The Emergence of Systems Biology : The Emergence of Systems Biology Vijay Saraswat
IBM TJ Watson Research Center and
The Pennsylvania State University
Systems Biology : Systems Biology Develop system-level understanding of biological systems
Genomic DNA, Messenger RNA, proteins, information pathways, signaling networks
Intra-cellular systems, Inter-cell regulation…
Cells, Organs, Organisms
~12 orders of magnitude in space and time!
Key question: Function from Structure
How do various components of a biological system interact in order to produce complex biological functions?
How do you design systems with specific properties (e.g. organs from cells)?
Share Formal Theories, Code, Models …
Promises profound advances in Biology and Computer Science Goal: To help the biologist model, simulate, analyze, design and diagnose biological systems.
Systems Biology : Systems Biology Work subsumes past work on mathematical modeling in biology:
Hodgkin-Huxley model for neural firing
Michaelis-Menten equation for Enzyme Kinetics
Gillespie algorithm for Monte-Carlo simulation of stochastic systems.
Bifurcation analysis for Xenopus cell cycle
Flux balance analysis, metabolic control analysis…
Why Now?
Exploiting genomic data
Scale
Across the internet, across space and time.
Integration of computational tools
Integration of new analysis techniques
Collaboration using markup-based interlingua
Moore’s Law! This is not the first time…
Integrating Computation into experimentation : Integrating Computation into experimentation Use all of Comp Sci
Logic and Hybrid systems
Symbolic Analysis Tools
Machine learning and pattern recognition
Algorithms
Databases
Modeling languages
Area is Exploding in interest… : Area is Exploding in interest… Conferences…
BioConcur ‘03
Pacific Sym BioComputing ‘04
International Workshop on Systems Biology
Comp Methods in Sys Bio, 2004
Systeomatics 2004
Websites
www.sbml.org
www.cellml.org
www.systemsbiology.org
Projects
BioSpice (DARPA)
CellML (U Auckland)
SBML
CalTech, U Hertfordshire, Argonne, Virginia, U Conn…
Post-genomic institutes
Harvard/MIT, Princeton
Systems
BioSpice, Charon, Cellerator, COPASI, DBSolve, E-Cell, Gepasi, Jarnac, JDesigner, JigCell, NetBuilder, StochSim, Virtual Cell…
Hybrid Systems : Hybrid Systems Traditional Computer Science
Discrete state, discrete change (assignment)
E.g. Turing Machine
Brittleness:
Small error major impact
Devastating with large code!
Traditional Mathematics
Continuous variables (Reals)
Smooth state change
Mean-value theorem
E.g. computing rocket trajectories
Robustness in the face of change
Stochastic systems (e.g. Brownian motion) Hybrid Systems combine both
Discrete control
Continuous state evolution
Intuition: Run program at every real value.
Approximate by:
Discrete change at an instant
Continuous change in an interval
Primary application areas
Engineering and Control systems
Paper transport
Autonomous vehicles…
And now.. Biological Computation. Emerged in early 90s in the work of Nerode, Kohn, Alur, Dill, Henzinger…
Hcc: Hybrid Concurrent Constraint Progg. : Hcc: Hybrid Concurrent Constraint Progg. Has a built-in notion of continuous time
Supports smooth and discontinuous system evolution
Supports stochastic modeling
Provides powerful, extensible constraint solver
Can handle variable-structure systems Supports qualitative and quantitative modeling.
Built on a formal operational and denotational semantics
Supports meta-programming (dynamic generation of programs)
Completely integrated with Java Saraswat, Jagadeesan, Gupta “jcc: Integrating TDCC into Java” Very flexible programming and modeling language
Based on a general theory of concurrency and constraints
Hcc: A language for hybrid modeling : Hcc: A language for hybrid modeling Hcc is based on a very few primitives
c
Establish constraint c now
if(c){S}
Run S when c holds (at this instant)
unless(c){S}
Run S unless c holds (at this instant)
S,S
Run the two in parallel
hence S
Run S at every real after now Language can be used to express any pattern of evolution across time:
always{S}
run S at every time point
every(c){S}
run S at every time point at which c holds.
watching(c){S}
run S, aborting it as soon as c holds.
Gupta, Jagadeesan, Saraswat “Computing with continuous change”, SCP 1998
Hcc for Systems Biology : Hcc for Systems Biology Bockmayr, Courtois: “Using hccp to model dynamic biological systems”, ICLP 02
Basic example : Basic example Expression of gene x inhibits expression of gene y; above a certain threshold, gene y inhibits expression of gene x: if (y < 0.8) {x’= -0.02*x + 0.01},
If (y >= 0.8) {x’=-0.02*x, y’=0.01*x}
Bioluminescence in E Fischeri : Bioluminescence in E Fischeri Bioluminescence in V. fischeri
When density passes a certain threshold, (marine) bacteria suddenly become luminescent
Model:
Variables x7,x9 represents internal (ext) concentration of Ai.
Variables x1,..x6,x8 represent other species
Use generic balance eqn:
x’=vs - vd +/- vr +/- vt
vs: synthesis rate
vd: degradation rate
vr: reaction rate
vt: transportation rate
E.g.
The conditional ODEs governing 9 system variables can be directly transcribed into jcc. always{
if (x7 = Ai_plus) x1’=-mu1*x1,…
}
Delta-Notch signaling in X. Laevis : Delta-Notch signaling in X. Laevis Consider cell differentiation in a population of epidermic cells.
Cells arranged in a hexagonal lattice.
Each cell interacts concurrently with its neighbors.
The concentration of Delta and Notch proteins in each cell varies continuously.
Cell can be in one of four states: Delta and Notch inhibited or expressed. Experimental Observations:
Delta (Notch) concentrations show typical spike at a threshold level.
At equilibrium, cells are in only two states (D or N expressed; other inhibited).
Ghosh, Tomlin: “Lateral inhibition through Delta-Notch signaling: A piece-wise affine hybrid model”, HSCC 2001
Delta-Notch Signaling : Delta-Notch Signaling Model:
VD, VN: concentration of Delta and Notch protein in the cell.
UD, UN: Delta (Notch) production capacity of cell.
UN=sum_i VD_i (neighbors)
UD = -VN
Parameters:
Threshold values: HD,HN
Degradation rates: MD, MN
Production rates: RD, RN
Model:
Cell in 1 of 4 states: {D,N} x {Expressed (above), Inhibited (below)}
Stochastic variables used to set random initial state.
Model can be expressed directly in hcc.
if (UN(i,j) < HN) {VN’= -MN*VN},
if (UN(I,j)>=HN){VN’=RN-MN*VN},
if (UD(I,j)=HD){VD’=RD-MD*VD}, Results: Simulation confirms observations. Tiwari/Lincoln prove that States 2 and 3 are stable.
Alternative splicing regulation : Alternative splicing regulation Alternative splicing occurs in post transcriptional regulation of RNA
Through selective elimination of introns, the same premessenger RNA can be used to generate many kinds of mature RNA
The SR protein appears to control this process through activation and inhibition. Because of complexity, experimentation can focus on only one site at a time.
Bockmayr et al use Hybrid CCP to model SR regulation at a single site.
Michaelis-Menten model using 7 kinetic reactions
This is used to create an n-site model by abstracting the action at one site via a splice efficiency function. Results described in [Alt], uses default reasoning properties of HCC.
Programming Languages Issues : Programming Languages Issues Languages for large-scale modeling
Hi-perf num computation
Arrays
Stochastic methods
Large-scale parallelism (e.g SPMD)
Efficient compilation issues
Identify patterns, integrate libraries of high-performance code
Integration of reasoning techniques
Eg finite state analysis of hybrid systems
Syntax/Semantics
Integration of Spatial dimension
Moving to PDEs
Developing models across the Internet
Semantic web… Exciting time for the development of new languages!
Acknowledgements : Acknowledgements [Sys-Bio]: Kitano “Systems Biology: Towards system-level understanding of Biological Systems”, in Foundations of Systems Biology, MIT Press, 2001
[Delta-Notch]: Tiwari, Lincoln “Automatic Techniques for stability analysis of Delta-Notch lateral inhibition mechanism”, CSB 2002.
[HCC-Bio]: Bockmayr, Courtois “Using hybrid concurrent constraint programming to model dynamic biological systems”, ICLP 2002
[Alt]: Eveillard, Ropers, de Jong, Branlant, Bockmayr “A multi-site constraint programming model of alternate splicing regulation”, INRIA Tech Rep, May 2003
HCC references : HCC references Gupta, Jagadeesan, Saraswat “Computing with Continuous Change”, Science of Computer Programming, Jan 1998, 30 (1—2), pp 3--49
Saraswat, Jagadeesan, Gupta “Timed Default Concurrent Constraint Programming”, Journal of Symbolic Computation, Nov-Dec1996, 22 (5—6), pp 475-520.
Gupta, Jagadeesan, Saraswat “Programming in Hybrid Constraint Languages”, Nov 1995, Hybrid Systems II, LNCS 999.
Alenius, Gupta “Modeling an AERCam: A case study in modeling with concurrent constraint languages”, CP’98 Workshop on Modeling and Constraints, Oct 1998.
CFP: Wkshp Comp Methods in Sys Bio : CFP: Wkshp Comp Methods in Sys Bio Deadline : March 1, 2004
Call for Papers - International Workshop on Computational Methods in Systems Biology 2004 (CMSB’04)
Organized by Genoscope, Evry – Génopole, Evry – CNRS – University of Paris VII – BioPathways Consortium
Hotel Meridien Montparnasse, Paris, France 26-28 May, 2004
Deadline : March 1st, 2004
http://www.genoscope.cns.fr/biopathways/CMSB04/