Conditions for the origin of homochirality in primordial catalytic reaction networks.

We study the generation of homochirality in a general chemical model (based on the homogeneous, fully connected Smoluchowski aggregation-fragmentation model) that obeys thermodynamics and can be easily mapped onto known origin of life models (e.g. autocatalytic sets, hypercycles, etc.), with essential aspects of origin of life modeling taken into consideration. Using a combination of theoretical modeling and numerical simulations, we look for minimal conditions for which our general chemical model exhibits spontaneous mirror symmetry breaking. We show that our model spontaneously breaks mirror symmetry in various catalytic configurations that only involve a small number of catalyzed reactions and nothing else. Of particular importance is that mirror symmetry breaking occurs in our model without the need for single-step autocatalytis or mutual inhibition, which may be of relevance for prebiotic chemistry.

Numerical and renormalization group analysis of the phase diagram of a stochastic cubic autocatalytic reaction-diffusion system.

The renormalization group is a set of tools that can be used to incorporate the effect of fluctuations in a dynamical system as a rescaling of the system's parameters. Here, we apply the renormalization group to a pattern-forming stochastic cubic autocatalytic reaction-diffusion model and compare its predictions with numerical simulations. Our results demonstrate a good agreement within the range of validity of the theory and show that external noise can be used as a control parameter in such systems.

Renormalization of stochastic differential equations with multiplicative noise using effective potential methods.

We present a method to renormalize stochastic differential equations subjected to multiplicative noise. The method is based on the widely used concept of effective potential in high-energy physics and has already been successfully applied to the renormalization of stochastic differential equations subjected to additive noise. We derive a general formula for the one-loop effective potential of a single ordinary stochastic differential equation (with arbitrary interaction terms) subjected to multiplicative Gaussian noise (provided the noise satisfies a certain normalization condition). To illustrate the usefulness (and limitations) of the method, we use the effective potential to renormalize a toy chemical model based on a simplified Gray-Scott reaction. In particular, we use it to compute the scale dependence of the toy model's parameters (in perturbation theory) when subjected to a Gaussian power-law noise with short time correlations.

From chemical soup to computing circuit: transforming a contiguous chemical medium into a logic gate network by modulating its external conditions.

It has been shown that it is possible to transform a well-stirred chemical medium into a logic gate simply by varying the chemistry's external conditions (feed rates, lighting conditions, etc.). We extend this work, showing that the same method can be generalized to spatially extended systems. We vary the external conditions of a well-known chemical medium (a cubic autocatalytic reaction-diffusion model), so that different regions of the simulated chemistry are operating under particular conditions at particular times. In so doing, we are able to transform the initially uniform chemistry, not just into a single logic gate, but into a functionally integrated network of diverse logic gates that operate as a basic computational circuit known as a full-adder.

Dynamic modulation of external conditions can transform chemistry into logic gates.

We introduce a new method for transforming chemical systems into desired logical operators (e.g. NAND gates) or similar signal-manipulation components. The method is based upon open-loop dynamic regulation, where external conditions such as feed-rate, lighting conditions, etc., are modulated according to a prescribed temporal sequence that is independent of the input to the network. The method is first introduced using a simple didactic model. We then show its application in transforming a well-stirred cubic autocatalytic reaction (often referred to as the Selkov-Gray-Scott model) into a logical NAND gate. We also comment on the applicability of the method to biological and other systems.

Effects of spatial and temporal noise on a cubic-autocatalytic reaction-diffusion model.

We characterize the influence that external noise, with both spatial and temporal correlations, has on the scale dependence of the reaction parameters of a cubic autocatalytic reaction diffusion (CARD) system. Interpreting the CARD model as a primitive reaction scheme for a living system, the results indicate that power-law correlations in environmental fluctuations can either decrease or increase the rates of nutrient decay and the rate of autocatalysis (replication) on small spatial and temporal scales.

Small-scale properties of a stochastic cubic-autocatalytic reaction-diffusion model.

We investigate the small-scale properties of a stochastic cubic-autocatalytic reaction-diffusion (CARD) model using renormalization techniques. We renormalize noise-induced ultraviolet divergences and obtain ß functions for the decay rate and coupling at one loop. Assuming colored (power-law) noise, our results show that the behavior of both decay rate and coupling with scale depends crucially on the noise exponent. Interpreting the CARD model as a proxy for a (very simple) living system, our results suggest that power-law correlations in environmental fluctuations can both decrease or increase the growth of structures at smaller scales.