Human physiology is an ensemble of various biological processes spanning from intracellular molecular interactions to the whole body phenotypic response. Systems biology endures to decipher these multi-scale biological networks and bridge the link between genotype to phenotype. The structure and dynamic properties of these networks are responsible for controlling and deciding the phenotypic state of a cell. Several cells and various tissues coordinate together to generate an organ level response which further regulates the ultimate physiological state. The overall network embeds a hierarchical regulatory structure, which when unusually perturbed can lead to undesirable physiological state termed as disease. Here, we treat a disease diagnosis problem analogous to a fault diagnosis problem in engineering systems. Accordingly we review the application of engineering methodologies to address human diseases from systems biological perspective. The review highlights potential networks and modeling approaches used for analyzing human diseases. The application of such analysis is illustrated in the case of cancer and diabetes. We put forth a concept of cell-to-human framework comprising of five modules (data mining, networking, modeling, experimental and validation) for addressing human physiology and diseases based on a paradigm of system level analysis. The review overtly emphasizes on the importance of multi-scale biological networks and subsequent modeling and analysis for drug target identification and designing efficient therapies.

Unused CPU time on desktop computers could be put to good use, if distributed computing succeeds in capturing people's imagination. In their Perspective, Shirts and Pande describe existing distributed computing projects and recent efforts to overcome parallelization problems. This vast underused resource could raise biological and other scientific computation to fundamentally new predictive levels.

To introduce this special issue on biological switches and clocks, we review the historical development of mathematical models of bistability and oscillations in chemical reaction networks. In the 1960s and 1970s, these models were limited to well-studied biochemical examples, such as glycolytic oscillations and cyclic AMP signalling. After the molecular genetics revolution of the 1980s, the field of molecular cell biology was thrown wide open to mathematical modellers. We review recent advances in modelling the gene-protein interaction networks that control circadian rhythms, cell cycle progression, signal processing and the design of synthetic gene networks.

Biologists are increasingly recognising that computational modelling is crucial for making sense of the vast quantities of complex experimental data that are now being collected. The systems biology field needs agreed-upon information standards if models are to be shared, evaluated and developed cooperatively. Over the last four years, our team has been developing the Systems Biology Markup Language (SBML) in collaboration with an international community of modellers and software developers. SBML has become a de facto standard format for representing formal, quantitative and qualitative models at the level of biochemical reactions and regulatory networks. In this article, we summarise the current and upcoming versions of SBML and our efforts at developing software infrastructure for supporting and broadening its use. We also provide a brief overview of the many SBML-compatible software tools available today.

Complex assemblies of interacting proteins carry out most of the interesting jobs in a cell, such as metabolism, DNA synthesis, movement and information processing. These physiological properties play out as a subtle molecular dance, choreographed by underlying regulatory networks. To understand this dance, a new breed of theoretical molecular biologists reproduces these networks in computers and in the mathematical language of dynamical systems.

Human tumours are complex and unstable biological systems. New intellectual and mathematical approaches together with massive computing power are transforming our capacity to model and investigate such complexity. Computers also allow massive data sets to be collated and analysed. Such sets include the medical and epidemiological records of entire populations; the entire genetic code of the human being and of other species, including parasites and disease vectors; and the genotype of each and every individual. Massive data sets take us into new dimensions of complexity for which simple linear mathematics are insufficient. The analysis of the grades of complexity which determine protein and cell construction, cell to cell interactions within tissues and organs, the morphogenesis of entire organisms and population interactions with disease vectors require the sophisticated mathematical tools of non-linear analysis, neural networks, chaos and complexity theory. The capacity for closer representations of reality through powerful computational models also allows us to look afresh at the generalizations of conventional statistics. Within this computational cauldron, we may also find help in the better understanding of oncogenesis and cancer therapy. This paper, the third in our series on modelling in tumour biology, considers the breadth of opportunity and challenge at the interface between cell biology and biomathematics.