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Mechanically-driven growth and competition in a Voronoi model of tissues

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arXiv 2024年

作者： Brézin, Louis Korolev, Kirill S. Department of Physics Graduate Program in Bioinformatics Biological Design Center Boston University BostonMA02215 United States

The mechanisms leading cells to acquire a fitness advantage and establish themselves in a population are paramount to understanding the development and growth of cancer. Although there are many works that study separately either the evolutionary dynamics or the mechanics of cancer, little has been done to couple evolutionary dynamics to mechanics. To address this question, we study a confluent model of tissue using a Self-Propelled Voronoi (SPV) model with stochastic growth rates that depend on the mechanical variables of the system. The SPV model is an out-of-equilibrium model of tissue derived from an energy functional that has a jamming/unjamming transition between solid-like and liquid-like states. By considering several scenarios of mutants invading a resident population in both phases, we determine the range of parameters that confer a fitness advantage and show that the preferred area and perimeter are the most relevant ones. We find that the liquid-like state is more resistant to invasion and show that the outcome of the competition can be determined from the simulation of a non-growing mixture. Moreover, a mean-field approximation can accurately predict the fate of a mutation affecting mechanical properties of a cell. Our results can be used to infer evolutionary dynamics from tissue images, understand cancer-suppressing effects of tissue mechanics, and even search for mechanics-based therapies. © 2024, CC BY.

关键词： Tissue

New sector morphologies emerge from anisotropic colony growth

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arXiv 2024年

作者： Swartz, Daniel W. Lee, Hyunseok Kardar, Mehran Korolev, Kirill S. Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics Graduate Program in Bioinformatics and Biological Design Center Boston University BostonMA02215 United States

Competition during range expansions is of great interest from both practical and theoretical view points. Experimentally, range expansions are often studied in homogeneous Petri dishes, which lack spatial anisotropy that might be present in realistic populations. Here, we analyze a model of anisotropic growth, based on coupled Kardar-Parisi-Zhang and Fisher-Kolmogorov-Petrovsky-Piskunov equations that describe surface growth and lateral competition. Compared to a previous study of isotropic growth, anisotropy relaxes a constraint between parameters of the model. We completely characterize spatial patterns and invasion velocities in this generalized model. In particular, we find that strong anisotropy results in a distinct morphology of spatial invasion with a kink in the displaced strain ahead of the boundary between the strains. This morphology of the out-competed strain is similar to a shock wave and serves as a signature of anisotropic growth. Copyright © 2024, The Authors. All rights reserved.

关键词： Strain

Competition on the edge of an expanding population

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arXiv 2023年

In growing populations, the fate of mutations depends on their competitive ability against the ancestor and their ability to colonize new territory. Here we present a theory that integrates both aspects of mutant fitness by coupling the classic description of one-dimensional competition (Fisher equation) to the minimal model of front shape (KPZ equation). We solved these equations and found three regimes, which are controlled solely by the expansion rates, solely by the competitive abilities, or by both. Collectively, our results provide a simple framework to study spatial competition. Copyright © 2023, The Authors. All rights reserved.

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1 Slow expanders invade by forming dented fronts in microbial 2 colonies

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arXiv 2021年

作者： Lee, Hyunseok Gore, Jeff Korolev, Kirill S. Physics of Living Systems Group Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics Graduate Program in Bioinformatics Biological Design Center Boston University BostonMA02215 United States

Most organisms grow in space, whether they are viruses spreading within a host tissue or invasive species colonizing a new continent. Evolution typically selects for higher expansion rates during spatial growth, but it has been suggested that slower expanders can take over under certain conditions. Here, we report an experimental observation of such population dynamics. We demonstrate that the slower mutants win not only when the two types are intermixed at the front but also when they are spatially segregated into sectors. The latter was thought to be impossible because previous studies focused exclusively on the global competitions mediated by expansion velocities but overlooked the local competitions at sector boundaries. We developed a theory of sector geometry that accounts for both local and global competitions and describes all possible sector shapes. In particular, the theory predicted that a slower, but more competitive, mutant forms a dented V-shaped sector as it takes over the expansion front. Such sectors were indeed observed experimentally and their shapes matched up quantitatively with the theory. In simulations, we further explored several mechanism that could provide slow expanders with a local competitive advantage and showed that they are all well-described by our theory. Taken together, our results shed light on previously unexplored outcomes of spatial competition and establish a universal framework to understand evolutionary and ecological dynamics in expanding populations. © 2021, CC BY-NC-ND.

关键词： Competition

Ecological landscapes guide the assembly of optimal microbial communities

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arXiv 2021年

作者： George, Ashish B. Korolev, Kirill S. Department of Physics Boston University BostonMA02215 United States Biological Design Center Boston University BostonMA02215 United States Carl R. Woese Institute for Genomic Biology Department of Plant Biology University of Illinois at Urbana-Champaign UrbanaIL61801 United States Graduate Program in Bioinformatics Boston University BostonMA02215 United States

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. © 2021, CC BY-NC-ND.

关键词： Microorganisms

Data-driven modeling reveals a universal dynamic underlying the COVID-19 pandemic under social distancing

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arXiv 2020年

作者： Marsland, Robert Mehta, Pankaj Department of Physics Boston University BostonMA02215 United States Biological Design Center Boston University BostonMA02215 United States Microbiome Initiative Boston University BostonMA02215 United States Bioinformatics Program Boston University BostonMA02215 United States College of Data Science Boston University BostonMA02215 United States

We show that the COVID-19 pandemic under social distancing exhibits universal dynamics. The cumulative numbers of both infections and deaths quickly cross over from exponential growth at early times to a longer period of power law growth, before eventually slowing. In agreement with a recent statistical forecasting model by the IHME, we show that this dynamics is well described by the erf function. Using this functional form, we perform a data collapse across countries and US states with very different population characteristics and social distancing policies, confirming the universal behavior of the COVID-19 outbreak. We show that the predictive power of statistical models is limited until a few days before curves flatten, forecast deaths and infections assuming current policies continue and compare our predictions to the IHME models. We present simulations showing this universal dynamics is consistent with disease transmission on scale-free networks and random networks with non-Markovian transmission dynamics. Copyright © 2020, The Authors. All rights reserved.

关键词： Dynamics

Modern views of ancient metabolic networks

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Current Opinion in Systems Biology 2018年 8卷 117-124页

作者： Goldford, Joshua E. Segrè, Daniel Bioinformatics Program and Biological Design Center Boston University Boston 02215 MA United States Department of Biology Boston University Boston 02215 MA United States Department of Biomedical Engineering Boston University Boston 02215 MA United States Department of Physics Boston University Boston 02215 MA United States

Metabolism is a molecular, cellular, ecological and planetary phenomenon, whose fundamental principles are likely at the heart of what makes living matter different from inanimate one. Systems biology approaches developed for the quantitative analysis of metabolism at multiple scales can help understand metabolism's ancient history. In this review, we highlight work that uses network-level approaches to shed light on key innovations in ancient life, including the emergence of proto-metabolic networks, collective autocatalysis and bioenergetics coupling. Recent experiments and computational analyses have revealed new aspects of this ancient history, paving the way for the use of large datasets to further improve our understanding of life's principles and abiogenesis. © 2018 The Authors

关键词： Evolution Metabolism Network expansion Origin of life

Computation of Microbial Ecosystems in Time and Space (COMETS): An open source collaborative platform for modeling ecosystems metabolism

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arXiv 2020年

作者： Dukovski, Ilija Bajić, Djordje Chacón, Jeremy M. Quintin, Michael Vila, Jean C.C. Sulheim, Snorre Pacheco, Alan R. Bernstein, David B. Riehl, William J. Korolev, Kirill S. Sanchez, Alvaro Harcombe, William R. Segrè, Daniel Bioinformatics Program Boston University BostonMA United States Biological Design Center Boston University BostonMA United States Department of Ecology and Evolutionary Biology Yale University New HavenCT United States Microbial Sciences Institute Yale University West HavenCT United States Department of Ecology Evolution and Behavior University of Minnesota St. PaulMN United States BioTechnology Institute University of Minnesota St. PaulMN United States Department of Biotechnology and Food Science Norwegian University of Science and Technology Trondheim Norway Department of Biotechnology and Nanomedicine SINTEF Industry Trondheim Norway Environmental Genomics and Systems Biology Division Lawrence Berkeley National Laboratory BerkeleyCA United States Department of Biomedical Engineering Boston University BostonMA United States Department of Physics Boston University BostonMA United States Department of Biology Boston University BostonMA United States

Genome-scale stoichiometric modeling of metabolism has become a standard systems biology tool for modeling cellular physiology and growth. Extensions of this approach are also emerging as a valuable avenue for predicting, understanding and designing microbial communities. COMETS (Computation Of Microbial Ecosystems in Time and Space) was initially developed as an extension of dynamic flux balance analysis, which incorporates cellular and molecular diffusion, enabling simulations of multiple microbial species in spatially structured environments. Here we describe how to best use and apply the most recent version of this platform, COMETS 2, which incorporates a more accurate biophysical model of microbial biomass expansion upon growth, as well as several new biological simulation modules, including evolutionary dynamics and extracellular enzyme activity. COMETS 2 provides user-friendly Python and MATLAB interfaces compatible with the well-established COBRA models and methods, and comprehensive documentation and tutorials, facilitating the use of COMETS for researchers at all levels of expertise with metabolic simulations. This protocol provides a detailed guideline for installing, testing and applying COMETS 2 to different scenarios, with broad applicability to microbial communities across biomes and scales. Copyright © 2020, The Authors. All rights reserved.

关键词： Microorganisms

Custom CRISPR-Cas9 PAM variants via scalable engineering and machine learning

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Nature 2025年 2025 Apr 22页

作者： Rachel A Silverstein Nahye Kim Ann-Sophie Kroell Russell T Walton Justin Delano Rossano M Butcher Martin Pacesa Blaire K Smith Kathleen A Christie Leillani L Ha Ronald J Meis Aaron B Clark Aviv D Spinner Cicera R Lazzarotto Yichao Li Azusa Matsubara Elizabeth O Urbina Gary A Dahl Bruno E Correia Debora S Marks Shengdar Q Tsai Luca Pinello Suk See De Ravin Qin Liu Benjamin P Kleinstiver PhD Program in Biological and Biomedical Sciences Harvard Medical School Boston MA USA. Center for Genomic Medicine Massachusetts General Hospital Boston MA USA. Department of Pathology Massachusetts General Hospital Boston MA USA. Department of Pathology Harvard Medical School Boston MA USA. Institute of Pharmacy and Molecular Biotechnology (IPMB) Faculty of Engineering Sciences Heidelberg University Heidelberg Germany. Department of Biomedical Informatics Harvard Medical School Boston MA USA. Molecular Pathology Unit Massachusetts General Hospital Charlestown MA USA. Krantz Family Center for Cancer Research Massachusetts General Hospital Charlestown Charlestown MA USA. Broad Institute of Harvard and MIT Cambridge MA USA. Ocular Genomics Institute Massachusetts Eye and Ear Boston MA USA. Department of Ophthalmology Harvard Medical School Boston MA USA. Laboratory of Protein Design and Immunoengineering École Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics Lausanne Switzerland. CELLSCRIPTTM Madison WI USA. Department of Systems Biology Harvard Medical School Boston MA USA. Department of Hematology St. Jude Children's Research Hospital Memphis TN USA. Laboratory of Clinical Immunology and Microbiology National Institute of Allergy and Infectious Diseases National Institutes of Health Bethesda MD USA. Center for Genomic Medicine Massachusetts General Hospital Boston MA USA. bkleinstiver@mgh.harvard.edu. Department of Pathology Massachusetts General Hospital Boston MA USA. bkleinstiver@mgh.harvard.edu. Department of Pathology Harvard Medical School Boston MA USA. bkleinstiver@mgh.harvard.edu.

Engineering and characterizing proteins can be time-consuming and cumbersome, motivating the development of generalist CRISPR-Cas enzymes to enable diverse genome editing applications. However, such enzymes have caveats such as an increased risk of off-target editing. To enable scalable reprogramming of Cas9 enzymes, here we combined high-throughput protein engineering with machine learning (ML) to derive bespoke editors more uniquely suited to specific targets. Via structure/function-informed saturation mutagenesis and bacterial selections, we obtained nearly 1,000 engineered SpCas9 enzymes and characterized their protospacer-adjacent motif (PAM) requirements to train a neural network that relates amino acid sequence to PAM specificity. By utilizing the resulting PAM ML algorithm (PAMmla) to predict the PAMs of 64 million SpCas9 enzymes, we identified efficacious and specific enzymes that outperform evolution-based and engineered SpCas9 enzymes as nucleases and base editors in human cells while reducing off-targets. An in silico directed evolution method enables user-directed Cas9 enzyme design, including for allele-selective targeting of the RHO P23H allele in human cells and mice. Together, PAMmla integrates ML and protein engineering to curate a catalog of SpCas9 enzymes with distinct PAM requirements, and motivates the use of efficient and safe bespoke Cas9 enzymes instead of generalist enzymes for various applications.

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