[3] **viXra:1512.0413 [pdf]**
*submitted on 2015-12-23 21:05:27*

**Authors:** Davide Mezza, Marco Pappalettera, Diego Liberati

**Comments:** 2141 Pages. submitted

Experimental evidence indicates that cells under irradiation induce in the neighbour non- irradiated cells the same biological effects affecting the irradiated ones. This is the so called bystander effect. Up to now in the scientific literature this kind of effect does not appear to be fully understood, even if several experiments show evidence of its existence. It would be reasonable that bystander effect takes place by means of paracrine chemical transmission mediators that would be broadcasted by the damaged cells to the surrounding cells. Furthermore a subset of a special class of signaling proteins, namely the cytokines, are probably the very ones involved in such signaling phenomenon. Among them, Tumor Necrosis Factor (TNF) is a particularly relevant protein belonging to the class of cytokines, because it is known to contribute to mediate various relevant cell functions, like apoptosis, the programmed cell death. As a molecule, TNF is quite interesting, because it can issue two opposite signals through different intracellular molecular signaling chains. One signal induces apoptosis, while the other is opposite, inducing the cell resistance to apoptic signals. The crucial point is thus to understand what makes each of such two signals masking the other. Thus a mathematical model related to the TNF signaling pathway is of interest, paying special attention to the study of the TNF reception mechanisms by cells that are not passed through by the radiation beam. In this work we present a new mathematical model of cellular apoptosis - mediated by TNF - and its validation based on data existent in literature. The model that we present will result to be a stable model with respect to large variation of the parameters and simplified with respect to other models already existent.

**Category:** Quantitative Biology

[2] **viXra:1512.0411 [pdf]**
*replaced on 2015-12-27 09:48:34*

**Authors:** Silvia Strada, Diego Liberati

**Comments:** 10 Pages. submitted

A simple, multivariable and linearly initial- ized clustering is shown to be able to deal with unsu- pervised classification of the data originating from pan- creatic endocrine tumors (PET). Results are discussed almost only on the data science side, leaving a more biological discussion to future work, even in the quest of possible hidden pathways.

**Category:** Quantitative Biology

[1] **viXra:1512.0337 [pdf]**
*submitted on 2015-12-16 09:16:24*

**Authors:** Pauline Traynard, Adrien Fauré, François Fages, Denis Thieffry

**Comments:** 14 Pages.

Proper understanding of the behavior of complex biological regulatory networks requires the integration of heterogeneous data into predictive mathematical models. Logical modeling focuses on qualitative data and offers a flexible framework to delineate the main dynamical properties of such networks. However, formal analysis faces a combinatorial explosion as the number of regulatory components and interactions increases.
Here, we show how model-checking techniques can be used to verify sophisticated dynamical properties resulting from model regulatory structure. We demonstrate the power of this approach through the updating of a model of the molecular network controlling mammalian cell cycle. We use model-checking to progressively refine this model in order to fit recent experimental observations. The resulting model accounts for the sequential activation of cyclins, the role of Skp2, and emphasizes a multifunctional role for the cell cycle inhibitor Rb.

**Category:** Quantitative Biology