In this task, you are given an abstract of article. Your task is to generate title for this article. Preferred titles are under thirty words.

Ex Input:
The significant disparities in metabolism between tumor and normal cells have inspired the development of metabolism-based anti-tumor therapeutics. Arginine is a semi-essential amino acid because normal cells can not only synthesize arginine de novo but also take up extracellular arginine. Several types of tumors have abnormalities in arginine metabolism enzymes and completely rely on extracellular arginine to support necessary biological processes. This property is referred to as arginine auxotrophy. Taking advantage of characteristic arginine auxotrophy in tumors, arginine deprivation, which is generally induced by the use of arginine deiminase (ADI) and arginase I, has been investigated as a novel strategy for cancer therapy. Arginine deprivation demonstrated promising efficacy against arginine-auxotrophic tumors. By integrating perspectives from both clinical oncologists and laboratory scientists, this article reviews the important aspects of arginine deprivation as a promising anticancer therapy.

Ex Output:
Targeting arginine metabolism pathway to treat arginine-dependent cancers.

Ex Input:
Expression quantitative trait loci (eQTL) mapping aims to determine genomic regions that regulate gene transcription. Expression QTL is used to study the regulatory structure of normal tissues and to search for genetic factors in complex diseases such as cancer, diabetes, and cystic fibrosis. A modern eQTL dataset contains millions of SNPs and thousands of transcripts measured for hundreds of samples. This makes the analysis computationally complex as it involves independent testing for association for every transcript-SNP pair. The heavy computational burden makes eQTL analysis less popular, often forces analysts to restrict their attention to just a subset of transcripts and SNPs. As larger genotype and gene expression datasets become available, the demand for fast tools for eQTL analysis increases. We present a new method for fast eQTL analysis via linear models, called Matrix eQTL. Matrix eQTL can model and test for association using both linear regression and ANOVA models. The models can include covariates to account for such factors as population structure, gender, and clinical variables. It also supports testing of heteroscedastic models and models with correlated errors. In our experiment on large datasets Matrix eQTL was thousands of times faster than the existing popular software for QTL/eQTL analysis. Matrix eQTL is implemented as both Matlab and R packages and thus can easily be run on Windows, Mac OS, and Linux systems. The software is freely available at the following address: http://www.bios.unc.edu/research/genomic_software/Matrix_eQTL

Ex Output:
Matrix eQTL: Ultra fast eQTL analysis via large matrix operations

Ex Input:
The importance of coordinating cell growth with proliferation has been recognized for a long time. The molecular basis of this relationship, however, is poorly understood. Here we show that the ribosomal protein L23 interacts with HDM2. The interaction involves the central acidic domain of HDM2 and an N-terminal domain of L23. L23 and L11, another HDM2-interacting ribosomal protein, can simultaneously yet distinctly interact with HDM2 together to form a ternary complex. We show that, when overexpressed, L23 inhibits HDM2-induced p53 polyubiquitination and degradation and causes a p53-dependent cell cycle arrest. On the other hand, knocking down L23 causes nucleolar stress and triggers translocation of B23 from the nucleolus to the nucleoplasm, leading to stabilization and activation of p53. Our data suggest that cells may maintain a steady-state level of L23 during normal growth; alternating the levels of L23 in response to changing growth conditions could impinge on the HDM2-p53 pathway by interrupting the integrity of the nucleolus.

Ex Output:
Inhibition of HDM2 and activation of p53 by ribosomal protein L23.