Synthetic regulatory tools for microbial engineering

Microbial engineering requires accurate information about cellular metabolic networks and a set of molecular tools that can be predictably applied to the efficient redesign of such networks. Recent advances in the field of metabolic engineering and synthetic biology, particularly the development of molecular tools for synthetic regulation in the static and dynamic control of gene expression, have increased our ability to efficiently balance the expression of genes in various biological systems. It would accelerate the creation of synthetic pathways and genetic programs capable of adapting to environmental changes in real time to perform the programmed cellular behavior. In this paper, we review current developments in the field of synthetic regulatory tools for static and dynamic control of microbial gene expression.     

Adjusting phenotypes by noise control

Genetically identical cells can show phenotypic variability. This is often caused by stochastic events that originate from randomness in biochemical processes involving in gene expression and other extrinsic cellular processes. From an engineering perspective, there have been efforts focused on theory and experiments to control noise levels by perturbing and replacing gene network components. However, systematic methods for noise control are lacking mainly due to the intractable mathematical structure of noise propagation through reaction networks. Here, we provide a numerical analysis method by quantifying the parametric sensitivity of noise characteristics at the level of the linear noise approximation. Our analysis is readily applicable to various types of noise control and to different types of system; for example, we can orthogonally control the mean and noise levels and can control system dynamics such as noisy oscillations. As an illustration we applied our method to HIV and yeast gene expression systems and metabolic networks. The oscillatory signal control was applied to p53 oscillations from DNA damage. Furthermore, we showed that the efficiency of orthogonal control can be enhanced by applying extrinsic noise and feedback. Our noise control analysis can be applied to any stochastic model belonging to continuous time Markovian systems such as biological and chemical reaction systems, and even computer and social networks. We anticipate the proposed analysis to be a useful tool for designing and controlling synthetic gene networks.     

The intriguing complexities of mammalian gene regulation: How to link enhancers to regulated genes. Are we there yet?

The information encoded in genomes supports the differentiation and function of the more than 200 unique cell types, which exist in various mammalian species. The major mechanism driving cellular differentiation and specification is differential gene expression regulation. Cis-acting enhancers and silencers appear to have key roles in regulating the expression of mammalian genes. However, these cis-acting elements are often located very far away from the regulated gene. Therefore, it is hard to find all of them and link them to the regulated gene. An intriguing and unresolved issue of the field is to identify all of the enhancers of a particular gene and link these short regulatory sequences to the genes they regulate and thus, reliably identify gene regulatory enhancer networks. Recent advances in molecular biological methods coupled with Next-Generation Sequencing (NGS) technologies have opened up new possibilities in this area of genomics. In this review we summarize the technological advances, bioinformatics challenges and the potential molecular mechanisms allowing the construction of enhancer networks operating in specific cell types and/or activated by various signals.     

A gene network engineering platform for lactic acid bacteria

Recent developments in synthetic biology have positioned lactic acid bacteria (LAB) as a major class of cellular chassis for applications. To achieve the full potential of LAB, one fundamental prerequisite is the capacity for rapid engineering of complex gene networks, such as natural biosynthetic pathways and multicomponent synthetic circuits, into which cellular functions are encoded. Here, we present a synthetic biology platform for rapid construction and optimization of large-scale gene networks in LAB. The platform involves a copy-controlled shuttle for hosting target networks and two associated strategies that enable efficient genetic editing and phenotypic validation. By using a nisin biosynthesis pathway and its variants as examples, we demonstrated multiplex, continuous editing of small DNA parts, such as ribosome-binding sites, as well as efficient manipulation of large building blocks such as genes and operons. To showcase the platform, we applied it to expand the phenotypic diversity of the nisin pathway by quickly generating a library of 63 pathway variants. We further demonstrated its utility by altering the regulatory topology of the nisin pathway for constitutive bacteriocin biosynthesis. This work demonstrates the feasibility of rapid and advanced engineering of gene networks in LAB, fostering their applications in biomedicine and other areas.     

Extraction of biological interaction networks from scientific literature

Biology can be regarded as a science of networks: interactions between various biological entities (eg genes, proteins, metabolites) on different levels (eg gene regulation, cell signalling) can be represented as graphs and, thus, analysis of such networks might shed new light on the function of biological systems. Such biological networks can be obtained from different sources. The extraction of networks from text is an important technique that requires the integration of several different computational disciplines. This paper summarises the most important steps in network extraction and reviews common approaches and solutions for the extraction of biological networks from scientific literature.     

Probabilistic representation of gene regulatory networks

MOTIVATION: Recent experiments have established unambiguously that biological systems can have significant cell-to-cell variations in gene expression levels even in isogenic populations. Computational approaches to studying gene expression in cellular systems should capture such biological variations for a more realistic representation. RESULTS: In this paper, we present a new fully probabilistic approach to the modeling of gene regulatory networks that allows for fluctuations in the gene expression levels. The new algorithm uses a very simple representation for the genes, and accounts for the repression or induction of the genes and for the biological variations among isogenic populations simultaneously. Because of its simplicity, introduced algorithm is a very promising approach to model large-scale gene regulatory networks. We have tested the new algorithm on the synthetic gene network library bioengineered recently. The good agreement between the computed and the experimental results for this library of networks, and additional tests, demonstrate that the new algorithm is robust and very successful in explaining the experimental data. AVAILABILITY: The simulation software is available upon request. SUPPLEMENTARY INFORMATION: Supplementary material will be made available on the OUP server.     

Tumor suppressor genes

Although tumor suppressor genes continue to be discovered, the most recent advances have been made in attributing new and exciting functions to existing ones - such as the apparent role of VHL as a regulator of proteolysis. Great insights have also come from piecing genes together into pathways and networks. For instance the discovery that cyclin D1 is regulated by beta-catenin/Tcf-4 allows us to tie the APC pathway to the RB pathway and cell cycle control. Similarly, tumor suppressor genes have been fitted together with oncogenes into the various pathways that regulate apoptosis such that tumor suppressor function is now attributed to some of the basic components of the apoptotic machinery, such as caspases and Apaf-1. The great pace at which mouse models of tumorigenesis continue to advance our knowledge of tumor suppressor gene function has led us to look anew at the role of genes such as TCF-1 and SMAD-3 in human cancer. Finally, the realisation that different growth regulatory pathways give rise to generic signals suggests that future work may lie in integrating the signals from different pathways and in understanding the importance of protein levels to cellular function.     

BioLayoutJava

Visualisation of biological networks is becoming a common task for the analysis of high-throughput data. These networks correspond to a wide variety of biological relationships, such as sequence similarity, metabolic pathways, gene regulatory cascades and protein interactions. We present a general approach for the representation and analysis of networks of variable type, size and complexity. The application is based on the original BioLayout program (C-language implementation of the Fruchterman-Rheingold layout algorithm), entirely re-written in Java to guarantee portability across platforms. BioLayout(Java) provides broader functionality, various analysis techniques, extensions for better visualisation and a new user interface. Examples of analysis of biological networks using BioLayout(Java) are presented.     

HCSGD:An integrated database of human cellular senescence genes

Cellular senescence is an irreversible cell cycle arrest program in response to various exogenous and endogenous stimuli like telomere dysfunction and DNA damage.It has been widely accepted as an antitumor program and is also found closely related to embryo development,tissue repair,organismal aging and age-related degenerative diseases.In the past decades,numerous efforts have been made to uncover the gene regulatory mechanisms of cellular senescence.There is a strong demand to integrate these data from various resources into one open platform.To facilitate researchers on cellular senescence,we have developed Human Cellular Senescence Gene Database(HCSGD) by integrating multiple online published data sources into a comprehensive senescence gene annotation platform(http://bioinfo.au.tsinghua.edu.cn/member/xwwang/HCSGD).Potential Human Cellular Senescence Genes(HCSGS)were collected by combining information from published literatures,gene expression profiling data and Protein-Protein Interaction networks.Additionally,genes are annotated with gene ontology annotation and microRNA/drug/compound target information.HCSGD provides a valuable resource to visualize cellular senescence gene networks,browse annotated functional information,and retrieve senescenceassociated genes with a user-friendly web interface.     

Chemical crosshairs on the central dogma

As cellular machines and processes that regulate the flow of genomic information have come into sharper focus, a new level of chemical control has become possible. The scope of such chemical intervention extends from the mechanistic dissection of biochemical processes in living cells to the targeted control of gene networks and cell fate.     

Characterization of stem cells and cancer cells on the basis of gene expression profile stability, plasticity, and robustness: dynamical systems theory of gene expressions under cell-cell interaction explains mutational robustness of differentiated cells and suggests how cancer cells emerge

Here I present and discuss a model that, among other things, appears able to describe the dynamics of cancer cell origin from the perspective of stable and unstable gene expression profiles. In identifying such aberrant gene expression profiles as lying outside the normal stable states attracted through development and normal cell differentiation, the hypothesis explains why cancer cells accumulate mutations, to which they are not robust, and why these mutations create a new stable state far from the normal gene expression profile space. Such cells are in strong contrast with normal cell types that appeared as an attractor state in the gene expression dynamical system under cell-cell interaction and achieved robustness to noise through evolution, which in turn also conferred robustness to mutation. In complex gene regulation networks, other aberrant cellular states lacking such high robustness are expected to remain, which would correspond to cancer cells.