Biomedical subjects

H H McAdams

Publications and source records attributed to H H McAdams.

9 recordsLinked to original sources

PubMed2004

Visualization of the movement of single histidine kinase molecules in live Caulobacter cells.

The bacterium Caulobacter crescentus divides asymmetrically as part of its normal life cycle. This asymmetry is regulated in part by the membrane-bound histidine kinase PleC, which localizes to one pole of the cell at specific times in the cell cycle. Here, we track single copies of PleC labeled with enhanced yellow fluorescent protein (EYFP) in the membrane of live Caulobacter cells over a time scale of seconds. In addition to the expected molecules immobilized at one cell pole, we observed molecules moving throughout the cell membrane. By tracking the positions of these molecules for several seconds, we determined a diffusion coefficient (D) of 12 +/- 2 x 10(-3) microm(2)/s for the mobile copies of PleC not bound at the cell pole. This D value is maintained across all cell cycle stages. We observe a reduced D at poles containing localized PleC-EYFP; otherwise D is independent of the position of the diffusing molecule within the bacterium. We did not detect any directional bias in the motion of the PleC-EYFP molecules, implying that the molecules are not being actively transported.

Bacterial Proteins↗

PubMed2000

Global analysis of the genetic network controlling a bacterial cell cycle.

This report presents full-genome evidence that bacterial cells use discrete transcription patterns to control cell cycle progression. Global transcription analysis of synchronized Caulobacter crescentus cells was used to identify 553 genes (19% of the genome) whose messenger RNA levels varied as a function of the cell cycle. We conclude that in bacteria, as in yeast, (i) genes involved in a given cell function are activated at the time of execution of that function, (ii) genes encoding proteins that function in complexes are coexpressed, and (iii) temporal cascades of gene expression control multiprotein structure biogenesis. A single regulatory factor, the CtrA member of the two-component signal transduction family, is directly or indirectly involved in the control of 26% of the cell cycle-regulated genes.

Bacterial Proteins↗

PubMed2000

Towards a circuit engineering discipline.

Genetic circuits can now be engineered that perform moderately complicated switching functions or exhibit predictable dynamical behavior. These 'forward engineering' techniques may have to be combined with directed evolution techniques to produce robustness comparable with naturally occurring circuits.

Escherichia coli↗

PubMed2000

Toggles and oscillators: new genetic circuit designs.

Two recent papers report the de novo design of a functioning biological circuit using well-characterized genetic elements.(1,2) Gardner et al. designed and constructed a genetic toggle switch while Elowitz and Leibler built an oscillating genetic circuit. Both circuits were designed with the aid of mathematical models. These papers demonstrate progress towards the unification of theory and experiment in the study of genetic circuits. Comparison of the predicted and observed behavior of the circuits, however, shows that the models explain only some of the circuits' properties. Further study of the observed behaviors not predicted by the model would lead to new insight into the properties of genetic networks. BioEssays 22:507-509, 2000.

Mathematics↗

PubMed1999

It's a noisy business! Genetic regulation at the nanomolar scale.

Many molecules that control genetic regulatory circuits act at extremely low intracellular concentrations. Resultant fluctuations (noise) in reaction rates cause large random variation in rates of development, morphology and the instantaneous concentration of each molecular species in each cell. To achieve regulatory reliability in spite of this noise, cells use redundancy in genes as well as redundancy and extensive feedback in regulatory pathways. However, some regulatory mechanisms exploit this noise to randomize outcomes where variability is advantageous.

Bacterial Physiological Phenomena↗

PubMed1998

Stochastic kinetic analysis of developmental pathway bifurcation in phage lambda-infected Escherichia coli cells.

Fluctuations in rates of gene expression can produce highly erratic time patterns of protein production in individual cells and wide diversity in instantaneous protein concentrations across cell populations. When two independently produced regulatory proteins acting at low cellular concentrations competitively control a switch point in a pathway, stochastic variations in their concentrations can produce probabilistic pathway selection, so that an initially homogeneous cell population partitions into distinct phenotypic subpopulations. Many pathogenic organisms, for example, use this mechanism to randomly switch surface features to evade host responses. This coupling between molecular-level fluctuations and macroscopic phenotype selection is analyzed using the phage lambda lysis-lysogeny decision circuit as a model system. The fraction of infected cells selecting the lysogenic pathway at different phage:cell ratios, predicted using a molecular-level stochastic kinetic model of the genetic regulatory circuit, is consistent with experimental observations. The kinetic model of the decision circuit uses the stochastic formulation of chemical kinetics, stochastic mechanisms of gene expression, and a statistical-thermodynamic model of promoter regulation. Conventional deterministic kinetics cannot be used to predict statistics of regulatory systems that produce probabilistic outcomes. Rather, a stochastic kinetic analysis must be used to predict statistics of regulatory outcomes for such stochastically regulated systems.

Bacteriophage lambda↗

PubMed1998

Simulation of prokaryotic genetic circuits.

Biochemical and genetic approaches have identified the molecular mechanisms of many genetic reactions, particularly in bacteria. Now a comparably detailed understanding is needed of how groupings of genes and related protein reactions interact to orchestrate cellular functions over the cell cycle, to implement preprogrammed cellular development, or to dynamically change a cell's processes and structures in response to environmental signals. Simulations using realistic, molecular-level models of genetic mechanisms and of signal transduction networks are needed to analyze dynamic behavior of multigene systems, to predict behavior of mutant circuits, and to identify the design principles applicable to design of genetic regulatory circuits. When the underlying design rules for regulatory circuits are understood, it will be far easier to recognize common circuit motifs, to identify functions of individual proteins in regulation, and to redesign circuits for altered functions.

Bacteria↗

PubMed1997

Stochastic mechanisms in gene expression.

In cellular regulatory networks, genetic activity is controlled by molecular signals that determine when and how often a given gene is transcribed. In genetically controlled pathways, the protein product encoded by one gene often regulates expression of other genes. The time delay, after activation of the first promoter, to reach an effective level to control the next promoter depends on the rate of protein accumulation. We have analyzed the chemical reactions controlling transcript initiation and translation termination in a single such "genetically coupled" link as a precursor to modeling networks constructed from many such links. Simulation of the processes of gene expression shows that proteins are produced from an activated promoter in short bursts of variable numbers of proteins that occur at random time intervals. As a result, there can be large differences in the time between successive events in regulatory cascades across a cell population. In addition, the random pattern of expression of competitive effectors can produce probabilistic outcomes in switching mechanisms that select between alternative regulatory paths. The result can be a partitioning of the cell population into different phenotypes as the cells follow different paths. There are numerous unexplained examples of phenotypic variations in isogenic populations of both prokaryotic and eukaryotic cells that may be the result of these stochastic gene expression mechanisms.

Algorithms↗

PubMed1995

Circuit simulation of genetic networks.

Genetic networks with tens to hundreds of genes are difficult to analyze with currently available techniques. Because of the many parallels in the function of these biochemically based genetic circuits and electrical circuits, a hybrid modeling approach is proposed that integrates conventional biochemical kinetic modeling within the framework of a circuit simulation. The circuit diagram of the bacteriophage lambda lysislysogeny decision circuit represents connectivity in signal paths of the biochemical components. A key feature of the lambda genetic circuit is that operons function as active integrated logic components and introduce signal time delays essential for the in vivo behavior of phage lambda.

Bacteriolysis↗