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      Coupling Cell Size Regulation and Proliferation Dynamics of C. glutamicum Reveals Cell Division Based on Surface Area

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          Abstract

          Single cells actively coordinate growth and division to regulate their size, yet how this size homeostasis at the single-cell level propagates over multiple generations to impact clonal expansion remains fundamentally unexplored. Classical timer models for cell proliferation (where the duration of the cell cycle is an independent variable) predict that the stochastic variation in colony size will increase monotonically over time. In stark contrast, implementing size control according to adder strategy (where on average a fixed size added from cell birth to division) leads to colony size variations that eventually decay to zero. While these results assume a fixed size of the colony-initiating progenitor cell, further analysis reveals that the magnitude of the intercolony variation in population number is sensitive to heterogeneity in the initial cell size. We validate these predictions by tracking the growth of isogenic microcolonies of Corynebacterium glutamicum in microfluidic chambers. Approximating their cell shape to a capsule, we observe that the degree of random variability in cell size is different depending on whether the cell size is quantified as per length, surface area, or volume, but size control remains an adder regardless of these size metrics. A comparison of the observed variability in the colony population with the predictions suggests that proliferation matches better with a cell division based on the cell surface. In summary, our integrated mathematical-experimental approach bridges the paradigms of single-cell size regulation and clonal expansion at the population levels. This innovative approach provides elucidation of the mechanisms of size homeostasis from the stochastic dynamics of colony size for rod-shaped microbes.

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          Robust growth of Escherichia coli.

          The quantitative study of the cell growth has led to many fundamental insights in our understanding of a wide range of subjects, from the cell cycle to senescence. Of particular importance is the growth rate, whose constancy represents a physiological steady state of an organism. Recent studies, however, suggest that the rate of elongation during exponential growth of bacterial cells decreases cumulatively with replicative age for both asymmetrically and symmetrically dividing organisms, implying that a "steady-state" population consists of individual cells that are never in a steady state of growth. To resolve this seeming paradoxical observation, we studied the long-term growth and division patterns of Escherichia coli cells by employing a microfluidic device designed to follow steady-state growth and division of a large number of cells at a defined reproductive age. Our analysis of approximately 10(5) individual cells reveals a remarkable stability of growth whereby the mother cell inherits the same pole for hundreds of generations. We further show that death of E. coli is not purely stochastic but is the result of accumulating damages. We conclude that E. coli, unlike all other aging model systems studied to date, has a robust mechanism of growth that is decoupled from cell death. Copyright 2010 Elsevier Ltd. All rights reserved.
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            DBS: a fast and informative segmentation algorithm for DNA copy number analysis

            Background Genome-wide DNA copy number changes are the hallmark events in the initiation and progression of cancers. Quantitative analysis of somatic copy number alterations (CNAs) has broad applications in cancer research. With the increasing capacity of high-throughput sequencing technologies, fast and efficient segmentation algorithms are required when characterizing high density CNAs data. Results A fast and informative segmentation algorithm, DBS (Deviation Binary Segmentation), is developed and discussed. The DBS method is based on the least absolute error principles and is inspired by the segmentation method rooted in the circular binary segmentation procedure. DBS uses point-by-point model calculation to ensure the accuracy of segmentation and combines a binary search algorithm with heuristics derived from the Central Limit Theorem. The DBS algorithm is very efficient requiring a computational complexity of O(n*log n), and is faster than its predecessors. Moreover, DBS measures the change-point amplitude of mean values of two adjacent segments at a breakpoint, where the significant degree of change-point amplitude is determined by the weighted average deviation at breakpoints. Accordingly, using the constructed binary tree of significant degree, DBS informs whether the results of segmentation are over- or under-segmented. Conclusion DBS is implemented in a platform-independent and open-source Java application (ToolSeg), including a graphical user interface and simulation data generation, as well as various segmentation methods in the native Java language.
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              Natural Selection and Random Genetic Drift in Phenotypic Evolution

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                Author and article information

                Journal
                bioRxiv
                BIORXIV
                bioRxiv
                Cold Spring Harbor Laboratory
                28 December 2023
                : 2023.12.26.573217
                Affiliations
                [a ]Department of Electrical and Computing Engineering, University of Delaware. Newark, DE 19716, USA
                [b ]CeBiTec, Bielefeld University. Bielefeld, Germany.
                [c ]Multiscale Bioengineering, Technical Faculty, Bielefeld University. Bielefeld, Germany.
                [d ]Institute of Process Engineering in Life Sciences: Microsystems in Bioprocess Engineering, Karlsruhe Institute of Technology. Karlsruhe, Germany.
                [e ]Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE 19716 USA.
                Author notes

                Author contributions

                S.T. and L.B. performed the experiments, C.N. designed the model and computational framework, C.N. and Z.V. analyzed the data. C.N. prepared the plots and wrote the manuscript with input from all authors. A.S. and A.G. directed the research.

                []Correspondence: absingh@ 123456udel.edu
                Author information
                http://orcid.org/0000-0001-6504-4619
                http://orcid.org/0000-0002-1515-1763
                http://orcid.org/0000-0002-3294-1695
                http://orcid.org/0000-0001-9794-5147
                http://orcid.org/0000-0002-7564-4957
                http://orcid.org/0000-0002-1451-2838
                Article
                10.1101/2023.12.26.573217
                10793411
                38234762
                3df8280c-0949-4787-bf45-929c64c772d6

                This work is licensed under a Creative Commons Attribution 4.0 International License, which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.

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