Parameter adaptations during phenotype transitions in progressive diseases

<p>Abstract</p> <p>Background</p> <p>The study of phenotype transitions is important to understand progressive diseases, e.g., diabetes mellitus, metabolic syndrome, and cardiovascular diseases. A challenge remains to explain phenotype transitions in terms of adaptations in molecular components and interactions in underlying biological systems.</p> <p>Results</p> <p>Here, mathematical modeling is used to describe the different phenotypes by integrating experimental data on metabolic pools and fluxes. Subsequently, trajectories of parameter adaptations are identified that are essential for the phenotypical changes. These changes in parameters reflect progressive adaptations at the transcriptome and proteome level, which occur at larger timescales. The approach was employed to study the metabolic processes underlying liver X receptor induced hepatic steatosis. Model analysis predicts which molecular processes adapt in time after pharmacological activation of the liver X receptor. Our results show that hepatic triglyceride fluxes are increased and triglycerides are especially stored in cytosolic fractions, rather than in endoplasmic reticulum fractions. Furthermore, the model reveals several possible scenarios for adaptations in cholesterol metabolism. According to the analysis, the additional quantification of one cholesterol flux is sufficient to exclude many of these hypotheses.</p> <p>Conclusions</p> <p>We propose a generic computational approach to analyze biological systems evolving through various phenotypes and to predict which molecular processes are responsible for the transition. For the case of liver X receptor induced hepatic steatosis the novel approach yields information about the redistribution of fluxes and pools of triglycerides and cholesterols that was not directly apparent from the experimental data. Model analysis provides guidance which specific molecular processes to study in more detail to obtain further understanding of the underlying biological system.</p

Extended Kalman Filter for Estimation of Parameters in Nonlinear State-Space Models of Biochemical Networks

It is system dynamics that determines the function of cells, tissues and organisms. To develop mathematical models and estimate their parameters are an essential issue for studying dynamic behaviors of biological systems which include metabolic networks, genetic regulatory networks and signal transduction pathways, under perturbation of external stimuli. In general, biological dynamic systems are partially observed. Therefore, a natural way to model dynamic biological systems is to employ nonlinear state-space equations. Although statistical methods for parameter estimation of linear models in biological dynamic systems have been developed intensively in the recent years, the estimation of both states and parameters of nonlinear dynamic systems remains a challenging task. In this report, we apply extended Kalman Filter (EKF) to the estimation of both states and parameters of nonlinear state-space models. To evaluate the performance of the EKF for parameter estimation, we apply the EKF to a simulation dataset and two real datasets: JAK-STAT signal transduction pathway and Ras/Raf/MEK/ERK signaling transduction pathways datasets. The preliminary results show that EKF can accurately estimate the parameters and predict states in nonlinear state-space equations for modeling dynamic biochemical networks

Paratransgenesis: a promising new strategy for mosquito vector control

The three main mosquito genera, Anopheles, Aedes and Culex, transmit respectively malaria, dengue and lymphatic filariasis. Current mosquito control strategies have proved unsuccessful, and there still is a substantial number of morbidity and mortality from these diseases. Genetic control methods have now arisen as promising alternative strategies, based on two approaches: the replacement of a vector population by disease-refractory mosquitoes and the release of mosquitoes carrying a lethal gene to suppress target populations. However, substantial hurdles and limitations need to be overcome if these methods are to be used successfully, the most significant being that a transgenic mosquito strain is required for every target species, making genetically modified mosquito strategies inviable when there are multiple vector mosquitoes in the same area. Genetically modified bacteria capable of colonizing a wide range of mosquito species may be a solution to this problem and another option for the control of these diseases. In the paratransgenic approach, symbiotic bacteria are genetically modified and reintroduced in mosquitoes, where they express effector molecules. For this approach to be used in practice, however, requires a better understanding of mosquito microbiota and that symbiotic bacteria and effector molecules be identified. Paratransgenesis could prove very useful in mosquito species that are inherently difficult to transform or in sibling species complexes. In this approach, a genetic modified bacteria can act by: (a) causing pathogenic effects in the host; (b) interfering with the host’s reproduction; (c) reducing the vector’s competence; and (d) interfering with oogenesis and embryogenesis. It is a much more flexible and adaptable approach than the use of genetically modified mosquitoes because effector molecules and symbiotic bacteria can be replaced if they do not achieve the desired result. Paratransgenesis may therefore become an important integrated pest management tool for mosquito control