Biology subjects

Korolev, K. S.

Publications and source records attributed to Korolev, K. S..

11 recordsLinked to original sources

bioRxiv2026

Dynamic flux balance analysis of cancer metabolism using expression-based human cell line models

Aberrant metabolism that supports dysregulated growth is a key feature of cancer. Flux balance analysis, a computational approach that can predict genome-scale metabolic rates based on stoichiometry and mass conservation, has proven valuable in the prediction of pathway utilization and possible drug targets in cancer cells, but deep insight into growth dynamics under physiologically relevant conditions has been limited. To address this gap, we revisited the human metabolism model and its integration of gene-expression data, and we implemented simulations of metabolism in multiple cancer cell lines using static and dynamic flux balance analysis. Our models predict a substantial dependence of intracellular and exchange fluxes, and of the timing of resource utilization, on growth media and specific cell-line makeup. Secretions include lactate and other metabolites that could contribute to shaping the tumor microenvironment. Despite these differences in flux phenotypes, virtual genetic knockout screens predict high similarity of lethality across cell lines and media, and suggest a handful of possible cell-line specific targets. We finally demonstrate the power of dynamic simulations by setting virtual competition assays, which form the foundation for future extensions into dynamic simulations of tumor-stromal interactions in spatially-structured environment.

systems biology↗

bioRxiv2024

A mechanistic statistical approach to infer invasion characteristics of human-dispersed species with complex life cycle

The rising introduction of invasive species through trade networks threatens biodiversity and ecosystem services. Yet, we have a limited understanding of how transportation networks determine patterns of range expansion. This is partly because current analytical models fail to integrate the invaders life-history dynamics with heterogeneity in human-mediated dispersal patterns. And partly because classical statistical methods often fail to provide reliable estimates of model parameters due to spatial biases in the presence-only records and lack of informative demographic data. To address these gaps, we first formulate an age-structured metapopulation model that uses a probability matrix to emulate human-mediated dispersal patterns. The model reveals that an invader spreads along the shortest network path, such that the inter-patch network distances decrease with increasing traffic volume and reproductive value of hitchhikers. Next, we propose a Bayesian statistical method to estimate model parameters using presence-only data and prior demographic knowledge. To show the utility of the statistical approach, we analyze zebra mussel (Dreissena polymorpha) expansion in North America through the commercial shipping network. Our analysis underscores the importance of correcting spatial biases and leveraging priors to answer questions, such as where and when the zebra mussels were introduced and what life-history characteristics make these mollusks successful invaders.

ecology↗

bioRxiv2022

A resource for the comparison and integration of heterogeneous microbiome networks

Naturally occurring microbial communities often comprise thousands of taxa involved in complex networks of interactions. These interactions can be mediated by several mechanisms, including the competition for resources, the exchange of signals and nutrients, cell-cell contact and antibiotic warfare. In addition to direct measurements and computational predictions of interactions, abundant data on microbial co-occurrence associations can be inferred from correlations of taxa across samples, which can be estimated from metagenomic, and amplicon datasets. The analysis and interpretation of interaction and correlation networks are limited by the challenge of comparing across different datasets, due to heterogeneity of the data itself and to the lack of a platform to facilitate such comparisons. Here, we introduce the Microbial Interaction Network Database (MIND) - a web-based platform for the integrative analysis of different types of microbial networks, freely available at http://microbialnet.org/. In addition to containing a growing body of curated data, including amplicon-based co-occurrence networks, genome-scale model-derived networks, metabolic influence networks and horizontal gene transfer networks, MIND allows users to upload and analyze newly generated networks using a JSON format and standard NCBI taxonomy. The platform provides convenient functions to compare and query multiple networks simultaneously, and to visualize and export networks and datasets. Through some illustrative examples, we demonstrate how the platform might facilitate discoveries and help generate new hypotheses on host-associated and environmentally important microbial ecosystems through the power of knowledge integration.

systems biology↗

bioRxiv2022

Growth instabilities shape morphology and genetic diversity of cellular aggregates

Cellular aggregates assume an incredible variety of shapes ranging from circular molds to irregular tumors. While we understand many of the mechanisms responsible for these spatial patterns, little is known about how the shape of an aggregate influences its ecology and evolution. Here, we investigate this relationship in the context of microbial colonies grown on hard agar plates. This a well-studied system that exhibits a transition from smooth circular disks to more irregular and rugged shapes as either the nutrient concentration or cellular motility is decreased. Starting from a mechanistic model of colony growth, we identify two dimensionless quantities that determine how morphology and genetic diversity of the population depend on the model parameters. Our simulations further reveal that population dynamics cannot be accurately described by the commonly-used surface growth models. Instead, one has to explicitly account for the emergent growth instabilities and demographic fluctuations. Overall, our work links together environmental conditions, colony morphology, and evolution. This link is essential for a rational design of concrete, biophysical perturbations to steer evolution in the desired direction.

biophysics↗

bioRxiv2021

Ecological landscapes guide the assembly of optimal microbial communities

Assembling optimal microbial communities is key for various applications in biofuel production, agriculture, and human health. Finding the optimal community is challenging because the number of possible communities grows exponentially with the number of species, and so an exhaustive search cannot be performed even for a dozen species. A heuristic search that improves community function by adding or removing one species at a time is more practical, but it is unknown whether this strategy can discover an optimal or nearly optimal community. Using consumer-resource models with and without cross-feeding, we investigate how the efficacy of search depends on the distribution of resources, niche overlap, cross-feeding, and other aspects of community ecology. We show that search efficacy is determined by the ruggedness of the appropriately-defined ecological landscape. We identify specific ruggedness measures that are both predictive of search performance and robust to noise and low sampling density. The feasibility of our approach is demonstrated using experimental data from a soil microbial community. Overall, our results establish the conditions necessary for the success of the heuristic search and provide concrete design principles for building high-performing microbial consortia. Author summaryResearch shows that microbial communities comprised of specific species combinations can cure disease, improve agricultural output, or synthesize valuable chemicals. But finding the species combinations that generate high-performing communities is challenging because there are too many species combinations to test exhaustively. So, scientists use heuristic strategies that test only a few species combinations to search for high-performing communities. However, these heuristic strategies often fail to find the best species combinations, and we still do not understand when they fail. Here, we develop a framework to analyze these heuristic strategies, building on the concept of fitness landscapes studied in evolution and computer science. We apply this framework to data from simulated microbial community models to identify biological properties that affect the success of heuristic search strategies, such as the extent to which microbes compete for the same metabolites. Further, we establish statistical measures of the landscape structure that can help estimate search success from preliminary data. We validate our findings using experimental data from communities of soil microbes. Together, our results develop a conceptual framework to analyze and develop heuristic search strategies and identify guiding principles to help scientists choose species and environmental conditions that make finding high-performing microbial communities easier.

ecology↗

bioRxiv2020

Genealogical structure changes as range expansions transition from pushed to pulled

Range expansions accelerate evolution through multiple mechanisms including gene surfing and genetic drift. The inference and control of these evolutionary processes ultimately relies on the information contained in genealogical trees. Currently, there are two opposing views on how range expansions shape genealogies. In invasion biology, expansions are typically approximated by a series of population bottlenecks producing genealogies with only pairwise mergers between lineages--a process known as the Kingman coalescent. Conversely, traveling-wave models predict a coalescent with multiple mergers, known as the Bolthausen-Sznitman coalescent. Here, we unify these two approaches and show that expansions can generate an entire spectrum of coalescent topologies. Specifically, we show that tree topology is controlled by growth dynamics at the front and exhibits large differences between pulled and pushed expansions. These differences are explained by the fluctuations in the total number of descendants left by the early founders. High growth cooperativity leads to a narrow distribution of reproductive values and the Kingman coalescent. Conversely, low growth cooperativity results in a broad distribution, whose exponent controls the merger sizes in the genealogies. These broad distribution and non-Kingman tree topologies emerge due to the fluctuations in the front shape and position and do not occur in quasi-deterministic simulations. Overall, our results show that range expansions provide a robust mechanism for generating different types of multiple mergers, which could be similar those observed in populations with strong selection or high fecundity. Thus, caution should be exercised in making inferences about the origin of non-Kingman genealogies. Significance statementSpatial dynamics are important for understanding genetic diversity in many contexts, such as cancer and infectious diseases. Coalescent theory offers a powerful framework for interpreting and predicting patters of genetic diversity in populations, but incorporating spatial structure into the theory has proven difficult. Here, we address this long-standing problem by studying the coalescent in a spatially expanding population. We find the topology of the coalescent changes depending on the growth dynamics at the front. Using analytical arguments, we show that the transition between coalescent topologies is universal and is controlled by a parameter related to the expansion velocity. Our theory makes precise predictions about the effects of population dynamics on genetic diversity at the expansion front, which we confirm in simulations.

evolutionary biology↗

bioRxiv2020

Traveling fronts in self-replicating persistent random walks with multiple internal states

Self-activation coupled to a transport mechanism results in traveling waves that describe polymerization reactions, forest fires, tumor growth, and even the spread of epidemics. Diffusion is a simple and commonly used model of particle transport. Many physical and biological systems are, however, better described by persistent random walks that switch between multiple states of ballistic motion. So far, traveling fronts in persistent random walk models have only been analyzed in special, simplified cases. Here, we formulate the general model of reaction-transport processes in such systems and show how to compute the expansion velocity for arbitrary number of states. For the two-state model, we obtain a closed-form expression for the velocity and report how it is affected by different transport and replication parameters. We also show that nonzero death rates result in a discontinuous transition from quiescence to propagation. We compare our results to a recent observation of a discontinuous onset of propagation in microtubule asters and comment on the universal nature of the underlying mechanism.

biophysics↗

bioRxiv2017

A simple rule for the evolution of fast dispersal at the edge of expanding populations

Predicting evolution of expanding populations is critical to control biological threats such as invasive species and cancer metastasis. Expansion is primarily driven by reproduction and dispersal, but nature abounds with examples of evolution where organisms pay a reproductive cost to disperse faster. When does selection favor this survival of the fastest? We searched for a simple rule, motivated by evolution experiments where swarming bacteria evolved into an hy-perswarmer mutant which disperses[~] 100% faster but pays a growth cost of [~] 10% to make many copies of its flagellum. We analyzed a two-species model based on the Fisher equation to explain this observation: the population expansion rate (v) results from an interplay of growth (r) and dispersal (D) and is independent of the carrying capacity: [Formula]. A mutant can take over the edge only if its expansion rate (v2) exceeds the expansion rate of the established species (v1); this simple condition (v2 > v1) determines the maximum cost in slower growth that a faster mutant can pay and still be able to take over. Numerical simulations and time-course experiments where we tracked evolution by imaging bacteria suggest that our findings are general: less favorable conditions delay but do not entirely prevent the success of the fastest. Thus, the expansion rate defines a traveling wave fitness, which could be combined with trade-offs to predict evolution of expanding populations.

evolutionary biology↗

bioRxiv2017

Neither pulled nor pushed: Genetic drift and front wandering uncover a new class of reaction-diffusion waves

Short AbstractTraveling waves describe diverse natural phenomena from crystal growth in physics to range expansions in biology. Two classes of waves exist with very different properties: pulled and pushed. Pulled waves are driven by high growth rates at the expansion edge, where the number of organisms is small and fluctuations are large. In contrast, fluctuations are suppressed in pushed waves because the region of maximal growth is shifted towards the population bulk. Although it is commonly believed that expansions are either pulled or pushed, we found an intermediate class of waves with bulk-driven growth, but exceedingly large fluctuations. These waves are unusual because their properties are controlled by both the leading edge and the bulk of the front.\n\nLong AbstractEpidemics, flame propagation, and cardiac rhythms are classic examples of reaction-diffusion waves that describe a switch from one alternative state to another. Only two types of waves are known: pulled, driven by the leading edge, and pushed, driven by the bulk of the wave. Here, we report a distinct class of semi-pushed waves for which both the bulk and the leading edge contribute to the dynamics. These hybrid waves have the kinetics of pushed waves, but exhibit giant fluctuations similar to pulled waves. The transitions between pulled, semi-pushed, and fully-pushed waves occur at universal ratios of the wave velocity to the Fisher velocity. We derive these results in the context of a species invading a new habitat by examining front diffusion, rate of diversity loss, and fluctuation-induced corrections to the expansion velocity. All three quantities decrease as a power law of the population density with the same exponent. We analytically calculate this exponent taking into account the fluctuations in the shape of the wave front. For fully-pushed waves, the exponent is -1 consistent with the central limit theorem. In semi-pushed waves, however, the fluctuations average out much more slowly, and the exponent approaches 0 towards the transition to pulled waves. As a result, a rapid loss of genetic diversity and large fluctuations in the position of the front occur even for populations with cooperative growth and other forms of an Allee effect. The evolutionary outcome of spatial spreading in such populations could therefore be less predictable than previously thought.

ecology↗

bioRxiv2017

Interactions between species introduce spurious associations in microbiome studies

Microbiota contribute to many dimensions of host phenotype, including disease. To link specific microbes to specific phenotypes, microbiome-wide association studies compare microbial abundances between two groups of samples. Abundance differences, however, reflect not only direct associations with the phenotype, but also indirect effects due to microbial interactions. We found that microbial interactions could easily generate a large number of spurious associations that provide no mechanistic insight. Using techniques from statistical physics, we developed a method to remove indirect associations and applied it to the largest dataset on pediatric inflammatory bowel disease. Our method corrected the inflation of p-values in standard association tests and showed that only a small subset of associations is directly linked to the disease. Direct associations had a much higher accuracy in separating cases from controls and pointed to immunomodulation, butyrate production, and the brain-gut axis as important factors in the inflammatory bowel disease.

microbiology↗

bioRxiv2017

Genetic Load Makes Cancer Cells More Sensitive To Common Drugs: Evidence From Cancer Cell Line Encyclopedia

Genetic alterations initiate tumors and enable the evolution of drug resistance. The pro-cancer view of mutations is however incomplete, and several studies show that mutational load can reduce tumor fitness. Given its negative effect, genetic load should make tumors more sensitive to anticancer drugs. Here, we test this hypothesis across all major types of cancer from the Cancer Cell Line Encyclopedia, that provides genetic and expression data of 496 cell lines together with their response to 24 common anticancer drugs. We found that the efficacy of 9 out of 24 drugs showed significant association with genetic load in a pan-cancer analysis. The associations for some tissue-drug combinations were remarkably strong with genetic load explaining up to 83% of the variance in the drug response. Overall, the role of genetic load depended on both the drug and the tissue type with 10 tissues being particularly vulnerable to genetic load. We also identified changes in gene expression associated with increased genetic load, which included cell-cycle checkpoints, DNA damage and apoptosis. Our results show that genetic load is an important component of tumor fitness and can predict drug sensitivity. Beyond being a biomarker, genetic load might be a new, unexplored vulnerability of cancer.

cancer biology↗