A declarative constraint-based method for analyzing discrete genetic regulatory networks

Dynamical modeling has proven useful for understanding how complex biological processes emerge from the many components and interactions composing genetic regulatory networks (GRNs). However, the development of models is hampered by large uncertainties in both the network structure and parameter values. To remedy this problem, the models are usually developed through an iterative process based on numerous simulations, confronting model predictions with experimental data and refining the model structure and/or parameter values to repair the inconsistencies. In this paper, we propose an alternative to this generate-and-test approach. We present a four-step method for the systematic construction and analysis of discrete models of GRNs by means of a declarative approach. Instead of instantiating the models as in classical modeling approaches, the biological knowledge on the network structure and its dynamics is formulated in the form of constraints. The compatibility of the network structure with the constraints is queried and in case of inconsistencies, some constraints are relaxed. Common properties of the consistent models are then analyzed by means of dedicated languages. Two such languages are introduced in the paper. Removing questionable constraints or adding interesting ones allows to further analyze the models. This approach allows to identify the best experiments to be carried out, in order to discriminate sets of consistent models and refine our knowledge on the system functioning. We test the feasibility of our approach, by applying it to the re-examination of a model describing the nutritional stress response in the bacterium Escherichia coli.     

A Logic Programming and Expert Statistical Systems Approach for Tissue Characterization in Magnetic Resonance Imaging

The main research goal has been to evaluate significant factors affecting the in vivo magnetic resonance imaging (MRI) parameters T, T, T₂, and ¹H density. This approach differs significantly from other such projects in that the experimental data analysis is being performed while concurrently developing automated, computer-aided analysis software for such MRI tissue parameters. In the experimental portion of the project, statistical analyses, and a heuristic minimum/maximum discriminant analysis algorithm have been explored. Both methods have been used to classify tissue types from 1.5 Tesla transaxial MR images of the human brain. The developing program, written in the logic programming language Prolog, is similar in a number of ways to many existing expert systems now in use for other medical applications; inclusion of the underlying statistical data base and advanced statistical analyses is the main differentiating feature of the current approach. First results indicate promising classification accuracy of various brain tissues such as gray and white matter, as well as differentiation of different types of gray matter and white matter (e.g.: caudate-nucleus vs. thalamus, both representatives of gray matter; and, cortical white matter vs. internal capsule as representative of white matter). Taking all four tissue types together, the percentage of correct classifications ranges from 73 to 87%.     

Contemplating 3-Hydroxypropionic Acid Biosynthesis in Klebsiella pneumoniae

3-Hydroxypropionic acid (3-HP) is a commercially valuable platform chemical from which an array of C₃ compounds can be generated. Klebsiella pneumoniae has been considered a promising species for biological production of 3-HP. Despite a wealth of reports related to 3-HP biosynthesis in K. pneumoniae, its commercialization is still in infancy. The major hurdle hindering 3-HP overproduction lies in the poor understanding of glycerol dissimilation in K. pneumoniae. To surmount this problem, this review aims to portray a picture of 3-HP biosynthesis, involving 3-HP-synthesizing strains, biochemical attributes, metabolic pathways and key enzymes. Inspired by the state-of-the-art advances in metabolic engineering and synthetic biology, here we advocate protocols for overproducing 3-HP in K. pneumoniae. These protocols range from cofactor regeneration, alleviation of metabolite toxicity, genome editing, remodeling of transport system, to carbon flux partition via logic gate. The feasibility for these protocols was also discussed.

Electronic supplementary material

The online version of this article (doi:10.1007/s12088-015-0513-0) contains supplementary material, which is available to authorized users.     

Recent advances and opportunities in synthetic logic gates engineering in living cells

Recently, a number of synthetic biologic gates including AND, OR, NOR, NOT, XOR and NAND have been engineered and characterized in a wide range of hosts. The hope in the emerging synthetic biology community is to construct an inventory of well-characterized parts and install distinct gene and circuit behaviours that are externally controllable. Though the field is still growing and major successes are yet to emerge, the payoffs are predicted to be significant. In this review, we highlight specific examples of logic gates engineering with applications towards fundamental understanding of network complexity and generating a novel socially useful applications.     

基于模糊层次分析法的黄河三角洲生态脆弱性评价

黄河三角洲位于黄河入海口,生态系统类型丰富,具有重要的生物多样性保护和生态屏障作用;但频繁的人为活动和自然灾害已对该地区生态环境造成严重破坏,生态脆弱性增强,故及时掌握黄河三角洲的生态环境现状,为区域生态环境保护和资源开发管理提供可靠的科学依据和重要的数据支撑是相当必要的。基于多元数据,尝试利用模糊层次分析法对黄河三角洲进行生态脆弱性评价,获取黄河三角洲的生态脆弱性空间分布状况并进行原因分析,并利用模糊逻辑理论模型对指标和评价结果进行定量化分级,提高了评价过程的可靠性。结果显示,黄河三角洲生态脆弱性等级空间分布具有一定的规律性,从沿海到内陆生态脆弱性逐渐减弱,有堤坝防护的沿海地区相对其他沿海区域生态脆弱性低,土壤质量和地下水位对生态脆弱性的影响较重,空间相关性分别达到-0.55和-0.74,人为开发活动对生态环境也产生较大压力。     

Reflections on the phytosociological approach and the epistemological basis of vegetation science

Summary It is tried to identify the logical basis of the phytosociological method. Both the inductive and the deductive approach are discussed. The starting point can be summarized with the axiom vegetation is organized in communities. Some problems related to the use of numerical methods and computers are discussed.This paper was written under the auspices of the Programma finalizzato Qualità dell'Ambiente of the C.N.R. (Roma) Subprogetto 5 Metodologie matematiche e basi di dati. I thank the following people for helpful comments: E. Feoli, D. Lausi, P. Nimis, E. Pignatti Wikus (all at the Istituto Botanico, Trieste), D. Dal Maso (Istituto di Matematica, Trieste), E. van der Maarel (Botanisch Laboratorium, Nijmegen).     

Intramolecular Charge Transfer aromatic amines and their application towards molecular logic gate

The emission response of 1-aminopyrene and 2-aminoanthracene was found to be switched ON or OFF by interrupting the Intramolecular Charge Transfer processes of these integrated systems using two binary ionic inputs like H⁺ or/and OH⁻. These fluorescence responses in the presence of added H⁺ or/and OH⁻ could be correlated with Half-subtractor logic operation, revealing the possibility of using simple molecules for demonstrating complex logic operations.     

The structural elucidation of a novel iridoid derivative from Tachiadenus longiflorus (Gentianaceae) using the LSD programme and quantum chemical computations

Oleanolic acid, scoparone, scopoletin and a novel iridoid derivative, angelone, were isolated from Tachiadenus longiflorus (Gentianaceae). The structure of angelone was determined from NMR data, given as input to the Logic for Structure Determination Programme, and was finally confirmed by comparison of experimental 13C-NMR chemical shifts with those obtained by quantum mechanical calculations.     

Bayesian logistic regression using a perfect phylogeny

Haplotype data capture the genetic variation among individuals in a population and among populations. An understanding of this variation and the ancestral history of haplotypes is important in genetic association studies of complex disease. We introduce a method for detecting associations between disease and haplotypes in a candidate gene region or candidate block with little or no recombination. A perfect phylogeny demonstrates the evolutionary relationship between single-nucleotide polymorphisms (SNPs) in the haplotype blocks. Our approach extends the logic regression technique of Ruczinski and others (2003) to a Bayesian framework, and constrains the model space to that of a perfect phylogeny. Environmental factors, as well as their interactions with SNPs, may be incorporated into the regression framework. We demonstrate our method on simulated data from a coalescent model, as well as data from a candidate gene study of sarcoidosis.