A bottom-up approach to translate multiscale clinical data into predictive model is to focus on the principles of cell decision-making. My starting point is how a single cell process microenvironmental information. By this approach I am aiming to* address challenges (C2) and (C3), i.e. to enable integration of multi scale data into a predictive theory that circumvents lack of knowledge of the underlying regulation mechanisms.* Regarding cells act as energetically constrained Bayesian inferrers of their intrinsic states (phenotype) from extrinsic ones (microenvironment), I proposed the principle of *least microenvironmental uncertainty* (LEUP). Limited energy poses a constraint on the microenvironmental sensing and forces cell to change their phenotype towards optimizing their microenvironmental prior. In this way, cell decisions minimize the microenvironmental uncertainty, i.e. cells phenotypic decisions follow microenvironmental entropy gradients. This rational can be interpreted in an elegant energy-like principle that assumes the entropy minimization of cell intrinsic (phenotype) and extrinsic (microenvironment) states. Therefore, LEUP implies a thermodynamic identity for spatially homogeneous phenotypic dynamics:

In the above formula, **X** denotes the intrinsic state vector of the cell, **Y** the extrinsic one and Z the intrinsic partition function. Free parameters of the model are (i) *β* models the compliance of the system to the LEUP and (ii) the local interaction radius of each cell. Such an energy-like principle addresses the two fundamental issues, since (i) it allows us to develop a bottom-up multicellular statistical mechanics theory for cell decision-making, and (ii) its Bayesian nature allows for circumventing the underlying uncertainty of the involved regulatory mechanisms of phenotypic decision (Fig. 2). Interestingly, we can integrate any type of related data, e.g. –*omics*, simply as constraints in the optimization of the energy functional. Similarly, upon the prior knowledge of a certain signalling pathway, we could integrate the model predictions of such pathways as constrains in our LEUP variational. As a proof of principle, we have applied the LEUP to modeling cell migration by treating cells as interacting decision-makers (e.g. velocity, locomotion force, receptor distribution) (Hatzikirou, JMBM, 2018, Barua et al, bioRxiv, 2018]).

*The development and validation of LEUP has been funded from the Volkswagenstiftung, within the call “Life? A Fresh Scientific Approach to the Basic Principles of Life”, with 1.5 million euros for 5 years*. The latter involves validating the LEUP in four different cell decision-making systems spanning from hematopoietic and immune cell differentiation (T-cell) and plasticity (macrophages) to bacterial motility and proliferation.