Inducible product gene expression technology tailored to bioprocess engineering

Bioprocess engineering has developed as a discipline to design optimal culture conditions and bioreactor operation protocols for production cell lines engineered for constitutive expression of desired protein pharmaceuticals. With the advent of heterologous gene regulation systems it has become possible to fine-tune expression of difficult-to-produce protein pharmaceuticals to optimal levels and to conditionally engineer cell metabolism for the best production performance. However, most of the small-molecules used to trigger expression of product or metabolic engineering product genes are incompatible with downstream processing regulations or process economics. Recent progress in product gene control design has resulted in the development of bioprocess-compatible regulation systems, which are responsive to physical parameters such as temperature or physiologic trigger molecules that are either an inherent part of host cell metabolism or intrinsic components of licensed protein-free cell culture media, such as redox status, vitamin H and gaseous acetaldehyde. While all of these systems have been shown to fine-tune product gene expression independent of the host cell metabolism some of them can be plugged into metabolic networks to capture critical physiologic parameters and convert them into an optimal production response. Assembly of individual product gene control modalities into synthetic networks has recently enabled construction of autonomously regulated time-delay or cell density-sensitive gene circuits, which trigger population-wide induction of product gene expression at a predefined time or culture density. We provide a comprehensive overview on the latest developments in the design of bioprocess-compatible product gene control systems.     

TDP-43: gumming up neurons through protein-protein and protein-RNA interactions

Since the discovery that 43 kDa TAR DNA binding protein (TDP-43) is involved in neurodegeneration, studies of this protein have focused on the global effects of TDP-43 expression modulation on cell metabolism and survival. The major difficulty with these global searches, which can yield hundreds to thousands of variations in gene expression level and/or mRNA isoforms, is our limited ability to separate specific TDP-43 effects from secondary dysregulations occurring at the gene expression and various mRNA processing steps. In this review, we focus on two biochemical properties of TDP-43: its ability to bind RNA and its protein-protein interactions. In particular, we overview how these two properties may affect potentially very important processes for the pathology, from the autoregulation of TDP-43 to aggregation in the cytoplasmic/nuclear compartments.     

Approximations and their consequences for dynamic modelling of signal transduction pathways

Signal transduction is the process by which the cell converts one kind of signal or stimulus into another. This involves a sequence of biochemical reactions, carried out by proteins. The dynamic response of complex cell signalling networks can be modelled and simulated in the framework of chemical kinetics. The mathematical formulation of chemical kinetics results in a system of coupled differential equations. Simplifications can arise through assumptions and approximations. The paper provides a critical discussion of frequently employed approximations in dynamic modelling of signal transduction pathways. We discuss the requirements for conservation laws, steady state approximations, and the neglect of components. We show how these approximations simplify the mathematical treatment of biochemical networks but we also demonstrate differences between the complete system and its approximations with respect to the transient and steady state behavior.     

Growth-phase-dependent gene expression profiling of poplar (Populus alba x Populus tremula var. glandulosa) suspension cells

Complex sequences of morphological and biochemical changes occur during the developmental course of a batch plant cell culture. However, little information is available about the changes in gene expression that could explain these changes, because of the difficulties involved in isolating specific cellular events or developmental phases in the overlapping phases of cell growth. In an attempt to obtain such information we have examined the global growth phase-dependent gene expression of poplar cells in suspension cultures by cDNA microarray analysis. Our results reveal that significant changes occur in the expression of genes with functions related to protein synthesis, cell cycling, hormonal responses and cell wall biosynthesis, as cultures progress from initiation to senescence, that are highly correlated with observed developmental and physiological changes in the cells. Genes encoding protein kinases, calmodulin and proteins involved in both ascorbate metabolism and water-limited stress responses also showed strong stage-specific expression patterns. Our report provides fundamental information on molecular mechanisms that control cellular changes throughout the developmental course of poplar cell cultures.     

Proteomic and physiological experiments to test Thermotoga neapolitana constraint-based model hypotheses of carbon source utilization

Constraint-based models of biochemical reaction networks require experimental validation to test model-derived hypotheses and iteratively improve the model. Physiological and proteomic analysis of Thermotoga neapolitana growth on cellotetraose was conducted to identify gene products related to growth on cellotetraose to improve a constraint-based model of T. neapolitana central carbon metabolism with incomplete cellotetraose pathways. In physiological experiments comparing cellotetraose to cellobiose and glucose as growth substrates, product formation yields on cellotetraose, cellobiose, and glucose were similar; however cell yields per mol carbon consumed were higher on cellotetraose than on cellobiose or glucose. Proteomic analysis showed increased expression of several proteins from cells grown on cellotetraose compared with glucose cell cultures, including cellobiose phosphorylase (CTN_0783), endo-1,4-β-glucosidase (CTN_1106), and an ATP-binding protein (CTN_1296). The CTN_1296 gene product should be evaluated further for participation in cellotetraose metabolism and is included as one of two hypothetical gene-protein-reaction associations in the T. neapolitana constraint-based model to reinstate cellotetraose metabolism in model simulations.     

从酵母表达时间序列估计基因调控网络

基因调控网络是生命功能在基因表达层面上的展现。用组合线性调控模型、调控元件识别和基因聚类等方法 ,从基因组表达谱解读酵母在细胞周期与环境胁迫中的基因调控网络。结果表明 ,细胞在不同环境条件下会调整基因调控网络。在适应的环境下 ,起主要作用的是细胞生长和增殖有关的基因调控网络 ;而在响应环境胁迫时 ,细胞会再规划调控网络 ,抑制细胞生长和增殖相关的基因 ,诱导跟适应性糖类代谢与结构修复相关的基因 ,还可能启动减数分裂产生孢子。分别从细胞周期和环境胁迫响应相关基因中 ,搜索到转录因子Mcm1结合位点TT CC T GGAAA ,和Dal82在尿囊素代谢途径相关基因上的结合位点TGAAAAWTTT。从而 ,从酵母表达时间序列估计基因调控网络是可行的 ,与至今已知的实验观察相当吻合     

RASSF1A对黑色素瘤A375细胞基因网络的影响

RASSF1A(Ras association domain family 1 isoform A)是定位于染色体3p21.3区域的抑瘤基因,编码一个由340个氨基酸残基构成的微管相关蛋白.该基因在包括恶性黑色素瘤在内的多种肿瘤中因启动子高甲基化而表达沉默.本研究建立了RASSF1A稳定表达的恶性黑色素瘤A375细胞系,通过全基因组表达谱基因芯片分析RASSF1A过表达对A375细胞基因表达谱的影响,发现RASSF1A引起184个基因表达上调,26个基因表达下调.通过Realtime RT-PCR对部分差异表达基因进行验证,结果表明与芯片筛选结果一致.RASSF1A影响的差异表达基因功能上归属于细胞生长与增殖、细胞周期、细胞凋亡、细胞间黏附、信号传导等生物过程.采用STRING软件构建了RASSF1A影响的差异表达基因调控网络,结果表明RASSF1A调控的差异表达基因构成一个高连接度的基因网络.其中,炎症细胞因子、转录因子位于网络中央.RASSF1A通过影响炎症细胞因子与转录因子之间的表达,影响A375细胞基因网络,调节黑色素瘤恶性生物学行为.     

Topological analysis of metabolic networks based on Petri net theory

Petri net concepts provide additional tools for the modelling of metabolic networks. Here, the similarities between the counterparts in traditional biochemical modelling and Petri net theory are discussed. For example the stoichiometry matrix of a metabolic network corresponds to the incidence matrix of the Petri net. The flux modes and conservation relations have the T-invariants, respectively, P-invariants as counterparts. We reveal the biological meaning of some notions specific to the Petri net framework (traps, siphons, deadlocks, liveness). We focus on the topological analysis rather than on the analysis of the dynamic behaviour. The treatment of external metabolites is discussed. Some simple theoretical examples are presented for illustration. Also the Petri nets corresponding to some biochemical networks are built to support our results. For example, the role of triose phosphate isomerase (TPI) in Trypanosoma brucei metabolism is evaluated by detecting siphons and traps. All Petri net properties treated in this contribution are exemplified on a system extracted from nucleotide metabolism.     

Application of Petri net theory for modelling and validation of the sucrose breakdown pathway in the potato tuber

MOTIVATION: Because of the complexity of metabolic networks and their regulation, formal modelling is a useful method to improve the understanding of these systems. An essential step in network modelling is to validate the network model. Petri net theory provides algorithms and methods, which can be applied directly to metabolic network modelling and analysis in order to validate the model. The metabolism between sucrose and starch in the potato tuber is of great research interest. Even if the metabolism is one of the best studied in sink organs, it is not yet fully understood. RESULTS: We provide an approach for model validation of metabolic networks using Petri net theory, which we demonstrate for the sucrose breakdown pathway in the potato tuber. We start with hierarchical modelling of the metabolic network as a Petri net and continue with the analysis of qualitative properties of the network. The results characterize the net structure and give insights into the complex net behaviour.     

The Modular Organization of Protein Interactions in Escherichia coli

Escherichia coli serves as an excellent model for the study of fundamental cellular processes such as metabolism, signalling and gene expression. Understanding the function and organization of proteins within these processes is an important step towards a ‘systems’ view of E. coli. Integrating experimental and computational interaction data, we present a reliable network of 3,989 functional interactions between 1,941 E. coli proteins (∼45% of its proteome). These were combined with a recently generated set of 3,888 high-quality physical interactions between 918 proteins and clustered to reveal 316 discrete modules. In addition to known protein complexes (e.g., RNA and DNA polymerases), we identified modules that represent biochemical pathways (e.g., nitrate regulation and cell wall biosynthesis) as well as batteries of functionally and evolutionarily related processes. To aid the interpretation of modular relationships, several case examples are presented, including both well characterized and novel biochemical systems. Together these data provide a global view of the modular organization of the E. coli proteome and yield unique insights into structural and evolutionary relationships in bacterial networks.     

A structured approach for the engineering of biochemical network models, illustrated for signalling pathways

Quantitative models of biochemical networks (signal transduction cascades, metabolic pathways, gene regulatory circuits) are a central component of modern systems biology. Building and managing these complex models is a major challenge that can benefit from the application of formal methods adopted from theoretical computing science. Here we provide a general introduction to the field of formal modelling, which emphasizes the intuitive biochemical basis of the modelling process, but is also accessible for an audience with a background in computing science and/or model engineering. We show how signal transduction cascades can be modelled in a modular fashion, using both a qualitative approach--qualitative Petri nets, and quantitative approaches--continuous Petri nets and ordinary differential equations (ODEs). We review the major elementary building blocks of a cellular signalling model, discuss which critical design decisions have to be made during model building, and present a number of novel computational tools that can help to explore alternative modular models in an easy and intuitive manner. These tools, which are based on Petri net theory, offer convenient ways of composing hierarchical ODE models, and permit a qualitative analysis of their behaviour. We illustrate the central concepts using signal transduction as our main example. The ultimate aim is to introduce a general approach that provides the foundations for a structured formal engineering of large-scale models of biochemical networks.     

Discovering gene networks with a neural-genetic hybrid

Recent advances in biology (namely, DNA arrays) allow an unprecedented view of the biochemical mechanisms contained within a cell. However, this technology raises new challenges for computer scientists and biologists alike, as the data created by these arrays is often highly complex. One of the challenges is the elucidation of the regulatory connections and interactions between genes, proteins and other gene products. In this paper, a novel method is described for determining gene interactions in temporal gene expression data using genetic algorithms combined with a neural network component. Experiments conducted on real-world temporal gene expression data sets confirm that the approach is capable of finding gene networks that fit the data. A further repeated approach shows that those genes significantly involved in interaction with other genes can be highlighted and hypothetical gene networks and circuits proposed for further laboratory testing.     

Prospects for Biotechnology in Oil Palm (Elaeis guineensis) and Coconut (Cocos nucifera) Improvement

Abstract

Research into human metabolism is expanding rapidly due to the emergence of metabolism as a key factor in common diseases. Mathematical modeling of human cellular metabolism has traditionally been performed via kinetic approaches whose applicability for large-scale systems is limited by lack of kinetic constants data. An alternative computational approach bypassing this hurdle called constraint-based modeling (CBM) serves to analyze the function of large-scale metabolic networks by solely relying on simple physical-chemical constraints. However, while extensive research has been performed on constraint-based modeling of microbial metabolism, large-scale modeling of human metabolism is still in its infancy. Utilizing constraint-based modeling to model human cellular metabolism is significantly more complicated than modeling microbial metabolism as in multi-cellular organisms the metabolic behavior varies across cell-types and tissues. It is further complicated due to lack of data on cell type- and tissue-specific metabolite uptake from the surrounding microenvironments and tissue-specific metabolic objective functions. To overcome these problems, several studies suggested CBM methods that integrate metabolic networks with gene expression data that is easily measurable under various conditions. This specific objective functions are expected to improve the prediction accuracy of the presented methods. Such objective functions may be derived based on computational learning that would give optimal correspondence between predicted and measured metabolic phenotypes (Burgard, 2003).

The CBM methods presented here open the way for future computational investigations of metabolic disorders given the relevant expression data. A first attempt to visualize and interpret changes in gene expression data measured following gastric bypass surgery via a genome-scale metabolic network was done by Duarte et al (Duarte, 2007). Another potential application would be the prediction of diagnostic biomarkers for metabolic diseases that could be identified via biofluid metabolomics (Kell, 2007). Towards this goal, we have recently developed a CBM method for predicting metabolic biomarkers for in-born errors of metabolism by searching for changes in metabolite uptake and secretion rate due to genetic alterations (Shlomi, 2009). Incorporating cell type- and tissue-specific gene expression data within this framework can potentially improve the identification of diagnostic biomarkers. Overall, the methods presented here lay the foundation for studying normal and abnormal human cellular metabolism in tissue-specific manner based on commonly measured gene expression data.