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Autocatalytic chemical networks at the origin of metabolism

Complexity Digest - Wed, 03/18/2020 - 07:22

Joana C. Xavier, Wim Hordijk, Stuart Kauffman, Mike Steel and William F. Martin

Proceedings of the Royal Society B: Biological Sciences


Modern cells embody metabolic networks containing thousands of elements and form autocatalytic sets of molecules that produce copies of themselves. How the first self-sustaining metabolic networks arose at life’s origin is a major open question. Autocatalytic sets smaller than metabolic networks were proposed as transitory intermediates at the origin of life, but evidence for their role in prebiotic evolution is lacking. Here, we identify reflexively autocatalytic food-generated networks (RAFs)—self-sustaining networks that collectively catalyse all their reactions—embedded within microbial metabolism. RAFs in the metabolism of ancient anaerobic autotrophs that live from H2 and CO2 provided with small-molecule catalysts generate acetyl-CoA as well as amino acids and bases, the monomeric components of protein and RNA, but amino acids and bases without organic catalysts do not generate metabolic RAFs. This suggests that RAFs identify attributes of biochemical origins conserved in metabolic networks. RAFs are consistent with an autotrophic origin of metabolism and furthermore indicate that autocatalytic chemical networks preceded proteins and RNA in evolution. RAFs uncover intermediate stages in the emergence of metabolic networks, narrowing the gaps between early Earth chemistry and life.

Source: royalsocietypublishing.org

Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV2)

Complexity Digest - Tue, 03/17/2020 - 18:52

Ruiyun Li, Sen Pei, Bin Chen, Yimeng Song, Tao Zhang, Wan Yang, Jeffrey Shaman

Science 16 Mar 2020:
DOI: 10.1126/science.abb3221


Estimation of the prevalence and contagiousness of undocumented novel coronavirus (SARS-CoV2) infections is critical for understanding the overall prevalence and pandemic potential of this disease. Here we use observations of reported infection within China, in conjunction with mobility data, a networked dynamic metapopulation model and Bayesian inference, to infer critical epidemiological characteristics associated with SARS-CoV2, including the fraction of undocumented infections and their contagiousness. We estimate 86% of all infections were undocumented (95% CI: [82%–90%]) prior to 23 January 2020 travel restrictions. Per person, the transmission rate of undocumented infections was 55% of documented infections ([46%–62%]), yet, due to their greater numbers, undocumented infections were the infection source for 79% of documented cases. These findings explain the rapid geographic spread of SARS-CoV2 and indicate containment of this virus will be particularly challenging.

Source: science.sciencemag.org

What China’s coronavirus response can teach the rest of the world

Complexity Digest - Tue, 03/17/2020 - 08:45

As the new coronavirus marches around the globe, countries with escalating outbreaks are eager to learn whether China’s extreme lockdowns were responsible for bringing the crisis there under control. Other nations are now following China’s lead and limiting movement within their borders, while dozens of countries have restricted international visitors.

Source: www.nature.com

End the Coronavirus

Complexity Digest - Sun, 03/15/2020 - 14:19

Spread the knowledge, not the virus.
Take part in eradicating this epidemic
Since the first confirmed case of a new, virulent strain of the coronavirus in December in Wuhan, China, the disease has spread to more than 100 countries and territories. As of March 12, 2020, there are 125,048 confirmed cases and 4,613 deaths. These numbers are still increasing.
Everyone can help.

Source: www.endcoronavirus.org

COVID-19 outbreak response: first assessment of mobility changes in Italy following lockdown

Complexity Digest - Fri, 03/13/2020 - 15:33

Emanuele Pepe, Paolo Bajardi, Laetitia Gauvin, Filippo Privitera, Ciro Cattuto, Michele Tizzoni


The mitigation measures enacted as part of the response to the unfolding SARS-CoV-2 pandemic are unprecedented in their breadth and societal burden. A major challenge in this situation is to quantitatively assess the impact of non-pharmaceutical interventions like mobility restrictions and social distancing, to better understand the ensuing reduction of mobility flows, individual mobility changes, and impact on contact patterns. Here we report preliminary results on tackling the above challenges by using de-identified, large-scale data from a location intelligence company, Cuebiq, that has instrumented smartphone apps with high-accuracy location-data collection software. We focus this initial analysis on Italy, where the COVID-19 epidemic has already triggered an unprecedented and escalating series of restrictions on travel and individual mobility of citizens.

Source: covid19mm.github.io

School closures, event cancellations, and the mesoscopic localization of epidemics in networks with higher-order structure

Complexity Digest - Fri, 03/13/2020 - 14:09

The COVID-19 epidemic is challenging in many ways, perhaps most obvious are failures of the surveillance system. Consequently, the official intervention has focused on conventional wisdom — social distancing, hand washing, etc. — while critical decisions such as the cancellation of large events like festivals, workshops and academic conferences are done on a case-by-case basis with limited information about local risks. Adding to this uncertainty is the fact that our mathematical models tend to assume some level of random mixing patterns instead of the higher-order structures necessary to describe these large events. Here, we discuss a higher-order description of epidemic dynamics on networks that provides a natural way of extending common models to interaction beyond simple pairwise contacts. We show that unlike the classic diffusion of standard epidemic models, higher-order interactions can give rise to mesoscopic localization, i.e., a phenomenon in which there is a concentration of the epidemic around certain substructures in the network. We discuss the implications of these results and show the potential impact of a blanket cancellation of events larger than a certain critical size. Unlike standard models of delocalized dynamics, epidemics in a localized phase can suddenly collapse when facing an intervention operating over structures rather than individuals.


Guillaume St-Onge, Vincent Thibeault, Antoine Allard, Louis J. Dubé, Laurent Hébert-Dufresne

Source: arxiv.org

Evolving Always-Critical Networks

Complexity Digest - Fri, 03/13/2020 - 09:51

Marco Villani , Salvatore Magrì, Andrea Roli and Roberto Serra


Living beings share several common features at the molecular level, but there are very few large-scale “operating principles” which hold for all (or almost all) organisms. However, biology is subject to a deluge of data, and as such, general concepts such as this would be extremely valuable. One interesting candidate is the “criticality” principle, which claims that biological evolution favors those dynamical regimes that are intermediaries between ordered and disordered states (i.e., “at the edge of chaos”). The reasons why this should be the case and experimental evidence are briefly discussed, observing that gene regulatory networks are indeed often found on, or close to, the critical boundaries. Therefore, assuming that criticality provides an edge, it is important to ascertain whether systems that are critical can further evolve while remaining critical. In order to explore the possibility of achieving such “always-critical” evolution, we resort to simulated evolution, by suitably modifying a genetic algorithm in such a way that the newly-generated individuals are constrained to be critical. It is then shown that these modified genetic algorithms can actually develop critical gene regulatory networks with two interesting (and quite different) features of biological significance, involving, in one case, the average gene activation values and, in the other case, the response to perturbations. These two cases suggest that it is often possible to evolve networks with interesting properties without losing the advantages of criticality. The evolved networks also show some interesting features which are discussed.

Source: www.mdpi.com

Concepts in Boolean network modeling: What do they all mean?

Complexity Digest - Thu, 03/12/2020 - 12:56

Julian D. Schwab, Silke D. Kühlwein, Nensi Ikonomi, Michael Kühl, Hans A. Kestler

Computational and Structural Biotechnology Journal


Boolean network models are one of the simplest models to study complex dynamic behavior in biological systems. They can be applied to unravel the mechanisms regulating the properties of the system or to identify promising intervention targets. Since its introduction by Stuart Kauffman in 1969 for describing gene regulatory networks, various biologically based networks and tools for their analysis were developed. Here, we summarize and explain the concepts for Boolean network modeling. We also present application examples and guidelines to work with and analyze Boolean network models.

Source: www.sciencedirect.com

Effect of non-pharmaceutical interventions for containing the COVID-19 outbreak: an observational and modelling study

Complexity Digest - Wed, 03/11/2020 - 17:50

Shengjie Lai, Nick W Ruktanonchai, Liangcai Zhou, Olivia Prosper, Wei Luo, Jessica R Floyd, Amy Wesolowski, Chi Zhang, Xiangjun Du, Hongjie Yu, Andrew J Tatem


Background: The COVID-19 outbreak containment strategies in China based on non-pharmaceutical interventions (NPIs) appear to be effective. Quantitative research is still needed however to assess the efficacy of different candidate NPIs and their timings to guide ongoing and future responses to epidemics of this emerging disease across the World. Methods: We built a travel network-based susceptible-exposed-infectious-removed (SEIR) model to simulate the outbreak across cities in mainland China. We used epidemiological parameters estimated for the early stage of outbreak in Wuhan to parameterise the transmission before NPIs were implemented. To quantify the relative effect of various NPIs, daily changes of delay from illness onset to the first reported case in each county were used as a proxy for the improvement of case identification and isolation across the outbreak. Historical and near-real time human movement data, obtained from Baidu location-based service, were used to derive the intensity of travel restrictions and contact reductions across China. The model and outputs were validated using daily reported case numbers, with a series of sensitivity analyses conducted. Findings: We estimated that there were a total of 114,325 COVID-19 cases (interquartile range [IQR] 76,776 – 164,576) in mainland China as of February 29, 2020, and these were highly correlated (p<0.001, R2=0.86) with reported incidence. Without NPIs, the number of COVID-19 cases would likely have shown a 67-fold increase (IQR: 44 – 94), with the effectiveness of different interventions varying. The early detection and isolation of cases was estimated to prevent more infections than travel restrictions and contact reductions, but integrated NPIs would achieve the strongest and most rapid effect. If NPIs could have been conducted one week, two weeks, or three weeks earlier in China, cases could have been reduced by 66%, 86%, and 95%, respectively, together with significantly reducing the number of affected areas. However, if NPIs were conducted one week, two weeks, or three weeks later, the number of cases could have shown a 3-fold, 7-fold, and 18-fold increase across China, respectively. Results also suggest that the social distancing intervention should be continued for the next few months in China to prevent case numbers increasing again after travel restrictions were lifted on February 17, 2020. Conclusion: The NPIs deployed in China appear to be effectively containing the COVID-19 outbreak, but the efficacy of the different interventions varied, with the early case detection and contact reduction being the most effective. Moreover, deploying the NPIs early is also important to prevent further spread. Early and integrated NPI strategies should be prepared, adopted and adjusted to minimize health, social and economic impacts in affected regions around the World.

Source: www.medrxiv.org

Landmark Computer Science Proof Cascades Through Physics and Math

Complexity Digest - Tue, 03/10/2020 - 15:27

In 1935, Albert Einstein, working with Boris Podolsky and Nathan Rosen, grappled with a possibility revealed by the new laws of quantum physics: that two particles could be entangled, or correlated, even across vast distances.

The very next year, Alan Turing formulated the first general theory of computing and proved that there exists a problem that computers will never be able to solve.

These two ideas revolutionized their respective disciplines. They also seemed to have nothing to do with each other. But now a landmark proof has combined them while solving a raft of open problems in computer science, physics and mathematics.

Source: www.quantamagazine.org

The effect of human mobility and control measures on the COVID-19 epidemic in China

Complexity Digest - Tue, 03/10/2020 - 09:42

Moritz U.G. Kraemer, Chia-Hung Yang, Bernardo Gutierrez, Chieh-Hsi Wu, Brennan Klein, David M. Pigott, open COVID-19 data working group, Louis du Plessis, Nuno R Faria, Ruoran Li, William P. Hanage, John S Brownstein, Maylis Layan, Alessandro Vespignani, Huaiyu Tian, Christopher Dye, Simon Cauchemez, Oliver Pybus, Samuel V Scarpino


The ongoing COVID-19 outbreak has expanded rapidly throughout China. Major behavioral, clinical, and state interventions are underway currently to mitigate the epidemic and prevent the persistence of the virus in human populations in China and worldwide. It remains unclear how these unprecedented interventions, including travel restrictions, have affected COVID-19 spread in China. We use real-time mobility data from Wuhan and detailed case data including travel history to elucidate the role of case importation on transmission in cities across China and ascertain the impact of control measures. Early on, the spatial distribution of COVID-19 cases in China was well explained by human mobility data. Following the implementation of control measures, this correlation dropped and growth rates became negative in most locations, although shifts in the demographics of reported cases are still indicative of local chains of transmission outside Wuhan. This study shows that the drastic control measures implemented in China have substantially mitigated the spread of COVID-19.

Source: www.medrxiv.org

Advanced Control and Optimization for Complex Energy Systems

Complexity Digest - Tue, 03/10/2020 - 09:37

Chun Wei, Xiaoqing Bai, and Taesic Kim

Editorial | Open Access

Complexity Volume 2020 |Article ID 5908102


The application of renewable energies such as wind and solar has become an inevitable choice for many countries in order to achieve sustainable and healthy economic development [1]. However, due to the intermittent characteristics of renewable energy, the issue with integrating a larger proportion of renewable energy into the grid becomes prominent. Currently, an energy system with weak coordination capability seriously affects the flexibility of power system operation [2]. As a result, this has led to the development of an effective way to integrate high-proportion renewable energy by developing multienergy systems including wind, solar, thermal, and energy storage to allow for the integration and coordination of different energy resources [3]. The major challenge of the multienergy system is its complexity with multispatial and multitemporal scales. Compared with the traditional power system, control and optimization of the complex energy system become more difficult in terms of modeling, operation, and planning [4, 5]. The main purpose of the complex energy system is to coordinate the operation with various distributed energy resources (DERs), energy storage systems, and power grids to ensure its reliability, while reducing the operating costs and achieving the optimal economic benefits.

Source: www.hindawi.com

See Also: Special Issue

On the Synthtesis of Affectivity Embodiment & AI

Complexity Digest - Mon, 03/09/2020 - 15:30


13-18 July 2020, Montreal, Canada 


Affective computing works mostly under a vision of emotions based on a functionalist conception of the mind in which emotions, as any other mental state, are understood as functional relations of information processing. The way in which these functional relations are achieved, whether through neuronal activity and organization or by artificial computer programming, is irrelevant to what emotions essentially are. These ideas are in stark contrast to the positions of embodied cognitive science, especially those emerging from the 4E approach to cognition (Embodied, Ecological, Embedded, Enactive), to which, in general, affectivity is seen as constitutive to cognition and cognition is always embodied.

In this workshop we discuss how relevant is embodiment for the synthesis of affectivity based in AI or other forms of implementation. The workshop is open to the widest possible disciplinary audience to tackle both the theoretical and philosophical aspects of synthetic affectivity, and how this is relevant for real-world implementations. We believe that this discussion is not only relevant in terms of advancing technology –which is exciting all by itself–, but it is a great opportunity to put the embodiment of emotions and affectivity in sharper relief by considering if and how this affective life can be shared with synthetic systems or even artificially implemented. We thus propose a dialogue in which the AI concern with artificial affectivity and the embodied methodologies of ALife can meet.

Source: cogsci4e.wixsite.com

Theme issue ‘Unifying the essential concepts of biological networks: biological insights and philosophical foundations’

Complexity Digest - Sun, 03/08/2020 - 16:20

Compiled and edited by Daniel Kostić, Claus C. Hilgetag and Marc Tittgemeyer

Philosophical Transactions of the Royal Society B: Biological Sciences: Vol 375, No 1796


Over the last decades, network-based approaches have become highly popular in diverse areas of biology. While these approaches continue to grow very rapidly, some of their conceptual and methodological aspects still require a programmatic foundation. In order to unify and systematize network approaches across biological sciences, this theme issue brings together scientists working in many diverse areas of biological sciences as well as philosophers working on foundational issues of network explanations and modelling, who together aim to develop universally applicable norms of network explanations, as well as systematize network concepts, such as levels and hierarchies.

Source: royalsocietypublishing.org

Crowdsourcing Moral Machines

Complexity Digest - Sat, 03/07/2020 - 14:16

Edmond Awad, Sohan Dsouza, Jean-François Bonnefon, Azim Shariff, Iyad Rahwan
Communications of the ACM, March 2020, Vol. 63 No. 3, Pages 48-55


Robots and other artificial intelligence (AI) systems are transitioning from performing well-defined tasks in closed environments to becoming significant physical actors in the real world. No longer confined within the walls of factories, robots will permeate the urban environment, moving people and goods around, and performing tasks alongside humans. Perhaps the most striking example of this transition is the imminent rise of automated vehicles (AVs). AVs promise numerous social and economic advantages. They are expected to increase the efficiency of transportation, and free up millions of person-hours of productivity. Even more importantly, they promise to drastically reduce the number of deaths and injuries from traffic accidents.12,30 Indeed, AVs are arguably the first human-made artifact to make autonomous decisions with potential life-and-death consequences on a broad scale. This marks a qualitative shift in the consequences of design choices made by engineers.

Source: cacm.acm.org

Statistical Consequences of Fat Tails: Real World Preasymptotics, Epistemology, and Applications, by Nassim Nicholas Taleb

Complexity Digest - Sat, 03/07/2020 - 14:08

The book investigates the misapplication of conventional statistical techniques to fat tailed distributions and looks for remedies, when possible.
Switching from thin tailed to fat tailed distributions requires more than "changing the color of the dress". Traditional asymptotics deal mainly with either n=1 or n=∞, and the real world is in between, under of the "laws of the medium numbers" –which vary widely across specific distributions. Both the law of large numbers and the generalized central limit mechanisms operate in highly idiosyncratic ways outside the standard Gaussian or Levy-Stable basins of convergence.
A few examples:
+ The sample mean is rarely in line with the population mean, with effect on "naive empiricism", but can be sometimes be estimated via parametric methods.
+ The "empirical distribution" is rarely empirical.
+ Parameter uncertainty has compounding effects on statistical metrics.
+ Dimension reduction (principal components) fails.
+ Inequality estimators (GINI or quantile contributions) are not additive and produce wrong results.
+ Many "biases" found in psychology become entirely rational under more sophisticated probability distributions
+ Most of the failures of financial economics, econometrics, and behavioral economics can be attributed to using the wrong distributions.
This book, the first volume of the Technical Incerto, weaves a narrative around published journal articles.

Source: www.researchers.one

Allotaxonometry and rank-turbulence divergence: A universal instrument for comparing complex systems

Complexity Digest - Sat, 03/07/2020 - 12:13

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

How Computation Is Helping Unravel the Dynamics of Morphogenesis

Complexity Digest - Fri, 03/06/2020 - 18:04

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

Disturbance in human gut microbiota networks by parasites and its implications in the incidence of depression

Complexity Digest - Fri, 03/06/2020 - 16:03

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

Living robots

Complexity Digest - Thu, 03/05/2020 - 20:07

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


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