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Hybrid Systems Modeling and Analysis
of Regulatory Pathways
Rajeev Alur
University of Pennsylvania www.cis.upenn.edu/~alur/
LSB, August 2006
State machines
offon
+ Dynamical systems
dx/dt=kxx<70
dx/dt=-k’xx>60
x>68
x<63
Automotive Robotics AnimationSystemsBiology
CoordinationProtocols
Computer Science Automata/Logic Concurrency Formal verification
+ Control Theory Optimal control Stability analysis Discrete-event
system
Software + Environment
Hybrid Systems
Talk Outline
1. A brief tour of hybrid systems research
2. Application to regulatory pathways
Thanks to many colleagues in Penn’s Bio-Hybrid Group, including
Calin Belta (Boston U)
Franjo Ivancic (NEC Labs)
Vijay Kumar
Harvey Rubin
Oleg Sokolsky …
See http://www.cis.upenn.edu/biocomp/
Hybrid Automata
Set L of of locations, and set E of edges
Set X of k continuous variables
State space: L X Rk, Region: subset of Rk
For each location l,
Initial states: region Init(l)
Invariant: region Inv(l)
Continuous dynamics: dX in Flow(l)(X)
For each edge e from location l to location l’
Guard: region Guard(e)
Update relation over Rk X Rk
Synchronization labels (communication information)
(Finite) Executions of Hybrid Automata
State: (l, x) such that x satisfies Inv(l)
Initialization: (l,x) s.t. x satisfies Init(l)
Two types of state updates
Discrete switches: (l,x) –a-> (l’,x’) if there is an a-labeled edge e from l to l’ s.t. x satisfies Guard(e) and (x,x’) satisfies update relation Jump(e)
Continuous flows: (l,x) –f-> (l,x’) where f is a continuous function from [0,] s.t. f(0)=x, f()=x’, and for all t<=, f(t) satisfies Inv(l) and df(t) satisfies Flow(l)(f(t))
CHARON Language Features
Individual components described as agents
Composition, instantiation, and hiding
Individual behaviors described as modes
Encapsulation, instantiation, and Scoping
Support for concurrency
Shared variables as well as message passing
Support for discrete and continuous behavior
Differential as well as algebraic constraints
Discrete transitions can call Java routines
• Input– touch sensors
• Output– desired angles of each
joint
• Components– Brain: control four legs– Four legs: control servo
motors• Instantiated from the
same pattern
Walking Model: Architecture and Agents
x
y
j1
j2
L1
(x, y)
v
L2
Walking Model: Behavior and Modes
dx = -vx > stride /2
dy = kv
dy = -kv dx = kvx < stride /2
CHARON ToolkitCHARON Toolkit
Reachability Analysis for Dynamical Systems
Goal: Given an initial region, compute whether a bad state can be reached
Key step: compute Reach(X) for a given set X under dx/dt = f(x)
X
Reach(X)
Polyhedral Flow Pipe Approximations
X0
t1
t2
t3
t4
t5t6 t7
t8
t9
• divide R[0,T](X0) into [tk,tk+1] segments
• enclose each segment with a convex polytope
• RM[0,T](X0) = union of polytopes
Abstraction and Refinement
Abstraction-based verificationGiven a model M, build an abstraction A
Check A for violation of properties
Either A is safe, or is adequate to indicate a bug in M, or gives false negatives (in that case, refine the abstraction and repeat)
Many projects exploring abstraction-based verification for hybrid systems
Predicate abstraction (Charon at Penn)
Counter-example guided abstraction refinement (CEGAR at CMU)
Qualitative abstraction using symbolic derivatives (SAL at SRI)
Predicate Abstraction
Input is a hybrid automaton and a set of k boolean predicates, e.g. x+y > 5-z.
The partitioning of the concrete state space is specified by the user-defined k predicates.
t
x
Concrete Space:L x R n
Abstract Space:L x {0,1} k
Overview of the Approach
Safetyproperty
Hybridsystem
Booleanpredicates
Search in abstract space
Analyze counter-example
Propertyholds
No!Counter-example
Realcounter-examplefound
additionalpredicates
Hybrid Systems Wrap-up
Efficient simulation
Accurate event detection
Symbolic simulation
Computing reachable state-space
Many new techniques emerging: level sets, Zenotopes, dimensionality reduction..
Scalability still remains a challenge
Cellular Networks
Networks of interacting biomolecules carry out many essential functions in living cells (gene regulation, protein production)
Both positive and negative feedback loops Design principles poorly understood Large amounts of data is becoming available Beyond Human Genome: Behavioral models of cellular
networks Modeling becoming increasingly relevant as an aid to
narrow the space of experiments
Model-based Systems Biology
Goal A: Provide notations for describing complex systems in a modular, structured manner
Principles of concurrency theory (e.g. compositionality)Hierarchy, encapsulation, reuseVisual programming tools
Goal B: Simulation and analysis for better understanding
Classical debugging toolsReachability and stability analysisModel-based experiments to combat the combinatorial explosion due to multiplicity of parameters
What to Model ?
Cellular networks exhibit a complex mix of featuresDiscrete switching as genes are turned on/offHigh degree of concurrencyStochastic behavior (particularly at low concentrations)Chemical reactions
Models possible at different levels of abstractionsDiscrete graph models capturing dependenciesBoolean models capturing qualitative statesPurely continuous modelsHybrid systemsStochastic modelsLocation-aware models
Regulatory Networks
cell-to-cellsignaling
START STOPgene
transcription
translation
regulation
nascentprotein
chemicalreaction
+
-
negative
positive
gene expression
Luminescence / Quorum Sensingin Vibrio Fischeri
Hybrid Modeling
START STOPluxR gene
transcription
translation
regulation
proteinLuxR
chemicalreaction
-+
negative
positive
Ai
Ai
CRP
Traditionally, biological systems are modeled using smooth functions.
Xm
Xm
X
),,X( XmXm
10.5
2swX1
swX
transporttransformdecaysynthesisdt
]x[d
luxRkHluxR
)b)),,AiLuxR(1(
),,CRP([Tdt
)luxR(d
GRNA
AiLuxRAiLuxR
CRPCRPc
LuxRkCorLuxRAir
HLuxR
luxRTdt
)LuxR(d
Gdb
spl
Ai/LuxRAi/LuxR
Hybrid Modeling
At low concentrations, a continuous approximation model might not be appropriate. Instead, a stochastic model should be used.
stochastic model
low conc
continuous model
high conc
In some cases, the biological description of a system is itself hybrid.
Essentiallyhybrid systemDiscrete jump
(mRNA)
Nonlinear dynamics(proteins involved in chemical reactions)
Linear dynamics(proteins not involvedin chemical reactions)
moderegulatoryprotein/complex
Luminescence Regulation
CRP
luxICDABEGluxR
Ai
LuxA
LuxB
luciferase
LuxI
Substrate
LuxR
lux box
CRP binding site
LuxR Ai
OL OR
-
+-
+ cAMP
Reachability
)Co(x8
)Ai(x7
)LuxI(x4
switching surface
lum dynamics
nonlum dynamics
sw88 xx
10bAxx
sw88 xx
lum
00bAxx
sw88 xx
non-lum
sw88 xx
Under what conditionscan the bacterium switch on the light?
sw8x
0ibAxx
8
7
4
x
x
x
x
0
0
)bc(TTH
biclRNA
0i
1c,0c 10
Simulation Results
external Ai
(input)
concentrationsfor various
entities
luminesence(output)
switchhistory
switchhistory
BioSketchPad
Interactive tool for graphical models of biomolecular and cellular networks
Nodes and edges with attributesHierarchical
Intended for use by biologists
Compiler to translate BioSketchPad models to Charon
BioSketchPad Concepts
Species nodesName (e.g. Ca, alcohol dehydrogenase, notch)Type (e.g. gene, protein)Location (e.g. cell membrane, nucleus)N-mer polymerization, electrical chargeInitial concentration
Reaction nodesInput and output connectorsType (e.g. transformation, transcription)Parameters for rate laws
Regulation nodesConnected to species nodes and/or reaction nodes to modulate the rate of reaction by concentration of speciesWeighted sum, tabular, product forms
Summary
Hybrid systems are useful to model some biological regulatory networks.
The simulation/reachability results of the luminescence control in Vibrio fischeri are in accordance with phenomena observed in experiments.
Modeling concepts such as hierarchy, concurrency, reuse, are relevant for modular specifications
BioSketchPad integrates many of these ideas
Challenges
Finding all the information needed to build a model is difficult
Finding people who can build models is even more difficult
Finding a common format for exchanging models among tools can make more models available
Scalability of analysis