整合受体调控基因表达信息构建细胞通信网络

目的 构建细胞通信网络有助于揭示细胞间协同工作机制、生物学过程和疾病发病机理。目前基于配体-受体相互作用构建细胞通信网络的方法大多只考虑配体和受体的表达信息，忽略了受体对其调控基因的信号传递影响，导致构建的细胞通信网络可靠性较低。鉴于此，本文提出IRRG算法，旨在构建更为准确的细胞通信网络，并挖掘具有生物学意义的细胞通信模式。方法 本文提出了一种整合受体调控基因表达信息构建细胞通信网络的方法（命名为IRRG）。该方法通过随机游走方式计算受体对下游基因的影响得分，进而与配体-受体共表达量结合构建细胞通信网络。结果 使用IRRG构建了小鼠滤泡间表皮（IFE）细胞通信网络并分析了配体-受体对的生物学意义，验证了IRRG计算受体影响得分的稳定性和细胞通信网络构建的可靠性。此外，使用IRRG构建了透明细胞肾细胞癌（ccRCC）的细胞通信网络，挖掘并分析其肿瘤微环境细胞通信模式。结论 IRRG可以构建富有生物学意义并且可靠的细胞通信网络，帮助人们从细胞通信的角度更深入地了解多种生物过程。IRRG算法代码可从GitHub获取：https://github.com/NWPU-903PR/IRRG。     

Detection of silent cells,synchronization and modulatory activity in developing cellular networks

Developing networks in the immature nervous system and in cellular cultures are characterized by waves of synchronous activity in restricted clusters of cells. Synchronized activity in immature networks is proposed to regulate many different developmental processes, from neuron growth and cell migration, to the refinement of synapses, topographic maps, and the mature composition of ion channels. These emergent activity patterns are not present in all cells simultaneously within the network and more immature “silent” cells, potentially correlated with the presence of silent synapses, are prominent in different networks during early developmental periods. Many current network analyses for detection of synchronous cellular activity utilize activity‐based pixel correlations to identify cellular‐based regions of interest (ROIs) and coincident cell activity. However, using activity‐based correlations, these methods first underestimate or ignore the inactive silent cells within the developing network and second, are difficult to apply within cell‐dense regions commonly found in developing brain networks. In addition, previous methods may ignore ROIs within a network that shows transient activity patterns comprising both inactive and active periods. We developed analysis software to semi‐automatically detect cells within developing neuronal networks that were imaged using calcium‐sensitive reporter dyes. Using an iterative threshold, modulation of activity was tracked within individual cells across the network. The distribution pattern of both inactive and active, including synchronous cells, could be determined based on distance measures to neighboring cells and according to different anatomical layers. © 2015 Wiley Periodicals, Inc. Develop Neurobiol 76: 357–374, 2016     

Modeling regulatory networks at Virginia Tech

The life of a cell is governed by the physicochemical properties of a complex network of interacting macromolecules (primarily genes and proteins). Hence, a full scientific understanding of and rational engineering approach to cell physiology require accurate mathematical models of the spatial and temporal dynamics of these macromolecular assemblies, especially the networks involved in integrating signals and regulating cellular responses. The Virginia Tech Consortium is involved in three specific goals of DARPA's computational biology program (Bio-COMP): to create effective software tools for modeling gene-protein-metabolite networks, to employ these tools in creating a new generation of realistic models, and to test and refine these models by well-conceived experimental studies. The special emphasis of this group is to understand the mechanisms of cell cycle control in eukaryotes (yeast cells and frog eggs). The software tools developed at Virginia Tech are designed to meet general requirements of modeling regulatory networks and are collected in a problem-solving environment called JigCell.     

Interplay between gene expression noise and regulatory network architecture

Complex regulatory networks orchestrate most cellular processes in biological systems. Genes in such networks are subject to expression noise, resulting in isogenic cell populations exhibiting cell-to-cell variation in protein levels. Increasing evidence suggests that cells have evolved regulatory strategies to limit, tolerate or amplify expression noise. In this context, fundamental questions arise: how can the architecture of gene regulatory networks generate, make use of or be constrained by expression noise? Here, we discuss the interplay between expression noise and gene regulatory network at different levels of organization, ranging from a single regulatory interaction to entire regulatory networks. We then consider how this interplay impacts a variety of phenomena, such as pathogenicity, disease, adaptation to changing environments, differential cell-fate outcome and incomplete or partial penetrance effects. Finally, we highlight recent technological developments that permit measurements at the single-cell level, and discuss directions for future research.     

Modelling evolutionary cell behaviour using neural networks: Application to tumour growth

In this paper, we present a modelling framework for cellular evolution that is based on the notion that a cell’s behaviour is driven by interactions with other cells and its immediate environment. We equip each cell with a phenotype that determines its behaviour and implement a decision mechanism to allow evolution of this phenotype. This decision mechanism is modelled using feed-forward neural networks, which have been suggested as suitable models of cell signalling pathways. The environmental variables are presented as inputs to the network and result in a response that corresponds to the phenotype of the cell. The response of the network is determined by the network parameters, which are subject to mutations when the cells divide. This approach is versatile as there are no restrictions on what the input or output nodes represent, they can be chosen to represent any environmental variables and behaviours that are of importance to the cell population under consideration. This framework was implemented in an individual-based model of solid tumour growth in order to investigate the impact of the tissue oxygen concentration on the growth and evolutionary dynamics of the tumour. Our results show that the oxygen concentration affects the tumour at the morphological level, but more importantly has a direct impact on the evolutionary dynamics. When the supply of oxygen is limited we observe a faster divergence away from the initial genotype, a higher population diversity and faster evolution towards aggressive phenotypes. The implementation of this framework suggests that this approach is well suited for modelling systems where evolution plays an important role and where a changing environment exerts selection pressure on the evolving population.     

An evolutionary hybrid cellular automaton model of solid tumour growth

We propose a cellular automaton model of solid tumour growth, in which each cell is equipped with a micro-environment response network. This network is modelled using a feed-forward artificial neural network, that takes environmental variables as an input and from these determines the cellular behaviour as the output. The response of the network is determined by connection weights and thresholds in the network, which are subject to mutations when the cells divide. As both available space and nutrients are limited resources for the tumour, this gives rise to clonal evolution where only the fittest cells survive. Using this approach we have investigated the impact of the tissue oxygen concentration on the growth and evolutionary dynamics of the tumour. The results show that the oxygen concentration affects the selection pressure, cell population diversity and morphology of the tumour. A low oxygen concentration in the tissue gives rise to a tumour with a fingered morphology that contains aggressive phenotypes with a small apoptotic potential, while a high oxygen concentration in the tissue gives rise to a tumour with a round morphology containing less evolved phenotypes. The tissue oxygen concentration thus affects the tumour at both the morphological level and on the phenotype level.     

Viruses associated with human cancer

It is estimated that viral infections contribute to 15-20% of all human cancers. As obligatory intracellular parasites, viruses encode proteins that reprogram host cellular signaling pathways that control proliferation, differentiation, cell death, genomic integrity, and recognition by the immune system. These cellular processes are governed by complex and redundant regulatory networks and are surveyed by sentinel mechanisms that ensure that aberrant cells are removed from the proliferative pool. Given that the genome size of a virus is highly restricted to ensure packaging within an infectious structure, viruses must target cellular regulatory nodes with limited redundancy and need to inactivate surveillance mechanisms that would normally recognize and extinguish such abnormal cells. In many cases, key proteins in these same regulatory networks are subject to mutation in non-virally associated diseases and cancers. Oncogenic viruses have thus served as important experimental models to identify and molecularly investigate such cellular networks. These include the discovery of oncogenes and tumor suppressors, identification of regulatory networks that are critical for maintenance of genomic integrity, and processes that govern immune surveillance.     

An agent based model for real-time signaling induced in osteocytic networks by mechanical stimuli

We recently observed that insertion of unloaded rest between each load cycle substantially enhanced bone formation induced by mild loading regimens. To begin to explore this result, we have developed an agent based model for real-time signaling induced when osteocytic networks are challenged by mechanical stimuli. In the model, activity induced in individual osteocytes were governed by the following cellular functions: (1) threshold levels of tissue strain magnitudes were required to initiate and maximally activate cells, (2) cell activity beyond thresholds were propagated within localized neighborhoods and influenced recipient cell activity, (3) cellular activity was modulated by 'molecular' stores and the rates at which stores were replenished when cells were quiescent. Using this model, the real-time response of osteocyte networks was determined as the average of individual cell activity. While not explicitly embedded within the model, interactions between cellular functions served as positive, negative, and end-point feedback mechanisms and resulted in unique real-time network responses to distinct mechanical stimuli. Specifically, the real-time network response to cyclic stimuli consisted of a large magnitude transient followed by low-level steady state fluctuations, while rest-inserted stimuli induced multiple secondary transients. Analysis of interaction patterns suggested that rest-inserted stimuli induced this enhanced and sustained signaling within osteocytic networks by enabling cell recovery of expended molecular stores and by efficiently utilizing properties inherent to cell-cell communication in bone. Importantly, this emergence based approach suggested mechanisms potentially underlying the benefit of rest-inserted stimuli and provides a unique framework for a broader exploration of mechanotransduction function within bone.     

Modelling and analysis of gene regulatory networks

Gene regulatory networks have an important role in every process of life, including cell differentiation, metabolism, the cell cycle and signal transduction. By understanding the dynamics of these networks we can shed light on the mechanisms of diseases that occur when these cellular processes are dysregulated. Accurate prediction of the behaviour of regulatory networks will also speed up biotechnological projects, as such predictions are quicker and cheaper than lab experiments. Computational methods, both for supporting the development of network models and for the analysis of their functionality, have already proved to be a valuable research tool.     

Interactions between myosin and actin crosslinkers control cytokinesis contractility dynamics and mechanics

INTRODUCTION: Contractile networks are fundamental to many cellular functions, particularly cytokinesis and cell motility. Contractile networks depend on myosin-II mechanochemistry to generate sliding force on the actin polymers. However, to be contractile, the networks must also be crosslinked by crosslinking proteins, and to change the shape of the cell, the network must be linked to the plasma membrane. Discerning how this integrated network operates is essential for understanding cytokinesis contractility and shape control. Here, we analyzed the cytoskeletal network that drives furrow ingression in Dictyostelium. RESULTS: We establish that the actin polymers are assembled into a meshwork and that myosin-II does not assemble into a discrete ring in the Dictyostelium cleavage furrow of adherent cells. We show that myosin-II generates regional mechanics by increasing cleavage furrow stiffness and slows furrow ingression during late cytokinesis as compared to myoII nulls. Actin crosslinkers dynacortin and fimbrin similarly slow furrow ingression and contribute to cell mechanics in a myosin-II-dependent manner. By using FRAP, we show that the actin crosslinkers have slower kinetics in the cleavage furrow cortex than in the pole, that their kinetics differ between wild-type and myoII null cells, and that the protein dynamics of each crosslinker correlate with its impact on cortical mechanics. CONCLUSIONS: These observations suggest that myosin-II along with actin crosslinkers establish local cortical tension and elasticity, allowing for contractility independent of a circumferential cytoskeletal array. Furthermore, myosin-II and actin crosslinkers may influence each other as they modulate the dynamics and mechanics of cell-shape change.     

RASSF1A对黑色素瘤A375细胞基因网络的影响

RASSF1A(Ras association domain family 1 isoform A)是定位于染色体3p21.3区域的抑瘤基因,编码一个由340个氨基酸残基构成的微管相关蛋白.该基因在包括恶性黑色素瘤在内的多种肿瘤中因启动子高甲基化而表达沉默.本研究建立了RASSF1A稳定表达的恶性黑色素瘤A375细胞系,通过全基因组表达谱基因芯片分析RASSF1A过表达对A375细胞基因表达谱的影响,发现RASSF1A引起184个基因表达上调,26个基因表达下调.通过Realtime RT-PCR对部分差异表达基因进行验证,结果表明与芯片筛选结果一致.RASSF1A影响的差异表达基因功能上归属于细胞生长与增殖、细胞周期、细胞凋亡、细胞间黏附、信号传导等生物过程.采用STRING软件构建了RASSF1A影响的差异表达基因调控网络,结果表明RASSF1A调控的差异表达基因构成一个高连接度的基因网络.其中,炎症细胞因子、转录因子位于网络中央.RASSF1A通过影响炎症细胞因子与转录因子之间的表达,影响A375细胞基因网络,调节黑色素瘤恶性生物学行为.     

The transition from differential equations to Boolean networks: a case study in simplifying a regulatory network model

Methods for modeling cellular regulatory networks as diverse as differential equations and Boolean networks co-exist, however, without much closer correspondence to each other. With the example system of the fission yeast cell cycle control network, we here discuss these two approaches with respect to each other. We find that a Boolean network model can be formulated as a specific coarse-grained limit of the more detailed differential equations model for this system. This demonstrates the mathematical foundation on which Boolean networks can be applied to biological regulatory networks in a controlled way.     

Protein interaction networks in medicine and disease

The physical interaction of proteins is subject to intense investigation that has revealed that proteins are assembled into large densely connected networks. In this review, we will examine how signaling pathways can be combined to form higher order protein interaction networks. By using network graph theory, these interaction networks can be further analyzed for global organization, which has revealed unique aspects of the relationships between protein networks and complex biological phenotypes. Moreover, several studies have shown that the structure and dynamics of protein networks are disturbed in complex diseases such as cancer progression. These relationships suggest a novel paradigm for treatment of complex multigenic disease where the protein interaction network is the target of therapy more so than individual molecules within the network.     

Dynamic proteomics in modeling of the living cell. Protein-protein interactions

This review is devoted to describing, summarizing, and analyzing of dynamic proteomics data obtained over the last few years and concerning the role of protein-protein interactions in modeling of the living cell. Principles of modern high-throughput experimental methods for investigation of protein-protein interactions are described. Systems biology approaches based on integrative view on cellular processes are used to analyze organization of protein interaction networks. It is proposed that finding of some proteins in different protein complexes can be explained by their multi-modular and polyfunctional properties; the different protein modules can be located in the nodes of protein interaction networks. Mathematical and computational approaches to modeling of the living cell with emphasis on molecular dynamics simulation are provided. The role of the network analysis in fundamental medicine is also briefly reviewed.     

Analyzing protein–protein interactions in the post-interactomic era. Are we ready for the endgame?

Mapping protein–protein interactions in genome-wide scales revealed thousands of novel binding partners in each of the explored model organisms. Organizing these hits in comprehensive ways is becoming increasingly important for systems biology approaches to understand complex cellular processes and diseases. However, proteome wide interaction techniques and their resulting global networks are not revealing the topologies of networks that are truly operating in the cell. In this short review I will discuss which prerequisites have to be fulfilled and which experimental methods might be practicable to translate primary protein interaction data into network presentations that help in understanding cellular processes.