A fascinating observation made decades ago, and subsequently reinforced in the post-genomics era, is the lack of correlation between the genome size and the complexity of an organism, a concept that became known as the C-value paradox.
For example, the genome of the small protozoan Amoeba proteus, estimated to have 670 billion base pairs, is approximately 200 time larger than the human genome; the unicellular eukaryote Paramecium tetraurelia has approximately 40,000 genes; Drosophila melanogaster has approximately 13,000 genes; and the flowering plant Arabidopsis thaliana has around 28,000 genes, comparable to the 20,000–25,000 protein-coding genes from the human genome.
These findings sparked an interest in understanding how certain organisms can achieve much more complex tasks than others, despite using comparable numbers of genes. Alternative splicing and noncoding genomic regions are two of the features that explain the complexity of higher eukaryotes. An additional theme that emerged in recent years is the need to shift the focus from exploring isolated components toward visualizing the complexity of the biological networks, which often results from the modularity of the components and the novel combinations that they can establish.
Understanding how the different components of a circuit function together helps unveil evolutionary concepts, reveals basic principles behind their design, and facilitates the construction of novel synthetic circuits that can perform a desired function.
One of the most recent inter-disciplinary undertakings at the juncture between life sciences and engineering, synthetic biology emerges as a dynamic area of investigation that proposes to design and generate complex biological systems with novel properties. Recent progress in areas that include molecular biology, protein engineering, computational biology, and the development of the -omics sciences helped this vibrant discipline become reality.
One of the challenging topics in science has revolved around the fact that measurements of cellular components, such as mRNA or proteins, often reflect population averages but are less informative about individual cells. As a result, cell-to-cell differences are lost in the average. More recent approaches, powered to examine biological processes within individual cells, reveal a considerable cell-to-cell variability, or noise, even among genetically identical cells.
In 1957, Novick and Weiner reported that at intermediate inducer concentrations, an E. coli population consisted of cells fully induced for lac expression and cells that were not induced at all, with individual cells being able to switch stochastically between these two states. Intercellular variability is advantageous for cell populations that are exposed to fluctuating environments.
Cohen et al. revealed that cancer cells respond differently to the same therapeutic agent and, as a result, a small population may survive. The authors monitored the expression over time of almost 1,000 different proteins in human cancer cells, and found bimodal behavior in a subset of proteins, with increased cell-to-cell variability after addition of the anticancer drug camptothecin, an observation that explains the ability of some cells to escape anticancer therapy.
Previously, modeling studies proposed that negative feedback regulation could provide stability to biological systems, but for a long time, this was not tested experimentally. Becskei and Serrano designed a synthetic gene circuit in E. coli and demonstrated that negative feedback can considerably reduce variability in gene expression.
In this system, the tetracycline repressor-regulated promoter PLtetO1 drives the expression of TetR-EGFP, a fusion between the tetracycline repressor and the enhanced green fluorescence protein. Negative feedback is established because TetR represses transcription from PLtetO1. When the tet operator was replaced with the lac operator, or when TetR was replaced with the low binding affinity mutant TetRY42A, elimination of the feedback produced an unregulated system. This model revealed an over threefold decrease in gene-expression variability in the presence of the negative feedback, as compared to an unregulated system.
Around the same time, Elowitz and Leibler designed and constructed a synthetic oscillating network known as repressilator by using three transcriptional repressors that are not part of a naturally occurring biological system. In this system, E. coli LacI inhibits the transcription of the second repressor, tetR from the Tn10 tetracycline-resistance transposon, and the TetR protein inhibits the expression of a third gene, cI from bacteriophage λ.
The product of this gene, CI, inhibits lacI expression, completing the cycle. This system revealed that genetic components from multiple systems can be used and combined in a new genetic context to construct artificial networks with new properties.
Gardner et al. constructed a genetic toggle switch by using two repressors and two constitutive promoters, each of them inhibited by the repressor transcribed by the other promoter. This study brought a fundamental contribution to the field of synthetic biology, and represented a departure from the classical genetic engineering in the sense that DNA or protein engineering were replaced with an approach that involves manipulating the network architecture, a promising emerging concept in biotechnology and gene therapy.
Another study that pioneered the field of synthetic biology, conducted by Farmer and Liao, emerged from the observation that a common strategy used in life sciences—the induction of a particular enzyme or pathway—often caused cellular metabolic imbalances resulting in phenotypic changes.
Underscoring the importance of visualizing the dynamics of a metabolic pathway, as opposed to simply focusing on its genetic composition, the authors engineered, in E. coli, the synthesis pathway of lycopene, an antioxidant with applications in chemotherapy and neurodegenerative conditions, by coupling gene expression to the metabolic state of the cell.
Three of the five genes encoding enzymes in the lycopene synthesis pathway were placed under the lac promoter, and two genes, which control the rate limiting steps, were controlled by the glnAp2 promoter, whose transcription is initiated by the active, phosphorylated version of the response regulator NRI. A modified version of the E. coli Ntr regulon was generated, in which the sensor kinase NRII was deleted to make NRI more responsive to acetyl phosphate. This engineered strain produced 100 mg/L lycopene after growth in glucose-containing medium.