计算机科学与系统生物学杂志

This is a proposed business model for a systems approach in adapting or optimizing plants towards novel products that briefly explains the growth and recent trends in plant systems using three modeling approaches: L-systems, Dynamical-biosystems analysis and cellular automata. The model discusses some of the attempts made by scientists in this field such as model development in Faba bean crops and the auto regulation in nodulation of plants and some other simulated models (ALAMEDA). Its application in horticulture or life style industry is proposed based on the meristem model applied in the roots of Zea mays. L. A grid based approach that effectively simulates and models competition among chosen species of plants on a simulated environment helps in the study of species survival amidst competition from other species, threat from pests, weeds etc. A model for the ecological risk assessment enables the study of impacts of a newly introduced plant variety on the environment and the implications on plants by the environment that helps in decision making and public acceptance. There are attempts in building the virtual cells, by choosing to apply living cell conditions with some basic cell components working effectively, leading to a thrust in the field of systems biology where this would be an application on all kind of cells in any organisms. A plant competition model in an agronomic perspective uses some of the plant models that help in increase of crop yield in agriculture. Systems biology is an integration of genomics, transcriptomics, proteomics and metabolomics where approaches in identifying the regulatory elements and conserved regulatory motifs have been successfully made leading to a possibility of molecular level study where the input data is scarce and inappropriate beyond which it enables developing hypotheses to decipher poorly understood signaling pathways. A good example of direction and path deduction in plant systems using auxin transport applies best in a well studied business environment enabling a wide range of future perspectives. Some of the novel products or services from plant systems, derived using the systems approaches are briefly discussed. The context of business settings and commercial plausibility is well supported in all attempts of applying the systems biology approach in deriving plant products or services.

The value of computational modelling in improving our understanding of complex nutrient-based pathways is becoming increasingly recognised. This is due to the integral role that computer modelling is playing within the multidisciplinary field of systems biology, where in silico quantitative simulations are being used to compliment more traditional wet-laboratory investigations. A large number of computational models are accessible via the Biomodels database, an archive of openly available peer reviewed models of biological systems. Moreover, there has been an explosion in the availability of free modelling software tools that can be used to assemble and simulate the dynamic behaviour of nutrient mediated systems. Computational modelling will continue to play an increasingly significant role in nutrition research. Thus, it is important that freely accessible models and resources relevant to nutrition research are highlighted. In response to these needs, we firstly examined the Biomodels database, to identify and categorise nutrition themed models. The outcome of the analysis revealed 163 nutrition themed models. These models are mainly cellular in nature, with intracellular representations of calcium oscillations the most common. Secondly, a generic nutrition centred modelling framework was used, to explore recent advances, data repositories and software relevant to model building. We conclude this paper by using our review findings to discuss areas of nutrition that could further exploit the potential of computational modelling in the future.

Biological databases, containing genetic information of patients, are undergoing tremendous growth beyond our analysing capability. However such analysis can reveal new findings about the cause and subsequent treatment of any disease. Interactions between genes and the proteins they synthesize shape Genetic Regulatory Networks (GRN). In this context, it has been developed a model capable of representing small dominant GRN, combining characteristics from the Rough Set and Bayesian Network. The investigation has been carried out on the publicly available microarray dataset for Lung Adenocarcinoma, obtained from the National Center for Biotechnology Information (NCBI) website. The analysis revealed that Rough Set Theory (RST) is able to extract the various dominant genes in term of reducts which play an important role in causing the disease and also able to provide a unique simplified rule set for building expert systems in medical sciences with high accuracy and coverage factor. The next part of this work is based on reconstruction of GRN using Bayesian network, which is a mathematical tool for modelling conditional independences between stochastic variables like different gene expression. This proposed Bayesian approach using scaled mutual information for scoring is applied to the dataset corresponding to most dominant responsible genes for Adenocarcinoma to uncover, gene/protein interactions and key biological features of the cellular system. Finally different interacting regulatory path which are the gene signature for a particular disease, between dominating genes are inferred from the probability distribution table and Bayesian Graph. Such reconstructed regulatory network is attractive for their ability to describe complex stochastic processes like gene transcription, classification of biological sequencing and intuitive model of causal influence successfully. This may serve as a signature pattern of the disease Adenocarcinoma, which has been extracted from huge microarray dataset. Extraction of this signature pattern is very useful for diagnosis of this disease.

The recent application of Mass Spectrometry (MS) to Nucleic Acid Programmable Protein Array (NAPPA) technique for proteins identification by non-classical methods leads to the needs of more sophisticated algorithm for peak recognition. NAPPA technique allows for functional proteins to be synthesized in situ directly from printed cDNAs but faces the difficulty generated by the presence of master mix and lysate molecules peaks appearing as background in the overall spectra. A wide range of tools are available to analyze proteins conventional mass spectra corresponding to few molecular species. None of them is optimized for background subtraction. Moreover, peak identification is performed by statistical analysis on characteristics peaks and thus background subtraction can alter outcome by erasing characteristic peaks. A first attempt to overcome the so far discussed problem is here discussed. The result of this effort is the development of SpADS: Spectrum Analyzer and Data Set manager-an R script for MS data preprocessing-therein discussed. SpADS provides useful preprocessing functions such binning and peak extractions, as available tools, and provides functions of spectra background subtraction and dataset managing. It is entirely developed in R, thus free of charge. A cluster k means implementation is here used to improve results of SpADS preprocessing on test datasets and on NAPPA expressed proteins.

Microarrays have already produced huge amounts of valuable genetic data that is challenging to analyse due to its high dimensionality and complexity. An inherent problem with the microarray data which is characteristic of diseases such as Alzheimer’s is that they face computational complexity due to the sparseness of the points within the data, which affect both the accuracy and the efficiency of supervised learning methods. This paper proposes a data-adaptive rule-based classification system for Alzheimer’s disease classification that generates relevant rules by finding adaptive partitions using gradient-based partitioning of the data. The adaptive partitions are generated from the histogram by analyzing Tuple Tests following which efficient and relevant rules are discovered that assist in classifying the new data correctly. The proposed approach has been compared with other rule-based and machine learning classifiers, and detailed results and discussion of the experiments are presented to demonstrate comparative analysis and the efficacy of the results.