The future of whole-cell modeling

Abstract

Integrated whole-cell modeling is poised to make a dramatic impact on molecular and systems biology, bioengineering, and medicine — once certain obstacles are overcome. From our group’s experience building a whole-cell model of Mycoplasma genitalium, we identified several significant challenges to building models of more complex cells. Here we review and discuss these challenges in seven areas: (1) experimental interrogation, (2) data curation, (3) model building and integration, (4) accelerated computation, (5) analysis and visualization, (6) model validation, and (7) collaboration and community development. Surmounting these challenges will require the cooperation of an interdisciplinary group of researchers to create increasingly sophisticated whole-cell models and make data, models, and simulations more accessible to the wider community.

Complete atomistic model of a bacterial cytoplasm for integrating physics, biochemistry, and systems biology

“…However, a significant fraction of MG genes is annotated only as having ‘hypothetical function’ or is classified into a general functional category (such as ‘hydrolase’ or ‘integral membrane protein’) that provides only limited information about their biochemical roles. Past efforts of developing whole-cell models of MG have focused on reconstructing metabolic pathways with the goal of describing metabolic fluxes and overall growth rates under different conditions[2]. In such a context, non-metabolic proteins are not critical other than contributing to the overall biomass.…”

“…High-throughput experiments have transformed biology and we have reached a time where it is possible to develop comprehensive models of entire biological systems[1, 2]. One example is a mathematical model of the minimal bacterium Mycoplasma genitalium (MG) that integrates genomic, proteomic, and metabolomic data into a fully connected metabolic reaction network[3].…”

Formalizing knowledge in multi-scale agent-based simulations

“…Building such models involves creating and assembling many complex components in order to understand the larger phenomenon [18]. A biological modeling language must provide language constructs – the basic building blocks that comprise models – that correspond directly to the building blocks found in biology.…”

Integrating single-molecule experiments and discrete stochastic models to understand heterogeneous gene transcription dynamics

“…The goal of some models may be to create a comprehensive representation of a biological process by compiling all known understanding of that particular system [5, 6]. While such models are qualitative in nature, they can provide a complete picture of how a particular system is currently understood to behave and can be used to test broad qualitative hypotheses.…”