Systems and Synthetic Biology最新文献

Breast cancer metastasis is a complex and still weakly understood process that involves diverse cellular pathways. It accounts for the majority of deaths from breast cancer. Recently, microRNAs (miRNAs), small non-coding RNAs that regulate gene expression post-transcriptionally, have been shown to be involved in breast cancer metastasis. In particular, in a recent work it has been found that miR-429 may have a role in the inhibition of migration and invasion of breast cancer cells. Its target gene CRKL has been identified as a potential candidate. In this paper, by using systems biology tools we have shown that CRKL is involved in positive regulation of ERK1/2 signaling pathway and contribute to the regulation of LYN through a topological generalization of feed forward loop.

Expression of synthetic proteins from intergenic regions of E. coli and their functional association was recently demonstrated (Dhar et al. in J Biol Eng 3:2, 2009. doi:10.1186/1754-1611-3-2). This gave birth to the question: if one can make 'user-defined' genes from non-coding genome-how big is the artificially translatable genome? (Dinger et al. in PLoS Comput Biol 4, 2008; Frith et al. in RNA Biol 3(1):40-48, 2006a; Frith et al. in PLoS Genet 2(4):e52, 2006b). To answer this question, we performed a bioinformatics study of all reported E. coli intergenic sequences, in search of novel peptides and proteins, unexpressed by nature. Overall, 2500 E. coli intergenic sequences were computationally translated into 'protein sequence equivalents' and matched against all known proteins. Sequences that did not show any resemblance were used for building a comprehensive profile in terms of their structure, function, localization, interactions, stability so on. A total of 362 protein sequences showed evidence of stable tertiary conformations encoded by the intergenic sequences of E. coli genome. Experimental studies are underway to confirm some of the key predictions. This study points to a vast untapped repository of functional molecules lying undiscovered in the non-expressed genome of various organisms.

Being able to design genetic regulatory networks (GRNs) to achieve a desired cellular function is one of the main goals of synthetic biology. However, determining minimal GRNs that produce desired time-series behaviors is non-trivial. In this paper, we propose a 'top-down' approach to evolving small GRNs and then use these to recursively boot-strap the identification of larger, more complex, modular GRNs. We start with relatively dense GRNs and then use differential evolution (DE) to evolve interaction coefficients. When the target dynamical behavior is found embedded in a dense GRN, we narrow the focus of the search and begin aggressively pruning out excess interactions at the end of each generation. We first show that the method can quickly rediscover known small GRNs for a toggle switch and an oscillatory circuit. Next we include these GRNs as non-evolvable subnetworks in the subsequent evolution of more complex, modular GRNs. Successful solutions found in canonical DE where we truncated small interactions to zero, with or without an interaction penalty term, invariably contained many excess interactions. In contrast, by incorporating aggressive pruning and the penalty term, the DE was able to find minimal or nearly minimal GRNs in all test problems.

Synthetic biology is currently one of the most debated emerging biotechnologies. The societal assessment of this technology is primarily based on contributions by scientists and policy makers, who focus mainly on technical challenges and possible risks. While public dialogue is given, it is yet rather limited. This study explores public debates concerning synthetic biology based on a focus group study with citizens from Austria and Germany and contextualises the analysed public views with content from policy reports and previous empirical studies on public engagement. The findings suggest that discussants favoured a gradual implementation process of synthetic biology, which is receptive to questions about the distribution of possible benefits. The discussed topics correspond in many ways with content from policy reports and former investigations, yet the emphasis of the discussions was different for many aspects.

Brucellaphage Gadvasu (BpG) is a lytic phage infecting Brucella spp. Brucellaphages contain dsDNA as genetic material and are short-tailed particles with host-specificity. Here, we report the challenges on annotation in the complete genome sequence of BpG when compared with that of a recent broad host-range brucellaphage Pr, an original reference genome. The extracted DNA was subjected to genome sequencing with Illumina technology and assembled using SSAKE/Velvet. A significant number of genes were found to be similar between the phages with sequence analysis revealing conserved open reading frames that correspond to 33 gene ontology classifiers, transcriptional terminators and a few putative transcriptional promoters. The analyses revealed that the genome constitutes 1269 contigs and 275 genes encoding 260 proteins. The sequence comparison from the reference data indicated that the genome shares an approximately 70 % nucleotide similarity and differs mainly in the region encoding proteins. We bring this commentary providing an overview of how this exemplar genome can allow us to understand these known unknown regions in brucellaphages.

Brain cancers demonstrate a complex metabolic behavior so as to adapt the external hypoxic environment and internal stress generated by reactive oxygen species. To survive in these stringent conditions, glioblastoma cells develop an antagonistic metabolic phenotype as compared to their predecessors, the astrocytes, thereby quenching the resources expected for nourishing the neurons. The complexity and cumulative effect of the large scale metabolic functioning of glioblastoma is mostly unexplored. In this study, we reconstruct a metabolic network comprising of pathways that are known to be deregulated in glioblastoma cells as compared to the astrocytes. The network, consisted of 147 genes encoding for enzymes performing 247 reactions distributed across five distinct model compartments, was then studied using constrained-based modeling approach by recreating the scenarios for astrocytes and glioblastoma, and validated with available experimental evidences. From our analysis, we predict that glycine requirement of the astrocytes are mostly fulfilled by the internal glycine-serine metabolism, whereas glioblastoma cells demand an external uptake of glycine to utilize it for glutathione production. Also, cystine and glucose were identified to be the major contributors to glioblastoma growth. We also proposed an extensive set of single and double lethal reaction knockouts, which were further perturbed to ascertain their role as probable chemotherapeutic targets. These simulation results suggested that, apart from targeting the reactions of central carbon metabolism, knockout of reactions belonging to the glycine-serine metabolism effectively reduce glioblastoma growth. The combinatorial targeting of glycine transporter with any other reaction belonging to glycine-serine metabolism proved lethal to glioblastoma growth.

Synthetic biology is a recent scientific approach towards engineering biological systems from both pre-existing and novel parts. The aim is to introduce computational aided design approach in biology leading to rapid delivery of useful applications. Though the term reprogramming has been frequently used in the synthetic biology community, currently the technological sophistication only allows for a probabilistic approach instead of a precise engineering approach. Recently, several human health applications have emerged that suggest increased usage of synthetic biology approach in developing novel drugs. This mini review discusses recent translational developments in the field and tries to identify some of the upcoming future developments.

Biomolecular networks that present oscillatory behavior are ubiquitous in nature. While some design principles for robust oscillations have been identified, it is not well understood how these oscillations are affected when the kinetic parameters are constantly changing or are not precisely known, as often occurs in cellular environments. Many models of diverse complexity level, for systems such as circadian rhythms, cell cycle or the p53 network, have been proposed. Here we assess the influence of hundreds of different parameter sets on the sensitivities of two configurations of a well-known oscillatory system, the p53 core network. We show that, for both models and all parameter sets, the parameter related to the p53 positive feedback, i.e. self-promotion, is the only one that presents sizeable sensitivities on extrema, periods and delay. Moreover, varying the parameter set values to change the dynamical characteristics of the response is more restricted in the simple model, whereas the complex model shows greater tunability. These results highlight the importance of the presence of specific network patterns, in addition to the role of parameter values, when we want to characterize oscillatory biochemical systems.