Systems and Synthetic Biology最新文献

The paper describes some thermodynamic constrains and relations in biochemical or metabolic network and provides a basis for entropy enthalpy compensation. Conventional definition of macroscopic forces and fluxes leads to a paradox namely, non-existence of positive efficiency of a chemically driven process. This paradox is resolved by deriving an appropriate definition of macroscopic force using the local balance equations. Entropy enthalpy compensation, whose thermodynamic basis is so far unclear, also follows. The method provides an account of how reactive pathways are coupled, the strength of coupling between a pathway pair depending on the product of their respective enthalpies. The obligatory role of the presence of a common chemical intermediate in defining coupling becomes unnecessary; such intermediate-free coupling being a key feature of metabolic energy transduction. The redefined flux and force can also be exploited to explain surface to volume ratio dependence of coupled networks. Lastly, the thermodynamic rationale for the Bergman's eco-geographic rule, namely the reduced ability of larger animals to avoid stress follows from the generalized expression for coupling coefficients. Higher surface to volume ratio is shown to make the organism resistant to external perturbations.

The sequencing of genomes of the pathogenic Mycobacterial species causing pulmonary and extrapulmonary tuberculosis, leprosy and other atypical mycobacterial infections, offer immense opportunities for discovering new therapeutics and identifying new vaccine candidates. Enhanced RV, which uses additional algorithms to Reverse Vaccinology (RV), has increased potential to reduce likelihood of undesirable features including allergenicity and immune cross reactivity to host. The starting point for MycobacRV database construction includes collection of known vaccine candidates and a set of predicted vaccine candidates identified from the whole genome sequences of 22 mycobacterium species and strains pathogenic to human and one non-pathogenic Mycobacterium tuberculosis H37Ra strain. These predicted vaccine candidates are the adhesins and adhesin-like proteins obtained using SPAAN at Pad > 0.6 and screening for putative extracellular or surface localization characteristics using PSORTb v.3.0 at very stringent cutoff. Subsequently, these protein sequences were analyzed through 21 publicly available algorithms to obtain Orthologs, Paralogs, BetaWrap Motifs, Transmembrane Domains, Signal Peptides, Conserved Domains, and similarity to human proteins, T cell epitopes, B cell epitopes, Discotopes and potential Allergens predictions. The Enhanced RV information was analysed in R platform through scripts following well structured decision trees to derive a set of nonredundant 233 most probable vaccine candidates. Additionally, the degree of conservation of potential epitopes across all orthologs has been obtained with reference to the M. tuberculosis H37Rv strain, the most commonly used strain in M. tuberculosis studies. Utilities for the vaccine candidate search and analysis of epitope conservation across the orthologs with reference to M. tuberculosis H37Rv strain are available in the mycobacrvR package in R platform accessible from the "Download" tab of MycobacRV webserver. MycobacRV an immunoinformatics database of known and predicted mycobacterial vaccine candidates has been developed and is freely available at http://mycobacteriarv.igib.res.in.

Abstract: Development of bioorganic-inorganic composites has drawn eyes to extensive attention in biomedical fields and tissue engineering. So many attempts to prepare hydroxyapatite (HA), in conjunction with various binders including polyvinyl alcohol (PVA), and collagen has performed for late 20 years. We applied a method based on the phase separation for making of polymer porous membranes. This procedure is induced through the addition of a small quantity of water (polymer-rich phase) to a solution with HA precursors (polymer-poor phase). Thermal and structural composite properties of collagen Hydrolysate (CH)-PVA/HA Polymer-Nano-Porous Membranes were analyzed by Design of experiment that was undertaken using D-optimal approach, to select the optimal combination of nano composites precursor. The resulted composite characters were investigated by Fourier transform infrared, scanning electron microscopy (SEM) and thermal gravimetric analysis. Based on the SEM images, this new method could be clearly concluded to porous CH-PVA/HA hybrid materials. Finally the hemocompatibility of nanocomposite membranes were evaluated by the hemolysis study.

Network of signaling proteins and functional interaction between the infected cell and the leishmanial parasite, though are not well understood, may be deciphered computationally by reconstructing the immune signaling network. As we all know signaling pathways are well-known abstractions that explain the mechanisms whereby cells respond to signals, collections of pathways form networks, and interactions between pathways in a network, known as cross-talk, enables further complex signaling behaviours. In silico perturbations can help identify sensitive crosstalk points in the network which can be pharmacologically tested. In this study, we have developed a model for immune signaling cascade in leishmaniasis and based upon the interaction analysis obtained through simulation, we have developed a model network, between four signaling pathways i.e., CD14, epidermal growth factor (EGF), tumor necrotic factor (TNF) and PI3 K mediated signaling. Principal component analysis of the signaling network showed that EGF and TNF pathways can be potent pharmacological targets to curb leishmaniasis. The approach is illustrated with a proposed workable model of epidermal growth factor receptor (EGFR) that modulates the immune response. EGFR signaling represents a critical junction between inflammation related signal and potent cell regulation machinery that modulates the expression of cytokines.

Context-dependent nature of biological phenomena is well documented in every branch of biology. While there have been few previous attempts to (implicitly) model various (particular) facets of biological context-dependence, a formal and general mathematical construct to model the wide spectrum of context-dependence, eludes the students of biology. Such an objective model, from both 'bottom-up' as well as 'top-down' perspective, is proposed here to serve as the template to describe the various kinds of context-dependence that we encounter in different branches of biology. Interactions between biological contexts was found to be transitive but non-commutative. It is found that a hierarchical nature of dependence among the biological contexts models the emergent biological properties efficiently. Reasons for these findings are provided in a general model to describe biological reality. Scheme to algorithmically implement the hierarchic structure of organization of biological contexts was proposed with a construct named 'Context tree'. A 'Context tree' based analysis of context interactions among biophysical factors influencing protein structure was performed.

In the past decades, synthetic biology has gained interest regarding research and development efforts within the biotechnology domain. However, it is unclear to what extent synthetic biology has matured already into being commercially exploitable. By means of a patent analysis, this study shows that there is an increasing trend regarding synthetic biology related patent applications. The majority of retrieved patents relates to innovations facilitating the realisation of synthetic biology through improved understanding of biological systems. In addition, there is increased activity concerning the development of synthetic biology based applications. When looking at potential application areas, the majority of synthetic biology patents seems most relevant for the medical, energy and industrial sector. Furthermore, the analysis shows that most activity has been carried out by the USA, with Japan and a number of European countries considerably trailing behind. In addition, both universities and companies are major patent applicant actor types. The results presented here form a starting point for follow-up studies concerning the identification of drivers explaining the observed patent application trends in synthetic biology.

We present a very general model of epigenetic evolution unifying (neo-)Darwinian and (neo-)Lamarckian viewpoints. The evolution is represented in the form of adaptive dynamics given by the quantum(-like) master equation. This equation describes development of the information state of epigenome under the pressure of an environment. We use the formalism of quantum mechanics in the purely operational framework. (Hence, our model has no direct relation to quantum physical processes inside a cell.) Thus our model is about probabilities for observations which can be done on epigenomes and it does not provide a detailed description of cellular processes. Usage of the operational approach provides a possibility to describe by one model all known types of cellular epigenetic inheritance.

In this paper, an estimation of model parameters is performed by using the Alternative Regression (AR) approach on an experimental data set of Herpes Simplex Virus type-1 (HSV-1) infection with innate immune response. Throughout the specified course of time, the measurements of monocytes, neutrophils, and viral load were obtained from the corneas of infected mice. C57BL/6 (B6) mice were used at Oakland University, Department of Biological Sciences, and the outcome measurements were divided into training and testing data sets. The HSV-1 nonlinear dynamic model is proposed based on the observed data patterns and biological system information. The simulation results of the proposed model showed that they consistently fit the experimental data set. In addition, the sensitivity test and model validation diagnostics are considered to determine the most significant key parameters that affect the dynamics of the HSV-1 system.

Ligand bound-receptors in a signalosome complex trigger signals to determine cellular functions. Upon ligand binding, the ligand-receptor complexes form clusters on cell membrane. Guided by the previous experimental reports on the cluster formation of CD40, a trans membrane receptor for CD40-ligand, we built a minimal model of the receptor cluster formation. In this model, we studied co-operative and non-co-operative clustering of a maximum of four CD40 molecules assuming a positive mediator of clustering such as cholesterol to be present in both cases. We observed that co-operative interactions between CD40 molecules resulted in more of the largest CD40 clusters than that observed with the non-co-operatively interacting CD40 molecules. We performed global sensitivity analysis on the model parameters and the analyses suggested that cholesterol influenced only the initial stage of the co-operatively clustering CD40 molecules but it affects both the initial and the final stages in case of the non-co-operatively clustering CD40 molecules. Robustness analyses revealed that in both co-operative and non-co-operative interactions, the higher order clusters beyond a critical size are more robust with respect to alterations in the environmental parameters including the cholesterol. Thus, the role of co-operative and non-co-operative interactions in environment-influenced receptor clustering is reported for the first time.