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Hall R, Bandara A, Charlebois DA. Fitness effects of a demography-dispersal trade-off in expanding Saccharomyces cerevisiaemats. Phys Biol 2024; 21:026001. [PMID: 38194907 DOI: 10.1088/1478-3975/ad1ccd] [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: 04/12/2023] [Accepted: 01/09/2024] [Indexed: 01/11/2024]
Abstract
Fungi expand in space and time to form complex multicellular communities. The mechanisms by which they do so can vary dramatically and determine the life-history and dispersal traits of expanding populations. These traits influence deterministic and stochastic components of evolution, resulting in complex eco-evolutionary dynamics during colony expansion. We perform experiments on budding yeast strains genetically engineered to display rough-surface and smooth-surface phenotypes in colony-like structures called 'mats'. Previously, it was shown that the rough-surface strain has a competitive advantage over the smooth-surface strain when grown on semi-solid media. We experimentally observe the emergence and expansion of segments with a distinct smooth-surface phenotype during rough-surface mat development. We propose a trade-off between dispersal and local carrying capacity to explain the relative fitness of these two phenotypes. Using a modified stepping-stone model, we demonstrate that this trade-off gives the high-dispersing, rough-surface phenotype a competitive advantage from standing variation, but that it inhibits this phenotype's ability to invade a resident smooth-surface population via mutation. However, the trade-off improves the ability of the smooth-surface phenotype to invade in rough-surface mats, replicating the frequent emergence of smooth-surface segments in experiments. Together, these computational and experimental findings advance our understanding of the complex eco-evolutionary dynamics of fungal mat expansion.
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Affiliation(s)
- Rebekah Hall
- Department of Mathematical and Statistical Sciences, University of Alberta, 11455 Saskatchewan Drive NW, Edmonton, Alberta, Canada
| | - Akila Bandara
- Department of Physics, University of Alberta, 11455 Saskatchewan Drive NW, Edmonton, Alberta, Canada
| | - Daniel A Charlebois
- Department of Physics, University of Alberta, 11455 Saskatchewan Drive NW, Edmonton, Alberta, Canada
- Department of Biological Sciences, University of Alberta, 11455 Saskatchewan Drive NW, Edmonton, Alberta, Canada
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2
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Kocher C, Dill KA. Origins of life: first came evolutionary dynamics. QRB DISCOVERY 2023; 4:e4. [PMID: 37529034 PMCID: PMC10392681 DOI: 10.1017/qrd.2023.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 03/07/2023] [Accepted: 03/08/2023] [Indexed: 08/03/2023] Open
Abstract
When life arose from prebiotic molecules 3.5 billion years ago, what came first? Informational molecules (RNA, DNA), functional ones (proteins), or something else? We argue here for a different logic: rather than seeking a molecule type, we seek a dynamical process. Biology required an ability to evolve before it could choose and optimise materials. We hypothesise that the evolution process was rooted in the peptide folding process. Modelling shows how short random peptides can collapse in water and catalyse the elongation of others, powering both increased folding stability and emergent autocatalysis through a disorder-to-order process.
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Affiliation(s)
- Charles Kocher
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA
- Department of Physics and Astronomy, Stony Brook University, Stony Brook, NY, USA
| | - Ken A. Dill
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA
- Department of Physics and Astronomy, Stony Brook University, Stony Brook, NY, USA
- Department of Chemistry, Stony Brook University, Stony Brook, NY, USA
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3
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Kocher CD, Dill KA. Darwinian evolution as a dynamical principle. Proc Natl Acad Sci U S A 2023; 120:e2218390120. [PMID: 36881627 PMCID: PMC10089213 DOI: 10.1073/pnas.2218390120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 02/03/2023] [Indexed: 03/08/2023] Open
Abstract
Darwinian evolution (DE)-biology's powerful process of adaptation-is remarkably different from other known dynamical processes. It is antithermodynamic, driving away from equilibrium; it has persisted for 3.5 billion years; and its target, fitness, can seem like "Just So" stories. For insights, we make a computational model. In the Darwinian Evolution Machine (DEM) model, resource-driven duplication and competition operate inside a cycle of search/compete/choose. We find the following: 1) DE requires multiorganism coexistence for its long-term persistence and ability to cross fitness valleys. 2) DE is driven by resource dynamics, like booms and busts, not just by mutational change. And, 3) fitness ratcheting requires a mechanistic separation between variation and selection steps, perhaps explaining biology's use of separate polymers, DNA and proteins.
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Affiliation(s)
- Charles D. Kocher
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY11794
- Department of Physics and Astronomy, Stony Brook University, Stony Brook, NY11794
| | - Ken A. Dill
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY11794
- Department of Physics and Astronomy, Stony Brook University, Stony Brook, NY11794
- Department of Chemistry, Stony Brook University, Stony Brook, NY11794
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Janulevicius A, van Doorn GS. Selection for rapid uptake of scarce or fluctuating resource explains vulnerability of glycolysis to imbalance. PLoS Comput Biol 2021; 17:e1008547. [PMID: 33465070 PMCID: PMC7815144 DOI: 10.1371/journal.pcbi.1008547] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Accepted: 11/16/2020] [Indexed: 11/19/2022] Open
Abstract
Glycolysis is a conserved central pathway in energy metabolism that converts glucose to pyruvate with net production of two ATP molecules. Because ATP is produced only in the lower part of glycolysis (LG), preceded by an initial investment of ATP in the upper glycolysis (UG), achieving robust start-up of the pathway upon activation presents a challenge: a sudden increase in glucose concentration can throw a cell into a self-sustaining imbalanced state in which UG outpaces LG, glycolytic intermediates accumulate and the cell is unable to maintain high ATP concentration needed to support cellular functions. Such metabolic imbalance can result in "substrate-accelerated death", a phenomenon observed in prokaryotes and eukaryotes when cells are exposed to an excess of substrate that previously limited growth. Here, we address why evolution has apparently not eliminated such a costly vulnerability and propose that it is a manifestation of an evolutionary trade-off, whereby the glycolysis pathway is adapted to quickly secure scarce or fluctuating resource at the expense of vulnerability in an environment with ample resource. To corroborate this idea, we perform individual-based eco-evolutionary simulations of a simplified yeast glycolysis pathway consisting of UG, LG, phosphate transport between a vacuole and a cytosol, and a general ATP demand reaction. The pathway is evolved in constant or fluctuating resource environments by allowing mutations that affect the (maximum) reaction rate constants, reflecting changing expression levels of different glycolytic enzymes. We demonstrate that under limited constant resource, populations evolve to a genotype that exhibits balanced dynamics in the environment it evolved in, but strongly imbalanced dynamics under ample resource conditions. Furthermore, when resource availability is fluctuating, imbalanced dynamics confers a fitness advantage over balanced dynamics: when glucose is abundant, imbalanced pathways can quickly accumulate the glycolytic intermediate FBP as intracellular storage that is used during periods of starvation to maintain high ATP concentration needed for growth. Our model further predicts that in fluctuating environments, competition for glucose can result in stable coexistence of balanced and imbalanced cells, as well as repeated cycles of population crashes and recoveries that depend on such polymorphism. Overall, we demonstrate the importance of ecological and evolutionary arguments for understanding seemingly maladaptive aspects of cellular metabolism.
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Affiliation(s)
- Albertas Janulevicius
- Groningen Institute for Evolutionary Life Sciences, University of Groningen, the Netherlands
- * E-mail:
| | - G. Sander van Doorn
- Groningen Institute for Evolutionary Life Sciences, University of Groningen, the Netherlands
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5
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Stochastic models coupling gene expression and partitioning in cell division in Escherichia coli. Biosystems 2020; 193-194:104154. [PMID: 32353481 DOI: 10.1016/j.biosystems.2020.104154] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Revised: 04/03/2020] [Accepted: 04/16/2020] [Indexed: 12/18/2022]
Abstract
Regulation of future RNA and protein numbers is a key process by which cells continuously best fit the environment. In bacteria, RNA and proteins exist in small numbers and their regulatory processes are stochastic. Consequently, there is cell-to-cell variability in these numbers, even between sister cells. Traditionally, the two most studied sources of this variability are gene expression and RNA and protein degradation, with evidence suggesting that the latter is subject to little regulation, when compared to the former. However, time-lapse microscopy and single molecule fluorescent tagging have produced evidence that cell division can also be a significant source of variability due to asymmetries in the partitioning of RNA and proteins. Relevantly, the impact of this noise differs from noise in production and degradation since, unlike these, it is not continuous. Rather, it occurs at specific time points, at which moment it can introduce major fluctuations. Several models have now been proposed that integrate noise from cell division, in addition to noise in gene expression, to mimic the dynamics of RNA and protein numbers of cell lineages. This is expected to be particularly relevant in genetic circuits, where significant fluctuations in one component protein, at specific time moments, are expected to perturb near-equilibrium states of the circuits, which can have long-lasting consequences. Here we review stochastic models coupling these processes in Escherichia coli, from single genes to small circuits.
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Charlebois DA, Balázsi G. Modeling cell population dynamics. In Silico Biol 2019; 13:21-39. [PMID: 30562900 PMCID: PMC6598210 DOI: 10.3233/isb-180470] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2018] [Revised: 09/13/2018] [Accepted: 10/16/2018] [Indexed: 12/27/2022]
Abstract
Quantitative modeling is quickly becoming an integral part of biology, due to the ability of mathematical models and computer simulations to generate insights and predict the behavior of living systems. Single-cell models can be incapable or misleading for inferring population dynamics, as they do not consider the interactions between cells via metabolites or physical contact, nor do they consider competition for limited resources such as nutrients or space. Here we examine methods that are commonly used to model and simulate cell populations. First, we cover simple models where analytic solutions are available, and then move on to more complex scenarios where computational methods are required. Overall, we present a summary of mathematical models used to describe cell population dynamics, which may aid future model development and highlights the importance of population modeling in biology.
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Affiliation(s)
- Daniel A. Charlebois
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, NY, USA
- Department of Physics, University of Alberta, Edmonton, AB, Canada
| | - Gábor Balázsi
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, NY, USA
- Department of Biomedical Engineering, Stony Brook University, NY, USA
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7
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Abstract
Quantitative modeling is quickly becoming an integral part of biology, due to the ability of mathematical models and computer simulations to generate insights and predict the behavior of living systems. Single-cell models can be incapable or misleading for inferring population dynamics, as they do not consider the interactions between cells via metabolites or physical contact, nor do they consider competition for limited resources such as nutrients or space. Here we examine methods that are commonly used to model and simulate cell populations. First, we cover simple models where analytic solutions are available, and then move on to more complex scenarios where computational methods are required. Overall, we present a summary of mathematical models used to describe cell population dynamics, which may aid future model development and highlights the importance of population modeling in biology.
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Affiliation(s)
- Daniel A Charlebois
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, NY, USA.,Department of Physics, University of Alberta, Edmonton, AB, Canada
| | - Gábor Balázsi
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, NY, USA.,Department of Biomedical Engineering, Stony Brook University, NY, USA
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Heins AL, Weuster-Botz D. Population heterogeneity in microbial bioprocesses: origin, analysis, mechanisms, and future perspectives. Bioprocess Biosyst Eng 2018. [PMID: 29541890 DOI: 10.1007/s00449-018-1922-3] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Population heterogeneity is omnipresent in all bioprocesses even in homogenous environments. Its origin, however, is only so well understood that potential strategies like bet-hedging, noise in gene expression and division of labour that lead to population heterogeneity can be derived from experimental studies simulating the dynamics in industrial scale bioprocesses. This review aims at summarizing the current state of the different parts of single cell studies in bioprocesses. This includes setups to visualize different phenotypes of single cells, computational approaches connecting single cell physiology with environmental influence and special cultivation setups like scale-down reactors that have been proven to be useful to simulate large-scale conditions. A step in between investigation of populations and single cells is studying subpopulations with distinct properties that differ from the rest of the population with sub-omics methods which are also presented here. Moreover, the current knowledge about population heterogeneity in bioprocesses is summarized for relevant industrial production hosts and mixed cultures, as they provide the unique opportunity to distribute metabolic burden and optimize production processes in a way that is impossible in traditional monocultures. In the end, approaches to explain the underlying mechanism of population heterogeneity and the evidences found to support each hypothesis are presented. For instance, population heterogeneity serving as a bet-hedging strategy that is used as coordinated action against bioprocess-related stresses while at the same time spreading the risk between individual cells as it ensures the survival of least a part of the population in any environment the cells encounter.
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Affiliation(s)
- Anna-Lena Heins
- Institute of Biochemical Engineering, Technical University of Munich, Boltzmannstr. 15, 85748, Garching, Germany.
| | - Dirk Weuster-Botz
- Institute of Biochemical Engineering, Technical University of Munich, Boltzmannstr. 15, 85748, Garching, Germany
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Charlebois DA, Balázsi G. Frequency-dependent selection: a diversifying force in microbial populations. Mol Syst Biol 2016; 12:880. [PMID: 27487818 PMCID: PMC5119495 DOI: 10.15252/msb.20167133] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
The benefits of “bet‐hedging” strategies have been assumed to be the main cause of phenotypic diversity in biological populations. However, in their recent work, Healey et al (2016) provide experimental support for negative frequency‐dependent selection (NFDS) as an alternative driving force of diversity. NFDS favors rare phenotypes over common ones, resulting in an evolutionarily stable mixture of phenotypes that is not necessarily optimal for population growth.
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Affiliation(s)
- Daniel A Charlebois
- The Louis and Beatrice Laufer Center for Physical & Quantitative Biology and Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA
| | - Gábor Balázsi
- The Louis and Beatrice Laufer Center for Physical & Quantitative Biology and Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA
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