International Synthetic and Systems Biology Summer School

To understand biological systems, our lab has defined "network motifs": basic interaction patterns that recur throughout biological networks, much more often than expected at random. The same small set of network motifs appears to serve as the building blocks of the circuitry that processes information from bacteria to mammals. Specific network motifs may be universal building blocks of biological computation. We experimentally studied the function of each network motif in the bacterium E. coli using dynamic fluorescent measurements from living cells. Each network motifs can serve as an elementary circuit with a defined function: filters, pulse generators, response accelerators, temporal-pattern generators and more. Evolution seems to have rediscovered the same motifs again and again, perhaps because they are the simplest and most robust circuits that perform these information-processing functions.

Organisms, tissues and molecules often need to perform multiple tasks. But usually no phenotype can be optimal at all tasks at once. This leads to a fundamental tradeoff. We study this using the concept of Pareto optimality from engineering and economics. Tradeoffs lead to an unexpected simplicity in the range of optimal phenotypes- they fall on low dimensional shapes in trait space such as lines, triangles and tetrahedrons. At the vertices of these polygons are phenotypes that specialize at a single task. We demonstrate this using data from animal and fossil morphology, bacterial gene expression and other biological systems.

Biological networks change in response to genetic and environmental cues. Changes are reflected in the abundances of biomolecules, the composition of protein complexes, and other descriptors of the biological state. Methods to infer the dynamic state of a cell would have great value for understanding how cells change over time to accomplish biological goals. We describe methods that predict the dynamic state of protein complexes in a cell, combining dynamic information from mRNA transcript profiling with underlying protein-protein interaction networks.

While most genome-wide association studies are based on analysis of individual variants, the causal variants often cluster in a small set of genes. We describe Bayesian methods that exploit gene-based and pathway-based clustering to improve the search for disease-related genetic variation.

Genome Design and Synthesis strategies vary considerable. Most of the published work on genome synthesis involves resynthesis of the native or near-native genome of bacteria. In this case, the design considerations are rather minimal and the major challenges involved have to do with design of an assembly strategy that is effective. Hierarchical strategies involve going from 10s of bases in oligonucleotides up to several megabases of dsDNA. A wide variety of methods have been described that can be used to assemble DNA and the details of these methods have significant impacts on speed of synthesis, cost of synthesis, and accuracy of synthesis. Innovative methods have been described that operate at all different levels of the assembly hierarchy. When more radical designs that deviate from that of wild-type genomes are considered, appropriate design software tools are needed. Our group has focused on genome editing software which takes advantage of well-developed browser platforms for visualizing genome features, and adapting them to genome editing. A variety of design features were somewhat arbitrarily selected for incorporation into Sc 2.0, the synthetic yeast genome. These, as well as possible future more aggressive designs under consideration will be discussed, as will interesting targets for future genome synthesis projects.

A wide variety of pathways are available for engineering to make useful products or encode interesting behaviors in cells and organisms. As more and more complex pathways and gene networks are discovered and dissected, more and more efficient means of assembling pathways are needed. Even more importantly, libraries of these, in which the promoter strength or host of origin for the coding sequences can be systematically varied are especially valuable. Methodologies and strategies for accomplishing this are discussed, incorporating Golden Gate, MoClo, Gateway cloning, and combinations of such methods will be discussed. Examples of pathway optimization will also be reviewed.

The information for synthesizing the molecules that allow organisms to survive and replicate is encoded in genomic DNA. In the cell, DNA is copied to messenger RNA, and triplet codons (64) in the messenger RNA are decoded - in the process of translation - to synthesize polymers of the natural 20 amino acids. This process (DNA RNA protein) describes the central dogma of molecular biology and is conserved in terrestrial life. We are interested in re-writing the central dogma to create organisms that synthesize proteins containing unnatural amino acids and polymers composed of monomer building blocks beyond the 20 natural amino acids. I will discuss our invention and synthetic evolution of new 'orthogonal' translational components (including ribosomes and aminoacyl-tRNA synthetases) to address the major challenges in re-writing the central dogma of biology. I will discuss the application of the approaches we have developed for incorporating unnatural amino acids into proteins and investigating and synthetically controlling diverse biological processes, with a particular emphasis on understanding the role of post-translational modifications

TBA

Synthetic biology is an application-focused field attempting to apply a systematic engineering approach to the design of new biological systems at the molecular level primarily using DNA. Notable progress has been made in the implementation of synthetic genetic circuits in living-systems that mimic functions such as switches, oscillators, timers, pulse generators, band-pass filters and logic gates (1,2). These genetic circuits are anticipated to enable the engineering of biological systems to a complexity previously unattainable. Over the past decade synthetic biology has aided the development of novel biological systems for a range of applications from bacteria that seek and destroy herbicides to the biosynthesis of valuable drugs (3, 4). To accelerate the development of the field many groups are establishing foundational technologies, which can be used across a wide range of applications. Within the Centre for Synthetic Biology and Innovation at Imperial College London (CSynBI; www.imperial.ac.uk/syntheticbiology) we are developing a series of platform technologies to enable the systematic design assembly and testing of synthetic biology designs including integration into an information system. I will describe recent developments in CSynBI on high throughput in vivo part characterization and the automated production of datasheets as well as new approaches in combinatorial DNA assembly. I will also describe our recent in vitro platform for the rapid characterization of DNA regulatory elements using a cell free system (5).

Biosensors generally can be used to monitor parameters of interest or target analytes and in part they involve a biological component. Early biosensors started when techniques for immobilising enzymes onto solid supports were first developed (1). The relatively recent discovery of bioluminescent (e.g. luciferase) and fluorescent (e.g. green fluorescent protein, GFP) proteins has provided a convenient mechanism for visualising gene expression and protein synthesis in cells and has lead to their use as reporter proteins. By utilizing such constructs, whole cell biosensors have been developed for example to specifically detect certain compounds or act as indicators to monitor cell growth and fitness (2,3). Biological organisms naturally monitor their environment and react accordingly with highly evolved detection and sensing systems, which can now be refactored at the genetic level for specific biosensor applications. One overall aim of synthetic biology is to harness nature's toolbox by fusing molecular biology and engineering disciplines and thus the field of biosensor design and construction has been a major focus for synthetic biologists (4-8). In this lecture I will describe the principles behind biosensor design using synthetic biology approaches providing exemplars to illustrate these principles. I will also present recent data from my groups on developing synthetic biology biosensors for healthcare applications and show how additional societal implications can be integrated into biosensor designs.