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.
