Steve McKeever

Dealing with diversity in computational cancer modeling.

This paper discusses the need for interconnecting computational cancer models from different sources and scales within clinically relevant scenarios to increase the accuracy of the models and speed up their clinical adaptation, validation, and eventual translation. We briefly review current interoperability efforts drawing upon our experiences with the development of in silico models for predictive oncology within a number of European Commission Virtual Physiological Human initiative projects on cancer. A clinically relevant scenario, addressing brain tumor modeling that illustrates the need for coupling models from different sources and levels of complexity, is described. General approaches to enabling interoperability using XML-based markup languages for biological modeling are reviewed, concluding with a discussion on efforts towards developing cancer-specific XML markup to couple multiple component models for predictive in silico oncology.

Web-Based Workflow Planning Platform Supporting the Design and Execution of Complex Multiscale Cancer Models

Significant Virtual Physiological Human efforts and projects have been concerned with cancer modeling, especially in the European Commission Seventh Framework research program, with the ambitious goal to approach personalized cancer simulation based on patient-specific data and thereby optimize therapy decisions in the clinical setting. However, building realistic in silicopredictive models targeting the clinical practice requires interactive, synergetic approaches to integrate the currently fragmented efforts emanating from the systems biology and computational oncology communities all around the globe. To further this goal, we propose an intelligent graphical workflow planning system that exploits the multiscale and modular nature of cancer and allows building complex cancer models by intuitively linking/interchanging highly specialized models. The system adopts and extends current standardization efforts, key tools, and infrastructure in view of building a pool of reliable and reproducible models capable of improving current therapies and demonstrating the potential for clinical translation of these technologies.

The role of markup for enabling interoperability in health informatics

Interoperability is the faculty of making information systems work together. In this paper we will distinguish a number of different forms that interoperability can take and show how they are realized on a variety of physiological and health care use cases. The last 15 years has seen the rise of very cheap digital storage both on and off site. With the advent of the Internet of Things peoples expectations are for greater interconnectivity and seamless interoperability. The potential impact these technologies have on healthcare are dramatic: from improved diagnoses through immediate access to a patients electronic health record, to in silico modeling of organs and early stage drug trials, to predictive medicine based on top-down modeling of disease progression and treatment. We will begin by looking at the underlying technology, classify the various kinds of interoperability that exist in the field, and discuss how they are realized. We conclude with a discussion on future possibilities that big data and further standardizations will enable.

Connecting digital cancer model repositories with markup: introducing TumorML version 1.0

The cancer research community requires a standardized way of describing mathematical and computational models to enable interoperation between systems, repositories, and between the models themselves. In this paper we describe a new markup language, TumorML, for describing computational models that fall within the domain of cancer. TumorML is an XML-based markup language that wraps existing cancer model implementations with metadata for model curation, parametric interface description, implementation description, and compound model linking.n In this paper we first introduce the rationale for a new markup language for computational cancer model description based on our experiences and requirements from the European Commissions Transatlantic Tumor Model Repositories project. The aim of the project is to develop a European-based digital cancer model repository to link and interoperate with a similar established repository based in the United States. TumorML was developed to enable this interoperation between repositories. We introduce the language and describe the main features of the specification and go on to describe a real application of TumorML where a molecular pathway model has been packaged using the new markup language.

Simulating Market Entry Rewards for Antibiotics Development

We design an agent based Monte Carlo model of antibiotics research and development (R&D) to explore the effects of the policy intervention known as Market Entry Reward (MER) on the likelihood that an antibiotic entering pre-clinical development reaches the market. By means of sensitivity analysis we explore the interaction between the MER and four key parameters: projected net revenues, R&D costs, venture capitalists discount rates, and large pharmaceutical organizations financial thresholds. We show that improving revenues may be more efficient than reducing costs, and thus confirm that this pull-based policy intervention effectively stimulates antibiotics R&D.

The next 700 unit of measurement checkers

In scientific applications, physical quantities and units of measurement are used regularly. If the inherent incompatibility between these units is not handled properly it can lead to major, sometimes catastrophic, problems. Although the risk of a miscalculation is high and the cost equally so, almost none of the major programming languages has support for physical quantities. Instead, scientific code developers often make their own tools or rely on external libraries to help them spot or prevent these mistakes. We employed a systematic approach to examine and analyse all available physical quantity open-source libraries. Approximately 3700 search results across seven repository hosting sites were condensed into a list of 82 of the most comprehensive and well-developed libraries currently available. In this group, 30 different programming languages are represented. Out of these 82 libraries, 38 have been updated within the last two years. These 38 are summarised in this paper as they are deemed the most relevant. The conclusion we draw from these results is that there is clearly too much diversity, duplicated efforts, and a lack of code sharing and harmonisation which discourages use and adoption.

Compositional Relational Programming with Name Projection and Compositional Synthesis

CombInduce is a methodology for inductive synthesis of logic programs, which employs a reversible meta-interpreter for synthesis, and uses a compositional relational target language for efficient synthesis of recursive predicates. The target language, Combilog, has reduced usability due to the lack of variables, a feature enforced by the principle of compositionality, which is at the core of the synthesis process. We present a revision of Combilog, namely, Combilog with Name Projection (CNP), which brings improved usability by using argument names, whilst still staying devoid of variables, preserving the compositionality.

Model composition for biological mathematical systems

Mathematical models are frequently used to model biological process, such as cardiac electrophysiological systems. In order to separate the models from the implementations, and to facilitate curation, domain specific languages (DSLs) have become a popular and effective means of specifying models (Lloyd et al., 2004; Hucka et al., 2004). In previous papers (Gill et al., 2012a; Gill et al., 2012b; McKeever et al., 2013) we have argued for including parameterised modules as part of such DSLs. We presented our prototype Ode language and showed how models could be created in a generic fashion. In this paper we extend our work with concrete examples and simulation results. We show how complex heart models can be constructed by aggregation, encapsulation and subtyping. Our use-case retraces the steps taken by (Niederer et al., 2009), which investigated the common history between cardiac models, and shows how they can be cast in our language to be reused and extended. Our DSL enables ‘physiological model engineering’ through the development of generic modules exploiting high cohesion and low coupling.