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Review
. 2010 Jan;11(1):80-95.
doi: 10.1093/bib/bbp054. Epub 2009 Nov 11.

The challenges of informatics in synthetic biology: from biomolecular networks to artificial organisms

Affiliations
Review

The challenges of informatics in synthetic biology: from biomolecular networks to artificial organisms

Gil Alterovitz et al. Brief Bioinform. 2010 Jan.

Abstract

The field of synthetic biology holds an inspiring vision for the future; it integrates computational analysis, biological data and the systems engineering paradigm in the design of new biological machines and systems. These biological machines are built from basic biomolecular components analogous to electrical devices, and the information flow among these components requires the augmentation of biological insight with the power of a formal approach to information management. Here we review the informatics challenges in synthetic biology along three dimensions: in silico, in vitro and in vivo. First, we describe state of the art of the in silico support of synthetic biology, from the specific data exchange formats, to the most popular software platforms and algorithms. Next, we cast in vitro synthetic biology in terms of information flow, and discuss genetic fidelity in DNA manipulation, development strategies of biological parts and the regulation of biomolecular networks. Finally, we explore how the engineering chassis can manipulate biological circuitries in vivo to give rise to future artificial organisms.

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Figures

Figure 1:
Figure 1:
The synthetic biology infrastructure. Solid lines indicate the components of synthetic biology and the connections among them. Bold solid lines emphasize the main path from given requirements to finished product. Boxes with thin solid lines indicate support structures that need to be developed in order to make synthetic biology a practical reality. The cycles within the graph convey that the current technology requires an iterative approach to arrive at a useful biological system. In a larger context, synthetic biology (design) and systems biology (analysis) feed into each other (see dashed lines). For example, in vivo tests result in data which feed back into synthetic biology. The Database box represents organized knowledge from systems biology and quantitative data on biological parts.
Figure 2:
Figure 2:
The BioBrick Standard [7]. (a) Basic sequence template of the BioBrick Standard. The insert of the BioBrick is flanked upstream and downstream with restriction sites. EcoRI and XbaI restriction sites are at the 5′-end (prefix). SpeI and PstI restriction sites are at the 3′-end (suffix). Each insert is a genetic component that can code for a promoter, ribosome-binding site, open reading frame, transcription termination sequence, or any combination of these. Restriction site sequences are not allowed within the genetic component. (b) Schematic of the joining process. To attach insert 1 upstream of insert 2, use restriction enzymes SpeI and XbaI respectively. The ends can then be joined together to form a scar, which cannot be cleaved again by either one of the restriction enzymes.
Figure 3:
Figure 3:
SBGN network example. of inter-cellular signaling near the neuromuscular junction [22, 23]. Biological concepts are organized with glyphs, or named containers. Some glyphs represent entity pool nodes, each of which is a population of entities that are not distinguished from one another in the current SBGN framework. Circular (not ellipsoid) glyphs represent ‘simple molecules’ like ATP and calcium ions. Rectangles with four rounded corners represent ‘macromolecules’ such as myosin. Glyphs can be adorned with additional information, such as the nicotinic acetylcholine receptors (nAChR), which are attached to the ‘state variable’ glyphs ‘open’ and ‘closed’. Note that the transition from ‘closed’ to ‘open’ is designated by an arrow with the ‘transition’ glyph, a small square. Another used process glyph here is ‘association’, where to lines converge to form one arrow, and a filled disk is placed downstream of the connection. By having a carefully planned set of conventions for depicting biological processes, collaborators can communicate with each other with minimal ambiguity in graphical notation.
Figure 4:
Figure 4:
Transcription-based logic gates constructed from modular transcription units [67]. Electronic logic gates are the fundamental building blocks of computational ability. For each logic gate, the table presents the boolean logic (column 2), design a biological module (column 3) and emulate the electronic counterpart with an expression profile (column 4). Each network architecture represents a synthetically designed component.
Figure 5:
Figure 5:
Assembling DNA molecules with BioBrick parts [70]. Gene A is to be added to the standardized plasmid p1. Neither Gene A nor any gene within p1 may have sequences that can be recognized by the four restriction enzymes used during the main assembly process. Gene A is flanked by ‘prefix’ and ‘suffix’ sequences which are deliver by primers during PCR. Alternatively, one can acquire a plasmid pA that already has Gene A with the necessarily prefix and suffix. Plasmid p1 and Gene A undergo separate restriction enzyme digests, and are later combined to form p1A. The plasmid p1A is now ready to receive another gene.
Figure 6:
Figure 6:
Recursive construction of error-free DNA molecules from imperfect oligonucleotides [68]. (A) GFP DNA construction. The entire sequence is divided into overlapping ones in silico. These pieces are synthesized conventionally. Assembly by overlapping ssDNA results in a target molecule, which are then sequenced to find errors. Error-free segments are derived, amplified and assembled by overlapping ssDNA takes place again. This loop continues until an error-free target molecule is formed. (B) Construction of ssDNA from two overlapping sequences. During PCR, one primer is a phosphorylated primer, which becomes a degradation target of Lambda endonuclease.

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