Alternative Title
Multicellular Models Bridging Subcellular Biochemistry to Population Dynamics
Abstract
Cell signaling and gene transcription occur at faster time scales compared to cellular death, division, and evolution. Bridging these multiscale events in a model is computationally challenging. We introduce a framework for the systematic development of multiscale cell population models. Using message passing interface (MPI) parallelism, the framework creates a population model from a single-cell biochemical network model. It launches parallel simulations on a single-cell model and treats each stand-alone parallel process as a cell object. MPI mediates cell-to-cell and cell-to-environment communications in a server-client fashion. In the framework, model-specific higher level rules link the intracellular molecular events to cellular functions, such as death, division, or phenotype change. Cell death is implemented by terminating a parallel process, while cell division is carried out by creating a new process (daughter cell) from an existing one (mother cell). We first demonstrate these capabilities by creating two simple example models. In one model, we consider a relatively simple scenario where cells can evolve independently. In the other model, we consider interdependency among the cells, where cellular communication determines their collective behavior and evolution under a temporally evolving growth condition. We then demonstrate the framework's capability by simulating a full-scale model of bacterial quorum sensing, where the dynamics of a population of bacterial cells is dictated by the intercellular communications in a time-evolving growth environment.
Recommended Citation
M. A. Islam et al., "Multicellular Models Bridging Intracellular Signaling and Gene Transcription to Population Dynamics," Processes, vol. 6, no. 11, MDPI AG, Nov 2018.
The definitive version is available at https://doi.org/10.3390/pr6110217
Department(s)
Computer Science
Second Department
Chemical and Biochemical Engineering
Research Center/Lab(s)
Intelligent Systems Center
Second Research Center/Lab
Center for High Performance Computing Research
Keywords and Phrases
Cell population dynamics; Gillespie method; Message passing interface; Multiscale modeling; Quorum sensing
International Standard Serial Number (ISSN)
2227-9717
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2018 The Authors, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
01 Nov 2018
