Accounting for robustness in modeling signal transduction responses

Abstract

A classical question in systems biology is to find a Boolean model which is able to predict the observed responses of a signaling network. It has been previously shown that such models can be tailored based on experimental data. While fitting a minimum-size network to the experimentally observed data is a natural assumption, it can potentially result in a network which is not so robust against the noises in the training dataset. Indeed, it is widely accepted now that biological systems are generally evolved to be very robust. Therefore, in the present work, we extended the classical formulation of Boolean network construction in order to put weight on the robustness of the created network. We show that our method results generally in more relevant networks. Consequently, considering robustness as a design principle of biological networks can result in more realistic models.     

Prediction of methionine oxidation risk in monoclonal antibodies using a machine learning method

ABSTRACT

Monoclonal antibodies (mAbs) have become a major class of protein therapeutics that target a spectrum of diseases ranging from cancers to infectious diseases. Similar to any protein molecule, mAbs are susceptible to chemical modifications during the manufacturing process, long-term storage, and in vivo circulation that can impair their potency. One such modification is the oxidation of methionine residues. Chemical modifications that occur in the complementarity-determining regions (CDRs) of mAbs can lead to the abrogation of antigen binding and reduce the drug’s potency and efficacy. Thus, it is highly desirable to identify and eliminate any chemically unstable residues in the CDRs during the therapeutic antibody discovery process. To provide increased throughput over experimental methods, we extracted features from the mAbs’ sequences, structures, and dynamics, used random forests to identify important features and develop a quantitative and highly predictive in silico methionine oxidation model.     

Tourettesyndrome免疫病因学研究进展

多发性抽动症（Tourette syndrome）是一种慢性神经精神性疾病,好发于儿童和青少年,主要表现为一个或多个部位肌肉运动性或发声性抽动,发作期持续超过一年,间歇期不超过3个月。TS的病因和发病机制至今仍未明确,目前认为其发病与免疫因素,环境心理因素,遗传因素,生物化学因素等多种因素有关,目前,A族B溶血性链球菌与Ts的发生跟发展的关系已有所报道,较多研究是关于链球茵感染的,一些免疫相关研究发现与链球菌感染有关的小儿自身免疫性神经精神疾病会引发或加重抽动症状。另外,过敏性变态反应性疾病与Ts患儿的自身免疫应答活跃的关系也有所报道。神经．内分泌一免疫网络学说认为,外周免疫系统跟中枢神经系统之间,可通过直接接触或通过产生并释放出来的生物活性因子进行信息交流。细胞因子正是两大系统间相互作用的信使,不仅可以调节免疫,而且还可以调控神经元和神经胶质细胞的功能。细胞因子与神经介质、内分泌激素共同组成机体细胞间的信号分子,参与免疫系统并激活神经递质及激素间的信息传递,与精神障碍和心理机体反应密切相关。因此,有关免疫病因学异常可能是部分易感患儿发病的机制,文章从感染免疫学,过敏性变态反应性疾病,以及细胞因子调节网络等与免疫相关的几个方面加以综述。