A more complete understanding of the relationship of cell physiology to genomic structure is desirable. Because of the intrinsic complexity of biological organisms, only the simplest cells will allow complete definition of all components and their interactions. The theoretical and experimental construction of a minimal cell has been suggested as a tool to develop such an understanding. Our ultimate goal is to convert a ''coarse-grain'' lumped parameter computer model of Escherichia coli into a genetically and chemically detailed model of a ''minimal cell.'' The base E. coli model has been converted into a generalized model of a heterotrophic bacterium. This coarse-grain minimal cell model is functionally complete, with growth rate, composition, division, and changes in cell morphology as natural outputs from dynamic simulations where only the initial composition of the cell and of the medium are specified. A coarse-grain model uses pseudochemical species (or modules) that are aggregates of distinct chemical species that share similar chemistry and metabolic dynamics. This model provides a framework in which these modules can be ''delumped'' into chemical and genetic descriptions while maintaining connectivity to all other functional elements. Here we demonstrate that a detailed description of nucleotide precursors transport and metabolism is successfully integrated into the whole-cell model. This nucleotide submodel requires fewer (12) genes than other theoretical predictions in minimal cells. The demonstration of modularity suggests the possibility of developing modules in parallel and recombining them into a fully functional chemically and genetically detailed model of a prokaryote cell. T he basic design rules relating the regulation of cellular function to genomic structure is of broad interest. Bioinformatics emerged as an approach to convert static linear sequence genomic data into an understanding of the dynamic nonlinear function of living organisms. Initial efforts have focused on identifying the proteins encoded in the genome and, subsequently, identifying protein function and regulatory elements in the genome. These efforts are able to address specific questions but cannot translate genomic data broadly into an understanding of cell function. We propose a reverse approach. We ask how we would design a cell to achieve expected functions and, from that design, how we would write the genomic instructions. This approach follows the typical engineering design approach where desired performance dictates functional design, which is then translated into blueprints. To accomplish this goal, we are constructing a chemically and genetically minimal cell computer model. By modeling the essential regulatory structure and functions to maintain a living cell, we expect to better understand the relationship of genomic instructions to cell function and regulation.A "minimal cell" is a hypothetical cell possessing the minimum functions required for sustained growth and reproduction in a maximally supportive culture env...

A genomically and chemically detailed mathematical model of a "minimal cell" would be useful to understand better the "design logic" of cellular regulation. A "minimal cell" will be a prokaryote with the minimum number of genes necessary for growth and replication in an ideal environment (i.e., preformed precursors, constant temperature, etc.). The Cornell single-cell model of Escherichia coli serves as the basic framework upon which a minimal cell model can be constructed. A critical issue for any cell model is to describe a mechanism for control of initiation of chromosome replication. There is strong evidence that the essence of chromosome replication control is highly conserved in eubacteria and even extends to the archae. A generalized mechanism is possible based on binding of the protein DnaA-ATP to the origin of replication (oriC) as a primary control. Other features, such as regulatory inactivation of DnaA (RIDA) by conversion of DnaA-ATP to DnaA-ADP and titration of DnaA by binding to other DnaA boxes on the chromosome, have emerged as critical elements in obtaining a functional system to control initiation of chromosome synthesis. We describe a biologically realistic model of chromosome replication initiation control embedded in a complete whole-cell model that explicitly links the external environment to the mechanism of replication control. The base model is deterministic and then modified to include stochastic variation in the components for replication control. The stochastic model allows evaluation of the model's robustness, employing a low standard deviation of interinitiation time as a measure of robustness. Four factors were examined: DnaA synthesis rate; DnaA-ATP binding sites at oriC; the binding rate of DnaA-ATP to the nonfunctional DnaA boxes; and the effect of changing the number of nonfunctional binding sites. The observed DnaA synthesis rate (2000 molecules/cell) and the number of DnaA binding sites per origin (30) are close to the values predicted by the model to provide good control (low variance of interinitiation time), with a reasonable expenditure of cell resources. At relatively high binding rates for DnaA-ATP to the DnaA boxes (10(16) M(-1) s(-1)), increasing the number of DnaA binding sites to about 300, improved control (but little further improvement was seen by extension to 1000 boxes); however, at a low binding rate (10(10) M(-1) s(-1)), an increase in DnaA boxes had an adverse effect on control. The combination of all four factors is probably necessary to obtain a robust control system. Although this mechanism of replication initiation control is highly conserved, it is not clear if simpler control in a minimal cell might exist based on experimental observations with Mycoplasma. This issue is discussed in this investigation.

A minimal cell is a hypothetical cell defined by the essential functions required for life. We have developed a module for the synthesis of membrane precursors for a mathematical minimal cell model. This module describes, with chemical and genomic detail the production of the constituents required to build a cell membrane and identifies the corresponding essential genes. Membranes allow selective nutrient passage, harmful substance exclusion, and energy generation. Bacterial membrane components range from lipids to fatty acids with embedded proteins and are structurally similar to eukaryotic cell membranes. Membranes are dynamic structures and experimental analyses show great variations in bacterial membrane composition. The flexibility of the model is such that different membrane compositions could be obtained in response to simulated changes in culture conditions. The model's predictions are in close agreement with the observed biological trends. The model's predictions correspond well with the experimental values of total lipid content in cells grown in chemostat culture, but less well with data from batch growth. Cell shape and size results agree especially well for data for growth rate relative to maximum growth rate larger than 0.5; and DNA, RNA, and protein predictions are consistent with experimental observations. A better understanding of the simplest bacterial membrane should lead to insights on the more complex behavior of membranes of higher species as well as identification of potential targets for antimicrobials.