Journal: Journal of Theoretical Biology

Abbreviation

J Theor Biol

Publisher

Elsevier

Journal Volumes

ISSN

0022-5193
1095-8541

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Publications 1 - 10 of 48
  • Effects of population growth on the success of invading mutants
    Item type: Journal Article
    Ashcroft, Peter; Smith, Cassandra E.R.; Garrod, Matthew; et al. (2017)
    Journal of Theoretical Biology
  • Aging a little: On the optimality of limited senescence in Escherichia coli
    Item type: Journal Article
    Blitvić, Natasha; Fernandez, Vicente I. (2020)
    Journal of Theoretical Biology
  • Single muscle fiber contraction is dictated by inter-sarcomere dynamics
    Item type: Journal Article
    Denoth, Jachen; Stüssi, Edgar; Csucs, Gabor; et al. (2002)
    Journal of Theoretical Biology
  • Evolutionary isolation and phylogenetic diversity loss under random extinction events
    Item type: Journal Article
    Steel, Mike; Pourfaraj, Vahab; Chaudhary, Abhishek; et al. (2018)
    Journal of Theoretical Biology
  • On the statistical analysis of single cell lineage trees
    Item type: Journal Article
    Stadler, Tanja; Skylaki, Stavroula; Kokkaliaris, Konstantinos D.; et al. (2018)
    Journal of Theoretical Biology
    Stem cells play a central role in the regeneration and repair of multicellular organisms. However, it remains far from trivial to reliably identify them. Despite decades of work, current techniques to isolate hematopoietic stem cells (HSCs) based on cell-surface markers only result in 50% purity, i.e. half of the sorted cells are not stem cells when functionally tested. Modern microscopy techniques allow us to follow single cells and their progeny for up to weeks in vitro, while recording the cell fates and lifetime of each individual cell. This cell tracking generates so-called lineage trees. Here, we propose statistical techniques to determine if the initial cell in a lineage tree was a HSC. We apply these techniques to murine hematopoietic lineage trees, revealing that 18% of the trees in our HSC dataset display a unique signature, and this signature is compatible with these trees having started from a true stem cell. Assuming 50% purity of HSC empirical datasets, this corresponds to a 0.35 power of the test, and the type-1-error is estimated to be 0.047. In summary, this study shows that statistical analysis of lineage trees could improve the classification of cells, which is currently done based on bio-markers only. Our statistical techniques are not limited to mammalian stem cell biology. Any type of single cell lineage trees, be it from bacteria, single cell eukaryotes, or single cells in a multicellular organism can be investigated. We expect this to contribute to a better understanding of the molecules influencing cellular dynamics at the single cell level.
  • Spatial models of virus-immune dynamics
    Item type: Journal Article
    Funk, Georg A.; Jansen, Vincent A.A.; Bonhoeffer, Sebastian; et al. (2005)
    Journal of Theoretical Biology
  • Simulating the evolution of signal transduction pathways
    Item type: Journal Article
    Soyer, Orkun S.; Pfeiffer, Thomas; Bonhoeffer, Sebastian (2006)
    Journal of Theoretical Biology
  • Restricted Occupancy Models for Neutralization of HIV Virions and Populations
    Item type: Journal Article
    Magnus, Carsten; Regoes, Roland R. (2011)
    Journal of Theoretical Biology
  • The probability distribution of the ancestral population size conditioned on the reconstructed phylogenetic tree with occurrence data
    Item type: Journal Article
    Manceau, Marc; Gupta, Ankit; Vaughan, Timothy G.; et al. (2021)
    Journal of Theoretical Biology
    We consider a homogeneous birth-death process with three different sampling schemes. First, individuals can be sampled through time and included in a reconstructed phylogenetic tree. Second, they can be sampled through time and only recorded as a point ‘occurrence’ along a timeline. Third, extant individuals can be sampled and included in the reconstructed phylogenetic tree with a fixed probability. We further consider that sampled individuals can be removed or not from the process, upon sampling, with fixed probability. We derive the probability distribution of the population size at any time in the past conditional on the joint observation of a reconstructed phylogenetic tree and a record of occurrences not included in the tree. We also provide an algorithm to simulate ancestral population size trajectories given the observation of a reconstructed phylogenetic tree and occurrences. This distribution can be readily used to draw inferences about the ancestral population size in the field of epidemiology and macroevolution. In epidemiology, these results will allow data from epidemiological case count studies to be used in conjunction with molecular sequencing data (yielding reconstructed phylogenetic trees) to coherently estimate prevalence through time. In macroevolution, it will foster the joint examination of the fossil record and extant taxa to reconstruct past biodiversity.
  • Cancer-induced immunosuppression can enable effectiveness of immunotherapy through bistability generation: A mathematical and computational examination
    Item type: Journal Article
    Garcia, Victor; Bonhoeffer, Sebastian; Fu, Feng (2020)
    Journal of Theoretical Biology
    Cancer immunotherapies rely on how interactions between cancer and immune system cells are constituted. The more essential to the emergence of the dynamical behavior of cancer growth these interactions are, the more effectively they may be used as mechanisms for interventions. Mathematical modeling can help unearth such connections, and help explain how they shape the dynamics of cancer growth. Here, we explored whether there exist simple, consistent properties of cancer-immune system interaction (CISI) models that might be harnessed to devise effective immunotherapy approaches. We did this for a family of three related models of increasing complexity. To this end, we developed a base model of CISI, which captures some essential features of the more complex models built on it. We find that the base model and its derivates can plausibly reproduce biological behavior that is consistent with the notion of an immunological barrier. This behavior is also in accord with situations in which the suppressive effects exerted by cancer cells on immune cells dominate their proliferative effects. Under these circumstances, the model family may display a pattern of bistability, where two distinct, stable states (a cancer-free, and a full-grown cancer state) are possible. Increasing the effectiveness of immune-caused cancer cell killing may remove the basis for bistability, and abruptly tip the dynamics of the system into a cancer-free state. Additionally, in combination with the administration of immune effector cells, modifications in cancer cell killing may be harnessed for immunotherapy without the need for resolving the bistability. We use these ideas to test immunotherapeutic interventions in silico in a stochastic version of the base model. This bistability-reliant approach to cancer interventions might offer advantages over those that comprise gradual declines in cancer cell numbers.
Publications 1 - 10 of 48