Biomedical subjects

Timothy C Elston

Publications and source records attributed to Timothy C Elston.

17 recordsLinked to original sources

PubMed2006

A structural model reveals energy transduction in dynein.

Intracellular active transport is driven by ATP-hydrolyzing motor proteins that move along cytoskeletal filaments. In particular, the microtubule-associated dynein motor is involved in the transport of organelles and vesicles, the maintenance of the Golgi, and mitosis. However, unlike kinesin and myosin, the mechanism by which dynein converts chemical energy into mechanical force remains largely a mystery, due primarily to the lack of a high-resolution molecular structure. Using homology modeling and normal mode analysis, we propose a complete atomic structure and a mechanism for force generation by the motor protein dynein. In agreement with very recent electron microscopy (EM) reconstructions showing dynein as a ring-shaped heptamer, our model consists of six ATPases of the AAA (ATPases associated with various cellular activities) superfamily and a C-terminal domain, which is experimentally known to control motor function. Our model shows a coiled coil spanning the diameter of the motor that accounts for previously unidentified structures in EM studies and provides a potential mechanism for long-range communication between the AAA domains. Furthermore, normal mode analysis reveals that the subunits of the motor that contain the nucleotide binding sites exhibit minimal movement, whereas the rest of the motor is very mobile. Our analysis suggests the likely domain rearrangements of the motor unit that generate its power stroke. This study provides insights into the structure and function of dynein that can guide further experimental investigations into energy transduction in dynein.

Amino Acid Sequence↗

PubMed2006

Gene regulatory networks: a coarse-grained, equation-free approach to multiscale computation.

We present computer-assisted methods for analyzing stochastic models of gene regulatory networks. The main idea that underlies this equation-free analysis is the design and execution of appropriately initialized short bursts of stochastic simulations; the results of these are processed to estimate coarse-grained quantities of interest, such as mesoscopic transport coefficients. In particular, using a simple model of a genetic toggle switch, we illustrate the computation of an effective free energy Phi and of a state-dependent effective diffusion coefficient D that characterize an unavailable effective Fokker-Planck equation. Additionally we illustrate the linking of equation-free techniques with continuation methods for performing a form of stochastic "bifurcation analysis"; estimation of mean switching times in the case of a bistable switch is also implemented in this equation-free context. The accuracy of our methods is tested by direct comparison with long-time stochastic simulations. This type of equation-free analysis appears to be a promising approach to computing features of the long-time, coarse-grained behavior of certain classes of complex stochastic models of gene regulatory networks, circumventing the need for long Monte Carlo simulations.

Algorithms↗

PubMed2006

A bottom-up approach to gene regulation.

The ability to construct synthetic gene networks enables experimental investigations of deliberately simplified systems that can be compared to qualitative and quantitative models. If simple, well-characterized modules can be coupled together into more complex networks with behaviour that can be predicted from that of the individual components, we may begin to build an understanding of cellular regulatory processes from the 'bottom up'. Here we have engineered a promoter to allow simultaneous repression and activation of gene expression in Escherichia coli. We studied its behaviour in synthetic gene networks under increasingly complex conditions: unregulated, repressed, activated, and simultaneously repressed and activated. We develop a stochastic model that quantitatively captures the means and distributions of the expression from the engineered promoter of this modular system, and show that the model can be extended and used to accurately predict the in vivo behaviour of the network when it is expanded to include positive feedback. The model also reveals the counterintuitive prediction that noise in protein expression levels can increase upon arrest of cell growth and division, which we confirm experimentally. This work shows that the properties of regulatory subsystems can be used to predict the behaviour of larger, more complex regulatory networks, and that this bottom-up approach can provide insights into gene regulation.

Arabinose↗

PubMed2006

Causal mapping as a tool to mechanistically interpret phenomena in cell motility: application to cortical oscillations in spreading cells.

Biological processes that occur at the cellular level and consist of large numbers of interacting elements are highly nonlinear and generally involve multiple time and spatial scales. The quantitative description of these complex systems is of great importance but presents large challenges. We outline a new systems biology approach, causal mapping (CMAP), which is a coarse-grained biological network tool that permits description of causal interactions between the elements of the network and overall system dynamics. On one hand, the CMAP is an intermediate between experiments and physical modeling, describing major requisite elements, their interactions and paths of causality propagation. On the other hand, the CMAP is an independent tool to explore the hierarchical organization of cell and the role of uncertainties in the system. It appears to be a promising easy-to-use technique for cell biologists to systematically probe verbally formulated qualitative hypotheses. We apply the CMAP to study the phenomenon of contractility oscillations in spreading cells in which microtubules have been depolymerized. The precise mechanism by which these oscillations are governed by a complex mechano-chemical system is not known but the data observed in experiments can be described by a CMAP. The CMAP suggests that the source of the oscillations results from the opposing effects of Rho activation leading to a decreased level of myosin light chain phosphatase and a cyclic calcium influx caused by increased membrane tension and leading to a periodically enhanced activation of myosin light chain kinase.

Animals↗

PubMed2005

Bistability, stochasticity, and oscillations in the mitogen-activated protein kinase cascade.

Signaling pathways respond to stimuli in a variety of ways, depending on the magnitude of the input and the physiological status of the cell. For instance, yeast can respond to pheromone stimulation in either a binary or graded fashion. Here we present single cell transcription data indicating that a transient binary response in which all cells eventually become activated is typical. Stochastic modeling of the biochemical steps that regulate activation of the mitogen-activated protein kinase Fus3 reveals that this portion of the pathway can account for the graded-to-binary conversion. To test the validity of the model, genetic approaches are used to alter expression levels of Msg5 and Ste7, two of the proteins that negatively and positively regulate Fus3, respectively. Single cell measurements of the genetically altered cells are shown to be consistent with predictions of the model. Finally, computational modeling is used to investigate the effects of protein turnover on the response of the pathway. We demonstrate that the inclusion of protein turnover can lead to sustained oscillations of protein concentrations in the absence of feedback regulation. Thus, protein turnover can profoundly influence the output of a signaling pathway.

Biological Clocks↗

PubMed2005

A numerical algorithm for investigating the role of the motor-cargo linkage in molecular motor-driven transport.

We extend the numerical algorithm developed by Wang et al. (2003. J. Theor. Biol. 221, 491-511) for studying biomolecular transport processes to include the linkage that connects molecular motors to their cargo. The new algorithm is used to investigate how the stiffness of the linkage affects the average velocity, effective diffusion coefficient, and randomness parameter. Three different models for molecular motors are considered: (1) a discrete stepping motor (2) a motor moving in a tilted-periodic potential and (3) a motor driven by a flashing potential. We demonstrate that a flexible motor-cargo linkage can make inferences on motor behavior based on measurements of the cargo's position difficult. We also show that even for the case of a tilted-periodic potential there exists an optimal stiffness of the linkage at which transport is maximized. The MATLAB code used in this paper is available at: http://www.unc.edu/approximatelytelston/code/.

Algorithms↗

PubMed2005

A model for the oscillatory motion of single dynein molecules.

Axonemal dynein is the molecular motor responsible for the rhythmic beating of eukaryotic cilia and flagella. An individual axonemal dynein molecule is capable of both unidirectional and oscillatory motion along a microtubule (Nature 393 (1998) 711). We propose a model which links the physical motion of a two-headed dynein molecule to its ATP hydrolysis cycle, and which exhibits both processive and oscillatory behaviors. A mathematical analysis of the model is used to make experimentally testable predictions.

Animals↗

PubMed2005

Stochasticity in gene expression: from theories to phenotypes.

Genetically identical cells exposed to the same environmental conditions can show significant variation in molecular content and marked differences in phenotypic characteristics. This variability is linked to stochasticity in gene expression, which is generally viewed as having detrimental effects on cellular function with potential implications for disease. However, stochasticity in gene expression can also be advantageous. It can provide the flexibility needed by cells to adapt to fluctuating environments or respond to sudden stresses, and a mechanism by which population heterogeneity can be established during cellular differentiation and development.

Animals↗

PubMed2005

The hepatic transcriptome as a window on whole-body physiology and pathophysiology.

Transcriptomics can be a valuable aid to pathologists. The information derived from microarray studies may soon include the entire transcriptomes of most cell types, tissues and organs for the major species used for toxicology and human disease risk assessment. Gene expression changes observed in such studies relate to every aspect of normal physiology and pathophysiology. When interpreting such data, one is forced to look "far from the lamp post:' and in so doing, face one's ignorance of many areas of biology. The central role of the liver in toxicology, as well as in many aspects of whole-body physiology, makes the hepatic transcriptome an excellent place to start your studies. This article provides data that reveals the effects of fasting and circadian rhythm on the rat hepatic transcriptome, both of which need to be kept in mind when interpreting large-scale gene expression in the liver. Once you become comfortable with evaluating mRNA expression profiles and learn to correlate these data with your clinical and morphological observations, you may wonder why you did not start your studies of transcriptomics sooner. Additional study data can be viewed at the journal website at (www.toxpath.org). Two data files are provided in Excel format, which contain the control animal data from each of the studies referred to in the text,including normalized signal intensity data for each animal (n=5) in the 6-hour, 24-hour, and 5-day time points. These files are briefly described in the associated 'Readme' file, and the complete list of GenBank numbers and Affymetrix IDs are provided in a separate txt file. These files are available at http://taylorandfrancis.metapress.comlopenurl.asp?genre=journal&issn=0192-6233. Click on the issue link for 33(1), then select this article. A download option appears at the bottom of this abstract. In order to access the full article online, you must either have an individual subscription or a member subscription accessed through (www.toxpath.org).

Animals↗

PubMed2004

Biochemical Network Stochastic Simulator (BioNetS): software for stochastic modeling of biochemical networks.

BACKGROUND: Intrinsic fluctuations due to the stochastic nature of biochemical reactions can have large effects on the response of biochemical networks. This is particularly true for pathways that involve transcriptional regulation, where generally there are two copies of each gene and the number of messenger RNA (mRNA) molecules can be small. Therefore, there is a need for computational tools for developing and investigating stochastic models of biochemical networks. RESULTS: We have developed the software package Biochemical Network Stochastic Simulator (BioNetS) for efficiently and accurately simulating stochastic models of biochemical networks. BioNetS has a graphical user interface that allows models to be entered in a straightforward manner, and allows the user to specify the type of random variable (discrete or continuous) for each chemical species in the network. The discrete variables are simulated using an efficient implementation of the Gillespie algorithm. For the continuous random variables, BioNetS constructs and numerically solves the appropriate chemical Langevin equations. The software package has been developed to scale efficiently with network size, thereby allowing large systems to be studied. BioNetS runs as a BioSpice agent and can be downloaded from http://www.biospice.org. BioNetS also can be run as a stand alone package. All the required files are accessible from http://x.amath.unc.edu/BioNetS. CONCLUSIONS: We have developed BioNetS to be a reliable tool for studying the stochastic dynamics of large biochemical networks. Important features of BioNetS are its ability to handle hybrid models that consist of both continuous and discrete random variables and its ability to model cell growth and division. We have verified the accuracy and efficiency of the numerical methods by considering several test systems.

Algorithms↗

PubMed2004

Fluctuations in transcription factor binding can explain the graded and binary responses observed in inducible gene expression.

Inducible genes are expressed in the presence of an external stimulus. Individual cells may exhibit either a binary or graded response to such signals. It has been hypothesized that the chemical kinetics of transcription factor/DNA interactions can account for both these scenarios (EMBO J. 9(9) (1990) 2835; BioEssays 14(5) (1992) 341). To explore this question, we have conducted work based on the experimental results of Fiering et al. (Genes Dev. 4 (10) (1990) 1823). In these experiments, three upstream NF-AT binding sites control transcription of the lacZ gene, which codes for the enzyme beta-Galactosidase. The experimental data show a binary response for this system. We consider the effects of fluctuations in NF-AT binding on the response of the system. Our modeling results are in good qualitative agreement with the experimental data, and illustrate how the binary and graded responses can stem from the same underlying mechanism.

Animals↗

PubMed2004

Mathematical modeling of RGS and G-protein regulation in yeast.

G-protein-activated signaling pathways are capable of adapting to a persistent external stimulus. Desensitization is thought to occur at the receptor level as well as through negative feedback by a family of proteins called regulators of G-protein signaling (RGS). The pheromone response pathway in yeast is a typical example of such a system, and the relative simplicity of this pathway makes it an attractive system in investigating the regulatory role of RGS proteins. Two studies have used computational modeling to gain insight into how this pathway is regulated (Hao et al., 2003; Yi et al., 2003). This article provides an introduction to computational analysis of signaling pathways by developing a mathematical model of the pheromone response pathway that synthesizes the results of these two investigations. Our model qualitatively captures many features of the pathway and suggests an additional mechanism for pathway inactivation. It also illustrates that a complete understanding of signaling pathways requires an investigation of their time-dependent behavior.

Computational Biology↗

PubMed2003

Regulators of G protein signaling and transient activation of signaling: experimental and computational analysis reveals negative and positive feedback controls on G protein activity.

Cellular responses to hormones and neurotransmitters are necessarily transient. The mating pheromone signal in yeast is typical. Signal initiation requires cell surface receptors, a G protein heterotrimer, and down-stream effectors. Signal inactivation requires Sst2, a regulator of G protein signaling (RGS) protein that accelerates GTPase activity. We conducted a quantitative analysis of RGS and G protein expression and devised computational models that describe their activity in vivo. These results indicated that pheromone-dependent transcriptional induction of the RGS protein constitutes a negative feedback loop that leads to desensitization. Modeling also suggested the presence of a positive feedback loop leading to resensitization of the pathway. In confirmation of the model, we found that the RGS protein is ubiquitinated and degraded in response to pheromone stimulation. We identified and quantitated these positive and negative feedback loops, which account for the transient response to external signals observed in vivo.

Computer Simulation↗

PubMed2003

A robust numerical algorithm for studying biomolecular transport processes.

We present a numerical algorithm that is well suited for the study of biomolecular transport processes. In the algorithm a continuous Markov process is discretized as a jump process and the jump rates are derived from local solutions of the continuous system. Consequently, the algorithm has two advantages over standard numerical methods: (1) it preserves detailed balance for equilibrium processes, (2) it is able to handle discontinuous potentials. The formulation of the algorithm also allows us to calculate the effective diffusion coefficient or, equivalently, the randomness parameter. We provide several simple examples of how to implement the algorithm. All the MATLAB functions files needed to reproduce the results presented in the article are available from www.amath.unc.edu/Faculty/telston/matlab_functions.

Algorithms↗

PubMed2002

The brownian ratchet and power stroke models for posttranslational protein translocation into the endoplasmic reticulum.

A quantitative analysis of experimental data for posttranslational translocation into the endoplasmic reticulum is performed. This analysis reveals that translocation involves a single rate-limiting step, which is postulated to be the release of the signal sequence from the translocation channel. Next, the Brownian ratchet and power stroke models of translocation are compared against the data. The data sets are simultaneously fit using a least-squares criterion, and both models are found to accurately reproduce the experimental results. A likelihood-ratio test reveals that the optimal fit of the Brownian ratchet model, which contains one fewer free parameter, does not differ significantly from that of the power stroke model. Therefore, the data considered here cannot be used to reject this import mechanism. The models are further analyzed using the estimated parameters to make experimentally testable predictions.

Algorithms↗

PubMedDate not supplied

Frequent sampling reveals dynamic responses by the transcriptome to routine media replacement in HepG2 cells.

Cultured cell lines are employed extensively for biological research. Large-scale differential gene expression (LSDGE) is being used to study mechanisms of toxicity in such cultures. 'Normal' gene expression dynamics could have a major impact on the design and interpretation of these studies. In order to provide understanding of such dynamics, we investigated LSDGE responses to media replacement in human hepatoblastoma cells (HepG2) using 5-minute sampling frequencies for 6 hours post routine media replacement. Each mRNA transcript was found to exhibit a characteristic 'operating range' based on signal intensity. Following media replacement, which replenishes nutrients (eg, glucose and glutamate) and removes excretory products (eg, lactate), a complex set of gene expression changes was observed. Some transcripts appeared to switch on from a quiescent state to a very active one (eg, CYP1A1), others exhibited 'clocklike' oscillations (eg, asparagine synthetase), or a synchronous burst (chirp) of expression up regulation (eg, timeless). Mathematical analysis (Fourier Transform, Singular Value Decomposition, Wavelets, Phase Analysis) of oscillating expression patterns identified cycle lengths ranging from 11.8 to 210 minutes. There were prominent 36.5- and 17.4-minute cycles, for subsets of genes, and transcript-specific differences in phase angle with respect to these cycles. The functional consequences of these novel observations remain to be determined. It is clear that dense time-course studies provide a valuable approach to the investigation of physiological responses to nutrients, toxicants, and other environmental variables. This research also highlights the need for an understanding of biological dynamics when using cell culture systems. An Excel data file representing individual transcripts from the respective Clontech cDNA arrays referred to in this article is available at http://taylorandfrancis.metapress.com/openurl.asp?genre=journal&issn=0192-6233. Rows represent data for individual transcripts and columns represent the time-points from 0 to 360 minutes. To access this file, click on the issue link for 31(4), then select this article. In order to access the full article online, you must either have an individual subscription or a member subscription accessed through www.toxpath.org.

Cell Culture Techniques↗

PubMedDate not supplied

Complementary roles for toxicologic pathology and mathematics in toxicogenomics, with special reference to data interpretation and oscillatory dynamics.

Toxicogenomics is an emerging multidisciplinary science that will profoundly impact the practice of toxicology. New generations of biologists, using evolving toxicogenomics tools, will generate massive data sets in need of interpretation. Mathematical tools are necessary to cluster and otherwise find meaningful structure in such data. The linking of this structure to gene functions and disease processes, and finally the generation of useful data interpretation remains a significant challenge. The training and background of pathologists make them ideally suited to contribute to the field of toxicogenomics, from experimental design to data interpretation. Toxicologic pathology, a discipline based on pattern recognition, requires familiarity with the dynamics of disease processes and interactions between organs, tissues, and cell populations. Optimal involvement of toxicologic pathologists in toxicogenomics requires that they communicate effectively with the many other scientists critical for the effective application of this complex discipline to societal problems. As noted by Petricoin III et al (Nature Genetics 32, 474-479, 2002), cooperation among regulators, sponsors and experts will be essential for realizing the potential of microarrays for public health. Following a brief introduction to the role of mathematics in toxicogenomics, "data interpretation" from the perspective of a pathologist is briefly discussed. Based on oscillatory behavior in the liver, the importance of an understanding of mathematics is addressed, and an approach to learning mathematics "later in life" is provided. An understanding of pathology by mathematicians involved in toxicogenomics is equally critical, as both mathematics and pathology are essential for transforming toxicogenomics data sets into useful knowledge.

Animals↗