In an instant classic paper ; 2002: 179–182) biologist Yuri Lazebnik deplores the poor effectiveness of the approach adopted by biologists to understand and “fix” biological systems. Lazebnik suggests that to remedy this state of things biologist should take inspiration from the approach used by engineers to design, understand, and troubleshoot technological systems. In the present paper I substantiate Lazebnik’s analysis by concretely showing how to apply the engineering approach to biological problems. I use an actual example of electronic circuit (...) troubleshooting to ground the thesis that, in engineering, the crucial phases of any non-trivial troubleshooting process are aimed at generating a mechanistic explanation of the functioning of the system, which makes extensive recourse to problem-driven qualitative reasoning possibly based on cognitive artifacts applied to systems that are known to have been designed for function. To show how to translate these findings into biological practice I consider a concrete example of biological model building and “troubleshooting”, aimed at the identification of a “fix” for the human immune system in presence of progressing cancer, autoimmune disease, and transplant rejection. The result is a novel immune system model—the danger model with regulatory cells— and new, original hypotheses concerning the development, prophylaxis, and therapy of these unwanted biological processes. Based on the manifest efficacy of the proposed approach, I suggest a refocusing of the activity of theoretical biologists along the engineering-inspired lines illustrated in the paper. (shrink)

In the past decades, an enormous amount of precious information has been collected about molecular and genetic characteristics of cancer. This knowledge is mainly based on a reductionistic approach, meanwhile cancer is widely recognized to be a ‘system biology disease’. The behavior of complex physiological processes cannot be understood simply by knowing how the parts work in isolation. There is not solely a matter how to integrate all available knowledge in such a way that we can still deal with complexity, (...) but we must be aware that a deeply transformation of the currently accepted oncologic paradigm is urgently needed. We have to think in terms of biological networks: understanding of complex functions may in fact be impossible without taking into consideration influences outside of the genome. Systems Biology involves connecting experimental unsupervised multivariate data to mathematical and computational approach than can simulate biologic systems for hypothesis testing or that can account for what it is not known from high-throughput data sets. Metabolomics could establish the requested link between genotype and phenotype, providing informations that ensure an integrated understanding of pathogenic mechanisms and metabolic phenotypes and provide a screening tool for new targeted drug. (shrink)