ABSTRACT
We present a heuristic control theory model that describes smoking under restricted and unrestricted access to cigarettes. The model is based on the allostasis theory and uses a formal representation of a multiscale opponent process. The model simulates smoking behavior of an individual and produces both short-term ("loading up" after not smoking for a while) and long-term smoking patterns (e.g., gradual transition from a few cigarettes to one pack a day). By introducing a formal representation of withdrawal- and craving-like processes, the model produces gradual increases over time in withdrawal- and craving-like signals associated with abstinence and shows that after 3 months of abstinence, craving disappears. The model was programmed as a computer application allowing users to select simulation scenarios. The application links images of brain regions that are activated during the binge/intoxication, withdrawal, or craving with corresponding simulated states. The model was calibrated to represent smoking patterns described in peer-reviewed literature; however, it is generic enough to be adapted to other drugs, including cocaine and opioids. Although the model does not mechanistically describe specific neurobiological processes, it can be useful in prevention and treatment practices as an illustration of drug-using behaviors and expected dynamics of withdrawal and craving during abstinence.
ABSTRACT
Increasingly, researchers are turning to computational models to understand the interplay of important variables on systems' behaviors. Although researchers may develop models that meet the needs of their investigation, application limitations-such as nonintuitive user interface features and data input specifications-may limit the sharing of these tools with other research groups. By removing these barriers, other research groups that perform related work can leverage these work products to expedite their own investigations. The use of software engineering practices can enable managed application production and shared research artifacts among multiple research groups by promoting consistent models, reducing redundant effort, encouraging rigorous peer review, and facilitating research collaborations that are supported by a common toolset. This report discusses three established software engineering practices- the iterative software development process, object-oriented methodology, and Unified Modeling Language-and the applicability of these practices to computational model development. Our efforts to modify the MIDAS TranStat application to make it more user-friendly are presented as an example of how computational models that are based on research and developed using software engineering practices can benefit a broader audience of researchers.
