(51a) Do We Know Enough to Reconstruct Life In Silico?

Authors: 
Shuler, M. L. - Presenter, Cornell University
Atlas, J. C. - Presenter, Cornell University
Nikolaev, E. V. - Presenter, Thomas Jefferson University


The question of ?What is essential for life?? is one of the most fundamental questions humankind faces. If we fully understand a system, we should be able to reconstruct it. We propose to construct in silico a model of a minimal chemoheterotrophic bacterial cell. Such a model 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. In this paper we describe our approach to creating such a model and results demonstrating feasibility. We also discuss constraints on identifying all essential functions.

I. Purpose

We propose a dynamic modeling framework to integrate genomic detail and cellular physiology within functionally complete ?hybrid' bacterial cell models. 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 have demonstrated this possibility with modules for nucleotides [1] and lipids [2]. 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.

A minimal cell is a hypothetical entity defined by the essential functions required for life [1]. Although others have the goal of experimentally constructing a minimal cell [3], we seek to identify a minimum number of genes necessary and sufficient for the cell to divide and grow continuously in a rich environment with preformed nutrients and constant temperature and pH. The model, which contains kinetic, thermodynamic, and stoichiometric constraints, is used as a tool to identify the organizing principles which relate the dynamic non-linear functioning of the cell to the genome sequence. We recognize that a unique set of minimal genes may not exist, but we believe a unique set of essential functions exist and that we can generate a candidate minimal gene set to accomplish these functions. Functions may be chemical or physical such as constraints on DNA packing.

II. Approach

The project proposed here includes three main parts: 1) Development of novel algorithms for stability and sensitivity analysis of hybrid cell models, 2) Implementation of a system for parameter estimation, and 3) Construction of a genomically and chemically detailed Minimal Cell Model.

III. Results

Using an existing coarse-grained model of E. coli [4] as a basis, we have developed a framework for analyzing the stability of hybrid cell models [5] as well as a technique for sensitivity analysis in this class of models using an extension of classical Metabolic Control Analysis [6]. Currently, we are extending a statistical mechanics method for parameter estimation [7] to our models.

References

[1] Castellanos, M., D.B. Wilson, M.L. Shuler. (2004) A modular minimal cell model: purine and pyrimidine transport and metabolism. PNAS 101:6681-6686

[2] Castellanos, M., K. Kushiro, S.K. Lai, and M.L. Shuler. 2007. A genomically/chemically complete module for synthesis of lipid membrane in a minimal cell. Biotechnol. Bioeng. 97:397-409

[3] Forster, A.C., G.M. Church. (2006) Towards Synthesis of a Minimal Cell. Molecular Systems Biology 2, 45.

[4] Shuler, M.L. (2005) Computer Models of Bacterial Cells to Integrate Genomic Detail with Cell Physiology. Proceedings of the KBM International Symposium on Microorganisms and Human Well-Being, Seoul Korea.

[5] Nikolaev, E.V., J.C. Atlas, M.L. Shuler. (2006) Computer Models of Bacterial Cells: From Generalized Coarse-Grained to Genome-Specific Modular Models. Journal of Physical Conference Series 46, 322-326.

[6] Nikolaev, E.V., J.C. Atlas, M.L. Shuler. (2007) Sensitivity and control analysis of periodically forced reaction networks using the Green's function method. J. Theoretical Biol. 247:442-461

[7] Brown, K.D., J.P. Sethna. (2003) Statistical Mechanical Approaches to Models with Many Poorly Known Parameters. Phys. Rev. E.68

Acknowledgements: This work was funded by DOE grant DE-FG02-04ER63806 and by NYSTAR, the New York State Office of Science, Technology, and Academic Research. Jordan Atlas gratefully acknowledges support from the DOE Computational Science Graduate Fellowship Program of the Office of Science and National Nuclear Security Administration in the DOE under contract DE-FG02-97ER25308.

1School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY 14850

2Department of Biomedical Engineering, Cornell University, Ithaca, NY 14850