On systems and control approaches to therapeutic gain

BACKGROUND: Mathematical models of cancer relevant processes are being developed at an increasing rate. Conceptual frameworks are needed to support new treatment designs based on such models. METHODS: A modern control perspective is used to formulate two therapeutic gain strategies. RESULTS: Two conceptually distinct therapeutic gain strategies are provided. The first is direct in that its goal is to kill cancer cells more so than normal cells, the second is indirect in that its goal is to achieve implicit therapeutic gains by transferring states of cancer cells of non-curable cases to a target state defined by the cancer cells of curable cases. The direct strategy requires models that connect anti-cancer agents to an endpoint that is modulated by the cause of the cancer and that correlates with cell death. It is an abstraction of a strategy for treating mismatch repair (MMR) deficient cancers with iodinated uridine (IUdR); IU-DNA correlates with radiation induced cell killing and MMR modulates the relationship between IUdR and IU-DNA because loss of MMR decreases the removal of IU from the DNA. The second strategy is indirect. It assumes that non-curable patient outcomes will improve if the states of their malignant cells are first transferred toward a state that is similar to that of a curable patient. This strategy is difficult to employ because it requires a model that relates drugs to determinants of differences in patient survival times. It is an abstraction of a strategy for treating BCR-ABL pro-B cell childhood leukemia patients using curable cases as the guides. CONCLUSION: Cancer therapeutic gain problem formulations define the purpose, and thus the scope, of cancer process modeling. Their abstractions facilitate considerations of alternative treatment strategies and support syntheses of learning experiences across different cancers

Using GeneReg to construct time delay gene regulatory networks

<p>Abstract</p> <p>Background</p> <p>Understanding gene expression and regulation is essential for understanding biological mechanisms. Because gene expression profiling has been widely used in basic biological research, especially in transcription regulation studies, we have developed GeneReg, an easy-to-use R package, to construct gene regulatory networks from time course gene expression profiling data; More importantly, this package can provide information about time delays between expression change in a regulator and that of its target genes.</p> <p>Findings</p> <p>The R package GeneReg is based on time delay linear regression, which can generate a model of the expression levels of regulators at a given time point against the expression levels of their target genes at a later time point. There are two parameters in the model, time delay and regulation coefficient. Time delay is the time lag during which expression change of the regulator is transmitted to change in target gene expression. Regulation coefficient expresses the regulation effect: a positive regulation coefficient indicates activation and negative indicates repression. GeneReg was implemented on a real Saccharomyces cerevisiae cell cycle dataset; more than thirty percent of the modeled regulations, based entirely on gene expression files, were found to be consistent with previous discoveries from known databases.</p> <p>Conclusions</p> <p>GeneReg is an easy-to-use, simple, fast R package for gene regulatory network construction from short time course gene expression data. It may be applied to study time-related biological processes such as cell cycle, cell differentiation, or causal inference.</p

Assessment of the effect of betaine on p16 and c-myc DNA methylation and mRNA expression in a chemical induced rat liver cancer model

<p>Abstract</p> <p>Background</p> <p>The development and progression of liver cancer may involve abnormal changes in DNA methylation, which lead to the activation of certain proto-oncogenes, such as <it>c-myc</it>, as well as the inactivation of certain tumor suppressors, such as <it>p16</it>. Betaine, as an active methyl-donor, maintains normal DNA methylation patterns. However, there are few investigations on the protective effect of betaine in hepatocarcinogenesis.</p> <p>Methods</p> <p>Four groups of rats were given diethylinitrosamine (DEN) and fed with AIN-93G diets supplemented with 0, 10, 20 or 40 g betaine/kg (model, 1%, 2%, and 4% betaine, respectively), while the control group, received no DEN, fed with AIN-93G diet. Eight or 15 weeks later, the expression of <it>p16 </it>and <it>c-myc </it>mRNA was examined by Real-time PCR (Q-PCR). The DNA methylation status within the <it>p16 </it>and <it>c-myc </it>promoter was analyzed using methylation-specific PCR.</p> <p>Results</p> <p>Compared with the model group, numbers and areas of glutathione S-transferase placental form (GST-p)-positive foci were decreased in the livers of the rats treated with betaine (<it>P < 0.05</it>). Although the frequency of <it>p16 </it>promoter methylation in livers of the four DEN-fed groups appeared to increase, there is no difference among these groups after 8 or 15 weeks (<it>P > 0.05</it>). Betaine supplementation attenuated the down-regulation of <it>p16 </it>and inhibited the up-regulation of <it>c-myc </it>induced by DEN in a dose-dependent manner (<it>P </it>< 0.01). Meanwhile, increases in levels of malondialdehyde (MDA) and glutathione S-transferase (GST) in model, 2% and 4% betaine groups were observed (<it>P < 0.05</it>). Finally, enhanced antioxidative capacity (T-AOC) was observed in both the 2% and 4% betaine groups.</p> <p>Conclusion</p> <p>Our data suggest that betaine attenuates DEN-induced damage in rat liver and reverses DEN-induced changes in mRNA levels.</p

Timing the initiation of multiple myeloma

The evolution and progression of multiple myeloma and its precursors over time is poorly understood. Here, we investigate the landscape and timing of mutational processes shaping multiple myeloma evolution in a large cohort of 89 whole genomes and 973 exomes. We identify eight processes, including a mutational signature caused by exposure to melphalan. Reconstructing the chronological activity of each mutational signature, we estimate that the initial transformation of a germinal center B-cell usually occurred during the first 2nd-3rd decades of life. We define four main patterns of activation-induced deaminase (AID) and apolipoprotein B mRNA editing catalytic polypeptide-like (APOBEC) mutagenesis over time, including a subset of patients with evidence of prolonged AID activity during the pre-malignant phase, indicating antigen-responsiveness and germinal center reentry. Our findings provide a framework to study the etiology of multiple myeloma and explore strategies for prevention and early detection

SBML Level 3: an extensible format for the exchange and reuse of biological models

Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction-based models and packages that extend the core with features suited to other model types including constraint-based models, reaction-diffusion models, logical network models, and rule-based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single-cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution