Biomedical subjects

Timothy C Elston

Publications and source records attributed to Timothy C Elston.

9 recordsLinked to original sources

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↗

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↗