Neural networks and logical reasoning systems: a translation table

A correspondence is established between the basic elements of logic reasoning systems (knowledge bases, rules, inference and queries) and the structure and dynamical evolution laws of neural networks. The correspondence is pictured as a translation dictionary which might allow to go back and forth between symbolic and network formulations, a desirable step in learning-oriented systems and multicomputer networks. In the framework of Horn clause logics, it is found that atomic propositions with n arguments correspond to nodes with nth order synapses, rules to synaptic intensity constraints, forward chaining to synaptic dynamics and queries either to simple node activation or to a query tensor dynamics.     

Does the Type of Event Influence How User Interactions Evolve on Twitter?

The number of people using on-line social networks as a new way of communication is continually increasing. The messages that a user writes in these networks and his/her interactions with other users leave a digital trace that is recorded. Thanks to this fact and the use of network theory, the analysis of messages, user interactions, and the complex structures that emerge is greatly facilitated. In addition, information generated in on-line social networks is labeled temporarily, which makes it possible to go a step further analyzing the dynamics of the interaction patterns. In this article, we present an analysis of the evolution of user interactions that take place in television, socio-political, conference, and keynote events on Twitter. Interactions have been modeled as networks that are annotated with the time markers. We study changes in the structural properties at both the network level and the node level. As a result of this analysis, we have detected patterns of network evolution and common structural features as well as differences among the events.     

PATIKAmad: putting microarray data into pathway context

High-throughput experiments, most significantly DNA microarrays, provide us with system-scale profiles. Connecting these data with existing biological networks poses a formidable challenge to uncover facts about a cell's proteome. Studies and tools with this purpose are limited to networks with simple structure, such as protein-protein interaction graphs, or do not go much beyond than simply displaying values on the network. We have built a microarray data analysis tool, named PATIKAmad, which can be used to associate microarray data with the pathway models in mechanistic detail, and provides facilities for visualization, clustering, querying, and navigation of biological graphs related with loaded microarray experiments. PATIKAmad is freely available to noncommercial users as a new module of PATIKAweb at http://web.patika.org.     

Miniaturization in functional genomics and proteomics

Proteins are the key components of the cellular machinery responsible for processing changes that are ordered by genomic information. Analysis of most human proteins and nucleic acids is important in order to decode the complex networks that are likely to underlie many common diseases. Significant improvements in current technology are also required to dissect the regulatory processes in high-throughtput and with low cost. Miniaturization of biological assays is an important prerequisite to achieve these goals in the near future.     

KnowledgeEditor: a new tool for interactive modeling and analyzing biological pathways based on microarray data

KnowledgeEditor is a graphical workbench for biological experts to model biomolecular network graphs. The modeled network data are represented by SRML, and can be published via the internet with the help of plug-in module 'GSCope'. KnowledgeEditor helps us to model and analyze biological pathways based on microarray data. It is possible to analyze the drawn networks by simulating up-down regulatory cascade in molecular interactions. AVAILABILITY: KnowledgeEditor is available at http://gscope.gsc.riken.go.jp/.     

Low degree metabolites explain essential reactions and enhance modularity in biological networks

Background  

Recently there has been a lot of interest in identifying modules at the level of genetic and metabolic networks of organisms, as well as in identifying single genes and reactions that are essential for the organism. A goal of computational and systems biology is to go beyond identification towards an explanation of specific modules and essential genes and reactions in terms of specific structural or evolutionary constraints.     

From protein interaction networks to novel therapeutic strategies

Cellular mechanisms that sustain health or contribute to disease emerge mostly from the complex interplay among various molecular entities. To understand the underlying relationships between genotype, environment and phenotype, one has to consider the intricate and nonsequential interaction patterns formed between the different sets of cellular players. Biological networks capture a variety of molecular interactions and thus provide an excellent opportunity to consider physiological characteristics of individual molecules within their cellular context. In particular, the concept of network biology and its applications contributed largely to recent advances in biomedical research. In this review, we show (i) how biological networks, i.e., protein-protein interaction networks, facilitate the understanding of pathogenic mechanisms that trigger the onset and progression of diseases and (ii) how this knowledge can be translated into effective diagnostic and therapeutic strategies. In particular, we focus on the impact of network pharmacological concepts that go beyond the classical view on individual drugs and targets aiming for combinational therapies with improved clinical efficacy and reduced safety risks.     

Autocatalysis in Reaction Networks

The persistence conjecture is a long-standing open problem in chemical reaction network theory. It concerns the behavior of solutions to coupled ODE systems that arise from applying mass-action kinetics to a network of chemical reactions. The idea is that if all reactions are reversible in a weak sense, then no species can go extinct. A notion that has been found useful in thinking about persistence is that of “critical siphon.” We explore the combinatorics of critical siphons, with a view toward the persistence conjecture. We introduce the notions of “drainable” and “self-replicable” (or autocatalytic) siphons. We show that: Every minimal critical siphon is either drainable or self-replicable; reaction networks without drainable siphons are persistent; and nonautocatalytic weakly reversible networks are persistent. Our results clarify that the difficulties in proving the persistence conjecture are essentially due to competition between drainable and self-replicable siphons.     

Response Suppression Delays the Planning of Subsequent Stimulus-Driven Saccades

The completion of an antisaccade selectively increases the reaction time (RT) of a subsequent prosaccade: a result that has been interpreted to reflect the residual inhibition of stimulus-driven saccade networks [1], [2]. In the present investigation we sought to determine whether the increase in prosaccade RT is contingent on the constituent antisaccade planning processes of response suppression and vector inversion or is limited to response suppression. To that end, in one block participants alternated between pro- and antisaccades after every second trial (task-switching block), and in another block participants completed a series of prosaccades that were randomly (and infrequently) interspersed with no-go catch-trials (go/no-go block). Notably, such a design provides a framework for disentangling whether response suppression and/or vector inversion delays the planning of subsequent prosaccades. As expected, results for the task-switching block showed that antisaccades selectively increased the RTs of subsequent prosaccades. In turn, results for the go/no-go block showed that prosaccade RTs were increased when preceded by a no-go catch-trial. Moreover, the magnitude of the RT ‘cost’ was equivalent across the task-switching and go/no-go blocks. That prosaccades preceded by an antisaccade or a no-go catch-trial produced equivalent RT costs indicates that the conjoint processes of response suppression and vector inversion do not drive the inhibition of saccade planning mechanisms. Rather, the present findings indicate that a general consequence of response suppression is a residual inhibition of stimulus-driven saccade networks.     

EXTINCTIONS IN HETEROGENEOUS ENVIRONMENTS AND THE EVOLUTION OF MODULARITY

Extinctions of local subpopulations are common events in nature. Here, we ask whether such extinctions can affect the design of biological networks within organisms over evolutionary timescales. We study the impact of extinction events on modularity of biological systems, a common architectural principle found on multiple scales in biology. As a model system, we use networks that evolve toward goals specified as desired input–output relationships. We use an extinction–recolonization model, in which metapopulations occupy and migrate between different localities. Each locality displays a different environmental condition (goal), but shares the same set of subgoals with other localities. We find that in the absence of extinction events, the evolved computational networks are typically highly optimal for their localities with a nonmodular structure. In contrast, when local populations go extinct from time to time, we find that the evolved networks are modular in structure. Modular circuitry is selected because of its ability to adapt rapidly to the conditions of the free niche following an extinction event. This rapid adaptation is mainly achieved through genetic recombination of modules between immigrants from neighboring local populations. This study suggests, therefore, that extinctions in heterogeneous environments promote the evolution of modular biological network structure, allowing local populations to effectively recombine their modules to recolonize niches.     

Topological Phenotypes Constitute a New Dimension in the Phenotypic Space of Leaf Venation Networks

The leaves of angiosperms contain highly complex venation networks consisting of recursively nested, hierarchically organized loops. We describe a new phenotypic trait of reticulate vascular networks based on the topology of the nested loops. This phenotypic trait encodes information orthogonal to widely used geometric phenotypic traits, and thus constitutes a new dimension in the leaf venation phenotypic space. We apply our metric to a database of 186 leaves and leaflets representing 137 species, predominantly from the Burseraceae family, revealing diverse topological network traits even within this single family. We show that topological information significantly improves identification of leaves from fragments by calculating a “leaf venation fingerprint” from topology and geometry. Further, we present a phenomenological model suggesting that the topological traits can be explained by noise effects unique to specimen during development of each leaf which leave their imprint on the final network. This work opens the path to new quantitative identification techniques for leaves which go beyond simple geometric traits such as vein density and is directly applicable to other planar or sub-planar networks such as blood vessels in the brain.     

Cancer during adolescence: negative and positive consequences reported three and four years after diagnosis

Persons diagnosed with cancer during adolescence have reported negative and positive cancer-related consequences two years after diagnosis. The overall aim was to longitudinally describe negative and positive cancer-related consequences reported by the same persons three and four years after diagnosis. A secondary aim was to explore whether reports of using vs. not using certain coping strategies shortly after diagnosis are related to reporting or not reporting certain consequences four years after diagnosis. Thirty-two participants answered questions about coping strategies shortly after diagnosis and negative and positive consequences three and four years after diagnosis. Answers about consequences were analysed with content analysis, potential relations between coping strategies and consequences were analysed by Fisher's exact test. The great majority reported negative and positive consequences three and four years after diagnosis and the findings indicate stability over time with regard to perceived consequences during the extended phase of survival. Findings reveal a potential relation between seeking information shortly after diagnosis and reporting a more positive view of life four years after diagnosis and not using fighting spirit shortly after diagnosis and not reporting good self-esteem and good relations four years after diagnosis. It is concluded that concomitant negative and positive cancer-related consequences appear stable over time in the extended phase of survival and that dialectical forces of negative and positive as well as distress and growth often go hand-in-hand after a trauma such as cancer during adolescence.     

Complex networks approach to gene expression driven phenotype imaging

MOTIVATION: The need is to visualize and quantify gene expression spatial patterns. Because of their generality for representation of interaction among several elements, complex networks are used to measure the spatial interactions and adjacencies defined by gene expression patterns. RESULTS: Enhanced visualization of spatial interactions between elements where genes are expressed is possible, allowing the identification of structures which would go unnoticed by using conventional imaging. The quantification of the expression intensity in terms of the node degree and clustering coefficient allows the identification of different types of interactions, yielding insights about cell signaling and differentiation, and providing the basis for comparison and discrimination of the patterns along the developmental stages. AVAILABILITY: Supplementary Material, including visualizations as well as the basic routines for translating gene expression images into complex networks and obtaining node degree and clustering coefficient measurements, are provided. CONTACT: luciano@if.sc.usp.br; diambra@univap.br.     

The neurobiology of love

Romantic and maternal love are highly rewarding experiences. Both are linked to the perpetuation of the species and therefore have a closely linked biological function of crucial evolutionary importance. The newly developed ability to study the neural correlates of subjective mental states with brain imaging techniques has allowed neurobiologists to learn something about the neural bases of both romantic and maternal love. Both types of attachment activate regions specific to each, as well as overlapping regions in the brain's reward system that coincide with areas rich in oxytocin and vasopressin receptors. Both deactivate a common set of regions associated with negative emotions, social judgment and 'mentalizing' that is, the assessment of other people's intentions and emotions. Human attachment seems therefore to employ a push-pull mechanism that overcomes social distance by deactivating networks used for critical social assessment and negative emotions, while it bonds individuals through the involvement of the reward circuitry, explaining the power of love to motivate and exhilarate. Yet the biological study of love, and especially romantic love, must go beyond and look for biological insights that can be derived from studying the world literature of love, and thus bring the output of the humanities into its orbit.     

深度学习在药物活性预测研究中的应用

药物从研发到临床应用需要耗费较长的时间，研发期间的投入成本可高达十几亿元。而随着医药研发与人工智能的结合以及生物信息学的飞速发展，药物活性相关数据急剧增加，传统的实验手段进行药物活性预测已经难以满足药物研发的需求。借助算法来辅助药物研发，解决药物研发中的各种问题能够大大推动药物研发进程。传统机器学习方法尤其是随机森林、支持向量机和人工神经网络在药物活性方面能够达到较高的预测精度。深度学习由于具有多层神经网络，模型可以接收高维的输入变量且不需要人工限定数据输入特征，可以拟合较为复杂的函数模型，应用于药物研发可以进一步提高各个环节的效率。在药物活性预测中应用较为广泛的深度学习模型主要是深度神经网络（deep neural networks，DNN）、循环神经网络（recurrent neural networks，RNN）和自编码器（auto encoder，AE），而生成对抗网络（generative adversarial networks，GAN）由于其生成数据的能力常常被用来和其他模型结合进行数据增强。近年来深度学习在药物分子活性预测方面的研究和应用综述表明，深度学习模型的准确度和效率均高于传统实验方法和传统机器学习方法。因此，深度学习模型有望成为药物研发领域未来十年最重要的辅助计算模型。     

Discovering adaptation-capable biological network structures using control-theoretic approaches

Constructing biological networks capable of performing specific biological functionalities has been of sustained interest in synthetic biology. Adaptation is one such ubiquitous functional property, which enables every living organism to sense a change in its surroundings and return to its operating condition prior to the disturbance. In this paper, we present a generic systems theory-driven method for designing adaptive protein networks. First, we translate the necessary qualitative conditions for adaptation to mathematical constraints using the language of systems theory, which we then map back as ‘design requirements’ for the underlying networks. We go on to prove that a protein network with different input–output nodes (proteins) needs to be at least of third-order in order to provide adaptation. Next, we show that the necessary design principles obtained for a three-node network in adaptation consist of negative feedback or a feed-forward realization. We argue that presence of a particular class of negative feedback or feed-forward realization is necessary for a network of any size to provide adaptation. Further, we claim that the necessary structural conditions derived in this work are the strictest among the ones hitherto existed in the literature. Finally, we prove that the capability of producing adaptation is retained for the admissible motifs even when the output node is connected with a downstream system in a feedback fashion. This result explains how complex biological networks achieve robustness while keeping the core motifs unchanged in the context of a particular functionality. We corroborate our theoretical results with detailed and thorough numerical simulations. Overall, our results present a generic, systematic and robust framework for designing various kinds of biological networks.     

Efficient reverse-engineering of a developmental gene regulatory network

Understanding the complex regulatory networks underlying development and evolution of multi-cellular organisms is a major problem in biology. Computational models can be used as tools to extract the regulatory structure and dynamics of such networks from gene expression data. This approach is called reverse engineering. It has been successfully applied to many gene networks in various biological systems. However, to reconstitute the structure and non-linear dynamics of a developmental gene network in its spatial context remains a considerable challenge. Here, we address this challenge using a case study: the gap gene network involved in segment determination during early development of Drosophila melanogaster. A major problem for reverse-engineering pattern-forming networks is the significant amount of time and effort required to acquire and quantify spatial gene expression data. We have developed a simplified data processing pipeline that considerably increases the throughput of the method, but results in data of reduced accuracy compared to those previously used for gap gene network inference. We demonstrate that we can infer the correct network structure using our reduced data set, and investigate minimal data requirements for successful reverse engineering. Our results show that timing and position of expression domain boundaries are the crucial features for determining regulatory network structure from data, while it is less important to precisely measure expression levels. Based on this, we define minimal data requirements for gap gene network inference. Our results demonstrate the feasibility of reverse-engineering with much reduced experimental effort. This enables more widespread use of the method in different developmental contexts and organisms. Such systematic application of data-driven models to real-world networks has enormous potential. Only the quantitative investigation of a large number of developmental gene regulatory networks will allow us to discover whether there are rules or regularities governing development and evolution of complex multi-cellular organisms.     

Neural networks for perceptual processing: from simulation tools to theories

Neural networks are modelling tools that are, in principle, able to capture the input-output behaviour of arbitrary systems that may include the dynamics of animal populations or brain circuits. While a neural network model is useful if it captures phenomenologically the behaviour of the target system in this way, its utility is amplified if key mechanisms of the model can be discovered, and identified with those of the underlying system. In this review, we first describe, at a fairly high level with minimal mathematics, some of the tools used in constructing neural network models. We then go on to discuss the implications of network models for our understanding of the system they are supposed to describe, paying special attention to those models that deal with neural circuits and brain systems. We propose that neural nets are useful for brain modelling if they are viewed in a wider computational framework originally devised by Marr. Here, neural networks are viewed as an intermediate mechanistic abstraction between 'algorithm' and 'implementation', which can provide insights into biological neural representations and their putative supporting architectures.