Complexity Thoughts: Issue #42
Unraveling complexity: building knowledge, one paper at a time
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Foundations of network science and complex systems
Supramolecular Assemblies in Active Motor-Filament Systems: Micelles, Bilayers, and Foams
Active matter systems evade the constraints of thermal equilibrium, leading to the emergence of intriguing collective behavior. A paradigmatic example is given by motor-filament mixtures, where the motion of motor proteins drives alignment and sliding interactions between filaments and their self-organization into macroscopic structures. After defining a microscopic model for these systems, we derive continuum equations, exhibiting the formation of active supramolecular assemblies such as micelles, bilayers, and foams. The transition between these structures is driven by a branching instability, which destabilizes the orientational order within the micelles, leading to the growth of bilayers at high microtubule densities. Additionally, we identify a fingering instability, modulating the shape of the micelle interface at high motor densities. We study the role of various mechanisms in these two instabilities, such as contractility, active splay, and anchoring, allowing for generalization beyond the system considered here.
Epidemics
Preserving system activity while controlling epidemic spreading in adaptive temporal networks
Human behavior strongly influences the spread of infectious diseases: understanding the interplay between epidemic dynamics and adaptive behaviors is essential to improve response strategies to epidemics, with the goal of containing the epidemic while preserving a sufficient level of operativeness in the population. Through activity-driven temporal networks, we formulate a general framework which models a wide range of adaptive behaviors and mitigation strategies, observed in real populations. We analytically derive the conditions for a widespread diffusion of epidemics in the presence of arbitrary adaptive behaviors, highlighting the crucial role of correlations between agents behavior in the infected and in the susceptible state. We focus on the effects of sick leave, comparing the effectiveness of different strategies in reducing the impact of the epidemic and preserving the system operativeness. We show the critical relevance of heterogeneity in individual behavior: in homogeneous networks, all sick-leave strategies are equivalent and poorly effective, while in heterogeneous networks, strategies targeting the most vulnerable nodes are able to effectively mitigate the epidemic, also avoiding a deterioration in system activity and maintaining a low level of absenteeism. Interestingly, with targeted strategies both the minimum of population activity and the maximum of absenteeism anticipate the infection peak, which is effectively flattened and delayed, so that full operativeness is almost restored when the infection peak arrives. We also provide realistic estimates of the model parameters for influenza-like illness, thereby suggesting strategies for managing epidemics and absenteeism in realistic populations.
Evolution
How complex spatial structure can shape evolutionary dynamics of cellular and molecular organization? It seems that research on this matter focused mostly on well-mixed structures and other regular networks. This paper is interesting since it shows what can happen when topological heterogeneity steps in…
Spatially resolved datasets are revolutionizing knowledge in molecular biology, yet are under-utilized for questions in evolutionary biology. To gain insight from these large-scale datasets of spatial organization, we need mathematical representations and modeling techniques that can both capture their complexity, but also allow for mathematical tractability. Evolutionary graph theory utilizes the mathematical representation of networks as a proxy for heterogeneous population structure and has started to reshape our understanding of how spatial structure can direct evolutionary dynamics. However, previous results are derived for the case of a single new mutation appearing in the population and the role of network structure in shaping fitness landscape crossing is still poorly understood. Here we study how network-structured populations cross fitness landscapes and show that even a simple extension to a two-mutational landscape can exhibit complex evolutionary dynamics that cannot be predicted using previous single-mutation results. We show how our results can be intuitively understood through the lens of how the two main evolutionary properties of a network, the amplification and acceleration factors, change the expected fate of the intermediate mutant in the population and further discuss how to link these models to spatially resolved datasets of cellular organization.
Ecosystems
The growth of complex populations, such as microbial communities, forests, and cities, occurs over vastly different spatial and temporal scales. Although research in different fields has developed detailed, system-specific models to understand each individual system, a unified analysis of different complex populations is lacking; such an analysis could deepen our understanding of each system and facilitate cross-pollination of tools and insights across fields. Here, we use a shared framework to analyze time-series data of the human gut microbiome, tropical forest, and urban employment. We demonstrate that a single, three-parameter model of stochastic population dynamics can reproduce the empirical distributions of population abundances and fluctuations in all three datasets. The three parameters characterizing a species measure its mean abundance, deterministic stability, and stochasticity. Our analysis reveals that, despite the vast differences in scale, all three systems occupy a similar region of parameter space when time is measured in generations. In other words, although the fluctuations observed in these systems may appear different, this difference is primarily due to the different physical timescales associated with each system. Further, we show that the distribution of temporal abundance fluctuations is described by just two parameters and derive a two-parameter functional form for abundance fluctuations to improve risk estimation and forecasting.
Special issue: Connected interactions: enriching food web research by spatial and social interactions
In ecological research, different kinds of interactions among organisms are modelled by networks. In social groups, social interactions form social networks. In multi-species communities, inter-specific interactions are studied by food web models, for example. Also, longer-term, spatial processes are modelled by landscape connectivity networks. These various network types are traditionally studied in separation, by ethologists, community ecologists and landscape ecologists. However, all of these connections concern the same individuals that can socialize, eat and be eaten or migrate, being involved in all kinds of processes in parallel. The contributions of this theme issue present examples for how to connect various networks, considering several processes in parallel. We propose that studying multiple networks is highly beneficial, especially for better understanding socio-ecological systems and challenges across scales.
The abstract is self-explanatory and lists many interesting papers. Here, I recommend two of them in particular.
This is a review paper at intersection between evolution and ecology.
There has long been a fundamental divide in the study of cooperation: researchers focus either on cooperation within species, including but not limited to sociality, or else on cooperation between species, commonly termed mutualism. Here, we explore the ecologically and evolutionarily significant ways in which within- and between-species cooperation interact. We highlight two primary cross-linkages. First, cooperation of one type can change the context in which cooperation of the other type functions, and thus potentially its outcome. We delineate three possibilities: (i) within-species cooperation modulates benefits for a heterospecific partner; (ii) between-species cooperation affects the dynamics of within-species cooperation; and (iii) both processes take place interactively. The second type of cross-linkage emerges when resources or services that cooperation makes available are obtainable either from members of the same species or from different species. This brings cooperation at the two levels into direct interaction, to some extent obscuring the distinction between them. We expand on these intersections between within- and between-species cooperation in a diversity of taxa and interaction types. These interactions have the potential to weave together social networks and trophic dynamics, contributing to the structure and functioning of ecological communities in ways that are just beginning to be explored.
It also encourages us to frame different questions, such as: at what scales and under what conditions does social stability occur?
In this article, we argue that social systems with fission–fusion (FF) dynamics are best characterized within a complex adaptive systems (CAS) framework. We discuss how different endogenous and exogenous factors drive scale-dependent network properties across temporal, spatial and social domains. Importantly, this view treats the dynamics themselves as objects of study, rather than variously defined notions of static ‘social groups’ that have hitherto dominated thinking in behavioural ecology. CAS approaches allow us to interrogate FF dynamics in taxa that do not conform to more traditional conceptualizations of sociality and encourage us to pose new types of questions regarding the sources of stability and change in social systems, distinguishing regular variations from those that would lead to system-level reorganization.
Biological Systems
Modeling tumors as complex ecosystems
Yes, that’s the title and an important change of perspective on tumors, which can be seen as complex adaptive systems and modeled accordingly. If you didn’t hear about cancer ecology before this post, this is the right paper to start from.
This implies that the cancer T cell model benefits from an explicit multilayer description that can capture both competition and predation
Maybe I am a bit biased, but I strongly agree that a multilayer description might be beneficial in this context!
Many cancers resist therapeutic intervention. This is fundamentally related to intratumor heterogeneity: multiple cell populations, each with different phenotypic signatures, coexist within a tumor and its metastases. Like species in an ecosystem, cancer populations are intertwined in a complex network of ecological interactions. Most mathematical models of tumor ecology, however, cannot account for such phenotypic diversity or predict its consequences. Here, we propose that the generalized Lotka-Volterra model (GLV), a standard tool to describe species-rich ecological communities, provides a suitable framework to model the ecology of heterogeneous tumors. We develop a GLV model of tumor growth and discuss how its emerging properties provide a new understanding of the disease. We discuss potential extensions of the model and their application to phenotypic plasticity, cancer-immune interactions, and metastatic growth. Our work outlines a set of questions and a road map for further research in cancer ecology.
What a great way to start an abstract!
Regeneration is a heroic biological process that restores tissue architecture and function in the face of day-to-day cell loss or the aftershock of injury. Capacities and mechanisms for regeneration can vary widely among species, organs, and injury contexts. Here, we describe “hallmarks” of regeneration found in diverse settings of the animal kingdom, including activation of a cell source, initiation of regenerative programs in the source, interplay with supporting cell types, and control of tissue size and function. We discuss these hallmarks with an eye toward major challenges and applications of regenerative biology.
Language
The rising entropy of English in the attention economy
We present evidence that the word entropy of American English has been rising steadily since around 1900. We also find differences in word entropy between media categories, with short-form media such as news and magazines having higher entropy than long-form media, and social media feeds having higher entropy still. To explain these results we develop an ecological model of the attention economy that combines ideas from Zipf’s law and information foraging. In this model, media consumers maximize information utility rate taking into account the costs of information search, while media producers adapt to technologies that reduce search costs, driving them to generate higher entropy content in increasingly shorter formats.
Language follows a distinct mode of extra-genomic evolution
Linguistic evolution differs from technological and biological evolution by yielding a stationary dynamic rather than stable solutions, allowing the use of language change for social differentiation while maintaining the global adaptiveness of language
As one of the most specific, yet most diverse of human behaviors, language is shaped by both genomic and extra-genomic evolution. Sharing methods and models between these modes of evolution has significantly advanced our understanding of language and inspired generalized theories of its evolution. Progress is hampered, however, by the fact that the extra-genomic evolution of languages, i.e. linguistic evolution, maps only partially to other forms of evolution. Contrasting it with the biological evolution of eukaryotes and the cultural evolution of technology as the best understood models, we show that linguistic evolution is special by yielding a stationary dynamic rather than stable solutions, and that this dynamic allows the use of language change for social differentiation while maintaining its global adaptiveness. Linguistic evolution furthermore differs from technological evolution by requiring vertical transmission, allowing the reconstruction of phylogenies; and it differs from eukaryotic biological evolution by foregoing a genotype vs phenotype distinction, allowing deliberate and biased change. Recognising these differences will improve our empirical tools and open new avenues for analyzing how linguistic, cultural, and biological evolution interacted with each other when language emerged in the hominin lineage. Importantly, our framework will help to cope with unprecedented scientific and ethical challenges that presently arise from how rapid cultural evolution impacts language, most urgently from interventional clinical tools for language disorders, potential epigenetic effects of technology on language, artificial intelligence and linguistic communicators, and global losses of linguistic diversity and identity. Beyond language, the distinctions made here allow identifying variation in other forms of biological and cultural evolution, developing new perspectives for empirical research.
Wow - even these brief synopsis require a lot of brain glucose to understand! A simple abstract in layman's terms would be nice. Wondering what you think about the random mutation foundation of biology. Seems evidence for some kind of passing on of of behaviors ala Lamarckian like mutations via epi-genes - a methylation process yet not part of the actual DNA ladder, but over time/generations of repeated behaviors - would not it become part of the DNA? When I first studied evolutionary processes around 20 years ago for my first book - there was no agreement or consensus on how mutations happen over time - some scientists would claim 50-100,000 years and yet the evidence for just a few thousand showed up in the thinner blood of Tibetan people and lactose tolerant humans. Respected biologist Stephen J Gould had his 'punctuated equilibrium' hypothesis - which describes occasional sped up mutations but not how or why or where etc. I am just finishing Schrodinger's What is Life - his physics stab at how biology and evolution work from the 1940-50s - which he praises Lamarck but also went along with status quo Darwin selection concept denying any chromosomes being affected by behavior or reactions to the environment (epi-genes upends this strongly held idea) yet he spent pages thinking it over in writing, making the sold point that only random mutations can't satisfactorily explain all the diversity. I mean anyone who sees a hummingbird with a super long beak designed to go deep into the flower for nectar - common sense would dictate some kind of mutations over time that are selected - but also mutations occur making that beak grow a certain way - no? Anyway, from my own recent research - there is a debate heating up within the Evolutionary Biology field - based on 20 years of new evidence/proofs supporting the Lamarck concept that genetic traits are shaped by behaviors/reactions to the environment. The paradigm is shifting and long overdue in my humble opinion.