Harnessing epithelial homeostatic mechanisms to fight cancer.

Cancer treatments have, in general, targeted the cancer cell itself. This approach has often been unsuccessful in the long term, especially for solid tumors. Even targeted therapies based on sequencing cancer genomes can be thwarted by genetic heterogeneity within tumors. Furthermore, genomic instability in cancer cells accelerates the generation of variants that are resistant to the treatment. Immunotherapies and anti-angiogenic treatments, which target the tumor-interacting and tumor-adjacent cells, have overcome some of these challenges, suggesting that other methods that target wild-type cells could be valuable in arresting tumor progression. Studies in Drosophila have uncovered mechanisms by which cells within an epithelium can react to neighboring cells that have genetic differences, resulting in the elimination of one population at the expense of another. Some of these mechanisms are now known to be conserved in mammals. The possibility of harnessing such mechanisms to empower normal epithelial cells to eliminate their precancerous neighbors before they develop into fully fledged cancers is an area of research that merits more attention

Optimizing radiation therapy treatments by exploring tumour ecosystem dynamics in-silico

In this contribution, we propose a system-level compartmental population dynamics model of tumour cells that interact with the patient (innate) immune system under the impact of radiation therapy (RT). The resulting in silico - model enables us to analyse the system-level impact of radiation on the tumour ecosystem. The Tumour Control Probability (TCP) was calculated for varying conditions concerning therapy fractionation schemes, radio-sensitivity of tumour sub-clones, tumour population doubling time, repair speed and immunological elimination parameters. The simulations exhibit a therapeutic benefit when applying the initial 3 fractions in an interval of 2 days instead of daily delivered fractions. This effect disappears for fast-growing tumours and in the case of incomplete repair. The results suggest some optimisation potential for combined hyperthermia-radiotherapy. Regarding the sensitivity of the proposed model, cellular repair of radiation-induced damages is a key factor for tumour control. In contrast to this, the radio-sensitivity of immune cells does not influence the TCP as long as the radio-sensitivity is higher than those for tumour cells. The influence of the tumour sub-clone structure is small (if no competition is included). This work demonstrates the usefulness of in silico – modelling for identifying optimisation potentials

Set-Based Pre-Processing for Points-To Analysis

We present set-based pre-analysis: a virtually universal op- timization technique for flow-insensitive points-to analysis. Points-to analysis computes a static abstraction of how ob- ject values flow through a program’s variables. Set-based pre-analysis relies on the observation that much of this rea- soning can take place at the set level rather than the value level. Computing constraints at the set level results in sig- nificant optimization opportunities: we can rewrite the in- put program into a simplified form with the same essential points-to properties. This rewrite results in removing both local variables and instructions, thus simplifying the sub- sequent value-based points-to computation. E ectively, set- based pre-analysis puts the program in a normal form opti- mized for points-to analysis. Compared to other techniques for o -line optimization of points-to analyses in the literature, the new elements of our approach are the ability to eliminate statements, and not just variables, as well as its modularity: set-based pre-analysis can be performed on the input just once, e.g., allowing the pre-optimization of libraries that are subsequently reused many times and for di erent analyses. In experiments with Java programs, set-based pre-analysis eliminates 30% of the program’s local variables and 30% or more of computed context-sensitive points-to facts, over a wide set of bench- marks and analyses, resulting in a 20% average speedup (max: 110%, median: 18%)

Structured Review of Code Clone Literature

This report presents the results of a structured review of code clone literature. The aim of the review is to assemble a conceptual model of clone-related concepts which helps us to reason about clones. This conceptual model unifies clone concepts from a wide range of literature, so that findings about clones can be compared with each other

Characterization, cloning and immunogenicity of antigens released by lung-stage larvae of Schistosoma mansoni

Lung-stage schistosomula are the target of protective immunity in mice vaccinated with attenuated cercariae of Schistosoma mansoni. Therefore, proteins present at this developmental stage, and in particular those which are secreted, are a potential source of novel vaccine candidates. However, little information is available about such molecules. Here we describe the cDNA clones identified by screening expression libraries with serum raised against proteins released by lung-stage schistosomula. In total, 11 different cDNA species were identified, 6 of which have been described previously in S. mansoni; these included fructose 1,6-bisphosphate aldolase and Sm21.7 which together accounted for two-thirds of all positive clones. Of the 5 newly described schistosome genes, 1 cDNA had a high degree of homology to the s5a subunit of 26S proteasomes, most significant being with the human protein. The remaining 4 clones showed no significant homologies to any genes sequenced previously. Fructose 1,6-bisphosphate aldolase, Sm21.7, the proteasome homologue and 1 unknown clone (A26) have been expressed in a bacterial expression system and serum produced against each recombinant protein. Immunolocalization showed fructose 1,6-bisphosphate aldolase, Sm21.7 and the proteasome homologue to be most abundant in muscle cells whilst clone A26 was distributed throughout many tissues, but was most abundant in the tegument. Analysis of the cellular immune responses of vaccinated mice showed 3 of the 4 expressed clones to be highly immunogenic, inducing the secretion of large quantities of the Th1-type cytokine interferon gamma

The genetic basis for adaptation of model-designed syntrophic co-cultures.

Understanding the fundamental characteristics of microbial communities could have far reaching implications for human health and applied biotechnology. Despite this, much is still unknown regarding the genetic basis and evolutionary strategies underlying the formation of viable synthetic communities. By pairing auxotrophic mutants in co-culture, it has been demonstrated that viable nascent E. coli communities can be established where the mutant strains are metabolically coupled. A novel algorithm, OptAux, was constructed to design 61 unique multi-knockout E. coli auxotrophic strains that require significant metabolite uptake to grow. These predicted knockouts included a diverse set of novel non-specific auxotrophs that result from inhibition of major biosynthetic subsystems. Three OptAux predicted non-specific auxotrophic strains-with diverse metabolic deficiencies-were co-cultured with an L-histidine auxotroph and optimized via adaptive laboratory evolution (ALE). Time-course sequencing revealed the genetic changes employed by each strain to achieve higher community growth rates and provided insight into mechanisms for adapting to the syntrophic niche. A community model of metabolism and gene expression was utilized to predict the relative community composition and fundamental characteristics of the evolved communities. This work presents new insight into the genetic strategies underlying viable nascent community formation and a cutting-edge computational method to elucidate metabolic changes that empower the creation of cooperative communities