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While in silico models are critical components of more physically tractable scientific disciplines such as metrology and economics, biology has not fully embraced or integrated these more quantitative approaches into its research continuum. Beyond the inherent complexity of biology, there are a variety of reasons for this lag, including lack of appropriate datasets, ill- defined biological questions, inappropriate model application and often simple social separation of the disciplines. Advances in all of these areas are slowly changing this view to the benefit of modern biology and disease research. The US National Cancer Institute (NCI) has a simple mission to understand the biology of cancer in order to better prevent, manage and treat the disease for the health of the nation and the world. To accomplish this mission, the NCI supports and conducts a broad range of research enterprises. Indeed, we have seen positive shifts within the USA in overall cancer incidence and death rates for some specific cancers. Many of these shifts can be attributed to early detection and healthier lifestyles. Tremendous advances in the treatment of cancer have also been made with probably the most exciting advance in the area of targeted therapy. Despite this progress, cancer remains a serious problem with devastating impact at both a personal and societal level. Certain types of cancer and certain populations have not experienced the same advances, and resistance is emerging with some of our most promising drugs. Most sobering is the fact that cancer remains the leading cause of death worldwide, accounting for 7. Here are the top 25 Integrative Cancer Biology Program Fellow profiles on LinkedIn. Get all the articles, experts, jobs, and insights you need. Cancer biology is in the midst of major transition — from a primary focus on isolated components (e.g., oncogenes) to an integrated focus on systems (e.g., pathways). This transition has become feasible only recently. It is now well established that cancer is multiscale involving many processes at the molecular, cellular and organismal level. At a molecular level, there are a vast collection of mutations and signalling networks associated with cancer. The Integrative Cancer Biology Program (ICBP): Centers for Cancer Systems Biology focuses on the analysis of cancer as a complex biological system. The ICBP Program is managed by NCI’s Division of Cancer Biology. MIT was awarded an NCI Integrative Cancer Biology Program (ICBP) grant in 2004, for the period September 2004 through February 2015. This grant catalyzed formation of a community of faculty, postdoctoral associates, graduate. At a cellular level, we are beginning to understand the complex milieu of different cell types and interactions that make up the tumour microenvironment and, finally, the many processes such as inflammation, angiogenesis and the immune response within the host. All of these processes work in a coordinated fashion to progress cancer through initiation, metastasis and intervention. NIH Funding Opportunities and Notices in the NIH Guide for Grants and Contracts: INTEGRATIVE CANCER BIOLOGY PROGRAMS RFA-CA-04-013. The National Cancer Institute's Integrative Cancer Biology Program (ICBP) - Principal Investigators Meeting is being held at Johns Hopkins University - Montgomery County Campus (JHU-MCC) located in Rockville, MD on May 6-8. Not only is each level a system unto itself but the systems act across scales in a well- coordinated and adaptive manner. Modern cancer biology recognizes this and grapples with the methodologies to take our understanding to the next level. To add to this complexity of scales is the vast amount of high- dimensional data and information that are being generated covering all aspects of a patient's cancer. The challenge of course is how to make sense of this vast amount of information and complex interactions. Here is where we turn to mathematical modelling for the necessary structure and analytics to provide testable and predictive models to not only explain but also to probe the various mechanisms of cancer. Models, while not perfect and by their very nature wrong . Mathematical models are also a great tool for integrating multiple distinct processes/components of a system and bridging multiple spatial and temporal scales in ways that would be impossible for experimentation alone. Models can be roughly classified as descriptive or mechanistic and, importantly for this Theme Issue, models can also be used as a means to integrate data and extract crucial information from them. The research community has realized this and has increasingly embraced systems biology and mathematical modelling in biomedical research. In 2. 00. 4, the NCI began the Integrative Cancer Biology Program (ICBP) with the express purpose of developing these fields in the area of cancer biology. The ICBP is a large programme encompassing centres, individual research grants and educational activities, along with data infrastructure (http: //icbp. At its core are the Centers for Cancer Systems Biology, which focus on the analysis of cancer as a complex biological system. The cornerstone of the programme is the development and implementation of mathematical and computational models of processes relevant to cancer prevention, diagnostics and therapeutics. The integration of experimental biology with mathematical modelling leads to new insights in cancer biology and innovative approaches to the management of cancer. The programme brings clinical and basic cancer researchers together with researchers from mathematics, physics, information technology, imaging sciences and computer science to work on key questions in cancer biology. The individual centres work both as multi- discipline research teams and as collaborative units in an overall consortium. The centres cover a broad range of issues in cancer biology from (epi)- genetics and signalling networks to cellular biology and clinical issues. The biological components are complemented by mathematical and computational modelling. While always present, throughout the life of the ICBP the importance of modelling has steadily increased with both novel approaches and applications being developed hand in hand with emerging experimental techniques and data acquisition. As members of this programme, we began to see a need to bring all of the theoreticians together to discuss our techniques and results and how best to apply (and/or integrate) them to the diverse datasets that were being generated. This Theme Issue reflects the results of a series of workshops that began in Berkeley, CA, in October 2. Tampa, FL, in March 2. Claire Tomlin (UC Berkeley) and Alexander Anderson (Moffitt). The mathematics of the ICBP workshops brought together all of the mathematical and computational modellers across the 1. As part of the workshop participation, we asked each centre to provide a list of key questions that it considered critical to this new community. While there were many, some unifying questions emerged (Box 1). Box 1.(i)How can we deal with the multi- scale nature of cancer?(ii)How can we infer signalling networks from static and dynamic data?(iii)How do we integrate multiple distinct datasets into a useful predictive model?(iv)How does intratumour heterogeneity propagate both spatially and temporally?(v)How do we effectively treat a heterogeneous tumour?(vi)Can we exploit the dialogue between tumour and microenvironment therapeutically?(vii)What role does cooperation play in tumour prevention, progression and resistance to treatment?(viii)How can we design experiments to better facilitate mathematical and computational models? In addition to these questions, we saw some excellent presentations highlighting novel approaches to address them, which led us to ask for possible contributions to this Theme Issue of Interface Focus; from the initial 4. These papers cover a diverse spectrum of both modelling techniques and application scales. Methods which are based on data clustering, segmentation and statistical regression methods have been used to organize and understand large biological datasets: the methods in Zhu et al. Yet other methods deal with clever ways to constrain parameter uncertainty: the paper by Hagen et al. Information theoretic and statistical methods for inferring and understanding biological pathways have found great promise in cancer systems biology: the paper by Jang et al. Differential equation- based models are ideal for capturing both spatial and temporal dynamics and can be used in a modular- like manner to connect distinct processes and scales, employed here by Wilkie & Hahnfeldt . Single- cell- based models are ideal for examining interactions and characterizing the heterogeneous nature of tumours: Gallaher & Anderson . Finally, Basanta & Anderson . 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