Influenza infection and therapy: a systems approach

Seasonal flu affects 5–20% of the human population each year. Although mortality rates are typically <0.1% and the pandemic 2009 H1N1 influenza strain has been well contained by vaccination and strict hygiene, particularly virulent pandemic forms have emerged three times in the last century, resulting in millions of deaths. Current vaccine and therapeutic strategies are limited by the ability of the virus to generate variants that evade vaccine-induced immune responses and resist the therapeutic effects of antiviral drugs. Host genetic variations affect immune responses and may induce adverse effects during drug treatment or against vaccines. To develop new, first-in-class therapeutics, new antiviral targets and new chemical entities must be identified in the context of the immunogenomic repertoire of the patient. Since influenza and so many other viruses need to escape innate immunity to become pathogenic, the viral proteins responsible for this, as well as the host cell molecular pathways that lead to the antiviral response, are an excellent potential source of new therapeutic targets within a systems approach against influenza infections.

Combining imaging and pathway profiling: an alternative approach to cancer drug discovery

Conventional drug discovery strategies are typically 'target centric' based on the selection of lead compounds with optimised 'on-target' potency and selectivity profiles. However, high-attrition rates are often the result of compensatory or redundant cancer mechanisms and the fact that tumours do not find it difficult to escape inhibition of a single pathway. In this article, we highlight two emerging and complimentary technologies; namely phenotypic imaging and post-translational pathway profiling, which when combined with relevant disease models can provide pharmacodiagnostic and drug combination strategies that predict and counteract inherent and adaptive drug resistance. The implementation of such approaches at early stages of the drug discovery process enables more informed decisions on candidate drug selection and how to maximise and predict efficacy before clinical development.

Reliable and effective oxygen-ozone therapy at a crossroads with ozonated saline infusion and ozone rectal insufflation

OBJECTIVES

This review aims to highlight the advantages and safety of oxygen-ozone therapy (OOT) and to suggest ways to enhance its acceptance.

KEY FINDINGS

The treatment of a herniated disk by injecting a gaseous oxygen-ozone mixture inside the nucleus pulposus is a great clinical success. However, the use of OOT lags for a number of reasons, including lack of standardization, the need for numerous treatments, lack of knowledge and even denial. Anecdotally, several million treatments by OOT have been performed worldwide indicating its usefulness, mainly in peripheral arterial diseases and age-related macular degeneration. The scepticism that accompanies the systemic use of ozone can only be overcome by demonstrating the validity of OOT in controlled and randomized clinical trials. Cheaper and quicker methods, such as ozonating physiological saline with successive infusion as well as ozone rectal insufflations, are becoming popular, however, such alternative procedures are erratic, unstable and liable to be toxic, with deleterious consequences, and are likely to discredit the beneficial use of ozone.

SUMMARY

The approval of ozone in terms of both therapeutic efficacy and safety will depend on the results achieved by authoritative clinical trials.

Yeast systems biology: the challenge of eukaryotic complexity

In this chapter, we present an up-to-date view of the optimal characteristics of the yeast Saccharomyces cerevisiae as a model eukaryote for systems biology studies, with main molecular mechanisms, biological networks, and sub-cellular organization essentially conserved in all eukaryotes, derived from a complex common ancestor. The existence of advanced tools for molecular studies together with high-throughput experimental and computational methods, most of them being implemented and validated in yeast, with new ones being developed, is opening the way to the characterization of the core modular architecture and complex networks essential to all eukaryotes. Selected examples of the latest discoveries in eukaryote complexity and systems biology studies using yeast as a reference model and their applications in biotechnology and medicine are presented.

Systems biology and modeling in neuroblastoma: practicalities and perspectives

Neuroblastoma (NB) is a common pediatric malignancy characterized by clinical and biological heterogeneity. A host of prognostic markers are available, contributing to accurate risk stratification and appropriate treatment allocation. Unfortunately, outcome is still poor for many patients, indicating the need for a new approach with enhanced utilization of the available biological data. Systems biology is a holistic approach in which all components of a biological system carry equal importance. Systems biology uses mathematical modeling and simulation to investigate dynamic interactions between system components, as a means of explaining overall system behavior. Systems biology can benefit the biomedical sciences by providing a more complete understanding of human disease, enhancing the development of targeted therapeutics. Systems biology is largely contiguous with current approaches in NB, which already employ an integrative and pseudo-holistic approach to disease management. Systems modeling of NB offers an optimal method for continuing progression in this field, and conferring additional benefit to current risk stratification and management. Likewise, NB provides an opportunity for systems biology to prove its utility in the context of human disease, since the biology of NB is comprehensively characterized and, therefore, suited to modeling. The purpose of this review is to outline the benefits, challenges and fundamental workings of systems modeling in human disease, using a specific example of bottom-up modeling in NB. The intention is to demonstrate practical requirements to begin bridging the gap between biological research and applied mathematical approaches for the mutual gain of both fields, and with additional benefits for clinical management.

An eScience-Bayes strategy for analyzing omics data

BACKGROUND

The omics fields promise to revolutionize our understanding of biology and biomedicine. However, their potential is compromised by the challenge to analyze the huge datasets produced. Analysis of omics data is plagued by the curse of dimensionality, resulting in imprecise estimates of model parameters and performance. Moreover, the integration of omics data with other data sources is difficult to shoehorn into classical statistical models. This has resulted in ad hoc approaches to address specific problems.

RESULTS

We present a general approach to omics data analysis that alleviates these problems. By combining eScience and Bayesian methods, we retrieve scientific information and data from multiple sources and coherently incorporate them into large models. These models improve the accuracy of predictions and offer new insights into the underlying mechanisms. This "eScience-Bayes" approach is demonstrated in two proof-of-principle applications, one for breast cancer prognosis prediction from transcriptomic data and one for protein-protein interaction studies based on proteomic data.

CONCLUSIONS

Bayesian statistics provide the flexibility to tailor statistical models to the complex data structures in omics biology as well as permitting coherent integration of multiple data sources. However, Bayesian methods are in general computationally demanding and require specification of possibly thousands of prior distributions. eScience can help us overcome these difficulties. The eScience-Bayes thus approach permits us to fully leverage on the advantages of Bayesian methods, resulting in models with improved predictive performance that gives more information about the underlying biological system.

Bystander effects and their implications for clinical radiotherapy

Radiation-induced bystander effects are defined as those biological effects expressed, after irradiation, by cells whose nuclei have not been directly irradiated. Radiation oncologists are only gradually beginning to appreciate the clinical relevance of radiation-induced bystander effects and associated phenomena: adaptive responses, genomic instability and abscopal effects. Incorporating bystander effects into the science underpinning clinical radiotherapy will involve moving beyond simple mechanistic models and towards a more systems-based approach. It is, given the protean nature of bystander effects, difficult to devise a coherent research strategy to investigate the clinical impact and relevance of bystander phenomena. Epidemiological approaches will be required, the traditional research models based on randomised controlled trials are unlikely to be adequate for the task. Any consideration of bystander effects challenges not only clinicians' preconceptions concerning the effects of radiation on tumours and normal tissues but also their ingenuity. This review covers, from a clinical perspective, the issues and problems associated with radiation-induced bystander effects.