2013, 10(1): i-ii. doi: 10.3934/mbe.2013.10.1i

From the Guest Editors

1. 

Department of Experimental Oncology, European Institute of Oncology, Via Ripamonti 435, I-20141 Milan

2. 

Department of Mathematics, University of Pisa, Largo B. Pontecorvo 5, I-56127 Pisa

3. 

Istituto di Analisi dei Sistemi ed Informatica `Antonio Ruberti', National Research Council, Viale Manzoni 30, I-00185 Rome

Published  December 2012

There is an increasing awareness that to properly understand how tumors originate and grow, and then how to develop effective cures, it must be taken into account the dynamics of tumors and its great complexity. Tumors are characterized not only by the coexistence of multiple scales, both temporal and spatial, but also by multiple and quite different kinds of interactions, from chemical to mechanical. This makes the study of tumors remarkably complicated. A genuine explosion of data concerning the multi-faceted aspects of this family of phenomena collectively called cancers, is now becoming available. At the same time, it is becoming quite evident that traditional tools from biostatistics and bioinformatics cannot manage these data. Mathematics, theoretical biophysics and computer sciences are needed to qualitatively and quantitatively interpret experimental and clinical results, in order to make realistic predictions.

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Citation: Alberto d’Onofrio, Paola Cerrai, Alberto Gandolfi. From the Guest Editors. Mathematical Biosciences & Engineering, 2013, 10 (1) : i-ii. doi: 10.3934/mbe.2013.10.1i
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