Rugged Landscape Research Articles

Evolution of Amino Acid Propensities under Stability-Mediated Epistasis.

Site-specific amino acid preferences are influenced by the genetic background of the protein. The preferences for resident amino acids are expected to, on average, increase over time because of replacements at other sites—a nonadaptive phenomenon referred to as the “evolutionary Stokes shift.” Alternatively, decreases in resident amino acid propensity have recently been viewed as evidence of adaptations to external environmental changes. Using population genetics theory and thermodynamic stability constraints, we show that nonadaptive evolution can lead to both positive and negative shifts in propensities following the fixation of an amino acid, emphasizing that the detection of negative shifts is not conclusive evidence of adaptation. By examining propensity shifts from when an amino acid is first accepted at a site until it is subsequently replaced, we find that 50% of sites show a decrease in the propensity for the newly resident amino acid while the remaining sites show an increase. Furthermore, the distributions of the magnitudes of positive and negative shifts were comparable. Preferences were often conserved via a significant negative autocorrelation in propensity changes—increases in propensities often followed by decreases, and vice versa. Lastly, we explore the underlying mechanisms that lead propensities to fluctuate. We observe that stabilizing replacements increase the mutational tolerance at a site and in doing so decrease the propensity for the resident amino acid. In contrast, destabilizing substitutions result in more rugged fitness landscapes that tend to favor the resident amino acid. In summary, our results characterize propensity trajectories under nonadaptive stability-constrained evolution against which evidence of adaptations should be calibrated.

Characterizing the rugged conformational energy landscape of RNA

RNA plays crucial roles in almost all essential biological functions. To perform these functions, RNA must fold into complex three-dimensional structures and often undergo functionally important structural rearrangements. Perhaps the most fundamental and commonly found RNA structural elements are RNA stem-loops, which often nucleate the folding of larger, more complex structural motifs; serve as recognition sites for RNA binding proteins; and are components of conformational switches that regulate RNA function.

Dynamic RNA Fitness Landscapes of a Group I Ribozyme during Changes to the Experimental Environment.

Fitness landscapes of protein and RNA molecules can be studied experimentally using high-throughput techniques to measure the functional effects of numerous combinations of mutations. The rugged topography of these molecular fitness landscapes is important for understanding and predicting natural and experimental evolution. Mutational effects are also dependent upon environmental conditions, but the effects of environmental changes on fitness landscapes remains poorly understood. Here, we investigate the changes to the fitness landscape of a catalytic RNA molecule while changing a single environmental variable that is critical for RNA structure and function. Using high-throughput sequencing of in vitro selections, we mapped a fitness landscape of the Azoarcus group I ribozyme under eight different concentrations of magnesium ions (1–48 mM MgCl2). The data revealed the magnesium dependence of 16,384 mutational neighbors, and from this, we investigated the magnesium induced changes to the topography of the fitness landscape. The results showed that increasing magnesium concentration improved the relative fitness of sequences at higher mutational distances while also reducing the ruggedness of the mutational trajectories on the landscape. As a result, as magnesium concentration was increased, simulated populations evolved toward higher fitness faster. Curve-fitting of the magnesium dependence of individual ribozymes demonstrated that deep sequencing of in vitro reactions can be used to evaluate the structural stability of thousands of sequences in parallel. Overall, the results highlight how environmental changes that stabilize structures can also alter the ruggedness of fitness landscapes and alter evolutionary processes.

Learning a compass spin model with neural network quantum states

Neural network quantum states provide a novel representation of the many-body states of interacting quantum systems and open up a promising route to solve frustrated quantum spin models that evade other numerical approaches. Yet its capacity to describe complex magnetic orders with large unit cells has not been demonstrated, and its performance in a rugged energy landscape has been questioned. Here we apply restricted Boltzmann machines (RBMs) and stochastic gradient descent to seek the ground states of a compass spin model on the honeycomb lattice, which unifies the Kitaev model, Ising model and the quantum 120° model with a single tuning parameter. We report calculation results on the variational energy, order parameters and correlation functions. The phase diagram obtained is in good agreement with the predictions of tensor network ansatz, demonstrating the capacity of RBMs in learning the ground states of frustrated quantum spin Hamiltonians. The limitations of the calculation are discussed. A few strategies are outlined to address some of the challenges in machine learning frustrated quantum magnets.

SpeedyGenesXL: an Automated, High-Throughput Platform for the Preparation of Bespoke Ultralarge Variant Libraries for Directed Evolution.

Directed evolution of proteins is a highly effective strategy for tailoring biocatalysts to a particular application, and is capable of engineering improvements such as kcat, thermostability and organic solvent tolerance. It is recognized that large and systematic libraries are required to navigate a protein's vast and rugged sequence landscape effectively, yet their preparation is nontrivial and commercial libraries are extremely costly. To address this, we have developed SpeedyGenesXL, an automated, high-throughput platform for the production of wild-type genes, Boolean OR, combinatorial, or combinatorial-OR-type libraries based on the SpeedyGenes methodology. Together this offers a flexible platform for library synthesis, capable of generating many different bespoke, diverse libraries simultaneously.

New Oldowan localities at high level within Kilombe Caldera, Kenya

New occurrences of early artefacts ascribed to the Oldowan tradition come from localities at high level within the caldera of the extinct Kilombe volcano, located in the central rift valley of Kenya. The trachyte cone and caldera of Kilombe volcano formed at ca. 2.5 Ma, and the record of >130m of sediment-fill indicates that the caldera subsequently held a lake for long periods during the Early Pleistocene. The Oldowan artefact localities, dated by 40Ar/39Ar and palaeomagnetism to ∼1.78 Ma, lie east of the centre of the caldera, on the west side of an ancient small lake, which later drained away as a gorge formed on the east side of the mountain. The artefacts are dominantly made of Kilombe trachyte, and are associated with a fauna of large animals including Hippopotamus gorgops. These are the first Oldowan localities to be discovered in a new area of the Kenyan rift valley in the last thirty years, and their presence at high level in rugged landscape indicates that the associated hominins were exploiting a full range of environments.

How much lateral collaboration is optimal? Insights from computer simulations of MNEs as complex adaptive systems

The paper models the MNE as a complex adaptive system and investigates the effects of lateral collaboration on performance at both the MNE and subsidiary level. Applying the NKC and rugged landscapes methodology, computer simulations are conducted to explore the boundary conditions governing lateral collaboration in MNEs. At the MNE level, the results show that the success of lateral collaboration across subsidiaries is related to the level of within-subsidiaries collaboration, which is a measure of internal complexity. More specifically, increasing the level of lateral collaboration above the level of internal complexity results in inferior performance and hence internal complexity represents an upper limit on productive lateral collaboration. At the subsidiary level, the results indicate that flatter structures generally outperform hierarchical ones. They also point to the advantage of concentrating the responsibilities for lateral collaboration within the subsidiary in the hands of one or few boundary spanners.

Landscape context and dispersal ability as determinants of population genetic structure in freshwater fishes

Abstract Dispersal is a critically important process that dictates population persistence, gene flow, and evolutionary potential, and is an essential element for identifying species conservation risks. This study aims to investigate the contributions of dispersal syndromes and hydrographic barriers on patterns of population connectivity and genetic structure in fishes occupying the particularly rugged and fragmented landscape of the Kimberley Plateau, Western Australia. We assessed population genetic structure between three neighbouring catchments (the Mitchell, King Edward, and Drysdale rivers) in three congeneric groups of freshwater fishes that exhibit varied dispersal syndromes within and among groups: (1) Melanotaenia australis and M. gracilis; (2) Syncomistes trigonicus and S. rastellus; and (3) Hephaestus jenkinsi and H. epirrhinos. Within each species we sampled the upper, middle, and lower reaches of each catchment and assessed patterns of gene flow between and within catchments using microsatellite markers. Our results suggest that contemporary connectivity between catchments is greatly limited or absent in all study species, regardless of their dispersal syndromes. However, gene flow within catchments varied in line with predicted dispersal potential, with poor dispersers exhibiting limited gene flow and significant genetic structuring. We conclude that the rugged landscape and historical habitat isolation has contributed to patterns of population fragmentation among fish populations from different river catchments. However, it appears dispersal syndromes influence connectivity and gene flow within catchments, where landscape constraints are not as pervasive. This study presents a comparative population genetic analysis of freshwater fishes with differing dispersal syndromes and colonisation ability. Our findings provide new insights into factors shaping patterns of biodiversity on the Kimberley Plateau, and the evolutionary uniqueness of fish communities from different river catchments draining the plateau. More broadly, they highlight the importance of accounting for dispersal‐related traits when planning management and conservation strategies.

Manufacturing Process Innovation in the Pharmaceutical Industry

Problem definition: Process innovation is commonly claimed to be a major source of competitive advantage for firms. Despite this perceived influence, it has received substantially less attention than product innovation, and much uncertainty remains about its true association with firm performance. We investigate the relationship between a pharmaceutical firm’s portfolio of manufacturing process innovations and its economic performance. Academic/practical relevance: We uniquely conduct a multidimensional evaluation of a firm’s portfolio of manufacturing process innovations at the product level. This allows a quantitative evaluation of both the relative benefit of the different dimensions of a portfolio as well as the potential complementarities between these in different technological landscapes. Methodology: Through a collaboration with expert patent attorneys, we develop a unique longitudinal data set that combines secondary data and evaluations of a firm’s portfolio of process patents along two key dimensions: novelty and scope. We conduct econometric analyses for a large-scale sample of active pharmaceutical ingredients (APIs) whose product patents have expired and for which process innovation is thus the main source of competitive advantage. Results: We find a positive association between the presence of manufacturing process innovation and firm performance. However, although portfolio’s scope appears to always be beneficial to performance, the effect of novelty alone depends on the ruggedness of the technological landscape: negative in smoother landscapes and positive in more rugged landscapes. Results further suggest that novelty and scope of a portfolio of process innovations are complementary across technological landscapes. Managerial implications: Our results provide important practical insights that can inform the organization and execution of the research and development process across high-technology industries. In particular, although process innovations can be economically beneficial, investing in high-novelty process innovations without a corresponding high scope could jeopardize payoffs, especially in technological landscapes that are relatively smooth.

Phase Transition of Catenated DNA Networks in Poly(ethylene glycol) Solutions.

Conformational phase transitions of macromolecules are an important class of problems in fundamental polymer physics. While the conformational phase transitions of linear DNA have been extensively studied, this feature of topologically complex DNA remains unexplored. We report herein the polymer-and-salt-induced (Ψ) phase transition of 2D catenated DNA networks, called kinetoplasts, using single-molecule fluorescence microscopy. We observe that kinetoplasts can undergo a reversible transition from the flat phase to the collapsed phase in the presence of NaCl as a function of the crowding agent poly(ethylene glycol). The nature of this phase transition is tunable through varying ionic strengths. For linear DNA, the coexistence of coil and globule phases was attributed to a first order phase transition associated with a double well potential in the transition regime. Kinetoplasts, however, navigate from the flat to the collapsed phase by passing through an intermediate regime, characterized by the coexistence of a multipopulation with varying shapes and sizes. Conformations of individual molecules in the multipopulation are long-lived, which suggests a rugged energy landscape.

«…Отложить поход до зимы»: влияние природных факторов на военные действия XIV—XV вв. на псковских границах

The Pskov state, limited to its natural borders, seems to be a good object for the research of the influence of natural and geographical factors on military and political history. Thus, the military actions of 1323—1480 are explored to consider the influence that these factors had on the causes, course and results of the wars waged by Pskov. The inequality in natural resources in the border areas and the struggle for their redistribution determined the main directions of military expansion, forms of invasions and their seasonal dating. The rugged landscape characteristic for the western part of Pskov land and the dependence of the state of the Pskov-Peipus reservoir complicated the choice of the season for military operations. The incursions of small detachments, timed to the periods of economic activity (May-June, August) which were more diversionary than predatory in character, customarily prevailed. The February-March incursions into the fishing zones were usually carried out by larger forces and were aimed at capturing the product of fishery. The military clashes between Pskov and Veliky Novgorod and the Grand Duchy of Lithuania, as well as the full-scale wars with Livonia, were mainly political, which was reflected in the active actions of the parties against the fortified settlements. In these cases, the necessity of large military contingents and siege equipment moving determined the choice of a season with the most predictable weather conditions.

Determining Sequence-Dependent DNA Oligonucleotide Hybridization and Dehybridization Mechanisms Using Coarse-Grained Molecular Simulation, Markov State Models, and Infrared Spectroscopy.

A robust understanding of the sequence-dependent thermodynamics of DNA hybridization has enabled rapid advances in DNA nanotechnology. A fundamental understanding of the sequence-dependent kinetics and mechanisms of hybridization and dehybridization remains comparatively underdeveloped. In this work, we establish new understanding of the sequence-dependent hybridization/dehybridization kinetics and mechanism within a family of self-complementary pairs of 10-mer DNA oligomers by integrating coarse-grained molecular simulation, machine learning of the slow dynamical modes, data-driven inference of long-time kinetic models, and experimental temperature-jump infrared spectroscopy. For a repetitive ATATATATAT sequence, we resolve a rugged dynamical landscape comprising multiple metastable states, numerous competing hybridization/dehybridization pathways, and a spectrum of dynamical relaxations. Introduction of a G:C pair at the terminus (GATATATATC) or center (ATATGCATAT) of the sequence reduces the ruggedness of the dynamics landscape by eliminating a number of metastable states and reducing the number of competing dynamical pathways. Only by introducing a G:C pair midway between the terminus and the center to maximally disrupt the repetitive nature of the sequence (ATGATATCAT) do we recover a canonical “all-or-nothing” two-state model of hybridization/dehybridization with no intermediate metastable states. Our results establish new understanding of the dynamical richness of sequence-dependent kinetics and mechanisms of DNA hybridization/dehybridization by furnishing quantitative and predictive kinetic models of the dynamical transition network between metastable states, present a molecular basis with which to understand experimental temperature jump data, and furnish foundational design rules by which to rationally engineer the kinetics and pathways of DNA association and dissociation for DNA nanotechnology applications.

Path integral approach unveils role of complex energy landscape for activated dynamics of glassy systems

The complex dynamics of an increasing number of systems is attributed to the emergence of a rugged energy landscape with an exponential number of metastable states. To develop this picture into a predictive dynamical theory, I discuss how to compute the exponentially small probability of a jump from one metastable state to another. This is expressed as a path integral that can be evaluated by saddle-point methods in mean-field models, leading to a boundary value problem. The resulting dynamical equations are solved numerically by means of a Newton-Krylov algorithm in the paradigmatic spherical $p$-spin glass model that is invoked in diverse contexts from supercooled liquids to machine-learning algorithms. I discuss the solutions in the asymptotic regime of large times and the physical implications on the nature of the ergodicity-restoring processes.

Using LiDAR and Random Forest to improve deer habitat models in a managed forest landscape

Conservation strategies are hindered by a lack of accurate maps of important habitat for many wildlife species, but especially for species inhabiting managed forest landscapes. Prioritizing restoration efforts on Alaska’s Tongass National Forest from past extensive clearcut logging is extremely challenging given the difficulty in accurately mapping its remote, rugged temperate rainforest landscapes. We tested the application of airborne light detection and ranging (LiDAR) technology to build a winter habitat model for Sitka black-tailed deer (Odocoileus hemionus sitkensis), the primary herbivore in the coastal temperate rainforest. We analyzed the importance of geomorphometric and forest structure characteristics as predictors of deer winter habitat selection using Random Forest applied to a 3-year GPS relocation dataset collected from 40 adult female deer. The LiDAR-based habitat model had a predictive performance of 94% (Out-of-bag error = 6%), a 10% lower model error compared to air-photo interpreted polygons and modeled plot data. Random Forest also outperformed analogous resource selection function models based on a comprehensive k-fold cross-validation. Deer habitat selection patterns in the LiDAR-based model were nonlinear across geomorphometric and forest structure predictive variables, and generally supported existing studies of deer habitat selection. Besides improving deer conservation and management on the Tongass National Forest, our approach could greatly enhance the accuracy and resolution of habitat maps used for conservation and restoration planning across large managed forest landscapes.

Integrating demography and distribution modeling for the iconic Leontopodium alpinum Colm. in the Romanian Carpathians.

Both climate change and human exploitation are major threats to plant life in mountain environments. One species that may be particularly sensitive to both of these stressors is the iconic alpine flower edelweiss (Leontopodium alpinum Colm.). Its populations have declined across Europe due to over‐collection for its highly prized flowers. Edelweiss is still subject to harvesting across the Romanian Carpathians, but no study has measured to what extent populations are vulnerable to anthropogenic change.Here, we estimated the effects of climate and human disturbance on the fitness of edelweiss. We combined demographic measurements with predictions of future range distribution under climate change to assess the viability of populations across Romania.We found that per capita and per‐area seed number and seed mass were similarly promoted by both favorable environmental conditions, represented by rugged landscapes with relatively cold winters and wet summers, and reduced exposure to harvesting, represented by the distance of plants from hiking trails. Modeling these responses under future climate scenarios suggested a slight increase in per‐area fitness. However, we found plant ranges contracted by between 14% and 35% by 2050, with plants pushed into high elevation sites.Synthesis. Both total seed number and seed mass are expected to decline across Romania despite individual edelweiss fitness benefiting from a warmer and wetter climate. More generally, our approach of coupling species distribution models with demographic measurements may better inform conservation strategies of ways to protect alpine life in a changing world.

Does Expenditure in Public Governance Guarantee Less Corruption? Non-linearities and Complementarities of the Rule of Law

Corruption is an endemic societal problem with profound implications in the development of nations. In combating this issue, cross-national evidence supporting the effectiveness of the rule of law seems at odds with poorly realized outcomes from reforms inspired in such literature. This paper provides an explanation for such contradiction. By building a computational approach, we develop three methodological novelties into the empirical study of corruption: (1) modeling government expenditure as a more adequate intervention variable than traditional indicators, (2) generating large within-country variation by means of bottom-up simulation (instead of cross-national data pooling), and (2) accounting for all possible interactions between covariates through a spillover network. Our estimates suggest that, the least developed a country is, the more difficult is to find the right combination of policies that leads to reductions in corruption. We characterize this difficulty through a rugged landscape that governments navigate through changes in their total budget and in the relative expenditure towards the rule of law. Importantly our method helps identifying the--country-specific--policy issues that complement the rule of law in the fight against corruption.

Adaptive and nonadaptive causes of heterogeneity in genetic differentiation across the Mimulus guttatus genome.

Genetic diversity becomes structured among populations over time due to genetic drift and divergent selection. Although population structure is often treated as a uniform underlying factor, recent resequencing studies of wild populations have demonstrated that diversity in many regions of the genome may be structured quite dissimilar to the genome-wide pattern. Here, we explored the adaptive and nonadaptive causes of such genomic heterogeneity using population-level, whole genome resequencing data obtained from annual Mimulus guttatus individuals collected across a rugged environment landscape. We found substantial variation in how genetic differentiation is structured both within and between chromosomes, although, in contrast to other studies, known inversion polymorphisms appear to serve only minor roles in this heterogeneity. In addition, much of the genome can be clustered into eight among-population genetic differentiation patterns, but only two of these clusters are particularly consistent with patterns of isolation by distance. By performing genotype-environment association analysis, we also identified genomic intervals where local adaptation to specific climate factors has accentuated genetic differentiation among populations, and candidate genes in these windows indicate climate adaptation may proceed through changes affecting specialized metabolism, drought resistance, and development. Finally, by integrating our findings with previous studies, we show that multiple aspects of plant reproductive biology may be common targets of balancing selection and that variants historically involved in climate adaptation among populations have probably also fuelled rapid adaptation to microgeographic environmental variation within sites.

Time horizons and the staging of search on rugged landscapes: A laboratory experiment

This paper investigates how available time influences the staging of search. We inquire into this question using a laboratory experiment centered on a 2x2 design: shorter vs. longer time horizons and simple vs. complex search landscapes. When more time is available, search oscillates between later stopping and less risky experimentation. In contrast, when less time is available, it oscillates between earlier stopping and more risky experimentation, particularly when the level of complexity is higher. We further examine the frequency of search and the rhythm underlying the patterns of change. Search frequency improves the development of standard procedures able to enhance both average and maximum performance when more time is available. An irregular rhythm, characterized by an uneven pattern of changes, negatively moderates the effect of search frequency, especially when less time is available. On the whole, time surfaces as a unique type of resource that prompts decision makers to differently strike a balance in the way they stage exploration and exploitation. These findings contribute improving our understanding of the temporal unfolding nature of problemistic search.

Learning the ground state of a non-stoquastic quantum Hamiltonian in a rugged neural network landscape

Strongly interacting quantum systems described by non-stoquastic Hamiltonians exhibit rich low-temperature physics. Yet, their study poses a formidable challenge, even for state-of-the-art numerical techniques. Here, we investigate systematically the performance of a class of universal variational wave-functions based on artificial neural networks, by considering the frustrated spin-1/21/2J_1-J_2J1−J2 Heisenberg model on the square lattice. Focusing on neural network architectures without physics-informed input, we argue in favor of using an ansatz consisting of two decoupled real-valued networks, one for the amplitude and the other for the phase of the variational wavefunction. By introducing concrete mitigation strategies against inherent numerical instabilities in the stochastic reconfiguration algorithm we obtain a variational energy comparable to that reported recently with neural networks that incorporate knowledge about the physical system. Through a detailed analysis of the individual components of the algorithm, we conclude that the rugged nature of the energy landscape constitutes the major obstacle in finding a satisfactory approximation to the ground state wavefunction, and prevents learning the correct sign structure. In particular, we show that in the present setup the neural network expressivity and Monte Carlo sampling are not primary limiting factors.

Novelty and imitation within the brain: a Darwinian neurodynamic approach to combinatorial problems

Efficient search in vast combinatorial spaces, such as those of possible action sequences, linguistic structures, or causal explanations, is an essential component of intelligence. Is there any computational domain that is flexible enough to provide solutions to such diverse problems and can be robustly implemented over neural substrates? Based on previous accounts, we propose that a Darwinian process, operating over sequential cycles of imperfect copying and selection of neural informational patterns, is a promising candidate. Here we implement imperfect information copying through one reservoir computing unit teaching another. Teacher and learner roles are assigned dynamically based on evaluation of the readout signal. We demonstrate that the emerging Darwinian population of readout activity patterns is capable of maintaining and continually improving upon existing solutions over rugged combinatorial reward landscapes. We also demonstrate the existence of a sharp error threshold, a neural noise level beyond which information accumulated by an evolutionary process cannot be maintained. We introduce a novel analysis method, neural phylogenies, that displays the unfolding of the neural-evolutionary process.