A4
Evolution and Ecology

Mathematical modeling of interactions in evolving metaorganisms

Mathematical models can help us to enhance our understanding of a specific observation by exploring its characteristics under a wide range of conditions, including those that cannot be addressed by empirical tests. Moreover, mathematical models allow us to generalize specific observations made in a particular taxon and thereby transfer them to a large range of organisms.

In the project, we use mathematical models (A4.1, PI Traulsen) and fine-tune them with the help of empirical data from an experimentally accessible metaorganism system (A4.3, PI Schulenburg) with the aim to improve our general understanding of the evolution of host-microbiota interactions.

The origin and evolution of these interactions are still largely unclear. A particular challenge is that a multitude of microorganisms and a host organism are likely to have conflicting evolutionary “interests”, yet still form a novel unit that as a whole is subject to selection. How does selection at the higher hierarchical level (i.e., imposed by the host) interact with selection at the lower levels (i.e., determined by the microbes)? How important are different types of interactions or trophic levels within the microbial community for the characteristics of the metaorganism? Are the interactions mainly driven by ecological relationships and neutral processes? What exactly determines the initial formation of the association and thus evolution of a host-associated life cycle of the microbes?

During the first funding phase, we explored the influence of hierarchical levels (e.g., determined by phages) on metaorganism function and evolution. We also established a model on neutral dynamics within the metaorganism and applied it to wide range of study systems from the CRC.

In the second funding phase, we will use the developed mathematical models and experimental approaches to explore in an iterative form the exact fitness determinants of microbes within the metaorganism, taking into account their transmission dynamics, different interaction types, trophic levels, and also neutral processes. Microbe fitness components will be related to fitness of the host. We anticipate that the utilization of mathematical models and their combination with tailored experiments will provide an integrated understanding of the evolutionary and ecological processes that shape and determine the initial origin of the metaorganism, its subsequent evolution, and the resulting functions.

In sum, our project specifically focuses on microbe fitness which is still understudied, yet represents a highly potent driver of host-microbiota interactions. We postulate microbe fitness to be of particular importance during the initial formation of these associations. Moreover, our project specifically assesses the influence of neutral in comparison to selective processes during formation and maintenance of host-microbiota interactions. Our project takes advantage of the particular power of mathematical modelling to generalize across empirical findings and explore a wide range of conditions. Our project combines mathematical modelling with a tailored experimental approach in an iterative form, in order to validate theoretical predictions and yield insight for further development of the models.

Our project thus directly links the complementary expertise of two groups, one with particular competence in evolutionary mathematical model development and analysis (Traulsen group) and the other with comprehensive experience in experimental evolution and evolutionary analysis of a highly tractable study system (Schulenburg group).

A4
Researchers

Researchers

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A4.1: Mathematical model analysis of the metaorganism

A4.3: The evolution of fitness across hierarchical levels within the C. elegans metaorganism

A4: Alumni

A4
Related Publications

Related Publications

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2020
A4

Stochastic colonization of hosts with a finite lifespan can drive individual host microbes out of equilibrium

Zapien-Campos R, Sieber M, Traulsen A (2020) PLOS Computational Biology, in press

2020
A1
A4

Microbiome-mediated plasticity directs host evolution along several distinct time scale

2019
A3
A4
B2
C1
C2
INF

Advancing our functional understanding of host–microbiota interactions: a need for new types of studies

He J, Lange J, Marinos G, Bathia J, Harris D, Soluch R, Vaibhvi V, Deines P, Hassani MA, Wagner K-S, Zapien‐Campos R, Jaspers C, Sommer F (2019) BioEssays, 1900211 (1-5). doi: 10.1002/bies.201900211

 

2019
A4
B1
C2

A phage protein aids bacterial symbionts in eukaryote immune evasion

Jahn MT, Arkhipova K, Markert SM, Stigloher C, Lachnit T, Pita L, Kupczok A, Ribes M, Stengel ST, Rosenstiel P, Dutilh BE, Hentschel U (2019) Cell Host & Microbe, doi: 10.1016/j.chom.2019.08.019

2019
A1
A4
INF

The functional repertoire contained within the native microbiota of the model nematode Caenorhabditis elegans

Zimmermann J*, Obeng N*, Yang W, Pees B, Petersen C, Waschina S, Kissoyan KAB, Aidley J, Hoeppner MP, Bunk B, Spröer C, Leippe M, Dierking K, Kaleta C*, Schulenburg H* (2019) The ISME Journal. 1-13. * Shared first or senior authorship  doi: 10.1038/s41396-019-0504-y

2019
A1
A4
INF
Z3

The inducible response of the nematode Caenorhabditis elegans to members of its natural microbiome across development and adult life

Yang W#, Petersen C#, Pees B#, Zimmermann J, Waschina S, Dirksen P, Rosenstiel P, Tholey A, Leippe M, Dierking K, Kaleta C*, Schulenburg H*.  Front Microbiol. 10:1793. # Equal contribution as first authors, * Equal contribution as senior authors doi: 10.3389/fmicb.2019.01793.

2019
A4
B1
C1
C2

Temperature and insulin signaling regulate body size in Hydra by the Wnt and TGF-beta pathways

Mortzfeld BM*, Taubenheim J*, Klimovich AV, Fraune S, Rosenstiel P, Bosch TCG (2019) Nature Communications 10, 3257. doi: 10.1038/s41467-019-11136-6

2019
A2
A4
B1
B2

Neutrality in the metaorganism

Sieber M, Pita L, Weiland-Bräuer N, Dirksen P, Wang J, Mortzfeld B, Franzenburg S, Schmitz RA, Baines JF, Fraune S, Hentschel U, Schulenburg H, Bosch TCG, Traulsen A (2019) PLoS Biol., DOI: 10.1371/journal.pbio.3000298

2019
A4
C1

The microbiome mediates environmental effects on ageing

Finlay B, Pettersson S, Melby M, Bosch TCG (2019) BioEssays, 1800257, 1-7; doi: 10.1002/bies.201800257

2019
A4
C1

Evolutionary “experiments” in symbiosis: the study of model animals provides insights into the mechanisms underlying diversity of host-microbe interactions

Bosch TCG, Guillemin K, McFall-Ngai M (2019) BioEssays, 1800256 (1-8). doi: 10.1002/bies.201800256

2019
A4

Exposure of the host-associated microbiome to nutrient-rich conditions may lead to dysbiosis and disease development – an evolutionary perspective.

Lachnit T, Bosch TCG, Deines P (2019) mBio (Opinion article); 10:3, e00355-19, 1-8, doi: 10.1128/mBio.00355-19

2019
A4
B1

Bdellovibrio and like organisms are predictors of microbiome diversity across diverse host groups.

Johnke J, Fraune S, Bosch TCG, Hentschel U, Schulenburg H (2019) Microbial Ecology DOI: 10.1007/s00248-019-01395-7

2019
A4
B1
B2

Resolving structure and function of metaorganisms through a holistic framework 2 combining reductionist and integrative approaches

Jaspers C, Fraune S, Consortium of Australian Academy of Science Boden Research Conference Participants, Arnold AE, Miller DJ, Bosch TCG, Voolstra CR (2019) Zoology. doi: 10.1016/j.zool.2019.02.007

2018
A4
C1
Z1

Hydra as Model to Determine the Role of FOXO in Longevity

Bosch TCG (2018); Methods Mol Biol. 1890:231-238. doi: 10.1007/978-1-4939-8900-3_19

2018
A4
B1
C1
Z1

Metabolic co-dependence drives the evolutionarily ancient Hydra-Chlorella symbiosis.

Hamada M, Schröder K, Bathia J, Kürn U, Fraune S, Khalturina M, Khalturin K, Shinzato C, Satoh N, Bosch TC (2018); Elife 7. pii: e35122. doi: 10.7554/eLife.35122

2018
A4
C1
C2
Z1
Z3

Grow With the Challenge – Microbial Effects on Epithelial Proliferation, Carcinogenesis, and Cancer Therapy

Von Frieling J, Fink C, Hamm J, Klischies K, Forster M, Thomas C. G. Bosch TCG, Roeder T, P Rosenstiel P, Sommer F (2018); Front. Microbiol. doi: 10.3389/fmicb.2018.02020

2018
A4
C1
Z1

Non-senescent Hydra tolerates severe disturbances in the nuclear lamina.

Klimovich A, Rehm A, Wittlieb J, Herbst EM, Benavente R, Bosch TCG (2018); Aging (Albany NY) 10(5):951-972. doi: 10.18632/aging.101440

2018
A4
C1
Z1

Rethinking the Role of the Nervous System: Lessons From the Hydra Holobiont.

Klimovich AV, Bosch TCG (2018); Bioessays 40(9):e1800060. doi: 10.1002/bies.201800060

2018
A4
B1
C1
Z1

Stem cell transcription factor FoxO controls microbiome resilience in Hydra.

Mortzfeld B M, Taubenheim J,Fraune S, Klimovich A V, Bosch T C G (2018); Front Microbiol., doi: 10.3389/fmicb.2018.00629

2018
A1
A2
A3
A4
B1
B2
C1
Z1
Z2

Metaorganisms in extreme environments: do microbes play a role in organismal adaptation?

Bang C, Dagan T, Deines P, Dubilier N, Duschl W J, Fraune S, Hentschel U, Hirt H, Hülter N, Lachnit T, Picazo D, Galan P L, Pogoreutz C, Rädecker N, Saad M M, Schmitz R A, Schulenburg H, Voolstra C R, Weiland-Bräuer N, Ziegler M, Bosch T C G (2018); Zoology, doi: 10.1016/j.zool.2018.02.004

2018
A4
C1
Z1

How the microbiome challenges our concept of self.

Rees T, Bosch T G C, Douglas A E (2018); PloS Biol., 16(2):e2005358. doi:10.1371/journal.pbio.2005358

2017
A4
B1
C1
Z1

Temperate phages as self-replicating weapons in bacterial competition.

Li XY, Lachnit T, Fraune S, Bosch T C G, Traulsen A, Sieber M (2017); J R Soc Interface, 14(137). doi: 10.1098/rsif.2017.0563

2017
A4
C1
Z1

Spontaneous body contractions are modulated by the microbiome of Hydra.

Murillo-Rincon A P, Klimovich A, Pemöller E, Taubenheim J, Mortzfeld B, Augustin R, Bosch T C G (2017); Scientific Reports, 7(15937). doi:10.1038/s41598-017-16191-x

2017
A4
B1
C1
Z1

A secreted antibacterial neuropeptide shapes the microbiome of Hydra.

Augustin R, Schröder K, Murillo Rincón A P, Fraune S, Anton-Erxleben F, Herbst E M, Wittlieb J, Schwentner M, Grötzinger J, Wassenaar T M, Bosch T C G (2017); Nat Commun., 8(1):698. doi: 10.1038/s41467-017-00625-1

2017
A1
A4

The Natural Biotic Environment of Caenorhabditis elegans.

Schulenburg H, Félix M A (2017); Genetics., 206(1):55-86. doi: 10.1534/genetics.116.195511

2017
A1
A4

Caenorhabditis elegans as a model for microbiome research.

Zhang F, Berg M, Dierking K, Félix M A, Shapira M, Samuel B, Schulenburg H (2017); Front. Microbiol., 8:485. doi: 10.3389/fmicb.2017.00485

2016
A4
B1
Z2

Emerging Sponge Models of Animal-Microbe Symbioses.

Pita L, Fraune S, Hentschel U (2016); Front Microbiol., 7:2102. doi: 10.3389/fmicb.2016.02102

2016
A4
C1
Z1

The Origin of Mucosal Immunity: Lessons from the Holobiont Hydra.

Schröder K, Bosch T C (2016); MBio., 7(6):e01184-16. doi: 10.1128/mBio.01184-16

2016
A4
C1
Z1

Transitioning from Microbiome Composition to Microbial Community Interactions: The Potential of the Metaorganism Hydra as an Experimental Model.

Deines P, Bosch T C G (2016); Front. Microbiol., 7:1610. doi: 10.3389/fmicb.2016.01610

2016
A4
B1

Using Nematostella vectensis to Study the Interactions between Genome, Epigenome, and Bacteria in a Changing Environment.

Fraune S, Forêt S, Reitzel A M (2016); Front. Mar. Sci., 3:148. doi: 10.3389/fmars.2016.00148

2016
A1
A4
C2
Z3

The native microbiome of the nematode Caenorhabditis elegans: Gateway to a new host-microbiome model.

Dirksen P, Marsh SA, Braker I, Heitland N, Wagner S, Nakad R, Mader S, Petersen C, Kowallik V, Rosenstiel P C, Felix M A, Schulenburg H (2016); BMC Biology, 14:38. doi:10.1186/s12915-016-0258-1

2016
A1
A4

Antimicrobial effectors in the nematode C. elegans – an outgroup to the Arthropoda.

Dierking K, Yang W, Schulenburg H (2016); Phil Trans R Soc Lond B., 371. doi:

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