DBP3: Whole Cell Behavior
Being the fundamental unit of life, the cell is a test tube in which to observe effects of and responses to biomedical treatments. Even the smallest bacterial cell has much of the complexity we associate with eukaryotic cells: namely, a membrane separating it from the environment and neighboring cells, a nucleoid region containing its DNA, the universal components of transcription, translation, and replication, and thousands of reactions linked together into metabolic, regulatory, translation, and bioenergetic networks. Mutations, drugs, and environmental factors can effect the growth behavior and responses of networks that can require timescales of several cell cycles to observe. Successful biomedical treatments must take into account that even genetically identical cells can display variations in growth rate, response kinetics, and other quantitative phenotypes.
Whole cell model built from cryoelectron tomograms of an individual E. coli cell. (a) Location of ribosomes and nucleoid in the cell. The red circle shows the location of the modeled lac gene. (b) Snapshot from a Lattice Microbes simulation of the lac operon showing the lac gene being actively transcribed, lac mRNA being translated, and lactose permease proteins in the membrane. Also shown are the free lac repressors. (c) A slice through the original cryoelectron tomogram obtained by collaborators Wolfgang Baumeister and Julio Ortiz. The ribosomes are visible as dark spots of enhanced density and the nucleoid region is distinguished by a smoother density and a lack of ribosomes. (a) and (b) made with VMD.
Cell division and chromosome packing. Cell-to-cell differences can arise from non-genetic sources, including stochastic fluctuations in gene expression and asymmetric partitioning of cell components at cell division. Diversity in growth rates provided by cell-to-cell variation is critical for bacterial survival in unstable and stressful environments because it ensures that some individuals will survive a potentially lethal assault that would otherwise extinguish the population (persistence). Understanding the dynamics of non-pathogenic bacterial growth and cell division coupled with DNA occlusion is a critical first step in deciphering how pathogens use persistence to evade host immunity. Dynamic processes such as transcription-factor binding, cell division, and DNA re-organization in bacterial cells can be monitored in real time with single molecule techniques, providing necessary data to guide the development of more detailed computational models.
The cellular biochemical reaction network. Determining the changes in growth behavior and ultimately providing a more complete picture of the underlying dynamics requires at the minimum embedding the perturbed biochemical reactions into the response of the entire cell. Dynamic and systems models of biochemical networks each attempt to address different problems in the drive to create computational models of an entire cell. Dynamic models, by definition, attempt to capture the time-varying changes in a cell by describing a specific chemical event, but require extensive (and often difficult to obtain) information on the state of the cell and rate constants of the biochemical reactions being modeled. Systems-level models forego the microscopic details in order to capture the entire biochemical network of the cell and steady-state fluxes through subsystems that maximize a cellular property like its growth rate. Characterizing adaptation of the metabolic network for fast and slow growing bacteria during acute and chronic phases of bacterial infection requires the integration of both approaches. A few attempts have shown the potential of integrating dynamics of biochemical reactions into systems level approaches.
Structure of a bioenergetic pseudo-organelle, chromatophore, of Rhodobacter sphaeroides.
Supramolecular organization of organelles. Cellular biochemical reactions are typically compartmentalized into organelles featuring cooperating proteins. Subsequently, bridging dynamic models of cellular processes with single protein function requires the determination of structural models for the associated organelles. The Center has constructed the first such model at atomic detail for the chromatophore, a bioenergetic pseudo-organelle found in purple bacteria, which converts short-lived electronic excitations resulting from photon absorption to stable chemical energy in the form of ATP, sharing some similarity with respiratory components in mitochondria. Chromatophore organization, varying among species and in response to growth conditions, has been determined by combining atomic-force microscopy (AFM), cryo-electron microscopy (cryo-EM), crystallography, spectroscopy, and proteomics data. Such atomistic models permit quantitatively accurate descriptions of energy harvesting in bacteria and other organelle-scale dynamics.
