Motility at the origin of life: Its characterization and a model

Due to recent advances in synthetic biology and artificial life, the origin of life is currently a hot topic of research. We review the literature and argue that the two traditionally competing "replicator-first" and "metabolism-first" approaches are merging into one integrated theory of individuation and evolution. We contribute to the maturation of this more inclusive approach by highlighting some problematic assumptions that still lead to an impoverished conception of the phenomenon of life. In particular, we argue that the new consensus has so far failed to consider the relevance of intermediate timescales. We propose that an adequate theory of life must account for the fact that all living beings are situated in at least four distinct timescales, which are typically associated with metabolism, motility, development, and evolution. On this view, self-movement, adaptive behavior and morphological changes could have already been present at the origin of life. In order to illustrate this possibility we analyze a minimal model of life-like phenomena, namely of precarious, individuated, dissipative structures that can be found in simple reaction-diffusion systems. Based on our analysis we suggest that processes in intermediate timescales could have already been operative in prebiotic systems. They may have facilitated and constrained changes occurring in the faster- and slower-paced timescales of chemical self-individuation and evolution by natural selection, respectively.Comment: 29 pages, 5 figures, Artificial Lif

Modeling and inference of the dynamics of spatiotemporally evolving systems using evolving gaussian processes

In this work, we present a new differentially-constrained machine learning model, termed Evolving Gaussian Processes (E-GP), for modeling and inference of spatiotemporally evolving dynamical systems. We show that an E-GP model can be used to estimate the latent state of large-scale physical systems of this type, and furthermore that a single E-GP model can generalize over multiple physically-similar systems over a range of parameters using only a few training sets. It is also shown that an E-GP model provides access to practical physical insights into the dynamic structure of the system(s) it is trained on. In particular, from spectral analysis of the linear dynamic layer in the top level of the E-GP model, one may derive the Koopman modes and eigenvalues of the system. We are also able to derive the spatial distribution of the invariant subspaces of a system using a new clustering method. This information can be used for sensor placement and/or mobile agent path planning for robust inference of the state of the system using few measurements. We primarily demonstrate our method on computational flow dynamics (CFD) data sets on fluid flowing past a cylinder at different Reynolds numbers. Though these systems are governed by highly nonlinear partial differential equations (the Navier-Stokes equations), we show that their major dynamical modes can be captured by a linear dynamical layer over the temporal evolution of the weights of stationary kernels

The Hierarchic treatment of marine ecological information from spatial networks of benthic platforms

Measuring biodiversity simultaneously in different locations, at different temporal scales, and over wide spatial scales is of strategic importance for the improvement of our understanding of the functioning of marine ecosystems and for the conservation of their biodiversity. Monitoring networks of cabled observatories, along with other docked autonomous systems (e.g., Remotely Operated Vehicles [ROVs], Autonomous Underwater Vehicles [AUVs], and crawlers), are being conceived and established at a spatial scale capable of tracking energy fluxes across benthic and pelagic compartments, as well as across geographic ecotones. At the same time, optoacoustic imaging is sustaining an unprecedented expansion in marine ecological monitoring, enabling the acquisition of new biological and environmental data at an appropriate spatiotemporal scale. At this stage, one of the main problems for an effective application of these technologies is the processing, storage, and treatment of the acquired complex ecological information. Here, we provide a conceptual overview on the technological developments in the multiparametric generation, storage, and automated hierarchic treatment of biological and environmental information required to capture the spatiotemporal complexity of a marine ecosystem. In doing so, we present a pipeline of ecological data acquisition and processing in different steps and prone to automation. We also give an example of population biomass, community richness and biodiversity data computation (as indicators for ecosystem functionality) with an Internet Operated Vehicle (a mobile crawler). Finally, we discuss the software requirements for that automated data processing at the level of cyber-infrastructures with sensor calibration and control, data banking, and ingestion into large data portals.Peer ReviewedPostprint (published version