Complexity Thoughts: Issue #52
Unraveling complexity: building knowledge, one paper at a time
If you find Complexity Thoughts interesting, follow us! Click on the Like button, leave a comment, repost on Substack or share this post. It is the only feedback I can have for this free service. The frequency and quality of this newsletter relies on social interactions. Thank you!
→ Don’t miss the podcast version of this post: click on “Spotify/Apple Podcast” above!
Foundations of network science and complex systems
Dynamical theory for adaptive systems
This paper has the potential to become a classic, building on the Martin-Siggia-Rose-De Dominicis-Janssen formalism to derive, in the thermodynamic limit, the effective dynamics for both fast and slow variables from the moment generating functional of their joint trajectories, and applying this to gene-regulatory networks.
The study of adaptive dynamics, involving many degrees of freedom on two separated timescales, one for fast changes of state variables and another for the slow adaptation of parameters controlling the former's dynamics is crucial for understanding feedback mechanisms underlying evolution and learning. We present a path-integral approach à la Martin–Siggia–Rose-De Dominicis–Janssen to analyse non-equilibrium phase transitions in such dynamical systems. As an illustration, we apply our framework to the adaptation of gene-regulatory networks under a dynamic genotype-phenotype map: phenotypic variations are shaped by the fast stochastic gene-expression dynamics and are coupled to the slowly evolving distribution of genotypes, each encoded by a network structure. We establish that under this map, genotypes corresponding to reciprocal networks of coherent feedback loops are selected within an intermediate range of environmental noise, leading to phenotypic robustness.
Evolution
Constructing stability: optimal learning in noisy ecological niches
This is interesting: the authors find that an organism’s learning rate should change as the square root of the rate of environmental change.
“Life is adaptive and optimal phenotypic plasticity through learning would seem to depend on a diversity of properties of both organism and environment (electronic supplementary material, table S1). We show that this need not be the case by organizing different aspects of the adaptive process into a unified framework for time scales and a largely overlooked aspect of niche construction”
Organisms can learn in response to environmental inputs as well as actively modify their environments through niche construction on slower evolutionary time scales. How quickly should an organism respond to a changing environment, and when possible, should organisms adjust the time scale of environmental change? We formulate these questions using a model of learning costs that considers optimal time scales of both memory and environment. We derive a general, sublinear scaling law for optimal memory as a function of environmental persistence. This encapsulates a trade-off between remembering and forgetting. We place learning strategies within a niche construction dynamics in a game theoretic setting. Niche construction is found to reduce or stabilize environmental volatility when learned environmental resources can be monopolized. When learned resources are shared, niche destructors evolve to degrade the shared environment. We integrate these results into a metabolic scaling framework in order to derive learning strategies as a function of body size.
Biological Systems
The extent and characteristics of DNA transfer between plasmids and chromosomes
Plasmids are often described in the literature as vehicles of horizontal gene transfer in bacterial evolution. Plasmid-mediated gene transfer may be divided into two possible non-exclusive routes: the first route is transfer of the plasmid genetic element, whose encoded functions are available for the host as long as the plasmid persists in the host. The second route is the transfer of plasmid genes to the chromosome, such that plasmid functions may become available to the host also following plasmid loss. Gene transfer may also occur from chromosomes to the resident plasmid, leading to gene gain by the plasmid and the evolution of novel plasmid functions.
Plasmids are extrachromosomal genetic elements that reside in prokaryotes. The acquisition of plasmids encoding beneficial traits can facilitate short-term survival in harsh environmental conditions or long-term adaptation of new ecological niches. Due to their ability to transfer between cells, plasmids are considered agents of gene transfer. Nonetheless, the frequency of DNA transfer between plasmids and chromosomes remains understudied. Using a novel approach for detection of homologous loci between genome pairs, we uncover gene sharing with the chromosome in 1,974 (66%) plasmids residing in 1,016 (78%) taxonomically diverse isolates. The majority of homologous loci correspond to mobile elements, which may be duplicated in the host chromosomes in tens of copies. Neighboring shared genes often encode similar functional categories, indicating the transfer of multigene functional units. Rare transfer events of antibiotics resistance genes are observed mainly with mobile elements. The frequent erosion of sequence similarity in homologous regions indicates that the transferred DNA is often devoid of function. DNA transfer between plasmids and chromosomes thus generates genetic variation that is akin to workings of endosymbiotic gene transfer in eukaryotic evolution. Our findings imply that plasmid contribution to gene transfer most often corresponds to transfer of the plasmid entity rather than transfer of protein-coding genes between plasmids and chromosomes.
Ecosystems
Competition for resources can reshape the evolutionary properties of spatial structure
Many evolving ecosystems are characterized by topological constraints and heterogeneous spatial architectures. For example, in ductal carcinoma, tumor growth is highly spatially constrained, with cells confined to a tree-like network of ducts. Here we use the mathematical formalism of networks to study how heterogeneity in population structure shapes rates of evolution, and specifically, probabilities of fixation of new mutants in the population. Using simulations and analytic approximations, together with mathematical representations of architectures inferred from imaging studies, we show that the complex, heterogeneous topology of many real world evolving ecosystems can critically reshape their genetic makeup. In particular, we show that there are topological structures that, under particular ecological conditions, can reshape rates of accumulation of deleterious mutations in a population, compared to systems that are well-mixed or have lattice-based structures of growth. Our work develops an initial framework to understand the effect of the population topology on eco-evolutionary dynamics, as well as helps inform on how to spatially engineer these types of ecosystems to select for evolutionary outcomes of interest.
Many evolving ecosystems have spatial structures that can be conceptualized as networks, with nodes representing individuals or homogeneous subpopulations and links the patterns of spread between them. Prior models of evolution on networks do not take ecological niche differences and eco-evolutionary interplay into account. Here, we combine a resource competition model with evolutionary graph theory to study how heterogeneous topological structure shapes evolutionary dynamics under global frequency-dependent ecological interactions. We find that the addition of ecological competition for resources can produce a reversal of roles between amplifier and suppressor networks for deleterious mutants entering the population. We show that this effect is a nonlinear function of ecological niche overlap and discuss intuition for the observed dynamics using simulations and analytical approximations. We use these theoretical results together with spatial representations from imaging data to show that, for ductal carcinoma, where tumor growth is highly spatially constrained, with cells confined to a tree-like network of ducts, the topological structure can lead to higher rates of deleterious mutant hitchhiking with metabolic driver mutations, compared to tumors characterized by different spatial topologies.
Urban environments increase generalization of hummingbird–plant networks across climate gradients
Urbanization causes dramatic changes in biodiversity. However, we still understand little about its effects on species interactions over broad spatial gradients. We leveraged a large dataset on hummingbird interactions with nectar flowers from Mexico to Brazil and evaluated the influence of both urbanization and climate. Hummingbird–plant communities were consistently more generalized in urban than in natural habitats, and communities located in areas with higher precipitation showed more specialized interactions for both habitat types. Therefore, while urbanization consistently affected species interactions, such effects are still related to large-scale climate gradients. Our study highlights the complex ways in which human-induced land transformations and climate affect species interactions, which may have cascading ecological and evolutionary effects on plants, pollinators, and ecosystem functioning.
Urbanization has reshaped the distribution of biodiversity on Earth, but we are only beginning to understand its effects on ecological communities. While urbanization may have homogenization effects strong enough to blur the large-scale patterns in interaction networks, urban community patterns may still be associated with climate gradients reflecting large-scale biogeographical processes. Using 103 hummingbird–plant mutualistic networks across continental Americas, including 176 hummingbird and 1,180 plant species, we asked how urbanization affects species interactions over large climate gradients. Urban networks were more generalized, exhibiting greater interaction overlap. Higher generalization was also associated with lower precipitation in both urban and natural areas, indicating that climate affects networks irrespective of habitat type. Urban habitats also showed lower hummingbird functional trait diversity and over/underrepresentation of specific clades. From the plant side, urban communities had a higher prevalence of nonnative nectar plants, which were more frequently visited by the hummingbird species occurring in both urban and natural areas. Therefore, urbanization affected hummingbird–plant interactions through both the composition of species and traits, as well as floral resource availability. Taken together, we show that urbanization consistently modifies ecological communities and their interactions, but climate still plays a role in affecting the structure of these novel communities over the scale of continents.
Global Systems
Time persistence of climate and carbon flux networks
The persistence of the global climate system is critical for assuring the sustainability of the natural ecosystem. However, persistence at a network level has been rarely discussed. Here we develop a framework to analyze the time persistence of the yearly networks of climate and carbon flux, based on cross-correlations between sites, using daily data from China, the contiguous United States, and the Europe land region. Our framework for determining the persistence is based on analyzing the similarity between the network structures in different years. Our results reveal that the similarity of climate and carbon flux networks in different years are within the range of 0.57 ± 0.07, implying that the climate and carbon flux in the Earth’s climate system are generally persistent and in a steady state. We find a very small decay in similarity when the gap between years increases. Moreover, we find that the persistence of climate variables and carbon flux in the three regions decreases when considering only long range links. Analyzing the persistence and evolution of the climate and carbon flux networks, enhance our understanding of the spatial and temporal evolution of the global climate system.
Polar ice sheets are decisive contributors to uncertainty in climate tipping projections
“the uncertainties in climate models and our epistemic uncertainty should encourage us to focus our energies on the most productive ways to reduce our uncertainties, but also to develop better decision-making under high uncertainty”
The Earth’s climate is a complex system including key components such as the Arctic Summer Sea Ice and the El Niño Southern Oscillation alongside climate tipping elements including polar ice sheets, the Atlantic Meridional Overturning Circulation, and the Amazon rainforest. Crossing thresholds of these elements can lead to a qualitatively different climate state, endangering human societies. The cryosphere elements are vulnerable at current levels of global warming (1.3 °C) while also having long response times and large uncertainties. We assess the impact of interacting Earth system components on tipping risks using an established conceptual network model of these components. Polar ice sheets (Greenland and West Antarctic ice sheets) are most decisive for tipping likelihoods and cascading effects within our model. At a global warming level of 1.5 °C, neglecting the polar ice sheets can alter the expected number of tipped elements by more than a factor of 2. This is concerning as overshooting 1.5 °C of global warming is becoming inevitable, while current state-of-the-art IPCC-type models do not (yet) include dynamic ice sheets. Our results suggest that polar ice sheets are critical to improving understanding of tipping risks and cascading effects. Therefore, improved observations and integrated model development are crucial.
Human behavior
Misinformation exploits outrage to spread online
Misinformation remains a major threat to US democratic integrity, national security, and public health. However, social media platforms struggle to curtail the spread of the harmful but engaging content. Across platforms, McLoughlin et al. examined the role of emotions, specifically moral outrage (a mixture of disgust and anger), in the diffusion of misinformation. Compared with trustworthy news sources, posts from misinformation sources evoked more angry reactions and outrage than happy or sad sentiments. Users were motivated to reshare content that evoked outrage and shared it without reading it first to discern accuracy. Interventions that solely emphasize sharing accurately may fail to curb misinformation because users may share outrageous, inaccurate content to signal their moral positions or loyalty to political groups. —Ekeoma Uzogara
We tested a hypothesis that misinformation exploits outrage to spread online, examining generalizability across multiple platforms, time periods, and classifications of misinformation. Outrage is highly engaging and need not be accurate to achieve its communicative goals, making it an attractive signal to embed in misinformation. In eight studies that used US data from Facebook (1,063,298 links) and Twitter (44,529 tweets, 24,007 users) and two behavioral experiments (1475 participants), we show that (i) misinformation sources evoke more outrage than do trustworthy sources; (ii) outrage facilitates the sharing of misinformation at least as strongly as sharing of trustworthy news; and (iii) users are more willing to share outrage-evoking misinformation without reading it first. Consequently, outrage-evoking misinformation may be difficult to mitigate with interventions that assume users want to share accurate information.
A systematic review of worldwide causal and correlational evidence on digital media and democracy
One of today’s most controversial and consequential issues is whether the global uptake of digital media is causally related to a decline in democracy. We conducted a systematic review of causal and correlational evidence (N = 496 articles) on the link between digital media use and different political variables. Some associations, such as increasing political participation and information consumption, are likely to be beneficial for democracy and were often observed in autocracies and emerging democracies. Other associations, such as declining political trust, increasing populism and growing polarization, are likely to be detrimental to democracy and were more pronounced in established democracies. While the impact of digital media on political systems depends on the specific variable and system in question, several variables show clear directions of associations. The evidence calls for research efforts and vigilance by governments and civil societies to better understand, design and regulate the interplay of digital media and democracy.
Social media has become a vital tool for health care providers to quickly share information. However, its lack of content curation and expertise poses risks of misinformation and premature dissemination of unvalidated data, potentially leading to widespread harmful effects due to the rapid and large-scale spread of incorrect information.
We aim to determine whether social media had an undue association with the prescribing behavior of hydroxychloroquine, using the COVID-19 pandemic as the setting.
In this retrospective study, we gathered the use of hydroxychloroquine in 48 hospitals in the United States between January and December 2020. Social media data from X/Twitter was collected using Brandwatch, a commercial aggregator with access to X/Twitter’s data, and focused on mentions of “hydroxychloroquine” and “Plaquenil.” Tweets were categorized by sentiment (positive, negative, or neutral) using Brandwatch’s sentiment analysis tool, with results classified by date. Hydroxychloroquine prescription data from the National COVID Cohort Collaborative for 2020 was used. Granger causality and linear regression models were used to examine relationships between X/Twitter mentions and prescription trends, using optimum time lags determined via vector auto-regression.
A total of 581,748 patients with confirmed COVID-19 were identified. The median daily number of positive COVID-19 cases was 1318.5 (IQR 1005.75-1940.3). Before the first confirmed COVID-19 case, hydroxychloroquine was prescribed at a median rate of 559 (IQR 339.25-728.25) new prescriptions per day. A day-of-the-week effect was noted in both prescriptions and case counts. During the pandemic in 2020, hydroxychloroquine prescriptions increased significantly, with a median of 685.5 (IQR 459.75-897.25) per day, representing a 22.6% rise from baseline. The peak occurred on April 2, 2020, with 3411 prescriptions, a 397.6% increase. Hydroxychloroquine mentions on X/Twitter peaked at 254,770 per day on April 5, 2020, compared to a baseline of 9124 mentions per day before January 21, 2020. During this study’s period, 3,823,595 total tweets were recorded, with 10.09% (n=386,115) positive, 37.87% (n=1,448,030) negative, and 52.03% (n=1,989,450) neutral sentiments. A 1-day lag was identified as the optimal time for causal association between tweets and hydroxychloroquine prescriptions. Univariate analysis showed significant associations across all sentiment types, with the largest impact from positive tweets. Multivariate analysis revealed only neutral and negative tweets significantly affected next-day prescription rates.
During the first year of the COVID-19 pandemic, there was a significant association between X/Twitter mentions and the number of prescriptions of hydroxychloroquine. This study showed that X/Twitter has an association with the prescribing behavior of hydroxychloroquine. Clinicians need to be vigilant about their potential unconscious exposure to social media as a source of medical knowledge, and health systems and organizations need to be more diligent in identifying expertise, source, and quality of evidence when shared on social media platforms.
Bio-inspired computing
Poisson balanced spiking networks
A central idea in neuroscience is that populations of neurons work together to efficiently perform computations, although just how they do that remains unclear. Boerlin et al (2013) proposed a powerful framework for embedding linear dynamical systems into populations of spiking neurons, which they called balanced spiking networks (BSNs). Their approach starts from the principle that neurons greedily fire spikes to reduce error in the network output. Here we focus on a key limitation of this framework, which is that the network may become unbalanced in the presence of physiologically plausible communication delays. To overcome this shortcoming, propose two different extensions of the BSN framework that rely on probabilistic spiking. In our first model, we replace deterministic spiking of the original BSN with a Poisson spiking rule. In the second, we re-formulate the BSN objective so that Poisson spiking emerges as a way to reduce the expected network error. Our work brings the BSN framework closer to biological realism by increasing the stability and, most importantly, allowing communication delays between neurons without sacrificing accuracy. Furthermore, both probabilistic approaches reproduce key experimentally observed spiking behaviors of neural populations.
An important problem in computational neuroscience is to understand how networks of spiking neurons can carry out various computations underlying behavior. Balanced spiking networks (BSNs) provide a powerful framework for implementing arbitrary linear dynamical systems in networks of integrate-and-fire neurons. However, the classic BSN model requires near-instantaneous transmission of spikes between neurons, which is biologically implausible. Introducing realistic synaptic delays leads to an pathological regime known as “ping-ponging”, in which different populations spike maximally in alternating time bins, causing network output to overshoot the target solution. Here we document this phenomenon and provide a novel solution: we show that a network can have realistic synaptic delays while maintaining accuracy and stability if neurons are endowed with conditionally Poisson firing. Formally, we propose two alternate formulations of Poisson balanced spiking networks: (1) a “local” framework, which replaces the hard integrate-and-fire spiking rule within each neuron by a “soft” threshold function, such that firing probability grows as a smooth nonlinear function of membrane potential; and (2) a “population” framework, which reformulates the BSN objective function in terms of expected spike counts over the entire population. We show that both approaches offer improved robustness, allowing for accurate implementation of network dynamics with realistic synaptic delays between neurons. Both Poisson frameworks preserve the coding accuracy and robustness to neuron loss of the original model and, moreover, produce positive correlations between similarly tuned neurons, a feature of real neural populations that is not found in the deterministic BSN. This work unifies balanced spiking networks with Poisson generalized linear models and suggests several promising avenues for future research.
Oldies but goldies
“About 400 years ago, a collection of molecules organized as William Shakespeare wrote Hamlet. About a century after that another collection of molecules organized as Ludwig Van Beethoven wrote the Ninth Symphony. And just before that another such collection of molecules organized as Isaac Newton wrote the Principia.” — George Wald