Bulletin of the American Physical Society
2005 APS March Meeting
Monday–Friday, March 21–25, 2005; Los Angeles, CA
Session J22: Biochemical and Genetic Networks |
Hide Abstracts |
Sponsoring Units: DBP Chair: Gabor Balazsi, Northwestern University Room: LACC 409 B |
Tuesday, March 22, 2005 11:15AM - 11:27AM |
J22.00001: Understanding and Improving Massively Parallel DNA Detectors for Biomedical Assays Benjamin Smith, Richard Yeh, Jason Carpentier, Steven Rodriguez, Shannon Guiles, David Lin, Carl Franck Microarrays are highly parallel sequence specific DNA detectors used to quantitatively study genotypes and gene-expression levels. Commercial agitation systems aim to remove the coverslip diffusion bottleneck, but the efficiency increase provided by these devices is variable and occasionally even negative. The underlying causes of these variations are not well understood. We have investigated hybridization efficiency using liquid-on-liquid mixing, in which an impeller stirs a viscous oil phase covering a thin film of fluorescently labeled target solution, which lies on a glass microarray substrate. Absolute efficiency studies indicate the diffusion limit is generally well obeyed by static hybridizations, but stirring produces no significant improvement in efficiency. To check whether shear deformation of DNA is a limiting factor a pause cycle is added to the mixing procedure, but no further improvement is observed. Antibody delivery experiments with a comparable diffusion constant show a clear increase in efficiency due to mixing. [Preview Abstract] |
Tuesday, March 22, 2005 11:27AM - 11:39AM |
J22.00002: Label-less Fluorescence-based Detection for DNA Microarray Sanjun Niu, Gaurav Singh, Ravi Saraf Microarray technology is the key to rapid, inexpensive gene sequencing that is the corner stone of modern medicine with the potential to diagnose disease before clinical signs and personalize medicine. By coupling light scattering and fluorescence, we describe a quantitative, label-free assay for microarray analysis with a dynamic range of 1 in 10$^{4}$ at signal-to-noise ratio of 3:1. Since light scattering is intrinsically proportional to number of molecules, the change in fluorescence is highly linear with respect to percent binding of single stranded DNA (ssDNA) target with the immobilized ssDNA probes. Since the scattering is proportional to fourth power of refractive index, the detection of binding is an order of magnitude more sensitive compared to other optical methods based on change in thickness and refractive index, such as, reflectivity, ellipsometry and surface-plasmon resonance. Remarkably, polystyrene film of optimum thickness of 30 nm is the best fluorescent agent since its excitation wavelength matches (within 5 nm) with wavelength for the maximum refractive index difference between ssDNA and dsDNA. [Preview Abstract] |
Tuesday, March 22, 2005 11:39AM - 11:51AM |
J22.00003: Label-free optical detection of biochemical reactions in microarray format J. P. Landry, X. D. Zhu, J. P. Gregg We have developed an oblique-incidence optical reflectivity difference (OI-RD) scanning microscope for detecting biochemical reactions involving unlabeled macromolecules such as DNA, protein, or lipid membranes in microarray format. This optical microscope detects changes in density, thickness, and conformation of macromolecules as a result of the reactions of probe molecules with target molecules immobilized on a solid surface such as a chemically functionalized glass microscope slide. Of particular interest to our current investigation are microarrays of small ligands and macromolecules that are targeted for protein binding. Our OI-RD microscope is particularly desirable for such microarray-based proteomic investigations as it offers the capability to detect activities of protein molecules without the influence of extrinsic "tag" molecules attached to the protein (such as organic fluorophore molecules) and other undesirable effects such as photobleaching. We have used our OI-RD scanning microscope in a series of proof-of-principle studies of oligonucleotide hybridization and antibody-antigen capture reactions \textit{without labeling}. [Preview Abstract] |
Tuesday, March 22, 2005 11:51AM - 12:03PM |
J22.00004: Identifying pattern in microarray expression series using algorithmic information theory Sebastian Ahnert, Karen Willbrand, Francis Brown, Thomas Fink We introduce a method of detecting pattern in data series independent of the nature of the pattern. This is achieved by calculating a lower bound on the Algorithmic Information Content (AIC) of the data series, the exact value of the AIC being fundamentally uncomputable. This bound also provides us with a measure of the algorithmic compressibility. Data series which are highly compressible are more likely to result from simple underlying mechanisms than series which are incompressible. We show that the compression in bits is a universal currency by which we can order data series according to their significance, even if they are from different experiments or exhibit different kinds of pattern or noise. We test our method on microarray time series of yeast cell cycle and show that is very successful at blindly selecting genes identified by independent experimental studies, without making any assumptions about what kind of pattern these data series contain. [Preview Abstract] |
Tuesday, March 22, 2005 12:03PM - 12:15PM |
J22.00005: Microarray Studies of Arabidopsis Gene Response to High Magnetic Fields. J. Ch. Davis, M.W. Meisel, J.S. Brooks, A.-L. Paul, R.J. Ferl Microarray analyses indicate that a homogeneous magnetic field of 21~Tesla has a far reaching effect on the genome of Arabidopsis plants. Survey of an Affymetrix microarray populated with 8,000 genes from the arabidopsis genome reveals that although most of the genes in the array show less than a 2-fold difference in expression between the 21~Tesla treatment and control, many show striking differential expression (5-50 fold). These results were corroborated by quantitative real-time reverse transcriptase - polymerase chain reaction (qRT-PCR), a method often used in conjunction with microarrays to support the scatter plot data rendered from the two-way comparison (21~Tesla vs. control) of the arrays. Scatter plots of treatment vs.~control data are saturated where differential expression is less than 2-fold. In an attempt to extract additional information from this area, topographical plots were generated to reveal the numbers of genes represented by any given point on the plot, providing information that may prove insightful in future analyses of microarray data. [Preview Abstract] |
Tuesday, March 22, 2005 12:15PM - 12:27PM |
J22.00006: Using mutual information to infer gene-gene interactions from microarray expression series Thomas Fink, Sebastian Ahnert, Francois Radvanyi, Nicolas Stransky, Karen Willbrand Identifying network structure from microarray data rests crucially on what is meant by `similarity' between two gene expression patterns. We introduce a method of inferring gene-gene interactions without making assumptions about what kind of expression correlations to look for. Our approach is to bound the mutual algorithmic information, measured in bits, between sets of measurements for two genes; a higher level of mutual information corresponds to a greater confidence of interaction. We have applied our method to yeast cell cycle and bladder cancer. [Preview Abstract] |
Tuesday, March 22, 2005 12:27PM - 12:39PM |
J22.00007: Quantifying optimal accuracy of local primary sequence bioinformatics methods Daniel Aalberts, Eric Daub, Jesse Dill Traditional bioinformatics methods scan primary sequences for local patterns. It is important to assess how accurate local primary sequence methods can be. We study the problem of donor pre-mRNA splice site recognition, where the sequence overlaps between real and decoy data sets can be quantified, exposing the intrinsic limitations of the performance of local primary sequence methods. We assess the accuracy of local primary sequence methods generally by studying how they scale with dataset size and demonstrate that our new Primary Sequence Ranking methods have superior performance. Our Primary Sequence Ranking analysis tools are available at {tt http://rna.williams.edu/} [Preview Abstract] |
Tuesday, March 22, 2005 12:39PM - 12:51PM |
J22.00008: Evolution at the Nucleotide Level Jose Parra, Bernard Gerstman We carry out a quantitative analysis that supports the viewpoint that DNA mutations do not occur with equal probabilities. We find evidence that the identity of the neighboring nucleotide within a codon influences the probability of a point substitution and we use a mutation model to quantify the strength of these interactions. We find a set of neighbor dependent mutation parameter strengths that does the best job of explaining the current frequency spectrum of appearance of amino acids. We also show that this optimal solution does not fully explain the current frequency of appearance of amino acids, and therefore other effects, such as externally imposed survival advantage of amino acids sequences, must also play a role in the evolution of nucleotide sequences. We also explain how the relative importance for genetic evolution of internal nucleotide mutation versus external selection can be determined if the frequency spectrum of amino acids could be determined at various times in the past. [Preview Abstract] |
Tuesday, March 22, 2005 12:51PM - 1:03PM |
J22.00009: An entropic tool for genome analysis Chih-Yuan Tseng Shannon information (SI) defines the difference of Shannon entropy and its global maximum value. It was found SI in a genome tends to be much larger than that in its random match for all extant prokaryotic and eukaryotic complete genomes in Chang et al's work. Thus a better sense of the magnitude of the SI in a sequence is obtained by measuring it relative to the SI in the random match, the reduced SI. They observed a linear relation between reduced SI and sequence length L, which implies a k-dependent but genome-independent constant. This forms a universality class that indicates that reduced SI is a signature of complete genomes undiminished by the enormous diversity in growth and evolution experienced by individual genomes. Although their studies revealed intriguing results, the mechanism was not clear. Our main goal here is to investigate it through the method of maximum entropy (ME). The rationale hinges on the use of relative entropy. ME indicates preferred probability distribution of frequency- occurrence of k-string in real genome sequences updated from random sequences is the one that maximizes relative entropy of genomes and random sequences subject to certain constraints. Our result shows the existence of universality classes to be simply a trivial consequence if frequencies-occurrence of k-string is chosen as the relevant variable. However, the use of this result is far from being exhausted, which may provide a track to develop a genomic growth model. [Preview Abstract] |
Tuesday, March 22, 2005 1:03PM - 1:15PM |
J22.00010: Nonlinear degradation and the function of genetic circuits Nicolas Buchler, Ulrich Gerland, Terence Hwa The functions of most genetic circuits require a sufficient degree of cooperativity in the circuit components. We examine a simple source of cooperativity that stems from the nonlinear degradation of multimeric proteins. Ample experimental evidence suggests that protein subunits can degrade less rapidly when associated in multimeric complexes, an effect we refer to as ``cooperative stabilization,'' For homodimers, this effect leads to a concentration dependence in the protein degradation rate because monomers which are predominant at low protein concentrations will be more rapidly degraded. Theoretical analysis of two model gene circuits in bacteria, i.e. genetic switch and oscillator, demonstrates that a few-fold difference between the degradation rate of monomers and dimers can substantially enhance the function of these circuits. Our results suggest that cooperative stabilization needs to be considered explicitly and characterized quantitatively in any systematic experimental or theoretical study of gene circuits. [Preview Abstract] |
Tuesday, March 22, 2005 1:15PM - 1:27PM |
J22.00011: Topological units of environmental signal processing in the transcriptional-regulatory network of Escherichia coli Gabor Balazsi, Albert-Laszlo Barabasi, Zoltan Oltvai Recent evidence indicates that potential interactions within biochemical networks are differentially utilized according to the environmental conditions in which a cell exists. However, the topological units of this differential utilization have not been investigated. Here, we use the transcriptional regulatory network of Escherichia coli to identify such units, called origons, representing regulatory subnetworks which originate at a distinct class of sensor transcription factors. Using microarray data, we find that specific environmental signals affect mRNA expression levels significantly only within the origons responsible for their detection and processing. We also show that small regulatory interaction patterns, called subgraphs and motifs, occupy distinct positions in- and between origons, offering insights into their role in environmental information processing. [Preview Abstract] |
Tuesday, March 22, 2005 1:27PM - 1:39PM |
J22.00012: Power law rank-abundance relationships in marine phage populations Peter Salamon, Karl Heinz Hoffmann, Beltran Rodriguez-Brito, Mya Breitbart, David Bangor, Florent Angly, Ben Felts, James Nulton, Forest Rohwer Phage are the most abundant biological entities in the biosphere, with an estimated 10$^{31}$ particles on the planet. About 25{\%} of oceanic organic carbon is cycled through phage every day. Metagenomic analyses show that the rank-abundance curve for marine phage communities follows a power law distribution. This distribution is consistent with a proposed, modified version of Lotka-Volterra predator-prey dynamics, where blooms of a specific microbial species leads to blooms of their corresponding phage and a subsequent decrease in abundance. The model predicts that the majority of phage types in a population will be rare and it is unlikely that the most abundant phage will be the same at different time points. The model is based on spatial-temporal heterogeneity and a power law phage decay, which are both supported by empirical data. [Preview Abstract] |
Tuesday, March 22, 2005 1:39PM - 1:51PM |
J22.00013: Biological Networks: Does Function Follow Form? Etay Ziv, Manuel Middendorf, Ilya Nemenman, Chris Wiggins Recently, studies of biological networks have focused on various topological measures (primarily degree distributions and subgraphs). Relating such graph-theoretic statistics to function is difficult, since a given topology does not uniquely determine function. In fact, a topology's ability to support multiple functions may itself provide a selective advantage to an organism, since a topology with multiple functions can be adaptable (on the time scale of the individual) or evolvable (on the time scale of the species). Here we present a quantitative measure of circuit function and use this measure to test if circuits with well-defined function or functions are common, and if evolvable topologies exist among them. [Preview Abstract] |
Tuesday, March 22, 2005 1:51PM - 2:03PM |
J22.00014: Analysis of a yeast cell cycle model Chao Tang, Ying Lu, Fangting Li, Qi Ouyang, Mingyuan Zhong We have analyzed a model network of yeast cell-cycle regulation, which consists of a set of ordinary differential equations with about 90 parameters. We show that this dynamical system has very stable and robust global fixed points which correspond to the biological checkpoints. The biological pathway corresponds to a globally attracting trajectory of the system. [Preview Abstract] |
Tuesday, March 22, 2005 2:03PM - 2:15PM |
J22.00015: Self-Consistent Proteomic Field Theory of Stochastic Gene Switches Aleksandra M. Walczak, Masaki Sasai, Peter G. Wolynes The need for a computationally efficient treatment of genetic networks and cascades, which, while acknowledging their stochastic character, at the same time allows us to gain a better and deeper understanding of the global attractor structure is widely recognized. Even treating the building blocks of these systems, genetic switches, generally requires some approximations. We propose a powerful generically applicable method, a self-consistent proteomic field approximation in which the mean influence of the proteomic cloud created by one gene on the action of another is computed self-consistently [1]. The stochastic nature of protein synthesis and degradation, and DNA binding events are treated stochastically and on equal footing. For a large class of problems, in which the output proteins of one gene influence other genes, the probability distributions may be determined exactly without any further assumptions within the self-consistent proteomic field approximation. We compare the results for various versions of a toggle switch composed of two mutually repressing genes to solutions of deterministic rate equations and find that when proteins are produced in bursts, the deterministic approach can fail dramatically.\\ 1. Walczak, A.M., Sasai, M., Wolynes P.G., Self Consistent Proteomic Field Theory of Stochastic Gene Switches, to be published in Biophysical Journal [Preview Abstract] |
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