Open-Endedness in Genelife.

We explore the open-ended nature of evolution in Genelife, an evolutionary extension of Conway's Game of Life cellular automaton in which "live" cell states are endowed at birth with a genome that affects their local dynamics and can be inherited. Both genetic sequences and locally connected spatial patterns are analyzed for novelty, keeping track of all new structures, and innovation is quantified using activity statistics. The impacts of both spatial symmetry breaking with nontotalistic rules and superimposed density regulation of the live state proliferation on the open-ended nature of the evolution are explored. Conditions are found where both genetic and spatial patterns exhibit open-ended innovation. This innovation appears to fall short of functional biological innovation, however, and potential reasons for this are discussed.

High-Throughput Optimization Cycle of a Cell-Free Ribosome Assembly and Protein Synthesis System.

Building variant ribosomes offers opportunities to reveal fundamental principles underlying ribosome biogenesis and to make ribosomes with altered properties. However, cell viability limits mutations that can be made to the ribosome. To address this limitation, the in vitro integrated synthesis, assembly and translation (iSAT) method for ribosome construction from the bottom up was recently developed. Unfortunately, iSAT is complex, costly, and laborious to researchers, partially due to the high cost of reaction buffer containing over 20 components. In this study, we develop iSAT in Escherichia coli BL21Rosetta2 cell lysates, a commonly used bacterial strain, with a cost-effective poly sugar and nucleotide monophosphate-based metabolic scheme. We achieved a 10-fold increase in protein yield over our base case with an evolutionary design of experiments approach, screening 490 reaction conditions to optimize the reaction buffer. The computationally guided, cell-free, high-throughput technology presented here augments the way we approach multicomponent synthetic biology projects and efforts to repurpose ribosomes.

Coping with complexity: machine learning optimization of cell-free protein synthesis.

Biological systems contain complex metabolic pathways with many nonlinearities and synergies that make them difficult to predict from first principles. Protein synthesis is a canonical example of such a pathway. Here we show how cell-free protein synthesis may be improved through a series of iterated high-throughput experiments guided by a machine-learning algorithm implementing a form of evolutionary design of experiments (Evo-DoE). The algorithm predicts fruitful experiments from statistical models of the previous experimental results, combined with stochastic exploration of the experimental space. The desired experimental response, or evolutionary fitness, was defined as the yield of the target product, and new experimental conditions were discovered to have ∼ 350% greater yield than the standard. An analysis of the best experimental conditions discovered indicates that there are two distinct classes of kinetics, thus showing how our evolutionary design of experiments is capable of significant innovation, as well as gradual improvement.

Measuring the evolution of the drivers of technological innovation in the patent record.

We argue that technology changes over time by an evolutionary process that is similar in important respects to biological evolution. The process is adaptive in the sense that technologies are selected because of their specific adaptive value and not at random, but this adaptive evolutionary process differs from the Darwinian process of random variation followed by natural selection. We find evidence for the adaptive evolution of technology in the US patent record, specifically, the public bibliographic information of all utility patents issued in the United States from 1976 through 2010. Patents record certain innovations in the evolution of technology. The 1976-2010 patent record is huge, containing almost four million patents. We use a patent's incoming citations to measure its impact on subsequent patented innovations. Weighting innovative impact by the dissimilarity between parent and child technologies reveals that many of the most fecund inventions are door-opening technologies that spawn innovations in widely diverse categories.

Leaky membrane dynamics.

A concentration difference of particles across a membrane perforated by pores will induce a diffusive flux. If the diffusing objects are of the same length scale as the pores, diffusion may not be simple; objects can move into the pore in a configuration that requires them to back up in order to continue forward. A configuration that blocks flow through the pore may be statistically preferred, an attracting metastable state of the system. This effect is purely kinetic, and not dependent on potentials, friction, or dissipation. We discuss several geometries which generate this effect, and introduce a heuristic model which captures the qualitative features.

Living technology: exploiting life's principles in technology.

The concept of living technology-that is, technology that is based on the powerful core features of life-is explained and illustrated with examples from artificial life software, reconfigurable and evolvable hardware, autonomously self-reproducing robots, chemical protocells, and hybrid electronic-chemical systems. We define primary (secondary) living technology according as key material components and core systems are not (are) derived from living organisms. Primary living technology is currently emerging, distinctive, and potentially powerful, motivating this review. We trace living technology's connections with artificial life (soft, hard, and wet), synthetic biology (top-down and bottom-up), and the convergence of nano-, bio-, information, and cognitive (NBIC) technologies. We end with a brief look at the social and ethical questions generated by the prospect of living technology.

Evolutionary self-organization in complex fluids.

This paper explores the ability of molecular evolution to take control of collective physical phases, making the first decisive step from independent replicators towards cell-like collective structures. We develop a physical model of replicating combinatorial molecules in a ternary fluid of hydrocarbons, amphiphiles and water. Such systems are being studied experimentally in various laboratories to approach the synthesis of artificial cells, and are also relevant to the origin of cellular life. The model represents amphiphiles by spins on a lattice (with Ising coupling in the simplest case), coupled to replicating molecules that may diffuse on the lattice and react with each other. The presence of the replicating molecules locally modulates the phases of the complex fluid, and the physical replication process and/or mobility of the replicating molecules is influenced by the local amphiphilic configuration through an energetic coupling. Consequently, the replicators can potentially modify their environment to enhance their own replication. Through this coupling, the system can associate hereditary properties, and the potential for autonomous evolution, to self-assembling mesoscale structures in the complex fluid. This opens a route to analyse the evolution of artificial cells. The models are studied using Monte Carlo simulation, and demonstrate the evolution of phase control. We achieve a unified combinatorial framework for the description of isotropic families of spin-lattice models of complex phases, opening up the physical study of their evolution.

Evolution of evolvability via adaptation of mutation rates.

We examine a simple form of the evolution of evolvability-the evolution of mutation rates-in a simple model system. The system is composed of many agents moving, reproducing, and dying in a two-dimensional resource-limited world. We first examine various macroscopic quantities (three types of genetic diversity, a measure of population fitness, and a measure of evolutionary activity) as a function of fixed mutation rates. The results suggest that (i) mutation rate is a control parameter that governs a transition between two qualitatively different phases of evolution, an ordered phase characterized by punctuated equilibria of diversity, and a disordered phase of characterized by noisy fluctuations around an equilibrium diversity, and (ii) the ability of evolution to create adaptive structure is maximized when the mutation rate is just below the transition between these two phases of evolution. We hypothesize that this transition occurs when the demands for evolutionary memory and evolutionary novelty are typically balanced. We next allow the mutation rate itself to evolve, and we observe that evolving mutation rates adapt to values at this transition. Furthermore, the mutation rates adapt up (or down) as the evolutionary demands for novelty (or memory) increase, thus supporting the balance hypothesis.