Molecular Codes Through Complex Formation in a Model of the Human Inner Kinetochore

Görlich, Dennis; Escuela, Gabi; Gruenert, Gerd; Dittrich, Peter; Ibrahim, Bashar

doi:10.1007/s12304-013-9193-5

Cited by 12 publications

(4 citation statements)

References 50 publications

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“…Despite these initial successes, more gene therapy targets for prostate cancer are still needed. In this study, we investigated the role of one gene that has been previously associated with human prostate carcinoma cells-myelodysplasia/myeloid leukemia factor 1 interacting protein (MLF1IP; also known as centromere protein U (CENPU), Cenp-50/PBIP1, or KLIP1) [6,7]-in order to better ascertain its role in human prostate carcinogenesis.…”

Section: Introductionmentioning

confidence: 99%

MLF1 interacting protein: a potential gene therapy target for human prostate cancer?

Zhang

Shao

et al. 2015

Med Oncol

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Here, we investigated the role of one gene that has been previously associated with human prostate carcinoma cells-myelodysplasia/myeloid leukemia factor 1 interacting protein (MLF1IP)-in order to better ascertain its role in human prostate carcinogenesis. The prostate cancer cell line PC-3 was lentivirally transfected to silence endogenous MLF1IP gene expression, which was confirmed by real-time quantitative PCR (RT-qPCR). Cellomics ArrayScan VTI imaging and MTT assays were conducted to assess cell proliferation. Cell cycle phase arrest and apoptosis were assayed by flow cytometry. Colony formation was assessed by fluorescence microscopy. MLF1IP gene expression was also analyzed by RT-qPCR in sixteen prostate cancer tissue samples and six healthy control prostate tissue samples from human patients. Cell proliferation was significantly inhibited in MLF1IP-silenced cells relative to control cells. G1 phase, S and G2/M phase cell counts were not significantly changed in MLF1IP-silenced cells relative to control cells. Apoptosis was significantly increased in MLF1IP-silenced cells, while MLF1IP-silenced cells displayed a significantly reduced number of cell colonies, compared to control cells. The 16 human prostate cancer tissue samples revealed no clear upregulation or downregulation in MLF1IP gene expression. MLF1IP significantly promotes prostate cancer cell proliferation and colony formation and significantly inhibits apoptosis without affecting cell cycle phase arrest. Further study is required to conclusively determine whether MLF1IP is upregulated in human prostate cancer tumors and to determine the precise cellular mechanism(s) for MLF1IP in prostate carcinogenesis.

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Section: Introductionmentioning

confidence: 99%

MLF1 interacting protein: a potential gene therapy target for human prostate cancer?

Zhang

Shao

et al. 2015

Med Oncol

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“…So far, mathematical models have helped to elucidate the kinetochore structure and with that the mitotic checkpoint mechanism (Doncic et al, 2005;Görlich et al, 2014;Ibrahim et al, 2007;2008b;Ibrahim and Henze, 2014;Kreyssig et al, 2012;Lohel et al, 2009;Sear and Howard, 2006;Simonetta et al, 2009;Tschernyschkow et al, 2013). However, none of these models consider the APC regulatory pathways.…”

Section: Resultsmentioning

confidence: 99%

Systems Biology Modeling of Five Pathways for Regulation and Potent Inhibition of the Anaphase-Promoting Complex (APC/C): Pivotal Roles for MCC and BubR1

Ibrahim

2015

OMICS: A Journal of Integrative Biology

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Correct DNA segregation is a fundamental process that ensures the precise and reliable inheritance of genomic information for the propagation of cell life. Eukaryotic cells have evolved a conserved surveillance control mechanism for DNA segregation named the Spindle Assembly Checkpoint (SAC).The SAC ensures that the sister chromatids of the duplicated genome are not separated and distributed to the spindle poles before all chromosomes have been properly linked to the microtubules of the mitotic spindle. Biochemically, the SAC delays cell cycle progression by preventing activation of the anaphase-promoting complex (APC/C) or cyclosome whose activation by Cdc20 is required for sister-chromatid separation; this marks the transition into anaphase. In response to activation of the checkpoint, various species control the activity of both APC/C and Cdc20. However, the underlying regulatory pathways remain largely elusive. In this study, five possible model variants of APC/C regulation were constructed, namely BubR1, Mad2, MCC, MCF2, and an all-pathways model variant. These models were validated with experimental data from the literature. A wide range of parameter values has been tested to find the critical values of the APC/C binding rate. The results show that all variants are able to capture the wild-type behavior of the APC/C. However, only one model variant, which included both MCC as well as BubR1 as potent inhibitors of the APC/C, was able to reproduce both wild-type and mutant type behavior of APC/C regulation. In conclusion, the presented work informs the regulation of fundamental processes such as SAC and APC/C in cell biology and has successfully distinguished between five competing dynamical models using a systems biology approach. The results attest that systems-level approaches are vital for molecular and cell biology.

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“…NetLogo allows users to write their own extensions. However, it cannot incorporate formal rule-based languages such as BNGL (BioNetGen language) [10] or Kappa [16], nor molecular structure and geometry (for details, see [47,49,50,54,55,71,126]). In addition, it is challenging to handle very large network models or very low concentrations of agents with stochastic rules [68,74,75].…”

Section: Spatial Propertiesmentioning

confidence: 99%

Trends in mathematical modeling of host–pathogen interactions

Ewald

Sieber

Garde

et al. 2019

Cell. Mol. Life Sci.

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Pathogenic microorganisms entail enormous problems for humans, livestock, and crop plants. A better understanding of the different infection strategies of the pathogens enables us to derive optimal treatments to mitigate infectious diseases or develop vaccinations preventing the occurrence of infections altogether. In this review, we highlight the current trends in mathematical modeling approaches and related methods used for understanding host-pathogen interactions. Since these interactions can be described on vastly different temporal and spatial scales as well as abstraction levels, a variety of computational and mathematical approaches are presented. Particular emphasis is placed on dynamic optimization, game theory, and spatial modeling, as they are attracting more and more interest in systems biology. Furthermore, these approaches are often combined to illuminate the complexities of the interactions between pathogens and their host. We also discuss the phenomena of molecular mimicry and crypsis as well as the interplay between defense and counter defense. As a conclusion, we provide an overview of method characteristics to assist non-experts in their decision for modeling approaches and interdisciplinary understanding.

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Molecular Codes Through Complex Formation in a Model of the Human Inner Kinetochore

Cited by 12 publications

References 50 publications

MLF1 interacting protein: a potential gene therapy target for human prostate cancer?

MLF1 interacting protein: a potential gene therapy target for human prostate cancer?

Systems Biology Modeling of Five Pathways for Regulation and Potent Inhibition of the Anaphase-Promoting Complex (APC/C): Pivotal Roles for MCC and BubR1

Trends in mathematical modeling of host–pathogen interactions

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