Complexity Thoughts: Issue #37
Unraveling complexity: building knowledge, one paper at a time
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Dall-e 3 representation of this issue’s content
From the Lab
Emergence of Complex Network Topologies from Flow-Weighted Optimization of Network Efficiency
Transportation and distribution networks are a class of spatial networks that have been of interest in recent years. These networks are often characterized by the presence of complex structures such as central loops paired with peripheral branches, which can appear both in natural and manmade systems, such as subway and railway networks. In this study, we investigate the conditions for the emergence of these nontrivial topological structures in the context of human transportation in cities. We propose a simple model for spatial networks generation, where a network lattice acts as a planar substrate and edge speeds define an effective temporal distance which we aim to optimize and quantifies the efficiency in exploring the urban space. Complex network topologies can be recovered from the optimization of edges’ speeds and we study how the interplay between a flow probability between two nodes in space and the associated travel cost influences the resulting optimal network. In the perspective of urban transportation we simulate these flows by means of human mobility models to obtain origin-destination matrices. We find that when using simple lattices, the obtained optimal topologies transition from treelike structures to more regular networks, depending on the spatial range of flows. Remarkably, we find that branches paired to large loops structures appear as optimal structures when the network is optimized for an interplay between heterogeneous mobility patterns of small range travels and longer-range ones typical of commuting. Moreover, when congestion dynamics in traffic routing is considered, we study the emergence of additional edges from the tree structure to mitigate temporal delays. Finally, we show that our framework is able to recover the statistical spatial properties of the Greater London area subway network.
Foundations of network science and complex systems
On principles of emergent organization
As the temperature gradient is further increased it will eventually reach a critical value and a moment of magic occurs
After more than a century of concerted effort, physics still lacks basic principles of spontaneous self-organization. To appreciate why, we first state the problem, outline historical approaches, and survey the present state of the physics of self-organization. This frames the particular challenges arising from mathematical intractability and the resulting need for computational approaches, as well as those arising from a chronic failure to define structure. Then, an overview of two modern mathematical formulations of organization—intrinsic computation and evolution operators—lays out a way to overcome these challenges. Additionally, we show how intrinsic computation and evolution operators combine to produce a general framework showing physical consistency between emergent behaviors and their underlying physics. This statistical mechanics of emergence provides a theoretical foundation for data-driven approaches to organization necessitated by analytic intractability. Taken all together, the result is a constructive path towards principles of organization that builds on the mathematical identification of structure.
Distinguishing subsampled power laws from other heavy-tailed distributions
This paper adds on a long-standing problem that in the last few years has gained momentum again. To understand the behind-the-scenes I recommend to read at least this paper and this paper, first.
Distinguishing power-law distributions from other heavy-tailed distributions is challenging, and this task is often further complicated by subsampling effects. In this work, we evaluate the performance of two commonly used methods for detecting power-law distributions—the maximum likelihood method of Clauset et al. and the extreme value method of Voitalov et al.—in distinguishing subsampled power laws from two other heavy-tailed distributions, the lognormal and the stretched exponential distributions. We focus on a random subsampling method commonly applied in network science and biological sciences. In this subsampling scheme, we are ultimately interested in the frequency distribution of elements with a certain number of constituent parts—for example, species with 𝑘 individuals or nodes with 𝑘 connections—and each part is selected to the subsample with an equal probability. We investigate how well the results obtained from low-subsampling-depth subsamples generalize to the original distribution. Our results show that the power-law exponent of the original distribution can be estimated fairly accurately from subsamples, but classifying the distribution correctly is more challenging. The maximum likelihood method falsely rejects the power-law hypothesis for a large fraction of subsamples from power-law distributions. While the extreme value method correctly recognizes subsampled power-law distributions with all tested subsampling depths, its capacity to distinguish power laws from the heavy-tailed alternatives is limited. However, these false positives tend to result not from the subsampling itself but from the estimators' inability to classify the original sample correctly. In fact, we show that the extreme value method can sometimes be expected to perform better on subsamples than on the original samples from the lognormal and the stretched exponential distributions, while the contrary is true for the main tests included in the maximum likelihood method.
Causality detection is an important problem in complexity science. I don’t think that the question is correctly asked, in general, since a complex system is characterized by the co-evolution of its units forming feedback loops.
Nevertheless, from the famous Granger causality statistical test to the Transfer Entropy, to the elegant Convergent Cross-Mapping (video below), some methods succeed in detecting the signatures of causality.
We introduce an approach which allows detecting causal relationships between variables for which the time evolution is available. Causality is assessed by a variational scheme based on the Information Imbalance of distance ranks, a statistical test capable of inferring the relative information content of different distance measures. We test whether the predictability of a putative driven system Y can be improved by incorporating information from a potential driver system X, without explicitly modeling the underlying dynamics and without the need to compute probability densities of the dynamic variables. This framework makes causality detection possible even between high-dimensional systems where only few of the variables are known or measured. Benchmark tests on coupled chaotic dynamical systems demonstrate that our approach outperforms other model-free causality detection methods, successfully handling both unidirectional and bidirectional couplings. We also show that the method can be used to robustly detect causality in human electroencephalography data.
Evolution
[…] cellular evolution must begin in a collective mode.
A theory for the evolution of cellular organization is presented. The model is based on the (data supported) conjecture that the dynamic of horizontal gene transfer (HGT) is primarily determined by the organization of the recipient cell. Aboriginal cell designs are taken to be simple and loosely organized enough that all cellular componentry can be altered and/or displaced through HGT, making HGT the principal driving force in early cellular evolution. Primitive cells did not carry a stable organismal genealogical trace. Primitive cellular evolution is basically communal. The high level of novelty required to evolve cell designs is a product of communal invention, of the universal HGT field, not intralineage variation. It is the community as a whole, the ecosystem, which evolves. The individual cell designs that evolved in this way are nevertheless fundamentally distinct, because the initial conditions in each case are somewhat different. As a cell design becomes more complex and interconnected a critical point is reached where a more integrated cellular organization emerges, and vertically generated novelty can and does assume greater importance. This critical point is called the “Darwinian Threshold” for the reasons given.
Ecosystems
Species dynamics and interactions via metabolically informed consumer-resource models
Quantifying the strength, sign, and origin of species interactions, along with their dependence on environmental context, is at the heart of prediction and understanding in ecological communities. Pairwise interaction models like Lotka-Volterra provide an important and flexible foundation, but notably absent is an explicit mechanism mediating interactions. Consumer-resource models incorporate mechanism, but describing competitive and mutualistic interactions is more ambiguous. Here, we bridge this gap by modeling a coarse-grained version of a species’ true cellular metabolism to describe resource consumption via uptake and conversion into biomass, energy, and byproducts. This approach does not require detailed chemical reaction information, but it provides a more explicit description of underlying mechanisms than pairwise interaction or consumer-resource models. Using a model system, we find that when metabolic reactions require two distinct resources we recover Liebig’s Law and multiplicative co-limitation in particular limits of the intracellular reaction rates. In between these limits, we derive a more general phenomenological form for consumer growth rate, and we find corresponding rates of secondary metabolite production, allowing us to model competitive and non-competitive interactions (e.g., facilitation). Using the more general form, we show how secondary metabolite production can support coexistence even when two species compete for a shared resource, and we show how differences in metabolic rates change species’ abundances in equilibrium. Building on these findings, we make the case for incorporating coarse-grained metabolism to update the phenomenology we use to model species interactions.
Biological Systems
Genes emerging from non-genic sequences? Here we go, with an experiment demonstrating, using E. coli, that proto-genes can emerge and persist if they serve a function.
The phenomenon of de novo gene birth—the emergence of genes from non-genic sequences—has received considerable attention due to the widespread occurrence of genes that are unique to particular species or genomes. Most instances of de novo gene birth have been recognized through comparative analyses of genome sequences in eukaryotes, despite the abundance of novel, lineage-specific genes in bacteria and the relative ease with which bacteria can be studied in an experimental context. Here, we explore the genetic record of the Escherichia coli long-term evolution experiment (LTEE) for changes indicative of “proto-genic” phases of new gene birth in which non-genic sequences evolve stable transcription and/or translation. Over the time span of the LTEE, non-genic regions are frequently transcribed, translated and differentially expressed, with levels of transcription across low-expressed regions increasing in later generations of the experiment. Proto-genes formed downstream of new mutations result either from insertion element activity or chromosomal translocations that fused preexisting regulatory sequences to regions that were not expressed in the LTEE ancestor. Additionally, we identified instances of proto-gene emergence in which a previously unexpressed sequence was transcribed after formation of an upstream promoter, although such cases were rare compared to those caused by recruitment of preexisting promoters. Tracing the origin of the causative mutations, we discovered that most occurred early in the history of the LTEE, often within the first 20,000 generations, and became fixed soon after emergence. Our findings show that proto-genes emerge frequently within evolving populations, can persist stably, and can serve as potential substrates for new gene formation.
Neuroscience
A body–brain circuit that regulates body inflammatory responses
Fascinating: the link between nervous and immune systems (but there were hints).
The body-brain axis is emerging as a principal conductor of organismal physiology. It senses and controls organ function1,2, metabolism3 and nutritional state4-6. Here, we show that a peripheral immune insult powerfully activates the body-brain axis to regulate immune responses. We demonstrate that pro- and anti-inflammatory cytokines communicate with distinct populations of vagal neurons to inform the brain of an emerging inflammatory response. In turn, the brain tightly modulates the course of the peripheral immune response. Genetic silencing of this body-to-brain circuit produced unregulated and out-of-control inflammatory responses. By contrast, activating, rather than silencing, this circuit affords exceptional neural control of immune responses. We used single-cell RNA sequencing, combined with functional imaging, to identify the circuit components of this neuro-immune axis, and showed that its selective manipulation can effectively suppress the pro-inflammatory response while enhancing an anti-inflammatory state. The brain-evoked transformation of the course of an immune response offers new possibilities in the modulation of a wide range of immune disorders, from autoimmune diseases to cytokine storm and shock.
Human behavior
Induction of social contagion for diverse outcomes in structured experiments in isolated villages
Exploiting features of social networks (e.g., identifying “gossips”) has effectively diffused knowledge across populations. However, selecting ideal frontline individuals to receive information (influencers that serve as “seeds” to boost message diffusion) previously required mapping the entire social network, which is an expensive, time-consuming, and often impracticable task. Is it possible to identify the best seeds without having full maps of all individuals in networks? Field experiments by Airoldi and Christakis explored this question in a very challenging context: extremely poor, isolated Honduran villages. By expanding the “friendship paradox” strategy, in which friends nominate optimal friends as seeds, their algorithm efficiently distributed seeds throughout social networks without full network maps. This scalable strategy was more effective than random targeting, carrying far-reaching policy implications for enhancing low- and middle-income country welfare.
Ongoing debates
We have already discussed about Assembly Theory:
and provided a recent follow-up:
Now I recommend to read also this paper by Bob Hazen et al, adding some evidence-based material to the discussion.
Some naturally occurring heteropolyanion clusters, including those in ewingite and ilmajokite, have assembly indices greater than the proposed abiotic/biotic cutoff of 15, thus invalidating the claim that only biological processes can produce molecules with assembly indices ≥15
Molecular assembly indices, which measure the number of unique sequential steps theoretically required to construct a three-dimensional molecule from its constituent atomic bonds, have been proposed as potential biosignatures. A central hypothesis of assembly theory is that any molecule with an assembly index ≥15 found in significant local concentrations represents an unambiguous sign of life. We show that abiotic molecule-like heteropolyanions, which assemble in aqueous solution as precursors to some mineral crystals, range in molecular assembly indices from 2 for H2CO3 or Si(OH)4 groups to as large as 21 for the most complex known molecule-like subunits in the rare minerals ewingite and ilmajokite. Therefore, values of molecular assembly indices ≥15 do not represent unambiguous biosignatures.
Perspectives
Coarse-graining as a downward causation mechanism
Downward causation is the controversial idea that ‘higher’ levels of organization can causally influence behaviour at ‘lower’ levels of organization. Here I propose that we can gain traction on downward causation by being operational and examining how adaptive systems identify regularities in evolutionary or learning time and use these regularities to guide behaviour. I suggest that in many adaptive systems components collectively compute their macroscopic worlds through coarse-graining. I further suggest we move from simple feedback to downward causation when components tune behaviour in response to estimates of collectively computed macroscopic properties. I introduce a weak and strong notion of downward causation and discuss the role the strong form plays in the origins of new organizational levels. I illustrate these points with examples from the study of biological and social systems and deep neural networks.
Great stuff for one more time Manlio!
(the link to the C. Woese's paper appears broken from the email (it works from the page))