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Drug combinations, offering increased therapeutic efficacy and reduced toxicity, play an important role in treating multiple complex diseases. Yet, our ability to identify and validate effective combinations is limited by a combinatorial explosion, driven by both the large number of drug pairs as well as dosage combinations. Here we propose a network-based methodology to identify clinically efficacious drug combinations for specific diseases. By quantifying the network-based relationship between drug targets and disease proteins in the human protein–protein interactome, we show the existence of six distinct classes of drug–drug–disease combinations. Relying on approved drug combinations for hypertension and cancer, we find that only one of the six classes correlates with therapeutic effects: if the targets of the drugs both hit disease module, but target separate neighborhoods. This finding allows us to identify and validate antihypertensive combinations, offering a generic, powerful network methodology to identify efficacious combination therapies in drug development. Network-based prediction of drug combinations
Feixiong Cheng, István A. Kovács & Albert-László Barabási
Nature Communications volume 10, Article number: 1197 (2019)
The insertion of robotic and artificial intelligent (AI) systems in therapeutic settings is accelerating. In this paper, we investigate the legal and ethical challenges of the growing inclusion of social robots in therapy. Typical examples of such systems are Kaspar, Hookie, Pleo, Tito, Robota,Nao, Leka or Keepon. Although recent studies support the adoption of robotic technologies for therapy and education, these technological developments interact socially with children, elderly or disabled, and may raise concerns that range from physical to cognitive safety, including data protection. Research in other fields also suggests that technology has a profound and alerting impact on us and our human nature. This article brings all these findings into the debate on whether the adoption of therapeutic AI and robot technologies are adequate, not only to raise awareness of the possible impacts of this technology but also to help steer the development and use of AI and robot technologies in therapeutic settings in the appropriate direction. Our contribution seeks to provide a thoughtful analysis of some issues concerning the use and development of social robots in therapy, in the hope that this can inform the policy debate and set the scene for further research. “I’ll take care of you,” said the robot Reflecting upon the legal and ethical aspects of the use and development of social robots for therapy Eduard Fosch-Villaronga Jordi Albo-Canals Paladyn, Journal of Behavioral Robotics
The concept of social interaction is at the core of embodied and enactive approaches to social cognitive processes, yet scientifically it remains poorly understood. Traditionally, cognitive science had relegated all behavior to being the end result of internal neural activity. However, the role of feedback from the interactions between agent and their environment has become increasingly important to understanding behavior. We focus on the role that social interaction plays in the behavioral and neural activity of the individuals taking part in it. Is social interaction merely a source of complex inputs to the individual, or can social interaction increase the individuals' own complexity? Here we provide a proof of concept of the latter possibility by artificially evolving pairs of simulated mobile robots to increase their neural complexity, which consistently gave rise to strategies that take advantage of their capacity for interaction. We found that during social interaction, the neural controllers exhibited dynamics of higher-dimensionality than were possible in social isolation. Moreover, by testing evolved strategies against unresponsive ghost partners, we demonstrated that under some conditions this effect was dependent on mutually responsive co-regulation, rather than on the mere presence of another agent's behavior as such. Our findings provide an illustration of how social interaction can augment the internal degrees of freedom of individuals who are actively engaged in participation. Embodied Dyadic Interaction Increases Complexity of Neural Dynamics: A Minimal Agent-Based Simulation Model Madhavun Candadai, Matt Setzler, Eduardo J. Izquierdo and Tom Froese Front. Psychol., 21 March 2019 | https://doi.org/10.3389/fpsyg.2019.00540
A simulation of a half-million flock is studied using a simple boids model originally proposed by Craig Reynolds. It was modeled with a differential equation in 3D space with a periodic boundary. Flocking is collective behavior of active agents, which is often observed in the real world (e.g., starling swarms). It is, nevertheless, hard to rigorously define flocks (or their boundaries). First, even within the same swarm, the members are constantly updated, and second, flocks sometimes merge or divide dynamically. To define individual flocks and to capture their dynamic features, we applied a DBSCAN and a non-negative matrix factorization (NMF) to the boid dataset. Flocking behavior has different types of dynamics depending on the size of the flock. A function of different flocks is discussed with the result of NMF analysis. Dynamic organization of flocking behaviors in a large-scale boids model
Norihiro Maruyama Daichi Saito Yasuhiro Hashimoto Takashi Ikegami Journal of Computational Social Science
Self-organization can be broadly defined as the ability of a system to display ordered spatio-temporal patterns solely as the result of the interactions among the system components. Processes of this kind characterize both living and artificial systems, making self-organization a concept that is at the basis of several disciplines, from physics to biology to engineering. Placed at the frontiers between disciplines, Artificial Life (ALife) has heavily borrowed concepts and tools from the study of self-organization, providing mechanistic interpretations of life-like phenomena as well as useful constructivist approaches to artificial system design. Despite its broad usage within ALife, the concept of self-organization has been often excessively stretched or misinterpreted, calling for a clarification that could help with tracing the borders between what can and cannot be considered self-organization. In this review, we discuss the fundamental aspects of self-organization and list the main usages within three primary ALife domains, namely "soft" (mathematical/computational modeling), "hard" (physical robots), and "wet" (chemical/biological systems) ALife. Finally, we discuss the usefulness of self-organization within ALife studies, point to perspectives for future research, and list open questions. Self-Organization and Artificial Life
Carlos Gershenson, Vito Trianni, Justin Werfel, Hiroki Sayama
But adding artificial intelligence to our midst could be much more disruptive. Especially as machines are made to look and act like us and to insinuate themselves deeply into our lives, they may change how loving or friendly or kind we are—not just in our direct interactions with the machines in question, but in our interactions with one another. [...] The bots thus converted a group of generous people into selfish jerks.
"We want to learn how we can just tickle, or gently push, a system in the right direction to set it back into a synced state," says Michael L. Roukes, the Frank J. Roshek Professor of Physics, Applied Physics, and Bioengineering at Caltech, and principal investigator of the new Science study. "This could perhaps engender a form of new, less harsh defibrillators, for example, for shocking the heart back into rhythm."
Via june holley
Are scale-free networks rare or universal? Important or not? We present the recent research about degree distributions of networks. This is a controversial topic, but, we argue, with some adjustments of the terminology, it does not have to be. Rare and everywhere: Perspectives on scale-free networks
Petter Holme
Nature Communicationsvolume 10, Article number: 1016 (2019)
Economic inequality isn't just limited to neighborhoods. The restaurants, stores, and other places we visit in cities are all unequal in their own way.
The Atlas of Inequality shows the income inequality of people who visit different places in the Boston metro area. It uses aggregated anonymous location data from digital devices to estimate people's incomes and where they spend their time.
Using that data, we've made our own place inequality metric to capture how unequal the incomes of visitors to each place are. Economic inequality isn't just limited to neighborhoods, it's part of the places you visit every day.
This article explores the effect of ideological polarization on team performance. By analysing millions of edits to Wikipedia, the authors reveal that politically diverse editor teams produce higher-quality articles than homogeneous or moderate teams, and they identify the mechanisms responsible for producing these superior articles. The wisdom of polarized crowds
Feng Shi, Misha Teplitskiy, Eamon Duede & James A. Evans
Nature Human Behaviour (2019)
Via june holley
Radically new dynamics in the early 20th century transformed cars and, in turn, the world. Here’s why the next great inflection point is upon us, auguring changes no less profound.
Our mental models about mobility—individually owned cars, gas stations, traffic jams, the driver’s license as a rite of passage—are on the verge of disruption. Mobility is about to become cheaper, more convenient, a better experience, safer, and cleaner—not 50 or even 25 years from now, but perhaps within a dozen.
We describe the coming transformation as mobility’s Second Great Inflection Point, because it has the potential to be as profound as the one that put horses to pasture and revolutionized industries and societies worldwide. A defining characteristic of the new world taking shape is that the automotive industry, which has operated for more than a century alongside but decidedly disconnected from other components of what transportation has come to mean, will blend into a more interconnected, customer-centric ecosystem. That shift boosts the odds that the momentous changes afoot will affect your business, even if the closest you currently get to a car is your morning commute.
Ship scheduling optimization is one of the most effective ways to eliminate the bottlenecks of waterway transportation, especially in restricted one-way waterways. In this study, a novel scheduling model called self-organizing grouping is proposed to minimize two types of delay time, which are the waiting time and the extra navigation time caused by speed reduction. The proposed model schedules ships in an iterative way based on the distributed scheduling mode. To alleviate the impact of local scheduling on the overall traffic efficiency, a grouping method is proposed, in which the ships are divided into different groups based on their arrival time interval. Moreover, the ships in the same group are scheduled to minimize the interferences among them by incorporating a grouping improvement strategy. The strategy is used to deal with the influence of ships with very small speed. Experiments are carried out by comparing the proposed model with the first-come-first-serve model and the ship self-organizing cooperation model. Simulation results show that the delay time is reduced by 25%‐30% and approximately by 5% compared with that from the two models, respectively. Such advantage also exists for different combinations of ship traffic parameters. In addition, long-distance sailing with limited speed can be avoided using the proposed method, which is beneficial to relieve waterway traffic congestion. A Self-Organizing Grouping Approach for Ship Traffic Scheduling in Restricted One-Way Waterway
Authors: Xin, Xuri; Liu, Kezhong; Zhang, Jinfen; Chen, Shuzhe; Wang, Hongbo; Cheng, Zhiyou
Source: Marine Technology Society Journal, Volume 53, Number 1, January/February 2019, pp. 83-96(14)
DOI: https://doi.org/10.4031/MTSJ.53.1.9
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In this guest blog Nele van Hooste from Board of Innovation speaks about the 6 mistakes they made while introducting a network of sel-managing teams.
Via june holley
Biological complexity has impeded our ability to predict the dynamics of mutualistic interactions. Here, the authors deduce a general rule to predict outcomes of mutualistic systems and introduce an approach that permits making predictions even in the absence of knowledge of mechanistic details.
Via Samir
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Suggested by
Silvy Salinas
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Complexity science explains why efforts to reject the mainstream merely result in a new conformity.
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Suggested by
Silvy Salinas
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Elements composing complex systems usually interact in several different ways and as such the interaction architecture is well modelled by a multiplex network. However often this architecture is hidden, as one usually only has experimental access to an aggregated projection. A fundamental challenge is thus to determine whether the hidden underlying architecture of complex systems is better modelled as a single interaction layer or results from the aggregation and interplay of multiple layers. Here we show that using local information provided by a random walker navigating the aggregated network one can decide in a robust way if the underlying structure is a multiplex or not and, in the former case, to determine the most probable number of hidden layers. As a byproduct, we show that the mathematical formalism also provides a principled solution for the optimal decomposition and projection of complex, non-Markovian dynamics into a Markov switching combination of diffusive modes.
We validate the proposed methodology with numerical simulations of both (i) random walks navigating hidden multiplex networks (thereby reconstructing the true hidden architecture) and (ii) Markovian and non-Markovian continuous stochastic processes (thereby reconstructing an effective multiplex decomposition where each layer accounts for a different diffusive mode). We also state and prove two existence theorems guaranteeing that an exact reconstruction of the dynamics in terms of these hidden jump-Markov models is always possible for arbitrary finite-order Markovian and fully non-Markovian processes. Finally, we showcase the applicability of the method to experimental recordings from (i) the mobility dynamics of human players in an online multiplayer game and (ii) the dynamics of RNA polymerases at the single-molecule level. Multiplex decomposition of non-Markovian dynamics and the hidden layer reconstruction problem Lucas Lacasa, Inés P. Mariño, Joaquín Miguez, Vincenzo Nicosia, Edgar Roldán, Ana Lisica, Stephan W. Grill, Jesús Gómez-Gardeñes
Experiments show that evolutionary fitness landscapes can have a rich combinatorial structure due to epistasis. For some landscapes, this structure can produce a computational constraint that prevents evolution from finding local fitness optima -- thus overturning the traditional assumption that local fitness peaks can always be reached quickly if no other evolutionary forces challenge natural selection. Here, I introduce a distinction between easy landscapes of traditional theory where local fitness peaks can be found in a moderate number of steps and hard landscapes where finding local optima requires an infeasible amount of time. Hard examples exist even among landscapes with no reciprocal sign epistasis; on these semi-smooth fitness landscapes, strong selection weak mutation dynamics cannot find the unique peak in polynomial time. More generally, on hard rugged fitness landscapes that include reciprocal sign epistasis, no evolutionary dynamics -- even ones that do not follow adaptive paths -- can find a local fitness optimum quickly. Moreover, on hard landscapes, the fitness advantage of nearby mutants cannot drop off exponentially fast but has to follow a power-law that long-term evolution experiments have associated with unbounded growth in fitness. Thus, the constraint of computational complexity enables open-ended evolution on finite landscapes. Knowing this constraint allows us to use the tools of theoretical computer science and combinatorial optimization to characterize the fitness landscapes that we expect to see in nature. I present candidates for hard landscapes at scales from single genes, to microbes, to complex organisms with costly learning (Baldwin effect) or maintained cooperation (Hankshaw effect). Just how ubiquitous hard landscapes (and the corresponding ultimate constraint on evolution) are in nature becomes an open empirical question. Computational Complexity as an Ultimate Constraint on Evolution
Artem Kaznatcheev
Genetics, Early online March 4, 2019: 10.1534/genetics.119.302000
Cooperation is still an important issue for both evolutionary and social scientists. There are some remarkable methods for sustaining cooperation. On the other hand, various studies discuss whether human deliberative behaviour promotes or inhibits cooperation. As those studies of human behaviour develop, in the study of evolutionary game theory, models considering deliberative behaviour of game players are increasing. Based on that trend, the author considers that decision of a person requires certain time and imposes a psychological burden on him/her and defines such burden as the cost of decision. This study utilizes the model of evolutionary game theory that each player plays the spatial prisoner’s dilemma game with opponent players connected to him/her and introduces the cost of decision. The main result of this study is that the introduction of the cost of decision contributes to the evolution of cooperation, although there are some differences in the extent of its contribution regarding the three types of sparse topology of connections. Regarding the distribution of the cost of decision, especially in the case of the scale-free topology of connections, players with high cost of decision, which seem to be disadvantageous at first glance, sometimes become mainstream at the last. Coevolution between the cost of decision and the strategy contributes to the evolution of cooperation
Tetsushi Ohdaira
Scientific Reports volume 9, Article number: 4465 (2019) |
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Silvy Salinas
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Combining mobile phone technologies with infectious disease diagnostics can increase patients’ access to testing and treatment and provide public health authorities with new ways to monitor and control outbreaks of infectious diseases. Taking connected mobile-health diagnostics of infectious diseases to the field
Christopher S. Wood, Michael R. Thomas, Jobie Budd, Tivani P. Mashamba-Thompson, Kobus Herbst, Deenan Pillay, Rosanna W. Peeling, Anne M. Johnson, Rachel A. McKendry & Molly M. Stevens
Nature volume 566, pages 467–474 (2019)
Despite the tight historical links between science and philosophy, present-day scientists often perceive philosophy as completely different from, and even antagonistic to, science. We argue here that, to the contrary, philosophy can have an important and productive impact on science. Opinion: Why science needs philosophy
Lucie Laplane, Paolo Mantovani, Ralph Adolphs, Hasok Chang, Alberto Mantovani, Margaret McFall-Ngai, Carlo Rovelli, Elliott Sober, and Thomas Pradeu
PNAS March 5, 2019 116 (10) 3948-3952; https://doi.org/10.1073/pnas.1900357116
Religion is considered as a notable origin of interpersonal relations, as well as an effective and efficient tool to organize a huge number of people towards some challenging targets. At the same time, a believer prefers to make friend with other people of the same faith, and thus people of different faiths tend to form relatively isolated communities. The segregation between different religions is a major factor for many social conflicts. However, quantitative understanding of religious segregation is rare. Here we analyze a directed social network extracted from weibo.com (the largest directed social network in China, similar to twitter.com), which is consisted of 6875 believers in Christianity, Buddhism, Islam and Taoism. This religion network is highly segregative. Comparative analysis shows that the extent of segregation for different religions is much higher than that for different races and slightly higher than that for different political parties. Furthermore, we study the few cross-religion links and find 46.7% of them are probably related to charitable issues. Our findings provide quantitative insights into religious segregation and valuable evidence for religious syncretism. Segregation in religion networks
Jiantao Hu, Qian-Ming Zhang and Tao Zhou
EPJ Data Science 2019 8:6
https://doi.org/10.1140/epjds/s13688-019-0184-x
A theoretical model is presented for early evolutionary cell sorting within cellular aggregates. The model involves an energy-saving mechanism and principles of collective self-organization analogous to those observed in bicycle pelotons (groups of cyclists). The theoretical framework is applied to slime-mold slugs (Dictyostelium discoideum) and incorporated into a computer simulation which demonstrates principally the sorting of cells between the anterior and posterior slug regions. The simulation relies on an existing simulation of bicycle peloton dynamics which is modified to incorporate a limited range of cell metabolic capacities among heterogeneous cells, along with a tunable energy-expenditure parameter, referred to as an “output-level” or “starvation-level” to reflect diminishing energetic supply. Proto-cellular dynamics are modeled for three output phases: “active”, “suffering”, and “dying or dead.” Adjusting the starvation parameter causes cell differentiation and sorting into sub-groups within the cellular aggregate. Tuning of the starvation parameter demonstrates how weak or expired cells shuffle backward within the cellular aggregate. Cell pelotons: A model of early evolutionary cell sorting, with application to slime mold Dictyostelium discoideum HughTrenchard Journal of Theoretical Biology
Volume 469, 21 May 2019, Pages 75-95
Will the coming mobility revolution make urban traffic better, or worse?
Systems chemistry deals with the design and study of complex chemical systems. However, such systems are often difficult to investigate experimentally. We provide an example of how theoretical and simulation-based studies can provide useful insights into the properties and dynamics of complex chemical systems, in particular of autocatalytic sets. We investigate the issue of the required molecular diversity for autocatalytic sets to exist in random polymer libraries. Given a fixed probability that an arbitrary polymer catalyzes the formation of other polymers, we calculate this required molecular diversity theoretically for two particular models of chemical reaction systems, and then verify these calculations by computer simulations. We also argue that these results could be relevant to an origin of life scenario proposed recently by Damer and Deamer. Life 2019, 9(1), 23; https://doi.org/10.3390/life9010023
Molecular Diversity Required for the Formation of Autocatalytic Sets
Wim Hordijk, Mike Steel and Stuart A. Kauffman
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