Quantitative Operating Principles of Yeast Metabolism during Adaptation to Heat Stress

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Construction of human activity‐based phosphorylation networks

The landscape of human phosphorylation networks has not been systematically explored, representing vast, unchartered territories within cellular signaling networks. Although a large number of in vivo phosphorylated residues have been identified by mass spectrometry (MS)‐based approaches, assigning the upstream kinases to these residues requires biochemical analysis of kinase‐substrate relationships (KSRs). Here, we developed a new strategy, called CEASAR, based on functional protein microarrays and bioinformatics to experimentally identify substrates for 289 unique kinases, resulting in 3656 high‐quality KSRs. We then generated consensus phosphorylation motifs for each of the kinases and integrated this information, along with information about in vivo phosphorylation sites determined by MS, to construct a high‐resolution map of phosphorylation networks that connects 230 kinases to 2591 in vivo phosphorylation sites in 652 substrates. The value of this data set is demonstrated through the discovery of a new role for PKA downstream of Btk (Bruton's tyrosine kinase) during B‐cell receptor signaling. Overall, these studies provide global insights into kinase‐mediated signaling pathways and promise to advance our understanding of cellular signaling processes in humans.     

系统生物学中建模方法的研究现状及展望

系统生物学倡导利用系统论的思想和方法,从整体的高度分析、研究生命的复杂特性。这一点与实验生物学仅关注某一个或者某一些生物大分子是迥然不同的。系统生物学既要同时考虑多个层次、多种类型的生物信息,还要考虑时间因素。由于系统特性是由于不同组成部分、不同层次间相互作用而“涌现”出的新性质,因此,如果只是针对组成部分或单一层次的分析并不能真正准确地预测整体或高层次的行为。如何通过研究和整合去发现和理解“涌现”出的新的系统性质,是系统生物学面临的一个根本性的挑战。为了应对这一挑战,系统生物学,特别是计算系统生物学必须建立有效的方法,通过整合系统各个层次的信息,建立可反映该系统目前已知或已可测量的性质的物理、数学模型,并通过这样的模型来研究或预测目前还未知晓的系统性状。可以说：建模是系统生物学的最重要的研究手段之一。目前,生命科学的研究正逐步由对单一现象、单一过程的机械论式的描述型研究转向运用高通量实验技术获取海量生物信息,并在这些生物信息基础上建立物理、数学模型,最终通过建模与实验相接合的研究手段来定量阐述生命现象的本质规律。由于建模方法在系统生物学研究中的重要性,本文将对一些主要的建模类型,如定性建模方法;基于约束的建模方法;基于常微分/偏微分方程的定量建模和基于随机微分方程的定量建模方法等等分别予以简要介绍。     

Modeling and simulation of biological systems from image data

This essay provides an introduction to the terminology, concepts, methods, and challenges of image‐based modeling in biology. Image‐based modeling and simulation aims at using systematic, quantitative image data to build predictive models of biological systems that can be simulated with a computer. This allows one to disentangle molecular mechanisms from effects of shape and geometry. Questions like “what is the functional role of shape” or “how are biological shapes generated and regulated” can be addressed in the framework of image‐based systems biology. The combination of image quantification, model building, and computer simulation is illustrated here using the example of diffusion in the endoplasmic reticulum.     

Applications of genome‐scale metabolic reconstructions

The availability and utility of genome‐scale metabolic reconstructions have exploded since the first genome‐scale reconstruction was published a decade ago. Reconstructions have now been built for a wide variety of organisms, and have been used toward five major ends: (1) contextualization of high‐throughput data, (2) guidance of metabolic engineering, (3) directing hypothesis‐driven discovery, (4) interrogation of multi‐species relationships, and (5) network property discovery. In this review, we examine the many uses and future directions of genome‐scale metabolic reconstructions, and we highlight trends and opportunities in the field that will make the greatest impact on many fields of biology.     

The hidden switches undertying RORα-mediated circuits that critically regulate uncontrolled cell proliferation

Prostaglandin E2 （PGE2） is known to have a key role in the development of colorectal cancer, but previous experiments showed its contrasting （i.e. tumor-promoting or tu mor-suppressive） roles depending on experimental conditions. To elucidate the mechanisms underlying such contrasting roles of PG E2 in tumorigenesis, we investigated all the previous experiments and found a new signal transduction pathway mediated by retinoic acid receptor-related orphan receptor （ROR）α, in which PGE2/PKCα-dependent phosphorylation of RORα attenuates Wnt target gene expression in colon cancer ceils. From mathematical simulations combined with biochemical experimentation, we revealed that RORα induces a biphasic response of Wnt target genes to PGE2 stimulation through a regulatory switch formed by an incoherent feedforward loop, which provides a mechanistic explanation on the contrasting roles of PGE2 observed in previous experiments. More interestingly, we found that RORα constitutes another regulatory switch formed by coupled positive and negative feedback loops, which regulates the hysteretic response of Wnt signaling and eventually converts a proliferative cellular state into an anti-proliferative state in a very delicate way. Our results indicate that RORα is the key regulator at the center of these hidden switches that critically regulate cancer celt proliferation and thereby being a promising anti-cancer therapeutic target.