Enzyme-Based
Biomolecular Computing
Molecular and biomolecular
logic gates
and their networks processing chemical input signals similarly to
computers
received high attention and were rapidly developed in the last decade.
Being a
subarea of unconventional computing, they can process chemical
information
mimicking Boolean logic operations using binary definitions (1,0;
YES/NO) for concentrations of reacting
species. Using this approach,
chemical reactions could be reformulated as information processing
steps with
built-in logic operations. Then, the chemical processes could be
programmed
similar to computer programming yielding networks performing several
logic
operations.
As a network that processes
information becomes large, and the information is processed in greater
quantities and at higher complexity, noise inevitably builds up and can
ultimately degrade the useful “signal” which is the intended result of
the
computation. One then has to develop approaches to achieve what is
known as
“fault-tolerant” information processing that involves noise control and
suppression. One notable example is the “robustness” of many complex
processes
in cell functions.
Presently, we are aware of
three
primary fault-tolerant information processing paradigms. The first is
the
analog/digital electronics paradigm of the Si-chip technology in modern
computers. We know how to design such devices and they have been
successfully
built. Living organisms are the second paradigm: While we obviously
know that
this paradigm leads to scalability, we do not yet fully understand it,
though
significant strides have been made in Systems Biology to explore the
structure
and functioning of biological “networks.” The third, recent paradigm,
involves
massive parallelism: quantum (quantum computing) or ensemble (variants
of DNA
computing), both in the preliminary research stages.
Biochemical
computing — in
our case based on enzymatic reactions1-3 — attempts
to
process information with biomolecules and biological objects.4-13
However, the information processing paradigm assumed, has, in most
cases, been
that of the ordinary electronics. Indeed, most biochemical computing
studies
attempt to realize and, most recently, network “gates” that mimic
Boolean
digital logic.14,15
Networks with computational
steps that
solely involve biochemical processes,16,17 are being
researched for
new technological capabilities: multi-input biosensors with new
functionalities,18-20 as well as approaches that allow
removing the
batteries from, and generally reducing the need for inorganic leads and
electrical power supply for those stages of information processing that
occur
during biomedical testing, implantable devices, and other fast decision
making
steps in applications. Futuristic ideas for applications of biochemical
logic
include more direct brain-computer and body-computer interfacing for
both
reading out and inputting information, and generally erasing the
barrier
between the inorganic and organic information processing in computer
device
functioning.
The
primary present challenge in
the field of biochemical computing can be summarized as follows. Recent
studies
took the field somewhat beyond the earlier set of works that have
realized
simple gates mimicking two-input, one-output Boolean functions.
Specifically,
few-gate networking has been accomplished,16,17 first steps
in the
analysis of network scalability have been reported very recently,21
and first attempts at “smart” interfacing with ordinary electronics
have been
initiated.22-25 In these studies, biocomputing based on
enzymatic
reactions has emerged as an appealing approach for information
processing due
to the specificity and other useful chemical-kinetics properties of
enzyme
reactions.
However, these advances
have also set
the stage for new challenges. In particular, it has been realized that
large-scale networking and fault-tolerance cannot be achieved without
the
development of a “toolbox” of new network elements, specifically,
filters,
signal splitters (copying), signal balancers, resetting functions, etc.
These
non-Boolean network elements for biochemical computing might not follow
too
closely the analogy with ordinary electronic devices. In fact, concepts
borrowed from Nature, including the delayed identity — part of the
feed-forward network motif,26 or “memory” properties27
have recently received attention in information processing network
studies. The
present research program attempts to address these challenges, building
on the
earlier works in the fields of chemical and biochemical computing.
At present there are
chemical-computing studies that have used molecular or supra-molecular
systems28‑44
to mimic processes typical for electronic computing devices such as
simple
Boolean logic operations AND, OR, XOR, etc.,45-63 as well as more complex
systems
including molecular comparator, digital demultiplexer,
encoder-decoder, keypad lock,
write-read-erase memory units, half-adder/half-subtractor or
full-adder/full-subtractor,64-86 etc.
Chemical systems are, in principle, capable of performing computations
at the
level of a single molecule87 resulting in nano-scaling of
the
computing units88 and allowing parallel computation
performed by
molecules involved in various chemical reactions.89 However, one of the most important
challenges in the
chemical computing is networking and fault-tolerance.1,15
Complex
multi-component chemical logic systems usually require ingenious
molecular
assemblies to allow compatibility between information processing
sub-units28,77
and perform rather simple functions despite their extreme synthetic
complexity.90
Biomolecular systems, on
the other
hand, offer the advantage of specificity (of being very selective) in
their
chemical functions and therefore being more usable in complex “chemical
soup”
environments. As a result, complex enzyme-based information processing
units
could be more easily scaled up giving rise to artificial biocomputing
networks
performing various logic functions and mimicking natural biochemical
pathways.16,17,21,42,91
An added advantage of biomolecular computing systems is the capability
to
process biochemical information received in the form of chemical
signals directly from biological
systems and the ability to operate in a biological environment.92
This is important for interfacing of the resulting biochemical-logic
“devices”
with processes in living organisms,93 for potential
biomedical
applications.
Recent experimental
advances in
enzyme-based biocomputing have included not just experimental
demonstrations of
several single Boolean gates, such as AND,
OR, XOR, Inhibit,
etc.,1-4
but also networking of several, up to 3-5 presently, gates.14,16,17,21,24
Similar logic operations were also realized using non-biological
chemical
systems.64-85 However, the biochemical systems offered
relative
simplicity of the assembled logic schemes. Still, ultimately the
increasing
complexity of enzyme-based logic networks will require exploration of
noise
suppression approaches in biochemical logic-gates networks. Fault
tolerance
within the analog/digital information processing paradigm is
accomplished by
gate optimization for suppression of the “analog” noise amplification1,94
as well as by network design and/or network topology.21 For
larger
networks, another, “digital” mechanism95 of noise
amplification
emerges. It is combated by redundancy in network design and requires
truly
digital information processing with appropriate network elements for
filtering,
signal splitting, etc.
The present sizes of the
biochemical
computing networks have already allowed exploration of aspects of
design and
optimization issues related to suppression of the “analog” noise
amplification.
The work on this topic was initiated in our recent publications.1,21,94,95
The field is presently wide open for research developments. The
following brief
description of our
recent results attempts to summarize the group activity in the project.
Boolean Logic Gates
Using Enzymes
as Input Signals
G.
Strack, M. Pita, M. Ornatska,E. Katz, Boolean logic gates using enzymes
as input signals. ChemBioChem,
2008, 9, 1260-1266. |
|
Enzyme-based
NAND and NOR logic gates with modular design
The
logic gates NAND / NOR were mimicked by enzyme
biocatalyzed reactions activated by sucrose, maltose and phosphate. The
sub-units performing AND / OR Boolean logic operations were
designed using maltose phosphorylase and cooperative work of
invertase/amyloglucosidase, respectively. Glucose produced as the
output signal from the AND / OR sub-units was applied as the
input signal for the INVERTER
gate composed of alcohol dehydrogenase, glucose oxidase,
microperoxidase-11, ethanol and NAD+, which generated the
final output in the form of NADH inverting the logic signal from 0 to 1 or from 1 to 0. The final output signal was
amplified by a self-promoting biocatalytic system. In order to fulfill
the Boolean properties of associativity and commutativity in logic
networks, the final NADH output signal was converted to the initial
signals of maltose and phosphate, thus allowing assembling of the same
standard units in concatenated sequences. The designed modular
approach, signal amplification and conversion processes open the way
towards complex logic networks composed of standard elements resembling
electronic integrated circuitries.
J. Zhou, M.A. Arugula, J. Halámek, M. Pita, E. Katz, Enzyme-based NAND and NOR logic gates with modular design. J. Phys. Chem. B 2009, 113, 16065-16070. |
|
Analog Noise
Reduction in Enzymatic
Logic Gates
In this work we demonstrate both experimentally and theoretically that the analog noise generation by a single enzymatic logic gate can be dramatically reduced to yield gate operation with virtually no input noise amplification. We demonstrate that when a co-substrate with a much smaller affinity than the primary substrate is used, a negligible increase in the noise output from the logic gate is obtained as compared to the input noise level. Our general theoretical conclusions were confirmed by experimental realizations of the AND logic gate based on the enzyme horseradish peroxidase using hydrogen peroxide as the substrate, with 2,2'‑azino‑bis(3-ethylbenzthiazoline-6-sulphonic acid) (ABTS) or ferrocyanide as co‑substrates with vastly different rate constants. D. Melnikov, Strack, M. Pita, V. Privman, E. Katz, Analog noise reduction in enzymatic logic gates. J. Phys. Chem. B 2009, 113, 10472-10479. |
|
Realization
and Properties of Biochemical-Computing Biocatalytic XOR Gate Based on
Signal
Change We consider a realization of the XOR logic gate in a system involving
two competing biocatalytic reactions, for which the logic-1 output is defined by these two
processes causing a change in the optically detected signal. A model is
developed for describing such systems in an approach suitable for
evaluation of the analog noise amplification properties of the gate and
optimization of its functioning. The initial data are fitted for gate
quality evaluation within the developed model, and then modifications
are proposed and experimentally realized for improving the gate
functioning.
V. Privman, J. Zhou, J. Halámek, E.
Katz, Realization and properties of biochemical-computing biocatalytic
XOR gate based on signal change. J.
Phys. Chem. B 2010, 114, 13601-13608.
|
Biochemical
Filter with Sigmoidal Response:
The first
realization of a designed, rather than natural, biochemical filter
process is
reported and analyzed as a promising network component for increasing
the
complexity of biomolecular logic systems. Key challenge in biochemical
logic
research has been achieving scalability for complex network designs.
Various
logic gates have been realized, but a "toolbox" of analog elements
for interconnectivity and signal processing has remained elusive.
Filters are
important as network elements that allow control of noise in signal
transmission and conversion. We report a versatile biochemical
filtering
mechanism designed to have sigmoidal response in combination with
signal-conversion process. Horseradish peroxidase-catalyzed oxidation
of chromogenic
electron donor by H2O2, was altered by adding ascorbate,
allowing to selectively suppress the output signal, modifying the
response from
convex to sigmoidal. A kinetic model was developed for evaluation of
the
quality of filtering. The results offer improved capabilities for
design of
scalable biomolecular information processing systems.
V. Privman, J. Halámek, M.A. Arugula, D. Melnikov, V. Bocharova, E. Katz, Biochemical filter with sigmoidal response: Increasing the complexity of biomolecular logic. J. Phys. Chem. B 2010, 114, 14103-14109.
|
|
Towards
Biochemical Filter with Sigmoidal Response to pH Changes
We
realize a biochemical filtering process
based on the
introduction of a buffer in a biocatalytic signal-transduction logic
system
based on the function of an enzyme, esterase. The input, ethyl
butyrate, is
converted into butyric acid—the output signal, which in turn is
measured by the
drop in the pH value. The developed approach offers a versatile
"network
element" for increasing the complexity of biochemical information
processing systems. Evaluation of an optimal regime for quality
filtering is
accomplished in the framework of a kinetic rate-equation model.
The reaction biocatalyzed by an enzyme,
here esterase,
results in the hydrolysis of ethyl butyrate (the logic Input)
to yield
butyric acid which releases H+ ions upon dissociation. A limited
quantity of a buffer, here HEPES, if introduced, consumes most of the
biocatalytically produced H+ ions when the input is applied at a low
concentration. The pH change (the logic Output,
measured by the pH drop,
as indicated by an arrow) sets in when the biocatalytically produced H+
ions overwhelm the buffer. The biocatalytic process and buffering
combined,
yield a sigmoidal dependence of the pH change as a function of the
input
concentration. The inset illustrates the onset of the sigmoidal
response in our
experimental system. The solid curves show the output, y,
vs. the input, x, properly redefined/rescaled to vary in
the "binary-logic
ranges" from 0 to 1, as explained in the text. Experimental data were
fitted by using rate equations appropriate for the processes involved,
and the
results are shown, here for the reaction time 120 min, for
increasing
buffer (HEPES) concentrations. The top (red) curve corresponds to
[HEPES] = 0; middle (blue): [HEPES] = 50 mM,
bottom
(green): [HEPES] = 100 mM. The dashed black curve does
not
correspond to experimental data but rather illustrates a desirable,
"ideal" filter response with small slopes at both binary logic points 0
and 1, and with a steep, symmetrically
positioned inflection
region in the middle. M. Pita, V. Privman, M.A.
Arugula, D. Melnikov, V. Bocharova, E.
Katz, Towards biochemical
filter with sigmoidal response to pH changes: Buffered biocatalytic
signal transduction. PhysChemChemPhys 2011,
in press (DOI:
10.1039/c0cp02524k). |
|
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