A Whole-Cell Computational Model Predicts Phenotype from Genotype

By Jonathan R. Karr, Jayodita C. Sanghvi, Derek N. Macklin, Miriam V. Gutschow, J. M. Jacobs, Benjamin Bolival, N. Assad-Garcia, John I. Glass, Markus W. Covert. 2012

1,338 citations recorded by the source corpus.

SUMMARY Understanding how complex phenotypes arise from individual molecules and their interactions is a primary challenge in biology that computational approaches are poised to tackle. We report a whole-cell computational model of the life cycle of the human pathogen Mycoplasma genitalium that includes all of its molecular components and their interactions. An integrative approach to modeling that combines diverse mathematics enabled the simultaneous inclusion of fundamentally different cellular processes and experimental measurements. Our whole-cell model accounts for all annotated gene functions and was validated against a broad range of data. The model provides insights into many previously unobserved cellular behaviors, including in vivo rates of protein-DNA association and an inverse relationship between the durations of DNA replication initiation and replication rates. In addition, experimental analysis directed by model predictions identified previously undetected kinetic parameters and biological functions. We conclude that comprehensive whole-cell models can be used to facilitate biological discovery.

Subfields: Molecular Biology

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Citations in the corpus

Simulation-assisted machine learning (2018, positive): “, Figure 7)? Progress in detailed biological simulation, such as the full simulation of the cell cycle of the bacterium Mycoplasma genitalium (Karr et al., 2012), the OpenWorm project (Szigeti et al.”
Systematic Evaluation of Methods for Integration of Transcriptomic Data into Constraint-Based Models of Metabolism (2014, background): “These types of data can also be used to parameterize next generation of whole-cell models that explicitly represent proteins and transcripts in addition to metabolic fluxes [58,59] Finally, we would like to acknowledge the authors who published their source code with the respective articles.”
A mechanistic link between cellular trade-offs, gene expression and growth (2015, neutral): “A whole-cell model has been proposed as one way to make such predictions [11], but its level of detail may sometimes obscure the core biochemistry that underlies the observed phenotypes and potentially complicates further analyses.”
Improving the phenotype predictions of a yeast genome‐scale metabolic model by incorporating enzymatic constraints (2017, methodology): “Alternative approaches such as resource balance analysis (Goelzer et al, 2015), self-replicating models (Molenaar et al, 2009; Berkhout et al, 2013), and whole-cell models (Karr et al, 2012) have also been developed to account for protein limitations.”
Quantitative and logic modelling of gene and molecular networks (2015, methodology): “This model used a mix of ODEs, stochastic processes and flux balance analysis to simulate 28 modules representing gene networks, signalling and metabolism (4) .”
Mapping Transcriptomic Vector Fields of Single Cells (2022, positive): “Although efforts have been made to perform whole-cell simulations of bacteria (Karr et al., 2012; Macklin et al., 2020), it remains a grand challenge to reconstruct the vector field (a vector-valued function that assigns a vector, such as the transcriptomic velocity vector, to any point in the state”
Model-assisted phenotyping and ideotype design (2015, background): “…gene action based on interactions of regulators, gene products, and other metabolites (class 6) have only been developed for unicellular organisms (Karr et al., 2012; Sanghvi et al., 2013). overall, while models of classes 5 and 6 can be relevant for ‘simple’ traits associated to a restricted…”
Using deep learning to model the hierarchical structure and function of a cell (2018, result): “In contrast to other mechanistic models that have attempted large-scale genotype–phenotype prediction 13,36 , the framework of hierarchical neural networks is very general and expressive, such that a large class of biological structures and functions can be represented.”
Genome-scale models of metabolism and gene expression extend and refine growth phenotype prediction (2013, neutral): “On the heels of these developments, a whole-cell model (WCM) of the human pathogen Mycoplasma genitalium appeared (Karr et al, 2012).”
Digital twin in healthcare: Recent updates and challenges (2023, background): “In a whole-cell project, specific types of cell and organism were modelled to better understand the molecular basis of diseases.(27) Therefore, faster solving surrogates of these models may be developed in the form of machine learning models to solve problems in real-time.”
In vitro neurons learn and exhibit sentience when embodied in a simulated game-world (2021, positive): “This is something that is long sought after and extends beyond purely in silico models or predictions of molecular pathways alone (Karr et al., 2012; Whittington et al., 2020; Yu et al., 2018).”
Using Genome-Scale Models to Predict Biological Capabilities (2015, positive): “In addition to DNA replication and cell division (Karr et al., 2012), much of the remaining proteome mass involves cellular stress responses (e.g., pH, osmolarity, osmotic); like with temperature, GEM-PRO will aid in modeling these cellular stresses.”
Merging molecular mechanism and evolution: theory and computation at the interface of biophysics and evolutionary population genetics (2014, background): “Additionally, because the approach is bottom-up, it can be coupled to the current efforts that build comprehensive cellular model of the genotype–phenotype relationship [72].”