Toward a Whole-Cell Model of Escherichia coli

Derek Macklin, Stanford University

The field of synthetic biology seeks to harness cell physiology to develop biological organisms capable of performing a variety of functions, ranging from biofuel production to targeted disease therapies. While researchers have attempted to develop modular and composable biological devices, most engineered genetic circuits contain only a handful of elements, limiting the scope and complexity of engineerable behaviors. To address this limitation, we propose the construction of a computer-aided design (CAD) tool capable of predicting phenotypes of engineered cells, thereby enabling rapid and inexpensive iteration in the design cycle. At the core of this CAD tool is a computational model that explicitly represents every molecule in the cell and how these molecules interact as the cell cycle progresses.


Recently we reported the first such gene-complete whole-cell computational model that simulates the life cycle of the pathogen Mycoplasma genitalium. We are now aiming to extend our methodology to simulate Escherichia coli (E. coli), a genetically engineerable microorganism with more than 10 times more genes and 50 times more molecules than Mycoplasma genitalium. To model E. coli, we will represent each cellular process with its most appropriate mathematical representation, based both on available parameters and on the types of interactions that occur within the process. Simulation of the cell cycle is then achieved by independently simulating individual cell processes on a short time scale and synchronizing the overall cell state at a longer interval. Initially, we are modeling a core set of E. coli processes including DNA replication, transcription, translation, RNA decay, protein decay, and metabolism, incorporating 75 percent of E. coli's well-annotated gene functions. Additionally, we are improving the tools for whole-cell model development and analysis. We anticipate that the model will drive further exploration of E. coli physiology as well serve as the basis of a synthetic biology CAD tool.

Abstract Author(s): Derek N. Macklin, Nicholas A. Ruggero, Elsa W. Birch, Markus W. Covert