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Pathway Model (pathway + model)

Distribution by Scientific Domains

Life Sciences	50%

Selected Abstracts

A Computational Study of Feedback Effects on Signal Dynamics in a Mitogen-Activated Protein Kinase (MAPK) Pathway Model

BIOTECHNOLOGY PROGRESS, Issue 2 2001
Anand R. Asthagiri
Exploiting signaling pathways for the purpose of controlling cell function entails identifying and manipulating the information content of intracellular signals. As in the case of the ubiquitously expressed, eukaryotic mitogen-activated protein kinase (MAPK) signaling pathway, this information content partly resides in the signals' dynamical properties. Here, we utilize a mathematical model to examine mechanisms that govern MAPK pathway dynamics, particularly the role of putative negative feedback mechanisms in generating complete signal adaptation, a term referring to the reset of a signal to prestimulation levels. In addition to yielding adaptation of its direct target, feedback mechanisms implemented in our model also indirectly assist in the adaptation of signaling components downstream of the target under certain conditions. In fact, model predictions identify conditions yielding ultra-desensitization of signals in which complete adaptation of target and downstream signals culminates even while stimulus recognition (i.e., receptor-ligand binding) continues to increase. Moreover, the rate at which signal decays can follow first-order kinetics with respect to signal intensity, so that signal adaptation is achieved in the same amount of time regardless of signal intensity or ligand dose. All of these features are consistent with experimental findings recently obtained for the Chinese hamster ovary (CHO) cell lines (Asthagiri et al., J. Biol. Chem.1999, 274, 27119,27127). Our model further predicts that although downstream effects are independent of whether an enzyme or adaptor protein is targeted by negative feedback, adaptor-targeted feedback can "back-propagate" effects upstream of the target, specifically resulting in increased steady-state upstream signal. Consequently, where these upstream components serve as nodes within a signaling network, feedback can transfer signaling through these nodes into alternate pathways, thereby promoting the sort of signaling cross-talk that is becoming more widely appreciated. [source]

Identification of genes involved in the biosynthesis of the cytotoxic compound glidobactin from a soil bacterium

ENVIRONMENTAL MICROBIOLOGY, Issue 7 2007
Barbara Schellenberg
Summary Glidobactins (syn. cepafungins) are a family of structurally related cytotoxic compounds that were isolated from the soil bacterial strain K481-B101 (ATCC 53080; DSM 7029) originally assigned to Polyangium brachysporum and, independently, from an undefined species related to Burkholderia cepacia. Glidobactins are acylated tripeptide derivatives that contain a 12-membered ring structure consisting of the two unique non-proteinogenic amino acids erythro -4-hydroxy- l -lysine and 4(S)-amino-2(E)-pentenoic acid. Here we report the cloning and functional analysis of a gene cluster (glbA,glbH) involved in glidobactin synthesis from K481-B101, which according to its 16S rRNA sequence belongs to the Burkholderiales. The putative encoded proteins include a mixed non-ribosomal peptide/polyketide synthetase whose structure and architecture allowed to build a biosynthetic pathway model explaining the biosynthesis of the unique peptide part of glidobactins. Intriguingly, among the more than 600 bacterial strains whose genome sequence is currently available, homologous gene clusters were found in Burkholderia pseudomallei, the causing agent of melioidosis, and in the insect pathogen Photorhabdus luminescens, strongly suggesting that these organisms are capable to synthesize compounds similar to glidobactins. In addition, a glb gene cluster that was inactivated by transposon-mediated rearrangements was also present in Burkholderia mallei, a very close relative of B. pseudomallei and the causing agent of glanders in horse-like animals. [source]

Random Computer Generation of 3D Molecular Structures: Theoretical and Statistical Analysis

MACROMOLECULAR THEORY AND SIMULATIONS, Issue 2 2006
Alain Porquet
Abstract Summary: A computer program has been developed to generate three-dimensional molecular structures randomly from a given collection of elementary chemical functional groups: the so-called fragment database. The gradual assembly of the various fragments present in the database is performed according to a "self-generation algorithm" (SGA). The latter is based on the covalent binding, step by step, between the unoccupied electronic valencies associated with the fragments of the database, and those of the growing molecular structure. When the number of electronic valencies of the molecular structure is zero, the growth process for this particular molecule is completed. It is shown that SGA is able to reproduce the asymmetric mass distributions of some natural colloids, like humic substances. In this article, particular attention is given to the analysis of the relationship existing between the fragment composition of the database and that of the collection of molecules generated. Mathematical expressions are derived and discussed, to understand the relationship between the proportions of the different types of fragments and the final composition of the generated molecular ensembles. For that purpose, a "pathway" formalism is proposed to describe exhaustively the whole set of generated molecules by specifying the distribution function of all of the fragments therein integrated. A statistical analysis that satisfactorily reproduces the predictions of the pathway model is extensively discussed. Example of a three-dimensional structure obtained with the "self-generation algorithm" (SGA). [source]

Elucidating the Relationship Between Obesity and Depression: Recommendations for Future Research

CLINICAL PSYCHOLOGY: SCIENCE AND PRACTICE, Issue 1 2008
Joshua I. Hrabosky
The obese population is heterogeneous in its experiences of psychosocial disturbances, yet many obese individuals do experience such problems as body image disturbance, low self-esteem, diminished self-efficacy, and binge eating. Furthermore, recent research has repeatedly found obesity to correlate with negative affect, depressed mood, and clinical depression. In their comprehensive review, Markowitz, Friedman, and Arent (2008) identify numerous psychosocial and biological processes that they hypothesize to act as mediating factors in the relationship between obesity and depression. This commentary extends Markowitz and colleagues' review and proposed causal pathway model by (a) evaluating the specificity of the relationship between obesity and depression, and (b) providing recommendations for the empirical evaluation of causal hypotheses. [source]