Titles and Abstracts of Talks - 2nd Annual MoCSSy Symposium
TITLE: The Swarming Body: How Algorithmic Systems Biology can Help to Conquer Complexity
ABSTRACT: Mathematical and computational models are becoming more prominent in biological and bioinformatics research. However, the designing, programming, and utilization of computer models has not yet found the acceptance it deserves within the biological research community. Why are virtual biology labs not part of a biologist’s standard tool set? First, there seems to be a wide communication gap between how biologists think about biological “systems” and how computer scientists, bioinformaticians, and mathematical modelers implement their formal models of living systems. Looking closer, however, it turns out that the engineering of software, the building of computational models, and the investigation of biological systems faces similar challenges and approaches: modularization of subsystems to keep things organized, complex interaction networks among more basic computational units — elementary functions in software versus proteins and molecules in wetware. Therefore it is important to create a common language among “algorithmic systems biologists” and biologists who want to understand complex biological systems — on any level, from cells to ecosystems.
Recent advances in computer hardware and new programming methodologies (such as GPU programming) now make it possible to embrace agent-based, object-oriented and rule-based approaches for the modeling of biological systems on a wider scale. Agent-based models lend themselves to more comprehensive and ‘more natural’ representations of biological entities. To illustrate this viewpoint, I will present a variety of models with visualization examples, that demonstrate our latest swarm intelligence-based approaches to explore gene regulatory systems, blood clotting, immune system processes, and bacterial ecosystems on various scales: from bodies to organs to cells to proteins.
TITLE: An Informatics Theory of Democratic Journalism
ABSTRACT: Informatics Theory of Effective Democracy is used to assess the degree that journalism is contributing to the effectiveness of democracy. The paper suggests that the journalism that contributes to the deliberations of individuals or their representations, within general, weak or strong public spheres, enhances the effectiveness of democratic systems by facilitating the necessary processes. The paper identifies the major external parameters that influence the media institutions then uses field theory to provide a more precise explanation for the dynamics inside and for the mechanisms through which strong fields influence the weak journalism field. The paper will identify the mechanisms that are at work to decrease the autonomy of the journalistic field and the degree of freedom of journalists. The paper then looks at the changes in the networks of the actors and identifies the opportunities that may be there as a result of new technologies. At the end, an Informatics Theory of Effective Journalism is proposed that can be used to assess the degree that journalism in a society can be considered democratic, and to evaluate journalistic field and its practices.
TITLE: 10 Uncomfortable Truths about Dynamic Modeling for Health Policy
ABSTRACT: Dynamic modeling offers great potential for informing the evaluation of health interventions, aiding in the prioritization of data collection, interpreting health trends, and as a vehicle for communication with diverse stakeholders. However, before the full potential of such models can be realized for practical decision making, practitioners will need to overcome a diverse set of barriers -- many of them little discussed. This talk identifies ten such barriers, including but are not limited to shortcomings in existing formalisms for model specification, low capacity for endogenous simulation of intervention outcomes, data and metadata lacunae, limited model disclosure, an error-prone and needlessly cluttered modeling process, neglect of horizontal model boundaries, and methodological tribalism. For each barrier, we drawing on systems thinking principles and examples from modeling practice to describe the significance of that barrier, and suggest promising approaches for overcoming it. We conclude with some general thoughts on the evolution of the health modeling landscape.
TITLE: Shifting the Paradigm: Using Complex Systems Frameworks to Address the Challenge of Obesity
ABSTRACT: Conceptual models of obesity have evolved from early descriptions which suggested that obesity is simply a result of energy imbalance, to ecological models which acknowledge the importance of environmental factors both proximal and distal to the individual. Recently, the Foresight Programme of the UK Government Office of Science developed a conceptual model which illustrates how more than 100 variables from 8 clusters including food production, social psychology and the physical activity environment interact in a complex system where obesity is an emergent property. The Foresight system map is the first to illustrate the causes of obesity as complex, not just complicated, with the dominant feature being the interconnections and feedback loops between variables. We used social network analysis software (Pajek) to examine the connections between variables by cluster and to produce a reduced system map. This analysis helps us unpack current perceptions about the influence of specific clusters and provides evidence of the importance of feedback loops.
Given the complexity of the factors that give rise to obesity, it is not surprising that recent efforts to identify actions to address obesity and chronic disease give rise to long lists that include many sectors and require the engagement of many actors. In an effort to get a better sense of the “big picture” we sorted several sets of actions data into an Intervention Level framework adapted from D. Meadows “Places to Intervene in Complex Systems” (Sustainability Institute, 1999). Actions sorted into our 5 level framework show a similar distribution with the dominant level being structural elements followed by the structure as a whole. Paradigms and goals are less often described, and there is an apparent gap at the level of feedback and delays.
Together these analyses illustrate that systems thinking, conceptual models and systems science tools can help to reframe our approach to solving the complex problem of obesity.
TITLE: Patients Aren't Widgets: Non-technical considerations complicate challenges in applying OR/MS to Canadian health care system
ABSTRACT: Health care systems are among the most challenging systems from an operations research and management science (OR/MS) perspective. There is certainly no shortage of operational and strategic challenges that could benefit from evidence-based decision support perspectives. Seasoned practitioners in OR/MS understand that not all OR/MS projects yield the desired outcomes. The challenges often cited refer to technical issues such as data quality and computational limitations. However, often the barriers to success are related to softer, non-technical considerations such as inadequate understanding of the system to be analyzed or modeled, insufficient involvement of the client in developing solution alternatives, and insufficient change management support to ensure the client derives the intended value from the project. This talk will highlight the specific challenges typically encountered in applying OR/MS in the Canadian hospital-based acute care system, potential avenues to mitigate these challenges and present several successful applications of OR/MS in health care.
TITLE: A Social Network Model of Investment Behaviour in the Stock Market
ABSTRACT: The efficient market hypothesis states that all investors have full knowledge of market values and prices, and that all investors follow the basic buy-low sell-high rule. This hypothesis should quickly lead to market prices being accurate reflections of stock value, providing a stable market with no bubbles or crashes. This naturally leads to the question of why the efficient market hypothesis does not hold.
In this talk we examine the possibility that rational traders with full market knowledge are influenced by the behaviour of their peers. We develop a trust network model of the stock market, and test market behaviour using various network structures. We find that if the trust network takes the form of a scale-free social network, market stability is significantly delayed.
TITLE: Supporting older people at home using ambient technology- the SOPRANO project
ABSTRACT: SOPRANO (http://www.soprano-ip.org/ ) is an EU-funded project to develop an “ambient assisted living” (AAL) system to enhance the lives of frail and disabled older people. SOPRANO uses pervasive technologies such as sensors, actuators, smart interfaces and artificial intelligence to create a more supportive home environment by providing additional safety and security, supporting independent living and social participation and improving quality of life. SOPRANO (Service-oriented Programmable Smart Environments for Older Europeans) is a consortium of commercial companies, service providers and research institutes with over 20 partners from in Greece, Germany, UK, Netherlands, Spain, Slovenia, Ireland and Canada. The presentation describes the user-driven approach to research and development within the SOPRANO project and presents the results from initial requirements capture and prototype development and testing. The paper concludes by discussing the benefits of the user-driven approach and plans for system demonstration and large-scale field trials.
TITLE: Maxhist hypothesis shows weight transitions are not Markovian
ABSTRACT: The National Longitudinal Survey of Youth 1997 (NLSY97) is used to show how individuals transitioned between different BMI statuses. In this talk we will demonstrate that the changes in obesity status over time do not satisfy the Markov assumption, and is therefore the basic Markov model is invalid. We introduce a new model (the Maxhist Model) to test our hypothesis regarding probabilities of particular patterns of weight changes over time in the population. Our Maxhist hypothesis states that an individual’s most probable weight class two years into future is determined by their maximum historical weight class.