Complexity Thoughts: Issue #39
Unraveling complexity: building knowledge, one paper at a time
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Dall-e 3 representation of this issue’s content
Foundations of network science and complex systems
As the Reynolds number is increased, a laminar fluid flow becomes turbulent, and the range of time and length scales associated with the flow increases. Yet, in a turbulent reactive flow system, as we increase the Reynolds number, we observe the emergence of a single dominant timescale in the acoustic pressure fluctuations, as indicated by its loss of multifractality. Such emergence of order from chaos is intriguing. We perform experiments in a turbulent reactive flow system consisting of flame, acoustic, and hydrodynamic subsystems interacting nonlinearly. We study the evolution of short-time correlated dynamics between the acoustic field and the flame in the spatiotemporal domain of the system. The order parameter, defined as the fraction of the correlated dynamics, increases gradually from zero to one. We find that the susceptibility of the order parameter, correlation length, and correlation time diverge at a critical point between chaos and order. Our results show that the observed emergence of order from chaos is a continuous phase transition. Moreover, we provide experimental evidence that the critical exponents characterizing this transition fall in the universality class of directed percolation. Our paper demonstrates how a real-world complex, nonequilibrium turbulent reactive flow system exhibits universal behavior near a critical point.
Data Science
Dynamic visualization of high-dimensional data
See also “Dimensionality reduction under scrutiny”.
Dimensionality reduction (DR) is commonly used to project high-dimensional data into lower dimensions for visualization, which could then generate new insights and hypotheses. However, DR algorithms introduce distortions in the visualization and cannot faithfully represent all relations in the data. Thus, there is a need for methods to assess the reliability of DR visualizations. Here we present DynamicViz, a framework for generating dynamic visualizations that capture the sensitivity of DR visualizations to perturbations in the data resulting from bootstrap sampling. DynamicViz can be applied to all commonly used DR methods. We show the utility of dynamic visualizations in diagnosing common interpretative pitfalls of static visualizations and extending existing single-cell analyses. We introduce the variance score to quantify the dynamic variability of observations in these visualizations. The variance score characterizes natural variability in the data and can be used to optimize DR algorithm implementations.
Advantages and limitations of current network inference methods
This is a paper from 2010 and still, I think we face similar challenges, as we have recently highlighted in this paper with Leto Peel and Tiago Peixoto.
Network inference, which is the reconstruction of biological networks from high-throughput data, can provide valuable information about the regulation of gene expression in cells. However, it is an underdetermined problem, as the number of interactions that can be inferred exceeds the number of independent measurements. Different state-of-the-art tools for network inference use specific assumptions and simplifications to deal with underdetermination, and these influence the inferences. The outcome of network inference therefore varies between tools and can be highly complementary. Here we categorize the available tools according to the strategies that they use to deal with the problem of underdetermination. Such categorization allows an insight into why a certain tool is more appropriate for the specific research question or data set at hand.
Evolution
Strategy evolution on higher-order networks
See also this insightful News & Views.
Cooperation is key to prosperity in human societies. Population structure is well understood as a catalyst for cooperation, where research has focused on pairwise interactions. But cooperative behaviors are not simply dyadic, and they often involve coordinated behavior in larger groups. Here we develop a framework to study the evolution of behavioral strategies in higher-order population structures, which include pairwise and multi-way interactions. We provide an analytical treatment of when cooperation will be favored by higher-order interactions, accounting for arbitrary spatial heterogeneity and nonlinear rewards for cooperation in larger groups. Our results indicate that higher-order interactions can act to promote the evolution of cooperation across a broad range of networks, in public goods games. Higher-order interactions consistently provide an advantage for cooperation when interaction hyper-networks feature multiple conjoined communities. Our analysis provides a systematic account of how higher-order interactions modulate the evolution of prosocial traits.
Bacterial origin of a key innovation in the evolution of the vertebrate eye
See also this gentle introduction to the paper.
Since the time of Charles Darwin, explaining the stepwise evolution of the eye has been a challenge. Here, we describe the essential contribution of bacteria to the evolution of the vertebrate eye, via interdomain horizontal gene transfer (iHGT), of a bacterial gene that gave rise to the vertebrate-specific interphotoreceptor retinoid-binding protein (IRBP). We demonstrate that IRBP, a highly conserved and essential retinoid shuttling protein, arose from a bacterial gene that was acquired, duplicated, and neofunctionalized coincident with the development of the vertebrate-type eye >500 Mya. Importantly, our findings provide a path by which complex structures like the vertebrate eye can evolve: not just by tinkering with existing genetic material, but also by acquiring and functionally integrating foreign genes.
Ecosystems
Clarifying the definition of common mycorrhizal networks
Common mycorrhizal networks (CMNs) are an enigmatic feature of soil and mycorrhizal ecology. The current use of the term ‘common mycorrhizal network’ stipulates a direct, continuous physical link between plants formed by the mycelium of mycorrhizal fungal genets. This means that a specific case (involving hyphal continuity) is used to define a much broader phenomenon of hyphae interlinking among roots of different plants. We here embrace a more inclusive definition of the CMN as a network formed by mycorrhizal fungal genets among roots of different plants, irrespective of the type of connection or interaction, and not limited to direct hyphal linkages. Implicitly, this broader version of the term has been used by many researchers already. We propose using the term ‘common mycorrhizal networks with hyphal continuity’ (CMN-HC) to capture the more specific case of a continuous link via hyphae between the roots of different plants, which is important to study for some (notable carbon and nutrient exchange), but not all functions of a CMN (e.g. transfer of infochemicals or microbes). In addition, and becoming more general than CMN, we introduce the term ‘common fungal network’ (CFN) to include networks of any type of connection formed between different plants by any type of fungus; this includes also non-mycorrhizal fungi, and indeed a combination of non-mycorrhizal and mycorrhizal networks. We assert that this new conceptual framework incorporating three hierarchical terms (CMN-HC, CMN and CFN), ranging from the most specific to the very broad, can usher in a period of new research activity on fungal networks.
Biological Systems
Transient loss of Polycomb components induces an epigenetic cancer fate
Cancer typically begins when some cells start to grow uncontrollably due to the buildup of permanent changes, such as mutations, in their DNA. These specific mutations, which have been directly linked to the development of cancer, can alter how genes are expressed in a cell: accordingly, gene expression alters the production of proteins carrying out specific functions. So, when mutations affect gene expression, they can cause significant changes in several cellular activities, such as:
Proliferation: cells can multiply faster than normal, leading to an overgrowth that can form tumors;
Differentiation: mutations can prevent cells from developing into their mature/specialized forms, which means they may keep dividing as immature cells;
Metabolism: cancer cells often change how they process energy, which can support their rapid growth;
Survival: mutations can enable cells to evade the body’s normal mechanisms for removing unhealthy or unneeded cells, allowing them to survive longer than they should.
These permanent changes driven by mutations in genes disrupt the normal, controlled growth of cells, leading to the formation and spread of tumors, which is the hallmark of cancer.
This paper challenges this idea, reporting that epigenetic regulation — i.e., even when gene expression is regulated by transient (and not permanent) disrupting mechanics, not necessarily changing the DNA — can start tumours and sustain their progression.
Although cancer initiation and progression are generally associated with the accumulation of somatic mutations1,2, substantial epigenomic alterations underlie many aspects of tumorigenesis and cancer susceptibility3,4,5,6, suggesting that genetic mechanisms might not be the only drivers of malignant transformation7. However, whether purely non-genetic mechanisms are sufficient to initiate tumorigenesis irrespective of mutations has been unknown. Here, we show that a transient perturbation of transcriptional silencing mediated by Polycomb group proteins is sufficient to induce an irreversible switch to a cancer cell fate in Drosophila. This is linked to the irreversible derepression of genes that can drive tumorigenesis, including members of the JAK–STAT signalling pathway and zfh1, the fly homologue of the ZEB1 oncogene, whose aberrant activation is required for Polycomb perturbation-induced tumorigenesis. These data show that a reversible depletion of Polycomb proteins can induce cancer in the absence of driver mutations, suggesting that tumours can emerge through epigenetic dysregulation leading to inheritance of altered cell fates.
Collective intelligence: A unifying concept for integrating biology across scales and substrates
A defining feature of biology is the use of a multiscale architecture, ranging from molecular networks to cells, tissues, organs, whole bodies, and swarms. Crucially however, biology is not only nested structurally, but also functionally: each level is able to solve problems in distinct problem spaces, such as physiological, morphological, and behavioral state space. Percolating adaptive functionality from one level of competent subunits to a higher functional level of organization requires collective dynamics: multiple components must work together to achieve specific outcomes. Here we overview a number of biological examples at different scales which highlight the ability of cellular material to make decisions that implement cooperation toward specific homeodynamic endpoints, and implement collective intelligence by solving problems at the cell, tissue, and whole-organism levels. We explore the hypothesis that collective intelligence is not only the province of groups of animals, and that an important symmetry exists between the behavioral science of swarms and the competencies of cells and other biological systems at different scales. We then briefly outline the implications of this approach, and the possible impact of tools from the field of diverse intelligence for regenerative medicine and synthetic bioengineering.