Bio/Spice: Towards a Network

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Nov 15, 2013 (3 years and 9 months ago)

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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@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.