Biomedical subjects

Peter S Swain

Publications and source records attributed to Peter S Swain.

9 recordsLinked to original sources

PubMed2006

An entropic mechanism to generate highly cooperative and specific binding from protein phosphorylations.

Cooperative interactions are essential to the operation of many biochemical networks. Such networks then respond ultrasensitively in a nonlinear manner to linear changes in network input, and network output, for example, levels of a phosphorylated protein or of gene expression, becomes a sigmoidal function of concentrations of input molecules. We present a novel, entropic ultrasensitivity mechanism that generates highly cooperative and specific binding between two proteins. We consider a disordered protein with multiple phosphorylation sites that binds to a single binding site on an interacting protein. We assume that each phosphorylation locally orders the protein. Such local order affects protein conformational entropy nonlinearly and generates binding that is a highly cooperative function of the number of protein phosphorylations (with Hill coefficients well above 10). Substantial binding may only occur once the disordered protein is phosphorylated a critical number of times or more. Cooperativity is determined by the size of the disordered region of the protein, the binding affinity, and unusually the concentration of the interacting protein. Given the widespread occurrence of disordered, multiply phosphorylated proteins, its highly ultrasensitive character, and the ease of its control, entropic, phosphorylation-driven cooperativity may be extensively exploited intracellularly.

Binding Sites↗

PubMed2006

A fluctuation method to quantify in vivo fluorescence data.

Quantitative in vivo measurements are essential for developing a predictive understanding of cellular behavior. Here we present a technique that converts observed fluorescence intensities into numbers of molecules. By transiently expressing a fluorescently tagged protein and then following its dilution during growth and division, we observe asymmetric partitioning of fluorescence between daughter cells at each division. Such partition asymmetries are set by the actual numbers of proteins present, and thus provide a means to quantify fluorescence levels. We present a Bayesian algorithm that infers from such data both the fluorescence conversion factor and an estimate of the measurement error. Our algorithm works for arbitrarily sized data sets and handles consistently any missing measurements. We verify the algorithm with extensive simulation and demonstrate its application to experimental data from Escherichia coli. Our technique should provide a quantitative internal calibration to systems biology studies of both synthetic and endogenous cellular networks.

Algorithms↗

PubMed2006

Noise in genetic and neural networks.

Both neural and genetic networks are significantly noisy, and stochastic effects in both cases ultimately arise from molecular events. Nevertheless, a gulf exists between the two fields, with researchers in one often being unaware of similar work in the other. In this Special Issue, we focus on bridging this gap and present a collection of papers from both fields together. For each field, the networks studied range from just a single gene or neuron to endogenous networks. In this introductory article, we describe the sources of noise in both genetic and neural systems. We discuss the modeling techniques in each area and point out similarities. We hope that, by reading both sets of papers, ideas developed in one field will give insight to scientists from the other and that a common language and methodology will develop.

Animals↗

PubMed2005

Gene regulation at the single-cell level.

The quantitative relation between transcription factor concentrations and the rate of protein production from downstream genes is central to the function of genetic networks. Here we show that this relation, which we call the gene regulation function (GRF), fluctuates dynamically in individual living cells, thereby limiting the accuracy with which transcriptional genetic circuits can transfer signals. Using fluorescent reporter genes and fusion proteins, we characterized the bacteriophage lambda promoter P(R) in Escherichia coli. A novel technique based on binomial errors in protein partitioning enabled calibration of in vivo biochemical parameters in molecular units. We found that protein production rates fluctuate over a time scale of about one cell cycle, while intrinsic noise decays rapidly. Thus, biochemical parameters, noise, and slowly varying cellular states together determine the effective single-cell GRF. These results can form a basis for quantitative modeling of natural gene circuits and for design of synthetic ones.

Bacterial Proteins↗

PubMed2005

Tracing the sources of cellular variation.

As the adage says, variety is the spice of life, and despite our best attempts, cells, even those with the same genome, never seem to behave the same. By combining mathematical and experimental analyses, Colman-Lerner and colleagues propose, in a recent issue of Nature, a method to delicately unravel the sources of this variation. Applying their technique to the pheromone response in budding yeast, they show that much of the observed variation originates from cell cycle effects and is dependent on levels of pathway input.

Cell Cycle↗

PubMed2004

Efficient attenuation of stochasticity in gene expression through post-transcriptional control.

Thermal fluctuations can lead to significant, unpredictable concentration changes in intracellular molecules, potentially disrupting the functioning of cellular networks and challenging cellular efficiency. Biochemical systems might therefore be expected to have evolved network architectures and motifs that limit the effects of stochastic disturbances. During gene expression itself, stochasticity, or "noise", in protein concentrations is believed to be determined mostly by mRNA, rather than protein, levels. Here, we demonstrate in silico, and analytically, how a number of commonly occurring network architectures in bacteria use mRNA to efficiently attenuate fluctuations. Genes coded in operons share mRNA, which we show generates strongly correlated expression despite multiple ribosome binding sites. For autogeneous control, we provide general analytic expressions using Langevin theory, and demonstrate that negative translational feedback has a much greater efficiency at reducing stochasticity than negative transcriptional feedback. Using the ribosomal proteins as an example, we also show that translational, rather than transcriptional, feedback best coordinates gene expression during assembly of macromolecular complexes. Our findings suggest that selection of a gene controlled post-transcriptionally may be for the resulting low stochasticity in its expression. Such low noise genes can be speculated to play a central role in the local gene network.

Alkaloids↗

PubMed2002

Intrinsic and extrinsic contributions to stochasticity in gene expression.

Gene expression is a stochastic, or "noisy," process. This noise comes about in two ways. The inherent stochasticity of biochemical processes such as transcription and translation generates "intrinsic" noise. In addition, fluctuations in the amounts or states of other cellular components lead indirectly to variation in the expression of a particular gene and thus represent "extrinsic" noise. Here, we show how the total variation in the level of expression of a given gene can be decomposed into its intrinsic and extrinsic components. We demonstrate theoretically that simultaneous measurement of two identical genes per cell enables discrimination of these two types of noise. Analytic expressions for intrinsic noise are given for a model that involves all the major steps in transcription and translation. These expressions give the sensitivity to various parameters, quantify the deviation from Poisson statistics, and provide a way of fitting experiment. Transcription dominates the intrinsic noise when the average number of proteins made per mRNA transcript is greater than approximately 2. Below this number, translational effects also become important. Gene replication and cell division, included in the model, cause protein numbers to tend to a limit cycle. We calculate a general form for the extrinsic noise and illustrate it with the particular case of a single fluctuating extrinsic variable-a repressor protein, which acts on the gene of interest. All results are confirmed by stochastic simulation using plausible parameters for Escherichia coli.

Biophysical Phenomena↗

PubMed2002

Stochastic gene expression in a single cell.

Clonal populations of cells exhibit substantial phenotypic variation. Such heterogeneity can be essential for many biological processes and is conjectured to arise from stochasticity, or noise, in gene expression. We constructed strains of Escherichia coli that enable detection of noise and discrimination between the two mechanisms by which it is generated. Both stochasticity inherent in the biochemical process of gene expression (intrinsic noise) and fluctuations in other cellular components (extrinsic noise) contribute substantially to overall variation. Transcription rate, regulatory dynamics, and genetic factors control the amplitude of noise. These results establish a quantitative foundation for modeling noise in genetic networks and reveal how low intracellular copy numbers of molecules can fundamentally limit the precision of gene regulation.

Bacterial Proteins↗

PubMed2002

The role of proofreading in signal transduction specificity.

Many intracellular signaling proteins such as MAP kinases and transcription factors require multiple covalent modifications before activating downstream targets. This property suggests that signaling pathways are organized to facilitate proofreading, which expends energy to enhance the specificity of the pathway for the appropriate effector. Focusing on MAP kinases, we show that each phosphorylation of the kinase can act as an independent specificity test for that kinase. This is independent of whether MAP kinase activation is distributive, processive, or confined to a protein scaffold. We also highlight the importance of phosphatases in developing and maintaining specificity. Support for our proposals can be drawn from the existing literature.

Biophysical Phenomena↗