P. S. Dodds, J. R. Minot, M. V. Arnold, T. Alshaabi, J. L. Adams, D. R. Dewhurst, T. J. Gray, M. R. Frank, A. J. Reagan, C. M. Danforth

Complex systems often comprise many kinds of components which vary over many orders of magnitude in size: Populations of cities in countries, individual and corporate wealth in economies, species abundance in ecologies, word frequency in natural language, and node degree in complex networks. Comparisons of component size distributions for two complex systems—or a system with itself at two different time points—generally employ information-theoretic instruments, such as Jensen-Shannon divergence. We argue that these methods lack transparency and adjustability, and should not be applied when component probabilities are non-sensible or are problematic to estimate. Here, we introduce `allotaxonometry’ along with `rank-turbulence divergence’, a tunable instrument for comparing any two (Zipfian) ranked lists of components. We analytically develop our rank-based divergence in a series of steps, and then establish a rank-based allotaxonograph which pairs a map-like histogram for rank-rank pairs with an ordered list of components according to divergence contribution. We explore the performance of rank-turbulence divergence for a series of distinct settings including: Language use on Twitter and in books, species abundance, baby name popularity, market capitalization, performance in sports, mortality causes, and job titles. We provide a series of supplementary flipbooks which demonstrate the tunability and storytelling power of rank-based allotaxonometry.

Source: arxiv.org

David Pastor-Escuredo and Juan C. del Álamo

Front. Phys.

 

The growing availability of imaging data, calculation power, and algorithm sophistication are transforming the study of morphogenesis into a computation-driven discipline. In parallel, it is accepted that mechanics plays a role in many of the processes determining the cell fate map, providing further opportunities for modeling and simulation. We provide a perspective of this integrative field, discussing recent advances and outstanding challenges to understand the determination of the fate map. At the basis, high-resolution microscopy and image processing provide digital representations of embryos that facilitate quantifying their mechanics with computational methods. Moreover, innovations in in-vivo sensing and tissue manipulation can now characterize cell-scale processes to feed larger-scale representations. A variety of mechanical formalisms have been proposed to model cellular biophysics and its links with biochemical and genetic factors. However, there are still limitations derived from the dynamic nature of embryonic tissue and its spatio-temporal heterogeneity. Also, the increasing complexity and variety of implementations make it difficult to harmonize and cross-validate models. The solution to these challenges will likely require integrating novel in vivo measurements of embryonic biomechanics into the models. Machine Learning has great potential to classify spatio-temporally connected groups of cells with similar dynamics. Emerging Deep Learning architectures facilitate the discovery of causal links and are becoming transparent and interpretable. We anticipate these new tools will lead to multi-scale models with the necessary accuracy and flexibility to formulate hypotheses for in-vivo and in-silico testing. These methods have promising applications for tissue engineering, identification of therapeutic targets, and synthetic life.

Source: www.frontiersin.org

Elvia Ramírez-Carrillo, Osiris Gaona, Javier Nieto, Andrés Sánchez-Quinto, Daniel Cerqueda-García, Luisa I. Falcón, Olga A. Rojas-Ramos & Isaac González-Santoyo
Scientific Reports volume 10, Article number: 3680 (2020)

 

If you think you are in control of your behavior, think again. Evidence suggests that behavioral modifications, as development and persistence of depression, maybe the consequence of a complex network of communication between macro and micro-organisms capable of modifying the physiological axis of the host. Some parasites cause significant nutritional deficiencies for the host and impair the effectiveness of cognitive processes such as memory, teaching or non-verbal intelligence. Bacterial communities mediate the establishment of parasites and vice versa but this complexity approach remains little explored. We study the gut microbiota-parasite interactions using novel techniques of network analysis using data of individuals from two indigenous communities in Guerrero, Mexico. Our results suggest that Ascaris lumbricoides induce a gut microbiota perturbation affecting its network properties and also subnetworks of key species related to depression, translating in a loss of emergence. Studying these network properties changes is particularly important because recent research has shown that human health is characterized by a dynamic trade-off between emergence and self-organization, called criticality. Emergence allows the systems to generate novel information meanwhile self-organization is related to the system’s order and structure. In this way, the loss of emergence means a depart from criticality and ultimately loss of health.

Source: www.nature.com

Philip Ball 
Nature Materials volume 19, page 265(2020)

 

The original ‘robots’, described in the 1921 play R.U.R. by the Czech writer Karel Čapek (the word is Czech for ‘labourer’) were not made from steel and controlled by electronics, but were fleshy and autonomous. Čapek’s manufacturing process, in which organs and other parts were made from vats of flesh-like dough and assembled into bodies, took inspiration from the emerging technology of in vivo tissue culture. It blurred the boundaries between engineering and biotechnology in a way that seemed far beyond the technologies of the time.

 

The results now reported by Kriegman et al. make this vision seem almost unnervingly prescient1. They describe ‘reconfigurable organisms’ made from living cells assembled into conglomerates about a millimetre across with arbitrary shapes, which are designed in silico for particular functions such as locomotion. These structures have been dubbed xenobots — which might be given the literal and apt interpretation of ‘strange robots’, although here ‘xeno’ comes from the use of embryonic stem cells of the African clawed frog Xenopus laevis as the construction material.

Source: www.nature.com