5 research outputs found
Timing in chemical reaction networks
Chemical reaction networks (CRNs) formally model chemistry in a well-mixed
solution. CRNs are widely used to describe information processing occurring in
natural cellular regulatory networks, and with upcoming advances in synthetic
biology, CRNs are a promising programming language for the design of artificial
molecular control circuitry. Due to a formal equivalence between CRNs and a
model of distributed computing known as population protocols, results transfer
readily between the two models.
We show that if a CRN respects finite density (at most O(n) additional
molecules can be produced from n initial molecules), then starting from any
dense initial configuration (all molecular species initially present have
initial count Omega(n), where n is the initial molecular count and volume),
then every producible species is produced in constant time with high
probability.
This implies that no CRN obeying the stated constraints can function as a
timer, able to produce a molecule, but doing so only after a time that is an
unbounded function of the input size. This has consequences regarding an open
question of Angluin, Aspnes, and Eisenstat concerning the ability of population
protocols to perform fast, reliable leader election and to simulate arbitrary
algorithms from a uniform initial state
Computational Complexity of Atomic Chemical Reaction Networks
Informally, a chemical reaction network is "atomic" if each reaction may be
interpreted as the rearrangement of indivisible units of matter. There are
several reasonable definitions formalizing this idea. We investigate the
computational complexity of deciding whether a given network is atomic
according to each of these definitions.
Our first definition, primitive atomic, which requires each reaction to
preserve the total number of atoms, is to shown to be equivalent to mass
conservation. Since it is known that it can be decided in polynomial time
whether a given chemical reaction network is mass-conserving, the equivalence
gives an efficient algorithm to decide primitive atomicity.
Another definition, subset atomic, further requires that all atoms are
species. We show that deciding whether a given network is subset atomic is in
, and the problem "is a network subset atomic with respect to a
given atom set" is strongly -.
A third definition, reachably atomic, studied by Adleman, Gopalkrishnan et
al., further requires that each species has a sequence of reactions splitting
it into its constituent atoms. We show that there is a to decide whether a given network is reachably atomic, improving
upon the result of Adleman et al. that the problem is . We
show that the reachability problem for reachably atomic networks is
-.
Finally, we demonstrate equivalence relationships between our definitions and
some special cases of another existing definition of atomicity due to Gnacadja
Uniformity is weaker than semi-uniformity for some membrane systems
We investigate computing models that are presented as families of finite
computing devices with a uniformity condition on the entire family. Examples of
such models include Boolean circuits, membrane systems, DNA computers, chemical
reaction networks and tile assembly systems, and there are many others.
However, in such models there are actually two distinct kinds of uniformity
condition. The first is the most common and well-understood, where each input
length is mapped to a single computing device (e.g. a Boolean circuit) that
computes on the finite set of inputs of that length. The second, called
semi-uniformity, is where each input is mapped to a computing device for that
input (e.g. a circuit with the input encoded as constants). The former notion
is well-known and used in Boolean circuit complexity, while the latter notion
is frequently found in literature on nature-inspired computation from the past
20 years or so.
Are these two notions distinct? For many models it has been found that these
notions are in fact the same, in the sense that the choice of uniformity or
semi-uniformity leads to characterisations of the same complexity classes. In
other related work, we showed that these notions are actually distinct for
certain classes of Boolean circuits. Here, we give analogous results for
membrane systems by showing that certain classes of uniform membrane systems
are strictly weaker than the analogous semi-uniform classes. This solves a
known open problem in the theory of membrane systems. We then go on to present
results towards characterising the power of these semi-uniform and uniform
membrane models in terms of NL and languages reducible to the unary languages
in NL, respectively.Comment: 28 pages, 1 figur
Probability 1 computation with chemical reaction networks
The computational power of stochastic chemical reaction networks (CRNs) varies significantly with the output convention and whether or not error is permitted. Focusing on probability 1 computation, we demonstrate a striking difference between stable computation that converges to a state where the output cannot change, and the notion of limit-stable computation where the output eventually stops changing with probability 1. While stable computation is known to be restricted to semilinear predicates (essentially piecewise linear), we show that limit-stable computation encompasses the set of predicates ϕ:N→{0,1} in Δ^0_2 in the arithmetical hierarchy (a superset of Turing-computable). In finite time, our construction achieves an error-correction scheme for Turing universal computation. We show an analogous characterization of the functions f:N→N computable by CRNs with probability 1, which encode their output into the count of a certain species. This work refines our understanding of the tradeoffs between error and computational power in CRNs
Probability 1 computation with chemical reaction networks
The computational power of stochastic chemical reaction networks (CRNs) varies significantly with the output convention and whether or not error is permitted. Focusing on probability 1 computation, we demonstrate a striking difference between stable computation that converges to a state where the output cannot change, and the notion of limit-stable computation where the output eventually stops changing with probability 1. While stable computation is known to be restricted to semilinear predicates (essentially piecewise linear), we show that limit-stable computation encompasses the set of predicates ϕ:N→{0,1} in Δ^0_2 in the arithmetical hierarchy (a superset of Turing-computable). In finite time, our construction achieves an error-correction scheme for Turing universal computation. We show an analogous characterization of the functions f:N→N computable by CRNs with probability 1, which encode their output into the count of a certain species. This work refines our understanding of the tradeoffs between error and computational power in CRNs