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Ahmadi A, Courtney M, Ren C, Ingalls B. A benchmarked comparison of software packages for time-lapse image processing of monolayer bacterial population dynamics. Microbiol Spectr 2024; 12:e0003224. [PMID: 38980028 PMCID: PMC11302142 DOI: 10.1128/spectrum.00032-24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Accepted: 04/26/2024] [Indexed: 07/10/2024] Open
Abstract
Time-lapse microscopy offers a powerful approach for analyzing cellular activity. In particular, this technique is valuable for assessing the behavior of bacterial populations, which can exhibit growth and intercellular interactions in a monolayer. Such time-lapse imaging typically generates large quantities of data, limiting the options for manual investigation. Several image-processing software packages have been developed to facilitate analysis. It can thus be a challenge to identify the software package best suited to a particular research goal. Here, we compare four software packages that support the analysis of 2D time-lapse images of cellular populations: CellProfiler, SuperSegger-Omnipose, DeLTA, and FAST. We compare their performance against benchmarked results on time-lapse observations of Escherichia coli populations. Performance varies across the packages, with each of the four outperforming the others in at least one aspect of the analysis. Not surprisingly, the packages that have been in development for longer showed the strongest performance. We found that deep learning-based approaches to object segmentation outperformed traditional approaches, but the opposite was true for frame-to-frame object tracking. We offer these comparisons, together with insight into usability, computational efficiency, and feature availability, as a guide to researchers seeking image-processing solutions. IMPORTANCE Time-lapse microscopy provides a detailed window into the world of bacterial behavior. However, the vast amount of data produced by these techniques is difficult to analyze manually. We have analyzed four software tools designed to process such data and compared their performance, using populations of commonly studied bacterial species as our test subjects. Our findings offer a roadmap to scientists, helping them choose the right tool for their research. This comparison bridges a gap between microbiology and computational analysis, streamlining research efforts.
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Affiliation(s)
- Atiyeh Ahmadi
- Department of Biology, University of Waterloo, Waterloo, Ontario, Canada
| | - Matthew Courtney
- Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, Ontario, Canada
| | - Carolyn Ren
- Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, Ontario, Canada
| | - Brian Ingalls
- Department of Biology, University of Waterloo, Waterloo, Ontario, Canada
- Department of Applied Mathematics, University of Waterloo, Waterloo, Ontario, Canada
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2
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Puniya BL, Verma M, Damiani C, Bakr S, Dräger A. Perspectives on computational modeling of biological systems and the significance of the SysMod community. BIOINFORMATICS ADVANCES 2024; 4:vbae090. [PMID: 38948011 PMCID: PMC11213628 DOI: 10.1093/bioadv/vbae090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 05/12/2024] [Accepted: 06/14/2024] [Indexed: 07/02/2024]
Abstract
Motivation In recent years, applying computational modeling to systems biology has caused a substantial surge in both discovery and practical applications and a significant shift in our understanding of the complexity inherent in biological systems. Results In this perspective article, we briefly overview computational modeling in biology, highlighting recent advancements such as multi-scale modeling due to the omics revolution, single-cell technology, and integration of artificial intelligence and machine learning approaches. We also discuss the primary challenges faced: integration, standardization, model complexity, scalability, and interdisciplinary collaboration. Lastly, we highlight the contribution made by the Computational Modeling of Biological Systems (SysMod) Community of Special Interest (COSI) associated with the International Society of Computational Biology (ISCB) in driving progress within this rapidly evolving field through community engagement (via both in person and virtual meetings, social media interactions), webinars, and conferences. Availability and implementation Additional information about SysMod is available at https://sysmod.info.
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Affiliation(s)
- Bhanwar Lal Puniya
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE 68588, United States
| | - Meghna Verma
- Systems Medicine, Clinical Pharmacology and Quantitative Pharmacology, R&D BioPharmaceuticals, AstraZeneca, Gaithersburg, MD 20878, United States
| | - Chiara Damiani
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, Milan 20126, Italy
| | - Shaimaa Bakr
- Department of Medicine, Stanford Center for Biomedical Informatics Research (BMIR), Stanford University, Stanford, CA 94305-5479, United States
| | - Andreas Dräger
- Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Cluster of Excellence ‘Controlling Microbes to Fight Infections’, Institute for Bioinformatics and Medical Informatics (IBMI), Eberhard Karl University of Tübingen, Tübingen 72076, Germany
- German Center for Infection Research (DZIF), partner site Tübingen, Tübingen 72076, Germany
- Quantitative Biology Center (QBiC), Eberhard Karl University of Tübingen, Tübingen 72076, Germany
- Data Analytics and Bioinformatics, Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle (Saale) 06120, Germany
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3
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Cumming T, Levayer R. Toward a predictive understanding of epithelial cell death. Semin Cell Dev Biol 2024; 156:44-57. [PMID: 37400292 DOI: 10.1016/j.semcdb.2023.06.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 06/15/2023] [Accepted: 06/22/2023] [Indexed: 07/05/2023]
Abstract
Epithelial cell death is highly prevalent during development and tissue homeostasis. While we have a rather good understanding of the molecular regulators of programmed cell death, especially for apoptosis, we still fail to predict when, where, how many and which specific cells will die in a tissue. This likely relies on the much more complex picture of apoptosis regulation in a tissular and epithelial context, which entails cell autonomous but also non-cell autonomous factors, diverse feedback and multiple layers of regulation of the commitment to apoptosis. In this review, we illustrate this complexity of epithelial apoptosis regulation by describing these different layers of control, all demonstrating that local cell death probability is a complex emerging feature. We first focus on non-cell autonomous factors that can locally modulate the rate of cell death, including cell competition, mechanical input and geometry as well as systemic effects. We then describe the multiple feedback mechanisms generated by cell death itself. We also outline the multiple layers of regulation of epithelial cell death, including the coordination of extrusion and regulation occurring downstream of effector caspases. Eventually, we propose a roadmap to reach a more predictive understanding of cell death regulation in an epithelial context.
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Affiliation(s)
- Tom Cumming
- Department of Developmental and Stem Cell Biology, Institut Pasteur, Université de Paris Cité, CNRS UMR 3738, 25 rue du Dr. Roux, 75015 Paris, France; Sorbonne Université, Collège Doctoral, F75005 Paris, France
| | - Romain Levayer
- Department of Developmental and Stem Cell Biology, Institut Pasteur, Université de Paris Cité, CNRS UMR 3738, 25 rue du Dr. Roux, 75015 Paris, France.
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Starke S, Harris DMM, Zimmermann J, Schuchardt S, Oumari M, Frank D, Bang C, Rosenstiel P, Schreiber S, Frey N, Franke A, Aden K, Waschina S. Amino acid auxotrophies in human gut bacteria are linked to higher microbiome diversity and long-term stability. THE ISME JOURNAL 2023; 17:2370-2380. [PMID: 37891427 PMCID: PMC10689445 DOI: 10.1038/s41396-023-01537-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 10/06/2023] [Accepted: 10/09/2023] [Indexed: 10/29/2023]
Abstract
Amino acid auxotrophies are prevalent among bacteria. They can govern ecological dynamics in microbial communities and indicate metabolic cross-feeding interactions among coexisting genotypes. Despite the ecological importance of auxotrophies, their distribution and impact on the diversity and function of the human gut microbiome remain poorly understood. This study performed the first systematic analysis of the distribution of amino acid auxotrophies in the human gut microbiome using a combined metabolomic, metagenomic, and metabolic modeling approach. Results showed that amino acid auxotrophies are ubiquitous in the colon microbiome, with tryptophan auxotrophy being the most common. Auxotrophy frequencies were higher for those amino acids that are also essential to the human host. Moreover, a higher overall abundance of auxotrophies was associated with greater microbiome diversity and stability, and the distribution of auxotrophs was found to be related to the human host's metabolome, including trimethylamine oxide, small aromatic acids, and secondary bile acids. Thus, our results suggest that amino acid auxotrophies are important factors contributing to microbiome ecology and host-microbiome metabolic interactions.
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Affiliation(s)
- Svenja Starke
- Institute of Human Nutrition and Food Science, Nutriinformatics, Kiel University, Kiel, Germany
| | - Danielle M M Harris
- Institute of Human Nutrition and Food Science, Nutriinformatics, Kiel University, Kiel, Germany
- Institute of Clinical Molecular Biology, Kiel University, Kiel, Germany
| | - Johannes Zimmermann
- Zoological Institute, Research Group Evolutionary Ecology and Genetics, Kiel University, Kiel, Germany
- Max Planck Institute for Evolutionary Biology, Plön, Germany
| | - Sven Schuchardt
- Fraunhofer Institute for Toxicology and Experimental Medicine (ITEM), Hanover, Germany
| | - Mhmd Oumari
- Institute of Clinical Molecular Biology, Kiel University, Kiel, Germany
| | - Derk Frank
- Department of Internal Medicine III, University Medical Center Schleswig-Holstein, Kiel, Germany
- German Centre for Cardiovascular Research (DZHK), Partner site Hamburg, Kiel, Lübeck, Germany
| | - Corinna Bang
- Institute of Clinical Molecular Biology, Kiel University, Kiel, Germany
| | - Philip Rosenstiel
- Institute of Clinical Molecular Biology, Kiel University, Kiel, Germany
| | - Stefan Schreiber
- Institute of Clinical Molecular Biology, Kiel University, Kiel, Germany
- Department of Internal Medicine I, University Medical Center Schleswig-Holstein, Kiel, Germany
| | - Norbert Frey
- Department of Internal Medicine III, University Medical Center Schleswig-Holstein, Kiel, Germany
- German Centre for Cardiovascular Research (DZHK), Partner site Hamburg, Kiel, Lübeck, Germany
- Department of Internal Medicine III, University Hospital Heidelberg, Heidelberg, Germany
| | - Andre Franke
- Institute of Clinical Molecular Biology, Kiel University, Kiel, Germany
| | - Konrad Aden
- Institute of Clinical Molecular Biology, Kiel University, Kiel, Germany.
- Department of Internal Medicine I, University Medical Center Schleswig-Holstein, Kiel, Germany.
| | - Silvio Waschina
- Institute of Human Nutrition and Food Science, Nutriinformatics, Kiel University, Kiel, Germany.
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Simpson K, L'Homme A, Keymer J, Federici F. Spatial biology of Ising-like synthetic genetic networks. BMC Biol 2023; 21:185. [PMID: 37667283 PMCID: PMC10478219 DOI: 10.1186/s12915-023-01681-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 08/11/2023] [Indexed: 09/06/2023] Open
Abstract
BACKGROUND Understanding how spatial patterns of gene expression emerge from the interaction of individual gene networks is a fundamental challenge in biology. Developing a synthetic experimental system with a common theoretical framework that captures the emergence of short- and long-range spatial correlations (and anti-correlations) from interacting gene networks could serve to uncover generic scaling properties of these ubiquitous phenomena. RESULTS Here, we combine synthetic biology, statistical mechanics models, and computational simulations to study the spatial behavior of synthetic gene networks (SGNs) in Escherichia coli quasi-2D colonies growing on hard agar surfaces. Guided by the combined mechanisms of the contact process lattice simulation and two-dimensional Ising model (CPIM), we describe the spatial behavior of bi-stable and chemically coupled SGNs that self-organize into patterns of long-range correlations with power-law scaling or short-range anti-correlations. These patterns, resembling ferromagnetic and anti-ferromagnetic configurations of the Ising model near critical points, maintain their scaling properties upon changes in growth rate and cell shape. CONCLUSIONS Our findings shed light on the spatial biology of coupled and bistable gene networks in growing cell populations. This emergent spatial behavior could provide insights into the study and engineering of self-organizing gene patterns in eukaryotic tissues and bacterial consortia.
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Affiliation(s)
- Kevin Simpson
- ANID - Millennium Science Initiative Program, Millennium Institute for Integrative Biology (iBio), Santiago, Chile
| | - Alfredo L'Homme
- Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Juan Keymer
- Institute for Advanced Studies, Shenzhen X-Institute, Shenzhen, China.
- Schools of Physics and Biology, Pontificia Universidad Católica de Chile, Santiago, Chile.
- Department of Natural Sciences and Technology, Universidad de Aysén, Coyhaique, Chile.
| | - Fernán Federici
- ANID - Millennium Science Initiative Program, Millennium Institute for Integrative Biology (iBio), Santiago, Chile.
- Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences, Pontificia Universidad Católica de Chile, Santiago, Chile.
- FONDAP Center for Genome Regulation - Department of Molecular Genetics and Microbiology, Pontificia Universidad Católica de Chile, Santiago, Chile.
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Dal Co A, Ackermann M, van Vliet S. Spatial self-organization of metabolism in microbial systems: A matter of enzymes and chemicals. Cell Syst 2023; 14:98-108. [PMID: 36796335 DOI: 10.1016/j.cels.2022.12.009] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 08/14/2022] [Accepted: 12/21/2022] [Indexed: 02/17/2023]
Abstract
Most bacteria live in dense, spatially structured communities such as biofilms. The high density allows cells to alter the local microenvironment, whereas the limited mobility can cause species to become spatially organized. Together, these factors can spatially organize metabolic processes within microbial communities so that cells in different locations perform different metabolic reactions. The overall metabolic activity of a community depends both on how metabolic reactions are arranged in space and on how they are coupled, i.e., how cells in different regions exchange metabolites. Here, we review mechanisms that lead to the spatial organization of metabolic processes in microbial systems. We discuss factors that determine the length scales over which metabolic activities are arranged in space and highlight how the spatial organization of metabolic processes affects the ecology and evolution of microbial communities. Finally, we define key open questions that we believe should be the main focus of future research.
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Affiliation(s)
- Alma Dal Co
- Department of Computational Biology, University of Lausanne, 1015 Lausanne, Switzerland
| | - Martin Ackermann
- Department of Environmental Systems Science, ETH Zurich, 8092 Zurich, Switzerland; Department of Environmental Microbiology, Eawag, 8600 Duebendorf, Switzerland.
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Sharma N, Traulsen A. Suppressors of fixation can increase average fitness beyond amplifiers of selection. Proc Natl Acad Sci U S A 2022; 119:e2205424119. [PMID: 36067304 PMCID: PMC9478682 DOI: 10.1073/pnas.2205424119] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 08/03/2022] [Indexed: 11/18/2022] Open
Abstract
Evolutionary dynamics on graphs has remarkable features: For example, it has been shown that amplifiers of selection exist that-compared to an unstructured population-increase the fixation probability of advantageous mutations, while they decrease the fixation probability of disadvantageous mutations. So far, the theoretical literature has focused on the case of a single mutant entering a graph-structured population, asking how the graph affects the probability that a mutant takes over a population and the time until this typically happens. For continuously evolving systems, the more relevant case is that mutants constantly arise in an evolving population. Typically, such mutations occur with a small probability during reproduction events. We thus focus on the low mutation rate limit. The probability distribution for the fitness in this process converges to a steady state at long times. Intuitively, amplifiers of selection are expected to increase the population's mean fitness in the steady state. Similarly, suppressors of selection are expected to decrease the population's mean fitness in the steady state. However, we show that another set of graphs, called suppressors of fixation, can attain the highest population mean fitness. The key reason behind this is their ability to efficiently reject deleterious mutants. This illustrates the importance of the deleterious mutant regime for the long-term evolutionary dynamics, something that seems to have been overlooked in the literature so far.
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Affiliation(s)
- Nikhil Sharma
- Department of Evolutionary Theory, Max Planck Institute for Evolutionary Biology, 24306 Plön, Germany
| | - Arne Traulsen
- Department of Evolutionary Theory, Max Planck Institute for Evolutionary Biology, 24306 Plön, Germany
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Gutiérrez Mena J, Kumar S, Khammash M. Dynamic cybergenetic control of bacterial co-culture composition via optogenetic feedback. Nat Commun 2022; 13:4808. [PMID: 35973993 PMCID: PMC9381578 DOI: 10.1038/s41467-022-32392-z] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 07/29/2022] [Indexed: 12/19/2022] Open
Abstract
Communities of microbes play important roles in natural environments and hold great potential for deploying division-of-labor strategies in synthetic biology and bioproduction. However, the difficulty of controlling the composition of microbial consortia over time hinders their optimal use in many applications. Here, we present a fully automated, high-throughput platform that combines real-time measurements and computer-controlled optogenetic modulation of bacterial growth to implement precise and robust compositional control of a two-strain E. coli community. In addition, we develop a general framework for dynamic modeling of synthetic genetic circuits in the physiological context of E. coli and use a host-aware model to determine the optimal control parameters of our closed-loop compositional control system. Our platform succeeds in stabilizing the strain ratio of multiple parallel co-cultures at arbitrary levels and in changing these targets over time, opening the door for the implementation of dynamic compositional programs in synthetic bacterial communities.
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Affiliation(s)
- Joaquín Gutiérrez Mena
- Department of Biosystems Science and Engineering (D-BSSE), ETH Zürich, Mattenstrasse 26, 4058, Basel, Switzerland
| | - Sant Kumar
- Department of Biosystems Science and Engineering (D-BSSE), ETH Zürich, Mattenstrasse 26, 4058, Basel, Switzerland
| | - Mustafa Khammash
- Department of Biosystems Science and Engineering (D-BSSE), ETH Zürich, Mattenstrasse 26, 4058, Basel, Switzerland.
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Laws of Spatially Structured Population Dynamics on a Lattice. PHYSICS 2022. [DOI: 10.3390/physics4030052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
We consider spatial population dynamics on a lattice, following a type of a contact (birth–death) stochastic process. We show that simple mathematical approximations for the density of cells can be obtained in a variety of scenarios. In the case of a homogeneous cell population, we derive the cellular density for a two-dimensional (2D) spatial lattice with an arbitrary number of neighbors, including the von Neumann, Moore, and hexagonal lattice.are not allowed in the abstract, please move them to the main text, please remember to check the order of references in the full text after revision. We then turn our attention to evolutionary dynamics, where mutant cells of different properties can be generated. For disadvantageous mutants, we derive an approximation for the equilibrium density representing the selection–mutation balance. For neutral and advantageous mutants, we show that simple scaling (power) laws for the numbers of mutants in expanding populations hold in 2D and 3D, under both flat (planar) and range population expansion. These models have relevance for studies in ecology and evolutionary biology, as well as biomedical applications including the dynamics of drug-resistant mutants in cancer and bacterial biofilms.
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Abstract
Biofilm formation is an important and ubiquitous mode of growth among bacteria. Central to the evolutionary advantage of biofilm formation is cell-cell and cell-surface adhesion achieved by a variety of factors, some of which are diffusible compounds that may operate as classical public goods-factors that are costly to produce but may benefit other cells. An outstanding question is how diffusible matrix production, in general, can be stable over evolutionary timescales. In this work, using Vibrio cholerae as a model, we show that shared diffusible biofilm matrix proteins are indeed susceptible to cheater exploitation and that the evolutionary stability of producing these matrix components fundamentally depends on biofilm spatial structure, intrinsic sharing mechanisms of these components, and flow conditions in the environment. We further show that exploitation of diffusible adhesion proteins is localized within a well-defined spatial range around cell clusters that produce them. Based on this exploitation range and the spatial distribution of cell clusters, we constructed a model of costly diffusible matrix production and related these length scales to the relatedness coefficient in social evolution theory. Our results show that production of diffusible biofilm matrix components is evolutionarily stable under conditions consistent with natural biofilm habitats and host environments. We expect the mechanisms revealed in this study to be relevant to other secreted factors that operate as cooperative public goods in bacterial communities and the concept of exploitation range and the associated analysis tools to be generally applicable.
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