Systems biology and cancer (original) (raw)

Systems Biology: Did we know it all along?

Systems Biology: Definitions and Perspectives, 2005

It is often suggested that Systems Biology is nothing new, or that it is irrelevant. Its central paradigm, i.e. that much of biological function arises from the interactions of macromolecules, is not generally appreciated. We here contend that much like molecular biology in its past, Systems Biology is new and old at the same time. It looks in a new way and with new and improved reincarnations of existing and new technologies at scientific issues that the existing disciplines describe but do not resolve. Its main focus is to understand in quantitative, predictable ways the regulation of complex cellular pathways and of intercellular communication so as to shed light on complex biological functions (e.g. metabolism, cell signaling, cell cycle, apoptosis, differentiation, and transformation). It is for the lack of achieving this understanding of living systems that the existing paradigms for biomedical research fail for the majority of diseases on the Northern hemisphere. Systems Biology appears appropriate for these complex and multifactorial diseases.

Fundamental issues in systems biology

BioEssays, 2005

In the context of scientists' reflections on genomics, we examine some fundamental issues in the emerging postgenomic discipline of systems biology. Systems biology is best understood as consisting of two streams. One, which we shall call 'pragmatic systems biology', emphasises large-scale molecular interactions; the other, which we shall refer to as 'systems-theoretic biology', emphasises system principles. Both are committed to mathematical modelling, and both lack a clear account of what biological systems are. We discuss the underlying issues in identifying systems and how causality operates at different levels of organisation. We suggest that resolving such basic problems is a key task for successful systems biology, and that philosophers could contribute to its realisation. We conclude with an argument for more sociologically informed collaboration between scientists and philosophers.

Bridging the gaps in systems biology

Molecular Genetics and Genomics, 2014

and biotechnology needed to be solved is still in its early phases. The field requires novel approaches for abstraction, for modeling bioprocesses that follow different biochemical and biophysical rules, and for combining different modules into larger models that still allow realistic simulation with the computational power available today. we have identified and discussed currently most prominent problems in systems biology: (1) how to bridge different scales of modeling abstraction, (2) how to bridge the gap between topological and mechanistic modeling, and (3) how to bridge the wet and dry laboratory gap. The future success of systems biology largely depends on bridging the recognized gaps.

Systems Biology—an interdisciplinary approach

System-level approaches in biology are not new but foundations of "Systems Biology" are achieved only now at the beginning of the 21st century . Foundations of Systems Biology. MIT Press, Cambridge, MA]. The renewed interest for a system-level approach is linked to the progress in collecting experimental data and to the limits of the "reductionist" approach. System-level understanding of native biological and pathological systems is needed to provide potential therapeutic targets. Examples of interdisciplinary approach in Systems Biology are described in U.S., Japan and Europe. Robustness in biology, metabolic engineering and idiotypic networks are discussed in the framework of Systems Biology.

Systems biology and cancer: Promises and perils

Progress in Biophysics and Molecular Biology, 2011

Systems biology uses systems of mathematical rules and formulas to study complex biological phenomena. In cancer research there are three distinct threads in systems biology research: modeling biology or biophysics with the goal of establishing plausibility or obtaining insights, modeling based on statistics, bioinformatics, and reverse engineering with the goal of better characterizing the system, and modeling with the goal of clinical predictions. Using illustrative examples we discuss these threads in the context of cancer research.