Computational Synthetic Biology Research Articles

Special issue “International Symposium on Molecular Logic and Computational Synthetic Biology: MLCSB18”

Special issue “International Symposium on Molecular Logic and Computational Synthetic Biology: MLCSB18”

Computational Modeling and Engineering of Allosteric Regulatory Mechanisms in Signaling Proteins: Integration of Multiscale Simulations, Network Biology and Machine Learning

The allosteric interactions and regulation of molecular chaperones and protein kinases allow for molecular communication and event coupling in signal transduction networks. We report the results of integrative systems biology studies of the Hsp90 chaperone and protein kinases with an atomic level analysis of the communication pathways regulating conformational equilibrium of theses protein systems in signaling networks. Biophysical modeling of allosteric regulation in the protein kinases has offered additional insights into organizing principles of kinase activation by molecular chaperones that may be orchestrated by a cross-talk between key regulatory regions. The results of biophysical and computational systems biology analyses combined with proteomics experiments have been integrated into a graph-based network model of allosteric regulation. The evolution of protein structure networks in molecular chaperones and protein kinases during allosteric activation has revealed the increased cooperativity reflecting a preferential attachment of allosterically interacting functional sites. Among our primary findings is the emerging evidence that a small number of functional motifs may be utilized by the chaperone and protein kinases to act collectively as central regulators of the intermolecular communications, ATP hydrolysis, and protein client binding in signaling networks. Integration of computational systems biology and machine learning analysis of the Hsp90 interactions with oncogenic kinase mutants is then used to construct models of allosteric regulation of oncogenic proteins by molecular chaperones in signaling cascades. A computational synthetic biology framework is proposed for design and re-engineering signal transduction networks and pathways that involve cross-talk between molecular chaperones and protein kinase clients. We have also analyzed mechanisms by which kinase drugs and allosteric Hsp90 modulators can act synergistically and exert their pharmacological effect by depriving the client kinase of access to the molecular chaperone.

Informational laws of genome structures.

In recent years, the analysis of genomes by means of strings of length k occurring in the genomes, called k-mers, has provided important insights into the basic mechanisms and design principles of genome structures. In the present study, we focus on the proper choice of the value of k for applying information theoretic concepts that express intrinsic aspects of genomes. The value k = lg2(n), where n is the genome length, is determined to be the best choice in the definition of some genomic informational indexes that are studied and computed for seventy genomes. These indexes, which are based on information entropies and on suitable comparisons with random genomes, suggest five informational laws, to which all of the considered genomes obey. Moreover, an informational genome complexity measure is proposed, which is a generalized logistic map that balances entropic and anti-entropic components of genomes and is related to their evolutionary dynamics. Finally, applications to computational synthetic biology are briefly outlined.

Integrative metabolic engineering

Recent advances in experimental and computational synthetic biology are extremely useful for achieving metabolic engineering objectives. The integration of synthetic biology and metabolic engineering within an iterative design-build-test framework will improve the practice of metabolic engineering by relying more on efficient design strategies. Computational tools that aid in the design and <em>in silico</em> simulation of metabolic pathways are especially useful. However, software helpful for constructing, implementing, measuring and characterizing engineered pathways and networks should not be overlooked. In this review, we highlight computational synthetic biology tools relevant to metabolic engineering, organized in the context of the design-build-test cycle.

Introduction to the Special Issue on Computational Synthetic Biology

The goal of this special issue is to introduce the field of computational synthetic biology to engineers and computer scientists. The first article gives an introduction to the key biological principles and experimental techniques that support synthetic biology, and it draws analogies with the computing field. This issue also includes five original research articles in computational synthetic biology. The first research article discusses how standards can be used to modularize the design process for genetic circuits. The next two articles introduce new abstraction techniques to improve the efficiency of analysis of genetic circuit models. The last two articles introduce new design techniques that help decouple design from construction. We hope this sampling from the field will help to motivate others to join this exciting and rich area of research.

Synthetic Biology: Applying Engineering to Life Sciences to Develop Rationally Designed Biological Parts, Devices, and Systems

Synthetic biology (SB) has attracted considerable attention in recent years. The term “SB” can be traced back to 1912 to Stephane Leduc’s book La Biologie Synthetique. The next direct reference to the term is found in 1974 when Waclaw Szybalski, in only a few sentences, laid the foundations for the emerging field of SB. The new wave of SB aims to design and engineer biologically based parts, novel devices, and networks as well as redesigning existing, natural biological pathways. Hence, SB has the potential to deliver significant technological benefits and economic growth. Synthetic biology has already made a resounding impact in fundamental and applied research. Currently it has broad applications in a number of areas including medicine, the environment and energy, agriculture, and food industries. Selected milestones and significant achievements include the reconstruction of a complete microbial genome (Gibson et al., 2010), the synthesis of a yeast chromosome (Dymond et al., 2011), and most recently, the engineering of Saccharomyces cerevisiae to produce high levels of a key precursor of the antimalarial drug artemisinin (Paddon et al., 2013). Synthetic Biology: Tools and Applications, edited by Huimin Zhao (University of Illinois) provides a comprehensive coverage and a succinct overview of the state-of-the-art knowledge on SB (Zhao, 2013). The overall scope of the book is based on a review article (Zhao, 2013) published in Wiley Interdisciplinary Reviews: Systems Biology and Medicine (Liang et al., 2011). On more than 300 pages, structured into 17 chapters, Synthetic Biology: Tools and Applications brings together thought leaders and leading researchers in SB, by providing a systematic and integrated framework to examine key enabling components in SB. The era of SB has arrived. As tellingly described in the introduction by the editor, SB builds on the advances in molecular, and systems biology and seeks to revolutionize life sciences in the same way that chemical synthesis revolutionized chemistry and integrated circuit design revolutionized computing. In addition, the number of publications on SB has increased exponentially in recent years. As a consequence, it is almost impossible to follow the rush of SB-literature in detail. Furthermore, the emergence of SB has been accompanied by the development of new and improved experimental tools and approaches. However, more exciting applications in basic and applied research would be useful to make SB easier, safer, and more predictable. Therefore, it is sound and timely to collect and converge the various research tools, methodologies, and prospects of SB research in a single comprehensive book. The organization of the book into four well-chosen sections (rather than into 17 imposing chapters) lures the reader into reading a thematic section of several chapters at any one time, rather than the entire book. The order of topics is intended to represent a logical progression. Each chapter starts with an essential introduction; this allows reading the chapters independently from each other, a feature that readers will highly acknowledge. The first thematic section, entitled “Synthesis and Engineering Tools in SB.” This section (chapters 1–4) addresses the latest state-of-the-art experimental tools and approaches developed for engineering biological systems at molecular, cellular, and multi-cellular levels. In addition, this section discusses the technologies for DNA synthesis and their limitations, whereas it also provides an overview of the key methods for protein engineering. The section closes by describing the challenges and opportunities in the standardization and modularization of biological parts, which is one of the key distinguishing features of SB. The second thematic section (chapters 5–8) reviews the mathematical modeling approaches, and in silico tools, which can dramatically accelerate the design process, as well as reduce the cost of development of novel devices and networks. In addition, this section introduces a new computational SB tool, the Synthetic Biology Software Suite, whereas a wide variety of successful examples are also discussed to highlight the effectiveness of these computational tools and approaches. The next section (chapters 9–13) deals with exciting practical applications of SB. Chapters of this section provide an overview on the potential therapeutic applications of SB on human health and disease, and on microbial production of pharmaceuticals. In addition, this section discusses the industrial application of SB in the microbial production of biofuels, and the challenges associated with biofuels production on an industrial scale. Furthermore, the available technologies and strategies for designing and constructing bacterial genomes, and the recent efforts on the analysis and creation of microbial consortia are also overviewed elegantly. The final section (chapters 14–17), Future Prospects, describes the current status of designing and constructing synthetic or artificial cells. This section also provides a comprehensive overview of the recent advances in cell-free SB. Furthermore, it provides an overview on the use of several tools to engineer light-energy conversion and carbon fixation in diverse non-photosynthetic hosts. Moreover, the recent developments and future prospects in engineering synthetic microbial ecosystems are also described. Finally, although an index is present, a glossary would be desirable. Synthetic Biology: Tools and Applications uses a combination of elegant tables, figures, and graphs to present complicated information in a way that is accessible and understandable by the reader. All chapters are skillfully reviewed and highly citable. In summary, the book is an excellent addition to the current literature on SB. It is a perfect companion for policy makers and funding agencies. Lastly, but most importantly, Synthetic Biology: Tools and Applications will be greatly appreciated by geneticists, molecular biologists, bioinformaticians, physicists, chemists, and bioengineers, and those contemplating a future in SB.

Flow cytometry and data analysis: Basic concepts and statistics

Merging concepts from synthetic biology and computational biology may yield antibiotics that are less likely to elicit resistance than existing drugs and that yet can fight drug-resistant infections. Indeed, computer-guided strategies coupled with massively parallel high-throughput experimental methods represent a new paradigm for antibiotic discovery. Infections caused by multidrug-resistant microorganisms are increasingly deadly. In the current post-antibiotic era, many of these infections cannot be treated with our existing antimicrobial arsenal. Furthermore, we may have already exhausted the category of large molecules produced in nature having antimicrobial activity: the antibiotic scaffolds we have discovered so far may represent the majority of those that exist. The rise in drug-resistant bacteria and lack of new antibiotic classes clearly call for out-of-the-box strategies. Recent advances in computational synthetic biology have enabled the development of antimicrobials. New molecular descriptors and genetic and pattern recognition algorithms are powerful tools that bring us a step closer to developing efficient antibiotics. We review several computational tools for drug design and a number of recently generated antibiotic candidates, with an emphasis on peptide-based molecules. Design strategies can generate a diversity of synthetic antimicrobial peptides, which may help to mitigate the spread of resistance and combat multidrug-resistant microorganisms.