Biochemical Reactions and Biology
Biochemical Reactions and
Logic Computation
Complex behaviors of a living organism
originate from systems of biochemical
reactions
Engineering biochemical reactions may
sharpen our understanding on how nature
design living organisms (in contrast to
how human design electronic systems)
Jie-Hong Roland Jiang
(Introduction to EDA, 2013)
National Taiwan University
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Outline
Outline
Compiling program control flows into
biochemical reactions
Compiling program control flows into
biochemical reactions
 Joint work with Chi-Yun Cheng, De-An Huang,
Ruei-Yang Huang [ICCAD 2012]
Beyond logic computation
Beyond logic computation
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Computational Biochemistry
Previous Work
 In living organisms, biochemical reactions carry
out some form of “computations” which result in
complex behaviors
Synthesizing molecular reactions has been
pursued, e.g.,
 Arithmetic operations
Fett et al. (2007, 2008)
 Biochemical reactions may be exploited for
computation by combining a proper set of
biochemical reactions
 Digital signal processing
Jiang et al. (2010)
 Writing and compiling code into biochemistry
 Computation with biochemical reactions has
potential applications in synthetic biology
Shea et al. (2010), Senum et al. (2011)
 In synthetic biology, known biochemical parts (DNA,
mRNA, proteins, etc.) are assembled either naturally or
artificially to realize desired functions
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Previous Work
Our Focus
Still lack systematic methodology to
construct complex program control flows
Heavily rely on modularized reactions
Assume reactions are of small quantities
Robustness
 Work under stochastic simulation but not ODE
simulation
 Improved reaction regulation
 Enhanced fault tolerance
 Valid under both stochastic and ODE
simulations
Optimality
 Reduced number of reactions
 Not limited to modularized reactions
Systematic compilation methodology
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Model of Computation
Reaction Model
Computation with biochemical reactions
Classical chemical kinetic (CCK) model
 ODE based simulation
 Computation in terms of molecular quantities
 Quantity changing rules are defined by
reactions
(Empirical study shows our construction works for
discrete stochastic simulation as well)
Example:
Example
A B  C
[C ]  min{[ A], [ B ]}
A
k

A   B 
C
C
B
B
B

1 d [ A]
1 d [ B] 1 d [C ]


 k[ A] [ B ]
 dt
 dt
 dt
Petri net representation
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Boolean & Quantitative Abstraction
Reaction Execution Precedence
Data represented by molecular
concentrations
In information processing, computation
must be performed in a proper order
Control signals in terms of Boolean
abstraction
Data dependencies must be maintained to
ensure operational correctness
A 
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Precondition
Precondition
For reaction X  Z  Y  Z
Z is the precondition
For X  Z  Y  Z ,
 At the end of the reaction, X exhausts and Y
has the same amount as X before the reaction
X   Y to denote the
 We use
postcondition of the reaction
To represent the absence of X, let there be presence
of some molecule, called absence indicator.
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Absence Indicator
Absence Indicator
 Prior method [Senum and Riedel 2011]
Dimerized absence indicator
Reaction rate r f  rs
At equilibrium (when A is present),
At equilibrium (when A is present),
[ A' ] 
rs
[ A]
rf
[ A*] 
 The amount of A’ is sensitive to the amount of A
rs
[ A' ]2
rf
 A* is further suppressed by the presence of A’
 A* remains little even if there is a leakage of A
This “leakage” degrades the robustness
Can only be applied to stochastic model
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Absence Indicator
Reaction Buffer
Reaction series:
(1)
After execution for some time,
A exceeds reaction threshold; precondition is violated.
(2)
prior absence indicator
The “buffer” reaction avoids the leakage problem
dimerized absence indicator
(by ODE simulation with SBW simulator)
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Compilation Strategy
Linear Flow
1. Identify Linear, Looping, Branching
statements and create corresponding
control flow reactions.
2. Resolve violation of preconditions or
postconditions, and introduce buffer if
necessary.
3. Decompose reactions for practical
realization
4. Optimize
Without regulation, reactions are
concurrent
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Linear Flow
Branching Statements
When reactions are intended to be in a
sequential manner, precondition can be
imposed as follows:
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Branching Statements
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Branching Statements
Design judgment carefully.
Use mutually exclusive postconditions to enter different code block.
Same precondition to start if‐else judgment
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Branching Statements
Branching Statements
Different reaction leads to the same ending reaction
Same ending reaction to leave branching for each program block.
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Branching Statements
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Looping Statement
Petri net visualization of branching statements:
Using A  B  C to judge (A>B)?
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Looping Statement
Looping Statement
Judgment is made when
(1)Previous reaction
finished
(2)End of every single loop
Use judgment result as precondition
to determine whether entering loop
Loop body
or not.
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Looping Statement
Executed when
Looping condition not
satisfied
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Case Study: Division
Loop body
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Case Study: Division
Case Study: Division
A:=A-B
Q:=Q+1
C of unit amount initially
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Case Study: Division
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Case Study: Division
A AB
D  B
B  0
Q  Q C
If A > B , B will exhaust and A Awill have amount [A] – [B] remains. The amount of
Q
by
,
C 1
( We set
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increment
C
).
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Case Study: Division
Case Study: Division
Division 20÷3
B  D
B
must regain value before next
judgment
(ODE simulation with SBW)
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Case Study: GCD
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Case Study: GCD
GCD(30,12)
Failure due to imperfect condition A  B
may cause a serious problem because [A] almost never exactly equal [B]
1000 != 1001
(ODE simulation with SBW)
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Case Study: GCD
Case Study: GCD
A  A B
is a necessarily
Z
small constant ,
indicating the error
tolerant range.
swap ( A, B)
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Case Study: GCD
Future work
GCD(30,12)
Biological realization of the compilation
approach
Minimization of molecular species
Correct answer obtained by enhanced error tolerance From discrete/static to
continuous/dynamic computation?
 Feedbacks
 Robustness issues
(ODE simulation with SBW)
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Outline
Beyond Logic Computation
Compiling program control flows into
biochemical reactions
A signal processing perspective
Beyond logic computation
A control perspective
 Modulators, filters, and signal processing
 Sensors, actuators (motors), and decision
making
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Signal Processing Perspective
Signal Processing Perspective
Transcription factor translocation example
TF translocation example (cont’d)
Nuclear translocation of S. cerevisiae stress response TF Msn2
Hao and O’Shea. Nature Structural & Molecular Biology, 2012
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Signal Processing Perspective
Signal Processing Perspective
TF translocation example (cont’d)
TF translocation example (cont’d)
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Control Perspective
Control Perspective
Quorum sensing example
Chemotaxis example
Symbiotic association between
the squid Euprymna scolopes and
the luminous bacterium Vibrio
fischeri
http://chemotaxis.biology.utah.edu/Parkinson_Lab/projects/ecolichemotaxis/ecolichemotaxis.html
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Control Perspective
Control Perspective
Chemotaxis example (cont’d)
Chemotaxis example (cont’d)
Manson M D PNAS 2010;107:11151-11152
http://chemotaxis.biology.utah.edu/Parkinson_Lab/projects/ecolichemotaxis/ecolichemotaxis.html
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Beyond Systems Biology
Connection to Neuroscience
Systems biology share strong similarities
with systems neuroscience, although
fundamental mechanisms are quite
different
C. elegans nervous system
 In biology, biochemical reactions are the
fundamental mechanism
 In neuroscience, electrical communications are
the fundamental mechanism
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Connection to Neuroscience
Connection to Neuroscience
Connectome of C. elegans (302) neurons
C. elegans neuron circuitry
sensory neurons
interneurons
motorneurons
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Summary
 Program control flows can be systematically
compiled into biochemical reactions
 Discrete computation, though convenient
abstraction for genetic circuits, is not a universal
approach to systems and synthetic biology
 New computation models needed to decipher
various open questions in systems biology and
systems neuroscience
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