Computer Models of Bacterial Cells: From Generalized Coarse-Grained to Genome-Specific Modular Models

Jordan Atlas, Cornell University


The question of “What is essential for life?” is one of the most fundamental questions we face. The complete reconstruction of a minimal cell in silico is key to fully understanding and identifying underlying regulatory and organizational concepts central to life. The success of whole organism genome sequencing and high-throughput measurements provides an opportunity for system-level analysis of whole organisms, or what has been termed “systems biology”. Systems biology investigates the behavior of all of the elements in a biological system while it is functioning. As a systems biology approach, the Minimal Cell Model (MCM) depicts the total functionality of a minimal cell and its explicit response to perturbations in its environment.


We propose a dynamic modeling framework to integrate genomic detail and cellular physiology within functionally complete ‘hybrid' bacterial cell models as the start of the construction of the MCM. An initial step in this approach is the development of a whole-cell coarse-grained model which explicitly links DNA replication, metabolism, and cell geometry with the external environment. A hybrid model can then be constructed from chemically-detailed and genome-specific subsystems, called modules, inserted into the original coarse-grained model. We use the sensitivity analysis of the original coarse-grained model to identify which pseudo-molecular processes should be de-lumped into molecularly-detailed mathematical modules to implement a particular biological function. The project proposed here includes three main parts: 1) Development of novel algorithms that facilitate rapid addition of chemically detailed modules to the hybrid cell models, 2) Utilization of a statistical mechanics method for parameter estimation that takes advantage of high-performance computation, and 3) Application of the hybrid-cell-modeling framework to the Minimal Cell Model.

Abstract Author(s): Jordan C. Atlas, Evgeni V. Nikolaev, and Michael L. Shuler