TY - JOUR
T1 - The principles of whole-cell modeling
AU - Karr, Jonathan R.
AU - Takahashi, Koichi
AU - Funahashi, Akira
N1 - Funding Information:
We thank Markus Covert for numerous discussions on whole-cell modeling and Javier Carrera, Derek Macklin, Matthew Oberhardt, and Eytan Ruppin for valuable feedback on this manuscript. This work was supported a James S. McDonnell Foundation Postdoctoral Fellowship Award to JRK, MEXT HPCI Strategic Program Supercomputational Life Science and JSPS KAKENHI ( 25711012 ) Grants to KT, and JSPS KAKENHI Grants ( 23136513 and 24300112 ) to AF.
Publisher Copyright:
© 2015 Elsevier Ltd.
PY - 2015/10/1
Y1 - 2015/10/1
N2 - Whole-cell models which comprehensively predict how phenotypes emerge from genotype promise to enable rational bioengineering and precision medicine. Here, we outline the key principles of whole-cell modeling which have emerged from our work developing bacterial whole-cell models: single-cellularity; functional, genetic, molecular, and temporal completeness; biophysical realism including temporal dynamics and stochastic variation; species-specificity; and model integration and reproducibility. We also outline the whole-cell model construction process, highlighting existing resources. Numerous challenges remain to achieving fully complete models including developing new experimental tools to more completely characterize cells and developing a strong theoretical understanding of hybrid mathematics. Solving these challenges requires collaboration among computational and experimental biologists, biophysicists, biochemists, applied mathematicians, computer scientists, and software engineers.
AB - Whole-cell models which comprehensively predict how phenotypes emerge from genotype promise to enable rational bioengineering and precision medicine. Here, we outline the key principles of whole-cell modeling which have emerged from our work developing bacterial whole-cell models: single-cellularity; functional, genetic, molecular, and temporal completeness; biophysical realism including temporal dynamics and stochastic variation; species-specificity; and model integration and reproducibility. We also outline the whole-cell model construction process, highlighting existing resources. Numerous challenges remain to achieving fully complete models including developing new experimental tools to more completely characterize cells and developing a strong theoretical understanding of hybrid mathematics. Solving these challenges requires collaboration among computational and experimental biologists, biophysicists, biochemists, applied mathematicians, computer scientists, and software engineers.
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U2 - 10.1016/j.mib.2015.06.004
DO - 10.1016/j.mib.2015.06.004
M3 - Review article
C2 - 26115539
AN - SCOPUS:84934971193
SN - 1369-5274
VL - 27
SP - 18
EP - 24
JO - Current Opinion in Microbiology
JF - Current Opinion in Microbiology
ER -