B-cell epitope prediction for peptide-based vaccine design: towards a paradigm of biological outcomes for global health

Global health must address a rapidly evolving burden of disease, hence the urgent need for versatile generic technologies exemplified by peptide-based vaccines. B-cell epitope prediction is crucial for designing such vaccines; yet this approach has thus far been largely unsuccessful, prompting further inquiry into the underlying reasons for its apparent inadequacy. Two major obstacles to the development of B-cell epitope prediction for peptide-based vaccine design are (1) the prevailing binary classification paradigm, which mandates the problematic dichotomization of continuous outcome variables, and (2) failure to explicitly model biological consequences of immunization that are relevant to practical considerations of safety and efficacy. The first obstacle is eliminated by redefining the predictive task as quantitative estimation of empirically observable biological effects of antibody-antigen binding, such that prediction is benchmarked using measures of correlation between continuous rather than dichotomous variables; but this alternative approach by itself fails to address the second obstacle even if benchmark data are selected to exclusively reflect functionally relevant cross-reactivity of antipeptide antibodies with protein antigens (as evidenced by antibody-modulated protein biological activity), particularly where only antibody-antigen binding is actually predicted as a surrogate for its biological effects. To overcome the second obstacle, the prerequisite is deliberate effort to predict, a priori, biological outcomes that are of immediate practical significance from the perspective of vaccination. This demands a much broader and deeper systems view of immunobiology than has hitherto been invoked for B-cell epitope prediction. Such a view would facilitate comprehension of many crucial yet largely neglected aspects of the vaccine-design problem. Of these, immunodominance among B-cell epitopes is a central unifying theme that subsumes immune phenomena of tolerance, imprinting and refocusing; but it is meaningful for vaccine design only in the light of disease-specific pathophysiology, which for infectious processes is complicated by host-pathogen coevolution. To better support peptide-based vaccine design, B-cell epitope prediction would entail individualized quantitative estimation of biological outcomes relevant to safety and efficacy. Passive-immunization experiments could serve as an important initial proving ground for B-cell epitope prediction en route to vaccine-design applications, by restricting biological complexity to render epitope-prediction problems more computationally tractable.      

Interactive learning and action: realizing the promise of synthetic biology for global health

The emerging field of synthetic biology has the potential to improve global health. For example, synthetic biology could contribute to efforts at vaccine development in a context in which vaccines and immunization have been identified by the international community as being crucial to international development efforts and, in particular, the millennium development goals. However, past experience with innovations shows that realizing a technology’s potential can be difficult and complex. To achieve better societal embedding of synthetic biology and to make sure it reaches its potential, science and technology development should be made more inclusive and interactive. Responsible research and innovation is based on the premise that a broad range of stakeholders with different views, needs and ideas should have a voice in the technological development and deployment process. The interactive learning and action (ILA) approach has been developed as a methodology to bring societal stakeholders into a science and technology development process. This paper proposes an ILA in five phases for an international effort, with national case studies, to develop socially robust applications of synthetic biology for global health, based on the example of vaccine development. The design is based on results of a recently initiated ILA project on synthetic biology; results from other interactive initiatives described in the literature; and examples of possible applications of synthetic biology for global health that are currently being developed.     

The Silicon Cell initiative: working towards a detailed kinetic description at the cellular level

The Silicon Cell initiative aims to understand cellular systems on the basis of the characteristics of their components. As a tool to achieve this, detailed kinetic models at the network reaction level are being constructed. Such detailed kinetic models are extremely useful for medical and biotechnological applications and form strong tools for fundamental studies. Several recently constructed detailed kinetic models on metabolism (glycolysis), signal transduction (EGF receptor), and the eukaryotic cell cycle (Saccharomyces cerevisiae) have been used to exemplify the Silicon Cell project. These models are stored and made accessible via the JWS Online Cellular Systems Modeling project, a web-based repository of kinetic models. Using a web-browser the models can be interrogated via a user-friendly graphical interface. The goal of the two projects is to combine models on parts of cellular systems and ultimately to construct detailed kinetic models at the cellular level.     

Drifting towards a diffuse control model of exploratory motor learning: A comparison of global and within-trial performance measures

 Accurate measurement is crucial for understanding the processes that underlie exploratory patterns in motor learning. Accordingly, measures of learning should be sensitive to the changes that take place during skill acquisition. Most studies, however, use trial-based global measures that assess performance but do not actually measure gradual changes taking place within trials. The present study attempted to remedy this shortcoming by analysing a visual adaptation task, and comparing traditional global measures of learning with new, within-trial measures. Movement time was the only global measure sensitive to changes in the movement trajectory during learning. Three new measures were expected to reveal changes to the movement trajectory that are associated with learning: (i) the length of runs, (ii) change of trajectory angle in relation to the target, and (iii) drift (change in distance from the target). All three measures were sensitive to learning and indicated a gradual straightening of the movement trajectories over trials. Furthermore, three different methods to partition trajectories into segments were examined. The new within-trial measures, irrespective of partitioning method, prove promising for the development of a diffuse control model of exploratory learning. Received: 5 February 2001 / Accepted in revised form: 16 January 2002