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

Previously referred to as ‘miraculous’ in the scientific literature because of its powerful properties and its wide application as optimal solution to the problem of induction/inference, (approximations to) Algorithmic Probability (AP) and the associated Universal Distribution are (or should be) of the greatest importance in science. Here we investigate the emergence, the rates of emergence and convergence, and the Coding-theorem like behaviour of AP in Turing-subuniversal models of computation. We investigate empirical distributions of computing models in the Chomsky hierarchy. We introduce measures of algorithmic probability and algorithmic complexity based upon resource-bounded computation, in contrast to previously thoroughly investigated distributions produced from the output distribution of Turing machines. This approach allows for numerical approximations to algorithmic (Kolmogorov-Chaitin) complexity-based estimations at each of the levels of a computational hierarchy. We demonstrate that all these estimations are correlated in rank and that they converge both in rank and values as a function of computational power, despite fundamental differences between computational models. In the context of natural processes that operate below the Turing universal level because of finite resources and physical degradation, the investigation of natural biases stemming from algorithmic rules may shed light on the distribution of outcomes. We show that up to 60% of the simplicity/complexity bias in distributions produced even by the weakest of the computational models can be accounted for by Algorithmic Probability in its approximation to the Universal Distribution. Coding theorem-like behaviour and emergence of the Universal Distribution. Correlation in rank (distributions were sorted in terms of each other) of empirical output distributions as compared to the output distribution of TM(5, 2). A progression towards greater correlation is noticed as a function of increasing computational power. Bold black labels are placed at their Chomsky level and gray labels are placed within the highest correlated level. Shannon entropy and lossless compression (Compress) distribute values below or at about the first 2 Chomsky types, as expected. It is not surprising to see the LBA with runtime 107 further deviate in ranking, because LBA after 27 steps produced the highest frequency strings, which are expected to converge faster. Eventually LBA 107 (which is none other than TM(4,2)) will converge to TM(5,2). An empirical bound of non-halting models seems to be low LBA even when increasing the number of states (or symbols for CA).

Source: www.tandfonline.com

Innovative ideas, products or services spread on social networks that, in the digital age, are maintained to large extent via telecommunication tools such as emails or social media. One of the intriguing puzzles in social contagion under such conditions is the role of physical space. It is not understood either how geography influences the disappearance of products at the end of their life-cycle. In this paper, we utilize a unique dataset compiled from a Hungarian on-line social network (OSN) to uncover novel features in the spatial adoption and churn of digital technologies. The studied OSN was established in 2002 and failed in international competition about a decade later. We find that early adopter towns churn early; while individuals tend to follow the churn of nearby friends and are less influenced by the churn of distant contacts. An agent-based Bass Diffusion Model describes the process how the product gets adopted in the overall population. We show the limitations of the model regarding the spatial aspects of diffusion and identify the directions of model corrections. Assortativity of adoption time, urban scaling of adoption over the product life-cycle and a distance decay function of diffusion probability are the main factors that spatial diffusion models need to account for.

 

Spatial diffusion and churn of social media
Balázs Lengyel, Riccardo Di Clemente, János Kertész, Marta C. González

Source: arxiv.org