Avenues for emergent ecologies

In this work, we present some fascinating behaviour emerging from a simple synthetic chemistry model. The results of Ono and Ikegami (2001) demonstrated the spontaneous formation of primitive, self-reproducing cells from a random homogeneous mixture of chemical components. Their model made use of a simple, artificial reaction network. Discrete particles were placed on a triangular lattice and the dynamics consisted of the following particle transitions: translation over one lattice spacing and chemical transformation. The primary particle types were membrane-forming particles, catalysts and water. The membrane particles formed structures akin to lipid bilayers. Their synthesis was stimulated by the catalyst particles, which were also capable of template self-replication using precursors. The system readily exhibits protocell formation from a random initial condition. These protocells form, grow, divide and eventually decay in a continuous cycle. Such emergent dynamics were an illuminating result given that the simulation itself only defines local interactions between particles and a set of physical transition rules. The protocell structures are not explicitly represented or built into the model. Hence it demonstrated a basic physical logic wherein the concepts of self-maintenance and self-reproduction could arise spontaneously from a set of simpler, lower level rules. In essence, it was an in silico realisation of the principle of autopoiesis.We decided to extend this work by augmenting the particle species repertoire. An additional catalyst was added, which did not stimulate the synthesis of membrane particles, but rather stimulated their decay. It was expected that this would reduce the rate of protocell formation. However a surprising dynamic was uncovered with this new system. As one might expect the protocells did not arise in abundance as in the original model. Instead they formed in small, isolated colonies since this was the only means by which they could avoid the destructive effects of the new catalyst. However because this toxic particle was also autocatalytic (like the other, constructive catalyst), its concentration rose sharply in regions confined by membrane particles since the membranes slowed their outward diffusion. Thus membranes actually created a niche for the toxic catalyst. This in turn produced a predator-prey dynamic with clouds of the toxic particle growing near protocells and protocells being forced to grow in the opposite direction to avoid the destructive effects of the new particle. These results reveal that high level, ecological phenomena can manifest themselves even in simple physico-chemical systems. They demonstrate that ideas of natural selection and fitness are intimately bound with the basic principle of free energy minimisation. We have also now enhanced the model further by adding a second reaction network. It is similar, but independent to the first and allows for two "species" of protocell. It is also possible for hybrids to form, comprised of mixtures of the membrane particles from the two reaction networks. Results from this new version are currently being gathered and analyse

Attractor Landscapes and Information Processing by Convective Obstacle Flows

We present recent results concerning the attractor landscape, memory, hysteresis and computation that can emerge in simple convective obstacle flows. In these systems a single phase fluid is heated from below and cooled from above. Small obstacles (one or two) are placed on the horizontal mid plane of the system and extract some fraction of the fluid’s horizontal or vertical momentum. Horizontal momentum sinks tend to attract convection plumes. Vertical momentum sinks are bistable; the obstacle will either align with a convection cell centre or convection plume depending on initial conditions and the history of the system. The resulting attractor landscape can be exploited to produce a single bit memory or even elementary Boolean logic

Conversion Efficiencies of Heteronuclear Feshbach Molecules

We study the conversion efficiency of heteronuclear Feshbach molecules in population imbalanced atomic gases formed by ramping the magnetic field adiabatically. We extend the recent work [J. E. Williams et al., New J. Phys., 8, 150 (2006)] on the theory of Feshbach molecule formations to various combinations of quantum statistics of each atomic component. A simple calculation for a harmonically trapped ideal gas is in good agreement with the recent experiment [S. B. Papp and C. E. Wieman, Phys. Rev. Lett., 97, 180404 (2006)] without any fitting parameters. We also give the conversion efficiency as an explicit function of initial peak phase space density of the majority species for population imbalanced gases. In the low-density region where Bose-Einstein condensation does not appear, the conversion efficiency is a monotonic function of the initial peak phase space density, but independent of statistics of a minority component. The quantum statistics of majority atoms has a significant effect on the conversion efficiency. In addition, Bose-Einstein condensation of an atomic component is the key element determining the maximum conversion efficiency.Comment: 46 pages, 32 figure

Coming Phase to Phase with Surfactants

We introduce a fast cellular automata model for the simulation of surfactant dynamics based on a previous model by Ono and Ikegami (2001). Here, individual lipid-like particles undergo stochastic movement and rotation on a two-dimensional lattice in response to potential energy gradients. The particles are endowed with an internal structure that reflects their amphiphilic character. Their head groups are weakly repelled by water whereas their hydrophobic tails cannot be readily hydrated. This leads to the formation of a variety of structures when the particles are placed in solution. The model in its current form compels a myriad of potential self-organisation experiments. Heterogeneous boundary conditions, chemical interactions and an arbitrary diversity of particles can easily be modelled. Our main objective was to establish a computational platform for investigating how mechanisms of lipid homeostasis might evolve among populations of protocells

Defining Lyfe in the Universe: From Three Privileged Functions to Four Pillars

Motivated by the need to paint a more general picture of what life is—and could be—with respect to the rest of the phenomena of the universe, we propose a new vocabulary for astrobiological research. Lyfe is defined as any system that fulfills all four processes of the living state, namely: dissipation, autocatalysis, homeostasis, and learning. Life is defined as the instance of lyfe that we are familiar with on Earth, one that uses a specific organometallic molecular toolbox to record information about its environment and achieve dynamical order by dissipating certain planetary disequilibria. This new classification system allows the astrobiological community to more clearly define the questions that propel their research—e.g., whether they are developing a historical narrative to explain the origin of life (on Earth), or a universal narrative for the emergence of lyfe, or whether they are seeking signs of life specifically, or lyfe at large across the universe. While the concept of “life as we don’t know it” is not new, the four pillars of lyfe offer a novel perspective on the living state that is indifferent to the particular components that might produce it

Emergence, Construction, or Unlikely? Navigating the Space of Questions regarding Life's Origins

We survey some of the philosophical challenges and pitfalls within origins research. Several of these challenges exhibit circularities, paradoxes, or anthropic biases. We present origins approaches in terms of three broad categories: unlikely (life's origin was a chance event), construction (life's origin was a stepwise series of synthesis and assembly processes), and emergence (life was always an amalgam of many parallel processes from which the living state emerged as a natural outcome of physical driving forces). We critically examine some of the founding and possibly misleading assumptions in these categories. Such assumptions need not be detrimental to scientific progress as long as their limits are respected. We conclude by attempting to concisely state the most significant enigmas still remaining in the origins field and suggest routes to solve them

Attractor Landscapes and Information Processing by Convective Obstacle Flows

We present recent results concerning the attractor landscape, memory, hysteresis and computation that can emerge in simple convective obstacle flows. In these systems a single phase fluid is heated from below and cooled from above. Small obstacles (one or two) are placed on the horizontal mid plane of the system and extract some fraction of the fluid’s horizontal or vertical momentum. Horizontal momentum sinks tend to attract convection plumes. Vertical momentum sinks are bistable; the obstacle will either align with a convection cell centre or convection plume depending on initial conditions and the history of the system. The resulting attractor landscape can be exploited to produce a single bit memory or even elementary Boolean logic

Quantifying Mineral-Ligand Structural Similarities: Bridging the Geological World of Minerals with the Biological World of Enzymes

Metal compounds abundant on Early Earth are thought to play an important role in the origins of life. Certain iron-sulfur minerals for example, are proposed to have served as primitive metalloenzyme cofactors due to their ability to catalyze organic synthesis processes and facilitate electron transfer reactions. An inherent difficulty with studying the catalytic potential of many metal compounds is the wide range of data and parameters to consider when searching for individual minerals and ligands of interest. Detecting mineral-ligand pairs that are structurally analogous enables more relevant selections of data to study, since structural affinity is a key indicator of comparable catalytic function. However, current structure-oriented approaches tend to be subjective and localized, and do not quantify observations or compare them with other potential targets. Here, we present a mathematical approach that compares structural similarities between various minerals and ligands using molecular similarity metrics. We use an iterative substructure search in the crystal lattice, paired with benchmark structural similarity methods. This structural comparison may be considered as a first stage in a more advanced analysis tool that will include a range of chemical and physical factors when computing mineral-ligand similarity. This approach will seek relationships between the mineral and enzyme worlds, with applications to the origins of life, ecology, catalysis, and astrobiology