Computational Design of Biological Systems: From Systems to Synthetic Biology

Today biology is overwhelmed with ‘big data’, amassed from genomic projects carried out in various laboratories around the world using efficient high throughput technologies. Biologists are co-opting mathematical and computational techniques developed to address these data and derive meaningful interpretations. These developments have led to new disciplines: systems and synthetic biology. To explore these two evolving branches of biology one needs to be familiar with technologies such as genomics, bioinformatics and proteomics, mathematical and computational modeling techniques that help predict the dynamic behavior of the biological system, ruling out the trial-and-error methods of traditional genetic engineering. Systems and synthetic biology have developed hand-in-hand towards building artificial biological devices using engineered biological units as basic building blocks. Systems biology is an integrated approach for studying the dynamic and complex behaviors of biological components, which may be difficult to interpret and predict from properties of individual constituents making up the biological systems. While, synthetic biology aims to engineer biologically inspired devices, such as cellular regulatory circuits that do not exist in nature but are designed using well characterized genes, proteins and other biological components in appropriate combinations to perform a desired function. This is analogous to an electronic circuit board design that is fabricated using well characterized electrical components such as resistors, capacitors and so on. The in silico abstractions and predictions should be tightly linked to experimentation to be proved in vitro and in vivo systems for their successful applications in biotechnology. This chapter focuses on mathematical approaches and computational tools available to engineer biological regulatory circuits and how they can be implemented as next generation therapeutics in infectious disease.

Keywords: Abstraction, bioengineering, bioinspired, biological parts, computational modelling, computational tools, constructs, dynamic, infectious disease, interdisciplinary, linearization, mathematical framework, nextgen therapeutics, omics, ordinary differential equations, parameters, physical systems, reactions, regulatory circuits, simulation.