Bio Spice Towards a Network Bioinformatics NIH July 2001

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

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. 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. 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.

Complex Behaviors of Cellular Systems Human neutrophil tracking a Staphylococcus. Myxococcus xanthus colony undergoing traveling wave selforganization on its way to sporulation. Photos from everyone but me Single cells in the wave Drosophila melanogaster embryo developing

Another Cytokine Response cytokine Primary chemoattractant >25 signals Inhomogenous environment Non-simple geometrical space Site of infection

Goals of “Network Biology Approach” 1. From the elementary interactions among the participating models, explain the complex behavior of a cellular function. • P 10 or SHi. P or Plx PIP 4, 5 PIPK Rac Actin PIPKg PIP 3, 4, 5 or 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 Asynchronous Analog Biological Switching Circuit Full knowledge of parts list Full knowledge of “device physics” Full knowledge of interactions 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. Designed and prototyped on a computer (SPICE analysis) Experimental implementation fault tested on computer We need a SPICE-like analysis for biological systems

SPICE: Simulation Program for Integrated Circuit Evaluation Parts database From subcircuit database Automated fault diagnosis Integrated circuit database

Tools for “multilevel” analysis Epidemiological/Ecological Models Organism Behavior Tissue Physiology/Development Cell Development/Signaling Cellular networks Cytomechanical/Spatial Processes Morphogenesis & Development Metabolic/Biosynthetic Analysis & Engineering Organismal Behavior Cell-Cell Interactions Homeostasis Signal Transduction Analysis Gene expression/network Analysis Biochemical and Genetic Network Prediction Molecular Interaction Prediction Biochemical Pathways/Dynamics Other Chemical Species Multi-organism function: e. g. Infectious disease Cell Behavior & Engineering Tissue Mechanics Cytomechanical Analysis Cancer Dynamics Physical properties Protein 3° Struct Macromolecular Dynamics Chromatin Structure Protein Function ID Protein Sequence ID RNA Function ID Homology Modeling RNA 3° Struct m. RNA Splicing RNA 2° Struct Proteins/RNAs m. RNA Regulation Genes/Regulatory Sequence Genome Sequence Finding Parts ORF Identification DNA Regulatory ID Assembled Genomes RNA Gene ID Polymorphisms

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 Database GUI Local DB Remote DBs Reflection of remote DBs Database access layer component 1 component 2 Component manager component 3 GUI component server Analysis Kernels 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 React. DB, Mech. DB, and Param. DB. 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 • 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 Local DB Remote DBs Quality measures: Edited/community Authorities: submission, revision Database access layer Reflection of remote DBs

Knowledge representation for data classification and analysis Aid to user in decision making. Allows for data fusion. Data Ontology Analysis Ontology Mathematical Ontology Cellular Ontology Differential, Algebraic, Stochastic Motion, Shape Change, Transport, Transformation

Leaves of the ontologies: Cellular Gene expression Transcription Initiation Elongation Termination Forms a hierarchy for modeling and data Translation RBS Binding

Levels of Abstraction Physical Mathematical Conceptual Molecular Mechanics ab initio Semiempirical (bioinformatic) Molecular Dynamics Chemical Master Equation Langevin Equations Deterministic Kinetics Reaction-Diffusion Time-scale separation Ensemble averaging Large system limits Global/Local stability Phenomenal Models Boolean Approximations Modularization Discrete Mechanical Continuum Mechanical Statistical/Thermodynamic

Analysis kernel • Configuration XML • Client/Server registry model Mathematica dispatcher MATLAB dispatcher Component manager Bio/Spice simulator component n

Automated Analysis/Target Hypothesis

Specific Hypothesis Testing Perturbation Sequence Design Experimental Replication Gene Expression “Significant” Effect Detection Protein Expression Data Generation Metabolite Expression Raw Data Storage Statistical Data Modeling/QC Data Filtering and Mining Bioinformatic Tool Integration Phenotype Catalog Knowledge Base Population Stage I Network Analytical Suite Network Construction Cellular Physiologic Imaging Literature Database Annotation Network Deduction Network Simulation Suite Biological Sub-model Production Data Linkage to Knowledge Base Stage III Stage IV Stage V

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.