Early microbiome research found numerous associations between microbial community patterns and host physiological states. These findings hinted at community-level explanations. “Top-down” experiments, working with whole communities, strengthened these explanatory expectations. Now, “bottom-up” mechanism-seeking approaches are dissecting communities to focus on specific microbes carrying out particular biochemical activities (e.g., choline metabolism pathways, Clostridium difficile suppression). To understand the interplay between methodological and explanatory scales, we examine claims of “dysbiosis,” when host illness is proposed as the consequence of a community state. Our analysis concludes with general observations about how methodologies relate to explanations, and the implications for microbiome research.

 

Methodological Strategies in Microbiome Research and their Explanatory Implications

Maureen A. O’Malley and Derek J. Skillings

Perspectives on Science
Volume 26 | Issue 2 | March-April 2018
p.239-265
https://doi.org/10.1162/POSC_a_00274

Source: www.mitpressjournals.org

Swarms are self-organized dynamical coupled agents which evolve from simple rules of communication. They are ubiquitous in nature, and be- coming more prominent in defense applications. Here we report on a preliminary study of swarm collisions for a swarm model in which each agent is self-propelling but globally communicates with other agents. We generalize previous models by investigating the interacting dynamics when delay is introduced to the communicating agents. One of our major find- ings is that interacting swarms are far less likely to flock cohesively if they are coupled with delay. In addition, parameter ranges based on coupling strength, incidence angle of collision, and delay change dramatically for other swarm interactions which result in flocking, milling, and scattering.

 

The Dynamics of Interacting Swarms
Carl Kolon, Ira B. Schwartz

Source: arxiv.org

How are granular details of stochastic growth and division of individual cells reflected in smooth deterministic growth of population numbers? We provide an integrated, multiscale perspective of microbial growth dynamics by formulating a data-validated theoretical framework that accounts for observables at both single-cell and population scales. We derive exact analytical complete time-dependent solutions to cell-age distributions and population growth rates as functionals of the underlying interdivision time distributions, for symmetric and asymmetric cell division. These results provide insights into the surprising implications of stochastic single-cell dynamics for population growth. Using our results for asymmetric division, we deduce the time to transition from the reproductively quiescent (swarmer) to the replication-competent (stalked) stage of the Caulobacter crescentus life cycle. Remarkably, population numbers can spontaneously oscillate with time. We elucidate the physics leading to these population oscillations. For C. crescentus cells, we show that a simple measurement of the population growth rate, for a given growth condition, is sufficient to characterize the condition-specific cellular unit of time and, thus, yields the mean (single-cell) growth and division timescales, fluctuations in cell division times, the cell-age distribution, and the quiescence timescale.

 

Bridging the Timescales of Single-Cell and Population Dynamics
Farshid Jafarpour, Charles S. Wright, Herman Gudjonson, Jedidiah Riebling, Emma Dawson, Klevin Lo, Aretha Fiebig, Sean Crosson, Aaron R. Dinner, and Srividya Iyer-Biswas
Phys. Rev. X 8, 021007 – Published 5 April 2018

Source: journals.aps.org

The emergence of life is best understood in terms of the dynamics and evolution of systems of chemical replicating entities endowed with genetic polymers. To understand the current emphasis of origin‐of‐life research in the abiotic appearance of replicative polymers and genetic information, this chapter considers Ernst Haeckel’s assumption of cell nuclei together with his characterization of the Monera as the oldest biological group. Haeckel’s ideas exerted an extraordinary influence in many 19th Century naturalists and philosophers, who conceived cells as chemical machines and developed research programs on experimental abiogenesis that can be considered as a direct intellectual predecessor of current efforts on synthetic biology. Some of their results have their contemporary equivalents in the proposals of the advocates of complexity theories that attempt to explain the origin and nature of life on the basis of complexity theory and self‐assembly phenomena.

 

The Emergence of Life: Some Notes on the Origin of Biological Information

Antonio Lazcano
Life Sciences, Information Sciences, 1

Book Editor(s): Thierry Gaudin Dominique Lacroix Marie‐Christine Maurel Jean‐Charles Pomerol

https://doi.org/10.1002/9781119452713.ch1 

Source: onlinelibrary.wiley.com

This paper provides the very first definition of “growing robots”: a category of robots that imitates biological growth by the incremental addition of material. Although this nomenclature is quite new, the concept of morphological evolution, which is behind growth, has been extensively addressed in engineering and robotics. In fact, the idea of reproducing processes that belong to living systems has always attracted scientists and engineers. The creation of systems that adapt reliably and effectively to the environment with their morphology and control would be beneficial for many different applications, including terrestrial and space exploration or the monitoring of disasters or dangerous environments. Different approaches have been proposed over the years for solving the morphological adaptation of artificial systems, e.g., self-assembly, self-reconfigurability, evolution of virtual creatures, plant inspiration. This work reviews the main milestones in relation to growing robots, starting from the original concept of a self-replicating automaton to the achievements obtained by plant inspiration, which provided an alternative solution to the challenges of creating robots with self-building capabilities. A selection of robots representative of growth functioning is also discussed, grouped by the natural element used as model: molecule, cell, or organism growth-inspired robots. Finally, the historical evolution of growing robots is outlined together with a discussion of the future challenges toward solutions that more faithfully can represent biological growth.

 

Toward Growing Robots: A Historical Evolution from Cellular to Plant-Inspired Robotics

Emanuela Del Dottore, Ali Sadeghi, Alessio Mondini, Virgilio Mattoli and Barbara Mazzolai

Front. Robot. AI, 14 March 2018 | https://doi.org/10.3389/frobt.2018.00016

Source: www.frontiersin.org