stage I
Stage II
Stage III
Stage IV
Stage V
Perturbation
Sequence
Design
Experimental
Replication
Specific Hypothesis
Testing
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.
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