  Medicine is moving from a curative discipline to a preventative discipline
relying on personalised and precise treatment plans. The complex and multi
level pathophysiological patterns of most diseases require a systemic medicine
approach and are challenging current medical therapies. On the other hand,
computational medicine is a vibrant interdisciplinary field that could help
move from an organ-centered approach to a process-oriented approach. The ideal
computational patient would require an international interdisciplinary effort,
of larger scientific and technological interdisciplinarity than the Human
Genome Project. When deployed, such a patient would have a profound impact on
how healthcare is delivered to patients. Here we present a computational
patient model that integrates, refines and extends recent mechanistic or
phenomenological models of cardiovascular, RAS and diabetic processes. Our aim
is twofold: analyse the modularity and composability of the model-building
blocks of the computational patient and to study the dynamical properties of
well-being and disease states in a broader functional context. We present
results from a number of experiments among which we characterise the dynamic
impact of COVID-19 and type-2 diabetes (T2D) on cardiovascular and inflammation
conditions. We tested these experiments under different exercise, meal and drug
regimens. We report results showing the striking importance of transient
dynamical responses to acute state conditions and we provide guidelines for
system design principles for the inter-relationship between modules and
components in systemic medicine. Finally this initial computational Patient can
be used as a toolbox for further modifications and extensions.
