Validation of a constraint-based model of <Emphasis Type="Italic">Pichia pastoris</Emphasis> metabolism under data scarcity

Background  

Constraint-based models enable structured cellular representations in which intracellular kinetics are circumvented. These models, combined with experimental data, are useful analytical tools to estimate the state exhibited (the phenotype) by the cells at given pseudo-steady conditions.     

A molecular diffusion based utility model for Drosophila larval phototaxis

Background  

Generally, utility based decision making models focus on experimental outcomes. In this paper we propose a utility model based on molecular diffusion to simulate the choice behavior of Drosophila larvae exposed to different light conditions.     

The impact of the competence quorum sensing system on <Emphasis Type="Italic">Streptococcus pneumoniae</Emphasis>biofilms varies depending on the experimental model

Background  

Different models for biofilm in Streptococcus pneumoniae have been described in literature. To permit comparison of experimental data, we characterised the impact of the pneumococcal quorum-sensing competence system on biofilm formation in three models. For this scope, we used two microtiter and one continuous culture biofilm system.     

Iterative approach to model identification of biological networks

Background  

Recent advances in molecular biology techniques provide an opportunity for developing detailed mathematical models of biological processes. An iterative scheme is introduced for model identification using available system knowledge and experimental measurements.     

CARMA: A platform for analyzing microarray datasets that incorporate replicate measures

Background  

The incorporation of statistical models that account for experimental variability provides a necessary framework for the interpretation of microarray data. A robust experimental design coupled with an analysis of variance (ANOVA) incorporating a model that accounts for known sources of experimental variability can significantly improve the determination of differences in gene expression and estimations of their significance.     

An editor for pathway drawing and data visualization in the Biopathways Workbench

Background  

Pathway models serve as the basis for much of systems biology. They are often built using programs designed for the purpose. Constructing new models generally requires simultaneous access to experimental data of diverse types, to databases of well-characterized biological compounds and molecular intermediates, and to reference model pathways. However, few if any software applications provide all such capabilities within a single user interface.     

Engineering and validation of a novel lipid thin film for biomembrane modeling in lipophilicity determination of drugs and xenobiotics

Background  

Determination of lipophilicity as a tool for predicting pharmacokinetic molecular behavior is limited by the predictive power of available experimental models of the biomembrane. There is current interest, therefore, in models that accurately simulate the biomembrane structure and function. A novel bio-device; a lipid thin film, was engineered as an alternative approach to the previous use of hydrocarbon thin films in biomembrane modeling.     

Interferon-γ inhibits interleukin-1β-induced matrix metalloproteinase production by synovial fibroblasts and protects articular cartilage in early arthritis

Introduction  

The first few months after symptom onset represents a pathologically distinct phase in rheumatoid arthritis (RA). We used relevant experimental models to define the pathological role of interferon-γ (IFN-γ) during early inflammatory arthritis.     

Stochastic modeling of oligodendrocyte generation in cell culture: model validation with time-lapse data

Background  

The purpose of this paper is two-fold. The first objective is to validate the assumptions behind a stochastic model developed earlier by these authors to describe oligodendrocyte generation in cell culture. The second is to generate time-lapse data that may help biomathematicians to build stochastic models of cell proliferation and differentiation under other experimental scenarios.     

Bifurcation analysis informs Bayesian inference in the Hes1 feedback loop

Background  

Ordinary differential equations (ODEs) are an important tool for describing the dynamics of biological systems. However, for ODE models to be useful, their parameters must first be calibrated. Parameter estimation, that is, finding parameter values given experimental data, is an inference problem that can be treated systematically through a Bayesian framework.     

Open access to novel dual flow chamber technology for <Emphasis Type="Italic">in vitro</Emphasis> cell mechanotransduction,toxicity and pharamacokinetic studies

Background  

A major stumbling block for researchers developing experimental models of mechanotransduction is the control of experimental variables, in particular the transmission of the mechanical forces at the cellular level. A previous evaluation of state of the art commercial perfusion chambers showed that flow regimes, applied to impart a defined mechanical stimulus to cells, are poorly controlled and that data from studies in which different chambers are utilized can not be compared, even if the target stress regimes are comparable.     

Computing phenomenologic Adair-Klotz constants from microscopic MWC parameters

Background  

Modellers using the MWC allosteric framework have often found it difficult to validate their models. Indeed many experiments are not conducted with the notion of alternative conformations in mind and therefore do not (or cannot) measure relevant microscopic constant and parameters. Instead, experimentalists widely use the Adair-Klotz approach in order to describe their experimental data.     

Towards the high-resolution protein structure prediction. Fast refinement of reduced models with all-atom force field

Background  

Although experimental methods for determining protein structure are providing high resolution structures, they cannot keep the pace at which amino acid sequences are resolved on the scale of entire genomes. For a considerable fraction of proteins whose structures will not be determined experimentally, computational methods can provide valuable information. The value of structural models in biological research depends critically on their quality. Development of high-accuracy computational methods that reliably generate near-experimental quality structural models is an important, unsolved problem in the protein structure modeling.     

Effect of interleukin-1β on spinal cord nociceptive transmission of normal and monoarthritic rats after disruption of glial function

Introduction  

Cytokines produced by spinal cord glia after peripheral injuries have a relevant role in the maintenance of pain states. Thus, while IL-1β is overexpressed in the spinal cords of animals submitted to experimental arthritis and other chronic pain models, intrathecal administration of IL-1β to healthy animals induces hyperalgesia and allodynia and enhances wind-up activity in dorsal horn neurons.     

Integrating protein structures and precomputed genealogies in the Magnum database: Examples with cellular retinoid binding proteins

Background  

When accurate models for the divergent evolution of protein sequences are integrated with complementary biological information, such as folded protein structures, analyses of the combined data often lead to new hypotheses about molecular physiology. This represents an excellent example of how bioinformatics can be used to guide experimental research. However, progress in this direction has been slowed by the lack of a publicly available resource suitable for general use.     

Parameter inference for discretely observed stochastic kinetic models using stochastic gradient descent

Background  

Stochastic effects can be important for the behavior of processes involving small population numbers, so the study of stochastic models has become an important topic in the burgeoning field of computational systems biology. However analysis techniques for stochastic models have tended to lag behind their deterministic cousins due to the heavier computational demands of the statistical approaches for fitting the models to experimental data. There is a continuing need for more effective and efficient algorithms. In this article we focus on the parameter inference problem for stochastic kinetic models of biochemical reactions given discrete time-course observations of either some or all of the molecular species.     

Validation of housekeeping genes for quantitative real-time PCR in in-vivo and in-vitro models of cerebral ischaemia

Background  

Studies of gene expression in experimental cerebral ischaemia models can contribute to understanding the pathophysiology of brain ischaemia and to identifying prognostic markers and potential therapeutic targets. The normalization of relative qRT-PCR data using a suitable reference gene is a crucial prerequisite for obtaining reliable conclusions. No validated housekeeping genes have been reported for the relative quantification of the mRNA expression profile activated in in-vitro ischaemic conditions, whereas for the in-vivo model different reference genes have been used.     

A crucial role for tumor necrosis factor receptor 1 in synovial lining cells and the reticuloendothelial system in mediating experimental arthritis

Introduction  

Rheumatoid arthritis (RA) is an autoimmune inflammatory disease that mainly affects synovial joints. Biologics directed against tumor-necrosis-factor (TNF)-α are efficacious in the treatment of RA. However, the role of TNF receptor-1 (TNFR1) in mediating the TNFα effects in RA has not been elucidated and conflicting data exist in experimental arthritis models. The objective is to investigate the role of TNFR1 in the synovial lining cells (SLC) and the reticuloendothelial system (RES) during experimental arthritis.     

Decision tree modeling predicts effects of inhibiting contractility signaling on cell motility

Background  

Computational models of cell signaling networks typically are aimed at capturing dynamics of molecular components to derive quantitative insights from prior experimental data, and to make predictions concerning altered dynamics under different conditions. However, signaling network models have rarely been used to predict how cell phenotypic behaviors result from the integrated operation of these networks. We recently developed a decision tree model for how EGF-induced fibroblast cell motility across two-dimensional fibronectin-coated surfaces depends on the integrated activation status of five key signaling nodes, including a proximal regulator of transcellular contractile force generation, MLC (myosin light chain) [Hautaniemi et al, Bioinformatics 21: 2027 {2005}], but we have not previously attempted predictions of new experimental effects from this model.