Complexity Thoughts: Issue #49
Unraveling complexity: building knowledge, one paper at a time
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Foundations of network science and complex systems
Nonequilibrium microbial dynamics unveil a new macroecological pattern beyond Taylor's law
A fascinating analogy between the stochastic dynamics of microbial populations and the classical physics of Brownian motion, reveals some parallels in their fluctuation dynamics. By formulating the behavior of microbial population through Langevin equations and drawing from Taylor's law, the authors uncover a microbial equivalent to the fluctuation-dissipation relation in physical systems. The analogy suggests that environmental inputs (playing a role similar to thermal energy in Brownian systems) govern microbial population fluctuations in a way that balances resource availability and stability.
We introduce a comprehensive analytical benchmark, relying on Fokker-Planck formalism, to study microbial dynamics in the presence of both biotic and abiotic forces. In equilibrium, we observe a balance between the two kinds of forces, leading to no correlations between species abundances. This implies that real microbiomes, where correlations have been observed, operate out of equilibrium. Therefore, we analyze nonequilibrium dynamics, presenting an ansatz for an approximate solution that embodies the complex interplay of forces in the system. This solution is consistent with Taylor's law as a coarse-grained approximation of the relation between species abundance and variance, but implies subtler effects, predicting unobserved structure beyond Taylor's law. Motivated by this theoretical prediction, we refine the analysis of existing metagenomic data, unveiling a novel universal macroecological pattern. Finally, we speculate on the physical origin of Taylor's law: building upon an analogy with Brownian motion theory, we propose that Taylor's law emerges as a fluctuation-growth relation resulting from equipartition of environmental resources among microbial species.
Emergent universal quench dynamics in randomly interacting spin models
Universal behaviour often emerges in the low-energy equilibrium physics of quantum many-body systems, despite their microscopic differences. Recently, there has been a growing interest in studying the far-from-equilibrium dynamics of quantum many-body systems. Such dynamics usually involve highly excited states beyond the traditional low-energy theory description. Whether universality can also emerge in such non-equilibrium dynamics is the subject of current research. Here, we report the experimental observation of universal dynamics by monitoring the spin depolarization process in a solid-state nuclear magnetic resonance system, described by an ensemble of randomly interacting spins. The spin depolarization can be related to temporal spin–spin correlation functions at high temperatures. We discover that these correlation functions obey a universal functional form. This finding helps us identify the dominant interacting processes in the spin depolarization dynamics that lead to universality. Our observation demonstrates the existence of universality even in non-equilibrium dynamics at high temperatures, thereby complementing the well-established universality in low-energy physics.
Evolution and Ecosystems
The case against simplistic genetic explanations of evolution
I argue that the long divided (and divisive) history of evolutionary and developmental genetics has created a biased expectation for simple mechanistic explanations that needs to be confronted
It seems that many disciplines are feeling that simple mechanistic (ie, reductionist) explanations cannot have enough explanatory power, with some hype and overselling of results that would need a more honest framing.
However, all studies should include an honest assessment of the findings that contemplates a wide range of possible explanations and fully acknowledges the limitations of the approach and its interpretation
Humans are curious to understand the causes of traits that distinguish us from other animals and that distinguish vastly different species from one another. We also have a proclivity for simple stories and sometimes tend toward seeking and accepting simple genetic explanations for large evolutionary shifts, even to a single gene. Here, I reveal how a biased expectation of mechanistic simplicity threads through the long history of evolutionary and developmental genetics. I argue, however, that expecting a simple mechanism threatens a deeper understanding of evolution, and I define the limitations for interpreting experimental evidence in evolutionary developmental genetics.
The evolving three-dimensional landscape of human adaptation
Over the past 3 million years, humans have expanded their ecological niche and adapted to more diverse environments. The temporal evolution and underlying drivers behind this niche expansion remain largely unknown. By combining archeological findings with landscape topographic data and model simulations of the climate and biomes, we show that human sites clustered in areas with increased terrain roughness, corresponding to higher levels of biodiversity. We find a gradual increase in human habitat preferences toward rough terrains until about 1.1 million years ago (Ma), followed by a 300 thousand-year-long contraction of the ecological niche. This period coincided with the Mid-Pleistocene Transition and previously hypothesized ancestral population bottlenecks. Our statistical analysis further reveals that from 0.8 Ma onward, the human niche expanded again, with human species (e.g., H. heidelbergensis, H. neanderthalensis, and H. sapiens) adapting to rougher terrain, colder and drier conditions, and toward regions of higher ecological diversity.
Evidence of a European seed dispersal crisis
This crisis is particularly worrying given that plants need to track rapidly shifting climatic envelopes in a continent strongly affected by habitat fragmentation
Seed dispersal is crucial for ecosystem persistence, especially in fragmented landscapes, such as those common in Europe. Ongoing defaunation might compromise effective seed dispersal, but the conservation status of pairwise interactions remains unknown. With a literature review, we reconstructed the first European-wide seed dispersal network and evaluated the conservation status of interactions by assessing each interacting partner’s IUCN (International Union for Conservation of Nature) conservation status and population trends. We found that a third of the disperser species and interactions face potential extinction and that 30% of the plant species have most of their dispersers threatened or declining. Our study reveals a developing seed dispersal crisis in Europe and highlights large knowledge gaps regarding the dispersers and conservation status of zoochorous plants, urging further scrutiny and action to conserve the seed dispersal service.
Biological Systems
A hierarchy of metabolite exchanges in metabolic models of microbial species and communities
Author summary. Pathway analysis of constraint-based metabolic models makes it possible to disentangle metabolism into formally defined metabolic pathways. A promising but underexplored application of pathway analysis is to analyze exchanges of metabolites between cells and their environment, which could also help overcome computational challenges and allow scaling to larger systems. Here, we used four different pathway definitions to enumerate combinations of metabolite exchanges that support growth in models of microbial species and a microbial community. We found that metabolite exchanges from different pathway definitions were related to each other through a previously unknown hierarchy, and we show that this hierarchical relationship between pathways holds more generally. Moreover, the fraction of pathways in which each metabolite was exchanged turned out to be remarkably consistent across pathway definitions despite large differences in pathway counts. In summary, our work shows how pathway definitions and their metabolite exchange predictions are related to each other, and it facilitates scalable and interpretable pathway analysis with applications in fields such as metabolic engineering.
The metabolic network of an organism can be analyzed as a constraint-based model. This analysis can be biased, optimizing an objective such as growth rate, or unbiased, aiming to describe the full feasible space of metabolic fluxes through pathway analysis or random flux sampling. In particular, pathway analysis can decompose the flux space into fundamental and formally defined metabolic pathways. Unbiased methods scale poorly with network size due to combinatorial explosion, but a promising approach to improve scalability is to focus on metabolic subnetworks, e.g., cells’ metabolite exchanges with each other and the environment, rather than the full metabolic networks. Here, we applied pathway enumeration and flux sampling to metabolite exchanges in microbial species and a microbial community, using models ranging from central carbon metabolism to genome-scale and focusing on pathway definitions that allow direct targeting of subnetworks such as metabolite exchanges (elementary conversion modes, elementary flux patterns, and minimal pathways). Enumerating growth-supporting metabolite exchanges, we found that metabolite exchanges from different pathway definitions were related through a hierarchy, and we show that this hierarchical relationship between pathways holds for metabolic networks and subnetworks more generally. Metabolite exchange frequencies, defined as the fraction of pathways in which each metabolite was exchanged, were similar across pathway definitions, with a few specific exchanges explaining large differences in pathway counts. This indicates that biological interpretation of predicted metabolite exchanges is robust to the choice of pathway definition, and it suggests strategies for more scalable pathway analysis. Our results also signal wider biological implications, facilitating detailed and interpretable analysis of metabolite exchanges and other subnetworks in fields such as metabolic engineering and synthetic biology.
Earth systems
Can we describe, like the following, what happened in the first hours after an asteroid struck Earth 66 million years ago? Yes, we can:
Chicxulub impact crater cores from the peak ring include ∼130 m of impact melt rock and breccia deposited on the first day of the Cenozoic. Within minutes of the impact, fluidized basement rocks formed a ring of hills, which were rapidly covered by ∼40 m of impact melt and breccia. Within an hour, ocean waters flooded the deep crater through a northeast embayment, depositing another 90 m of breccia. Within a day, a tsunami deposited material from distant shorelines, including charcoal. Charcoal and absence of sulfur-rich target rocks support the importance of impact-generated fires and release of sulfate aerosols for global cooling and darkness postimpact.
Neuroscience
Efficient codes and balanced networks
The balance between excitatory and inhibitory inputs plays a crucial role in how neurons encode and process information. Traditional theories of neural coding, like the rate coding hypothesis (Barlow, 1961), propose that neurons communicate through their firing rates. The problem is that the highly irregular spiking patterns observed in real neural activity challenge this idea.
More recently, balanced networks, where excitatory and inhibitory inputs are finely tuned, offered a solution by creating irregular spiking while preserving efficient information transmission. Tightly balanced networks, where inhibition closely tracks excitation on fine timescales, enable highly efficient population coding, reducing errors more effectively than other, more traditional, models. This tight balance also allows the brain to represent multiple variables simultaneously, as each neuron contributes uniquely to a collective representation of multidimensional information. Moreover, this coding strategy is robust, as the distributed nature of population coding ensures that information remains intact even when some neurons or spikes are lost.
Recent years have seen a growing interest in inhibitory interneurons and their circuits. A striking property of cortical inhibition is how tightly it balances excitation. Inhibitory currents not only match excitatory currents on average, but track them on a millisecond time scale, whether they are caused by external stimuli or spontaneous fluctuations. We review, together with experimental evidence, recent theoretical approaches that investigate the advantages of such tight balance for coding and computation. These studies suggest a possible revision of the dominant view that neurons represent information with firing rates corrupted by Poisson noise. Instead, tight excitatory/inhibitory balance may be a signature of a highly cooperative code, orders of magnitude more precise than a Poisson rate code. Moreover, tight balance may provide a template that allows cortical neurons to construct high-dimensional population codes and learn complex functions of their inputs.
Building functional networks of spiking model neurons
Most of the networks used by computer scientists and many of those studied by modelers in neuroscience represent unit activities as continuous variables. Neurons, however, communicate primarily through discontinuous spiking. We review methods for transferring our ability to construct interesting networks that perform relevant tasks from the artificial continuous domain to more realistic spiking network models. These methods raise a number of issues that warrant further theoretical and experimental study.
Bio-inspired computing
Network model with internal complexity bridges artificial intelligence and neuroscience
Click here to access the free online version. You can also check this News & Views.
Artificial intelligence (AI) researchers currently believe that the main approach to building more general model problems is the big AI model, where existing neural networks are becoming deeper, larger and wider. We term this the big model with external complexity approach. In this work we argue that there is another approach called small model with internal complexity, which can be used to find a suitable path of incorporating rich properties into neurons to construct larger and more efficient AI models. We uncover that one has to increase the scale of the network externally to stimulate the same dynamical properties. To illustrate this, we build a Hodgkin–Huxley (HH) network with rich internal complexity, where each neuron is an HH model, and prove that the dynamical properties and performance of the HH network can be equivalent to a bigger leaky integrate-and-fire (LIF) network, where each neuron is a LIF neuron with simple internal complexity.
