Engineering simulations for cancer systems biology

Computer simulation can be used to inform in vivo and in vitro experimentation, enabling rapid, low-cost hypothesis generation and directing experimental design in order to test those hypotheses. In this way, in silico models become a scientific instrument for investigation, and so should be developed to high standards, be carefully calibrated and their findings presented in such that they may be reproduced. Here, we outline a framework that supports developing simulations as scientific instruments, and we select cancer systems biology as an exemplar domain, with a particular focus on cellular signalling models. We consider the challenges of lack of data, incomplete knowledge and modelling in the context of a rapidly changing knowledge base. Our framework comprises a process to clearly separate scientific and engineering concerns in model and simulation development, and an argumentation approach to documenting models for rigorous way of recording assumptions and knowledge gaps. We propose interactive, dynamic visualisation tools to enable the biological community to interact with cellular signalling models directly for experimental design. There is a mismatch in scale between these cellular models and tissue structures that are affected by tumours, and bridging this gap requires substantial computational resource. We present concurrent programming as a technology to link scales without losing important details through model simplification. We discuss the value of combining this technology, interactive visualisation, argumentation and model separation to support development of multi-scale models that represent biologically plausible cells arranged in biologically plausible structures that model cell behaviour, interactions and response to therapeutic interventions

A cognitive task analysis of a visual analytic workflow: Exploring molecular interaction networks in systems biology

Background: Bioinformatics visualization tools are often not robust enough to support biomedical specialists’ complex exploratory analyses. Tools need to accommodate the workflows that scientists actually perform for specific translational research questions. To understand and model one of these workflows, we conducted a case-based, cognitive task analysis of a biomedical specialist’s exploratory workflow for the question: What functional interactions among gene products of high throughput expression data suggest previously unknown mechanisms of a disease? Results: From our cognitive task analysis four complementary representations of the targeted workflow were developed. They include: usage scenarios, flow diagrams, a cognitive task taxonomy, and a mapping between cognitive tasks and user-centered visualization requirements. The representations capture the flows of cognitive tasks that led a biomedical specialist to inferences critical to hypothesizing. We created representations at levels of detail that could strategically guide visualization development, and we confirmed this by making a trial prototype based on user requirements for a small portion of the workflow. Conclusions: Our results imply that visualizations should make available to scientific users “bundles of features” consonant with the compositional cognitive tasks purposefully enacted at specific points in the workflow. We also highlight certain aspects of visualizations that: (a) need more built-in flexibility; (b) are critical for negotiating meaning; and (c) are necessary for essential metacognitive support

A visual analytics approach to feature discovery and subspace exploration in protein flexibility matrices

The vast amount of information generated by domain scientists makes the transi- tion from data to knowledge difficult and often impedes important discoveries. For example, the knowledge gained from protein flexibility data sets can speed advances in genetic therapies and drug discovery. However, these models generate so much data that large scale analysis by traditional methods is almost impossible. This hinders biomedical advances. Visual analytics is a new field that can help alleviate this problem. Visual analytics attempts to seamlessly integrate human abilities in pattern recognition, domain knowledge, and synthesis with automatic analysis techniques. I propose a novel, visual analytics pipeline and prototype which eases discovery, com- parison, and exploration in the outputs of complex computational biology datasets. The approach utilizes automatic feature extraction by image segmentation to locate regions of interest in the data, visually presents the features to users in an intuitive way, and provides rich interactions for multi-resolution visual exploration. Functional- ity is also provided for subspace exploration based on automatic similarity calculation and comparative visualizations. The effectiveness of feature discovery and subspace exploration is shown through a user study and user scenarios. Feedback from analysts confirms the suitability of the proposed solution to domain tasks