Cardiomyocyte-specific inactivation of thyroid hormone in pathologic ventricular hypertrophy: an adaptative response or part of the problem?

Recent studies in various rodent models of pathologic ventricular hypertrophy report the re-expression of deiodinase type 3 (D3) in cardiomyocytes. D3 inactivates thyroid hormone (T3) and is mainly expressed in tissues during development. The stimulation of D3 activity in ventricular hypertrophy and subsequent heart failure is associated with severe impairment of cardiac T3 signaling. Hypoxia-induced signaling appears to drive D3 expression in the hypertrophic cardiomyocyte, but other signaling cascades implicated in hypertrophy are also capable of stimulating transcription of the DIO3 gene. Many cardiac genes are transcriptionally regulated by T3 and impairment of T3 signaling will not only reduce energy turnover, but also lead to changes in gene expression that contribute to contractile dysfunction in pathologic remodeling. Whether stimulation of D3 activity and the ensuing local T3-deficiency is an adaptive response of the stressed heart or part of the pathologic signaling network leading to heart failure, remains to be established

Experimental Animal Models in Periodontology: A Review

In periodontal research, animal studies are complementary to in vitro experiments prior to testing new treatments. Animal models should make possible the validation of hypotheses and prove the safety and efficacy of new regenerating approaches using biomaterials, growth factors or stem cells. A review of the literature was carried out by using electronic databases (PubMed, ISI Web of Science). Numerous animal models in different species such as rats, hamsters, rabbits, ferrets, canines and primates have been used for modeling human periodontal diseases and treatments. However, both the anatomy and physiopathology of animals are different from those of humans, making difficult the evaluation of new therapies. Experimental models have been developed in order to reproduce major periodontal diseases (gingivitis, periodontitis), their pathogenesis and to investigate new surgical techniques. The aim of this review is to define the most pertinent animal models for periodontal research depending on the hypothesis and expected results

Scalable rule-based modelling of allosteric proteins and biochemical networks

Much of the complexity of biochemical networks comes from the information-processing abilities of allosteric proteins, be they receptors, ion-channels, signalling molecules or transcription factors. An allosteric protein can be uniquely regulated by each combination of input molecules that it binds. This "regulatory complexity" causes a combinatorial increase in the number of parameters required to fit experimental data as the number of protein interactions increases. It therefore challenges the creation, updating, and re-use of biochemical models. Here, we propose a rule-based modelling framework that exploits the intrinsic modularity of protein structure to address regulatory complexity. Rather than treating proteins as "black boxes", we model their hierarchical structure and, as conformational changes, internal dynamics. By modelling the regulation of allosteric proteins through these conformational changes, we often decrease the number of parameters required to fit data, and so reduce over-fitting and improve the predictive power of a model. Our method is thermodynamically grounded, imposes detailed balance, and also includes molecular cross-talk and the background activity of enzymes. We use our Allosteric Network Compiler to examine how allostery can facilitate macromolecular assembly and how competitive ligands can change the observed cooperativity of an allosteric protein. We also develop a parsimonious model of G protein-coupled receptors that explains functional selectivity and can predict the rank order of potency of agonists acting through a receptor. Our methodology should provide a basis for scalable, modular and executable modelling of biochemical networks in systems and synthetic biology

PLS and dimension reduction for classification

Linear discriminant analysis, Ridge regression, Shrinkage estimation, Misclassification rates, Principal components,