Optimization-driven identification of genetic perturbations accelerates the convergence of model parameters in ensemble modeling of metabolic networks

The ensemble modeling (EM) approach has shown promise in capturing kinetic and regulatory effects in the modeling of metabolic networks. Efficacy of the EM procedure relies on the identification of model parameterizations that adequately describe all observed metabolic phenotypes upon perturbation. In this study, we propose an optimization-based algorithm for the systematic identification of genetic/enzyme perturbations to maximally reduce the number of models retained in the ensemble after each round of model screening. The key premise here is to design perturbations that will maximally scatter the predicted steady-state fluxes over the ensemble parameterizations. We demonstrate the applicability of this procedure for an Escherichia coli metabolic model of central metabolism by successively identifying single, double, and triple enzyme perturbations that cause the maximum degree of flux separation between models in the ensemble. Results revealed that optimal perturbations are not always located close to reaction(s) whose fluxes are measured, especially when multiple perturbations are considered. In addition, there appears to be a maximum number of simultaneous perturbations beyond which no appreciable increase in the divergence of flux predictions is achieved. Overall, this study provides a systematic way of optimally designing genetic perturbations for populating the ensemble of models with relevant model parameterizations.     

基于HP模型的蛋白质折叠问题的研究

基于蛋白质二维HP模型提出改进的遗传算法对真实蛋白质进行计算机折叠模拟。结果显示疏水能量函数最小值的蛋白质构象对应含疏水核心的稳定结构,疏水作用在蛋白质折叠中起主要作用。研究表明二维HP模型在蛋白质折叠研究中是可行的和有效的并为进一步揭示蛋白质折叠机理提供重要参考信息。     

基于遗传算法特征选择的HBV再激活分类预测模型

探讨原发性肝癌患者精确放疗后乙型肝炎病毒(hepatitis b virus,HBV)再激活的危险特征和分类预测模型。提出基于遗传算法的特征选择方法,从原发性肝癌数据的初始特征集中选择HBV再激活的最优特征子集。建立贝叶斯和支持向量机的HBV再激活分类预测模型,并预测最优特征子集和初始特征集的分类性能。实验结果表明,基于遗传算法的特征选择提高了HBV再激活分类性能,最优特征子集的分类性能明显优于初始特征子集的分类性能。影响HBV再激活的最优特征子集包括:HBV DNA水平,肿瘤分期TNM,Child-Pugh,外放边界和全肝最大剂量。贝叶斯的分类准确性最高可达82.89%,支持向量机的分类准确性最高可达83.34%。     

细菌性肝脓肿临床特点的回顾性分析

目的:总结细菌性肝脓肿(pyogenic liver abscesses,PLA)的临床特点、诊断及治疗经验,提高诊疗水平。方法:回顾性分析哈尔滨医科大学第二临床医学院2013年1月-2014年12月收治的75例PLA患者的临床资料。结果:75例患者的平均年龄57岁,最大78岁,最小34岁。其中,男45例,女30例,男:女为1.5:1。75例患者中表现为发热者69例(92.0%)、寒战28例(37.3%)、右上腹痛66例(88.0%),伴恶心呕吐者28例(37.3%),黄疸15例(20.0%),肝肿大8例(10.7%)。有糖尿病病史患者28例(37.3%)。胆系疾病(78.7%)是本组资料主要的易患因素,其中53例患有胆囊炎(70.7%)。外周血白细胞增高67例(89.3%),血红蛋白降低41例(54.7%),白蛋白降低46例(61.3%);75例患者转氨酶升高58例(77.3%)。肝右叶单发脓肿多见(75例患者中36例,占48%)。51例B超确诊,24例行B超+CT或CT检查。48例行穿刺液细菌培养及25例血培养,其中肺炎克雷伯氏菌为主要病原菌。平均住院天数为19天,多数患者经皮下肝脓肿穿刺或置管引流而好转,1例漏诊为肺炎,2例患者反复入院,1例因菌血症死亡。结论:PLA的临床表现多样,以发热最为常见;多为肝右叶单发,部分患者血红蛋白及血清白蛋白降低。糖尿病为PLA最常见基础疾病,胆系疾病是主要的易患因素。肺炎克雷伯杆菌是本组PLA患者的主要致病菌。     

不同性别急性心梗患者的临床特征及院内治疗预后分析

目的:观察首次确诊为急性心肌梗死(AMI)患者的临床特征并分析不同性别人群的在入院治疗及院内短期预后的差异。方法:回顾性分析2010年2月至2012年4月在沈阳军区总医院心内科初次确诊为AMI的患者271例,并按性别分为两组,其中男性组180例,女性组91例,统计其临床特征、入院后的药物和手术治疗情况,以及院内并发症及预后情况,分析总结两组患者各自的特点和差异。结果:女性患者的年龄要大于男性患者(63±14vs71±11,P0.001),且同时更易患有高血压等心血管疾病合并症(73.3%vs 58.2%,P0.05)。女性患者的发病至入院治疗时间长于男性(P0.001),且入院后行PCI治疗的比例要明显低于男性患者(67%vs 79.4%,P0.05)。初步分析结果表明,女性患者的院内死亡率高于男性(11%vs 3.3%,OR:3.11,95%CI:1.53-7.15),但排除不均衡因素的影响后,男女患者的院内死亡率无明显差异(OR:2.11,95%CI:0.68-5.12)。结论:两性AMI患者的临床特征、入院后治疗及院内短期预后均存在一定差异。根据女性患者的临床特征和院内治疗及预后的现状,应进一步加强对女性高危人群的冠心病诊治知识普及和教育,且对入院后的女性患者应采取更为积极的药物和介入手术治疗手段,以改善预后。     

木霉对草酸耐受和消除作用的初步分析

【目的】草酸(Oxalic acid,OA)是灰霉菌(Botrytis cinerea)等植物致病菌的致病因子,一些木霉(Trichoderma spp.)生防菌可消除草酸并降低植物发病率,但其消除草酸防治病害的途径和机制尚未研究透彻。【方法】首先从42株木霉菌株中筛选出一株在30 mmol/L草酸胁迫下耐受性最强的哈茨木霉T.harzianum LTR-2,通过形态学方法观察和原位分析了不同浓度草酸胁迫下LTR-2的发育特征变化,并测定了草酸消除水平、滤液p H和菌丝干重,分析了LTR-2在草酸为唯一碳源的生长情况。通过Real-time PCR分析了OXDC基因LTR_4445在草酸处理下的表达水平。【结果】在PDA固体培养基中,25°C半光照条件下培养5 d,在草酸浓度为50-80 mmol/L时,LTR-2可存活,但无正常菌落形态;在30-50 mmol/L浓度下,LTR-2先萌发厚垣孢子,而后再次萌发菌丝形成菌落;在30 mmol/L浓度下,LTR-2发育正常,仅生长速度减缓。25°C、160 r/min振荡培养5 d,LTR-2可消除草酸。在20 mmol/L浓度时,草酸消除率最高,为66.50%;10 mmol/L浓度中的消除率次之,为55.06%。草酸浓度50 mmol/L时,消除能力下降为6.75%-38.94%。相应地,培养液p H被不同程度地提高,在10、20 mmol/L草酸浓度下提高的幅度更大。当草酸浓度20 mmol/L时,木霉的菌丝干重有不同程度的提高。将草酸作为唯一碳源进行液体培养5 d时,在10、20 mmol/L浓度下,LTR-2可形成肉眼可见的绿色菌丝球,但高于50 mmol/L条件下无法生长。草酸处理下,LTR_4445表达水平上调。【结论】通过分析LTR-2在草酸胁迫条件下的形态特征变化及消除草酸的特性,暗示在木霉消除草酸作用中除了已知的草酸降解代谢途径,还存在响应草酸胁迫模式的转变、将草酸作为前体转化为营养物质的途径等其他消除途径。     

Machine learning approaches in MALDI-MSI: clinical applications

Introduction: Despite the unquestionable advantages of Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging in visualizing the spatial distribution and the relative abundance of biomolecules directly on-tissue, the yielded data is complex and high dimensional. Therefore, analysis and interpretation of this huge amount of information is mathematically, statistically and computationally challenging.

Areas covered: This article reviews some of the challenges in data elaboration with particular emphasis on machine learning techniques employed in clinical applications, and can be useful in general as an entry point for those who want to study the computational aspects. Several characteristics of data processing are described, enlightening advantages and disadvantages. Different approaches for data elaboration focused on clinical applications are also provided. Practical tutorial based upon Orange Canvas and Weka software is included, helping familiarization with the data processing.

Expert commentary: Recently, MALDI-MSI has gained considerable attention and has been employed for research and diagnostic purposes, with successful results. Data dimensionality constitutes an important issue and statistical methods for information-preserving data reduction represent one of the most challenging aspects. The most common data reduction methods are characterized by collecting independent observations into a single table. However, the incorporation of relational information can improve the discriminatory capability of the data.