Network medicine

To more effectively target complex diseases like cancer, diabetes and schizophrenia, we may need to rethink our strategies for drug development and the selection of molecular targets for pharmacological treatments. Here, we discuss the potential use of protein signaling networks as the targets for new therapeutic intervention. We argue that by targeting the architecture of aberrant signaling networks associated with cancer and other diseases new therapeutic strategies can be implemented. Transforming medicine into a network driven endeavour will require quantitative measurements of cell signaling processes; we will describe how this may be performed and combined with new algorithms to predict the trajectories taken by a cellular system either in time or through disease states. We term this approach, network medicine.     

Gene expression in the social behavior network of the wire‐tailed manakin (Pipra filicauda) brain

The vertebrate basal forebrain and midbrain contain a set of interconnected nuclei that control social behavior. Conserved anatomical structures and functions of these nuclei have now been documented among fish, amphibians, reptiles, birds and mammals, and these brain regions have come to be known as the vertebrate social behavior network (SBN). While it is known that nuclei (nodes) of the SBN are rich in steroid and neuropeptide activity linked to behavior, simultaneous variation in the expression of neuroendocrine genes among several SBN nuclei has not yet been described in detail. In this study, we use RNA‐seq to profile gene expression across seven brain regions representing five nodes of the vertebrate SBN in a passerine bird, the wire‐tailed manakin Pipra filicauda. Using weighted gene co‐expression network analysis, we reconstructed sets of coregulated genes, showing striking patterns of variation in neuroendocrine gene expression across the SBN. We describe regional variation in gene networks comprising a broad set of hormone receptors, neuropeptides, steroidogenic enzymes, catecholamines and other neuroendocrine signaling molecules. Our findings show heterogeneous patterns of brain gene expression across nodes of the avian SBN and provide a foundation for future analyses of how the regulation of gene networks may mediate social behavior. These results highlight the importance of region‐specific sampling in studies of the mechanisms of behavior.     

Fundamental Escherichia coli biochemical pathways for biomass and energy production: identification of reactions

Cells grow by oxidizing nutrients using a complex network of biochemical reactions. During this process new biological material is produced along with energy used for maintaining cellular organization. Because the metabolic network is highly branched, these tasks can be accomplished using a wide variety of unique reaction sequences. However, evolutionary pressures under carbon-limited growth conditions likely select organisms that utilize highly efficient pathways. Using elementary-mode analysis, we demonstrate that the metabolism of the bacterium Escherichia coli contains four unique pathways that most efficiently convert glucose and oxygen into new cells and maintenance energy under any level of oxygen limitation. Observed regulatory patterns and experimental findings suggest growing cells use these highly efficient pathways. It is predicted that five knockout mutations generate a strain that supports growth using only the most efficient reaction sequence. The analysis approach should be generally useful for predicting metabolic capabilities and efficient network designs based on only genomic information.     

全细胞网络重建与细胞工厂设计

在各种组学及其相应的网络研究相对成熟的基础上,集成各组学网络的细胞整合型网络或全细胞网络将大大提高对生物表型的预测能力,并成为代谢工程决策的有力武器.本文在阐述了细胞工厂设计中应该考虑细胞整合网络之后,综述了细胞整合网络的重建、分析、设计方法方面的有关问题,并进一步就研究细胞整合网络涉及的数据库、软件平台、并行计算几方面的作用作了介绍.     

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.     

Stability analysis of an artificial biomolecular oscillator with non-cooperative regulatory interactions

Oscillators are essential to fuel autonomous behaviours in molecular systems. Artificial oscillators built with programmable biological molecules such as DNA and RNA are generally easy to build and tune, and can serve as timers for biological computation and regulation. We describe a new artificial nucleic acid biochemical reaction network, and we demonstrate its capacity to exhibit oscillatory solutions. This network can be built in vitro using nucleic acids and three bacteriophage enzymes, and has the potential to be implemented in cells. Numerical simulations suggest that oscillations occur in a realistic range of reaction rates and concentrations.     

A systems biology view of blood vessel growth and remodelling

Blood travels throughout the body in an extensive network of vessels – arteries, veins and capillaries. This vascular network is not static, but instead dynamically remodels in response to stimuli from cells in the nearby tissue. In particular, the smallest vessels – arterioles, venules and capillaries – can be extended, expanded or pruned, in response to exercise, ischaemic events, pharmacological interventions, or other physiological and pathophysiological events. In this review, we describe the multi‐step morphogenic process of angiogenesis – the sprouting of new blood vessels – and the stability of vascular networks in vivo. In particular, we review the known interactions between endothelial cells and the various blood cells and plasma components they convey. We describe progress that has been made in applying computational modelling, quantitative biology and high‐throughput experimentation to the angiogenesis process.     

Recent Progress on the Analysis of Power-law Features in Complex Cellular Networks

Complex interactions between different kinds of bio-molecules and essential nutrients are responsible for cellular functions. Rapid advances in theoretical modeling and experimental analyses have shown that drastically different biological and non-biological networks share a common architecture. That is, the probability that a selected node in the network has exactly k edges decays as a power-law. This finding has definitely opened an intense research and debate on the origin and implications of this ubiquitous pattern. In this review, we describe the recent progress on the emergence of power-law distributions in cellular networks. We first show the internal characteristics of the observed complex networks uncovered using graph theory. We then briefly review some works that have significantly contributed to the theoretical analysis of cellular networks and systems, from metabolic and protein networks to gene expression profiles. This prevalent topology observed in so many diverse biological systems suggests the existence of generic laws and organizing principles behind the cellular networks.     

Neurons-on-a-Chip: In Vitro NeuroTools

Neurons-on-a-Chip technology has been developed to provide diverse in vitro neuro-tools to study neuritogenesis, synaptogensis, axon guidance, and network dynamics. The two core enabling technologies are soft-lithography and microelectrode array technology. Soft lithography technology made it possible to fabricate microstamps and microfluidic channel devices with a simple replica molding method in a biological laboratory and innovatively reduced the turn-around time from assay design to chip fabrication, facilitating various experimental designs. To control nerve cell behaviors at the single cell level via chemical cues, surface biofunctionalization methods and micropatterning techniques were developed. Microelectrode chip technology, which provides a functional readout by measuring the electrophysiological signals from individual neurons, has become a popular platform to investigate neural information processing in networks. Due to these key advances, it is possible to study the relationship between the network structure and functions, and they have opened a new era of neurobiology and will become standard tools in the near future.     

Network design meets in silico evolutionary biology

Cell fate is programmed through gene regulatory networks that perform several calculations to take the appropriate decision. In silico evolutionary optimization mimics the way Nature has designed such gene regulatory networks. In this review we discuss the basic principles of these evolutionary approaches and how they can be applied to engineer synthetic networks. We summarize the basic guidelines to implement an in silico evolutionary design method, the operators for mutation and selection that iteratively drive the network architecture towards a specified dynamical behavior. Interestingly, as it happens in natural evolution, we show the existence of patterns of punctuated evolution. In addition, we highlight several examples of models that have been designed using automated procedures, together with different objective functions to select for the proper behavior. Finally, we briefly discuss the modular designability of gene regulatory networks and its potential application in biotechnology.     

The Interactorium: Visualising proteins,complexes and interaction networks in a virtual 3‐D cell

Here, we describe the Interactorium, a tool in which a Virtual Cell is used as the context for the seamless visualisation of the yeast protein interaction network, protein complexes and protein 3‐D structures. The tool has been designed to display very complex networks of up to 40 000 proteins or 6000 multiprotein complexes and has a series of toolboxes and menus to allow real‐time data manipulation and control the manner in which data are displayed. It incorporates new algorithms that reduce the complexity of the visualisation by the generation of putative new complexes from existing data and by the reduction of edges through the use of protein “twins” when they occur in multiple locations. Since the Interactorium permits multi‐level viewing of the molecular biology of the cell, it is a considerable advance over existing approaches. We illustrate its use for Saccharomyces cerevisiae but note that it will also be useful for the analysis of data from simpler prokaryotes and higher eukaryotes, including humans. The Interactorium is available for download at http://www.interactorium.net .     

Two-Cell to N-Cell Heterogeneous,Inhibitory Networks: Precise Linking of Multistable and Coherent Properties

Inhibitory networks are now recognized as being the controllers of several brain rhythms. However, experimental work with inhibitory cells is technically difficult not only because of their smaller percentage of the neuronal population, but also because of their diverse properties. As such, inhibitory network models with tight links to the experimental data are needed to understand their contributions to population rhythms. However, mathematical analyses of network models with more than two cells is challenging when the cellular models involve biophysical details. We use bifurcation analyses and simulations to show that two-cell analyses can quantitatively predict N-cell (N = 20, 50, 100) network dynamics for heterogeneous, inhibitory networks. Interestingly, multistable states in the two-cell system are manifest as different and distinct coherent network patterns in the N-cell networks for the same parameter sets.     

The Problem of Colliding Networks and its Relation to Cell Fusion and?Cancer

Cell fusion, a process that merges two or more cells into one, is required for normal development and has been explored as a tool for stem cell therapy. It has also been proposed that cell fusion causes cancer and contributes to its progression. These functions rely on a poorly understood ability of cell fusion to create new cell types. We suggest that this ability can be understood by considering cells as attractor networks whose basic property is to adopt a set of distinct, stable, self-maintaining states called attractors. According to this view, fusion of two cell types is a collision of two networks that have adopted distinct attractors. To learn how these networks reach a consensus, we model cell fusion computationally. To do so, we simulate patterns of gene activities using a formalism developed to simulate patterns of memory in neural networks. We find that the hybrid networks can assume attractors that are unrelated to parental attractors, implying that cell fusion can create new cell types by nearly instantaneously moving cells between attractors. We also show that hybrid networks are prone to assume spurious attractors, which are emergent and sporadic network states. This finding means that cell fusion can produce abnormal cell types, including cancerous types, by placing cells into normally inaccessible spurious states. Finally, we suggest that the problem of colliding networks has general significance in many processes represented by attractor networks, including biological, social, and political phenomena.