Somatic complementation by fusion of two mutant cells and mixing of their cytoplasms occurs when the genetic defect of one fusion partner is cured by the functional gene product provided by the other. We have found that complementation of mutational defects in the network mediating stimulus‐induced commitment and sporulation of Physarum polycephalum may reflect time‐dependent changes in the signaling state of its molecular building blocks. Network perturbation by fusion of mutant plasmodial cells in different states of activation, and (...) the time‐resolved analysis of somatic complementation effects can be used to systematically probe network structure and dynamics. Time‐resolved somatic complementation quantitatively detects regulatory interactions between the functional modules of a network, independent of their biochemical composition or subcellular localization, and without being limited to direct physical interactions. BioEssays 25:950–960, 2003. © 2003 Wiley Periodicals, Inc. (shrink)

Recent years have witnessed tremendous growth in the epidermal growth factor (EGF) family of peptide growth factors and the ErbB family of tyrosine kinases, the receptors for these factors. Accompanying this growth has been an increased appreciation for the roles these molecules play in tumorigenesis and in regulating cell proliferation and differentiation during development. Consequently, a significant question has been how diverse biological responses are specified by these hormones and receptors. Here we discuss several characteristics of hormone-receptor interactions and receptor (...) coupling that contribute to specificity: 1) a single EGF family hormone can bind multiple receptors; 2) a single ErbB family receptor can bind multiple hormones; 3) there are three distinct functional groups of EGF family hormones; 4) EGF family hormones can activate receptors in trans, and this heterodimerization diversifies biological responses; 5) ErbB3 requires a receptor partner for signaling; and 6) ErbB family receptors differentially couple to signaling pathways and biological responses. BioEssays 20:41–48, 1998. © 1998 John Wiley & Sons, Inc. (shrink)

Wnt proteins can activate different intracellular signaling cascades in various organisms by interacting with receptors of the Frizzled family. The first identified Wnt signaling pathway, the Wnt/β‐catenin pathway, has been studied in much detail and is highly conserved among species. As to non‐canonical Wnt pathways, the current situation is more nebulous partly because the intracellular mediators of this pathway are not yet fully understood and, in some cases, even identified. However, there are increasing data that prove the existence of non‐canonical (...) Wnt signaling and demonstrate its involvement in different developmental processes. In vertebrates, Wnt‐11 and Wnt‐5A can activate the Wnt/JNK pathway, which resembles the planar cell polarity pathway in Drosophila. The Wnt/Ca2+‐pathway has only been described in Xenopus and zebrafish so far and it is unclear whether it also exists in other organisms. Two recent papers provide us with new insight into non‐canonical Wnt signaling by (1) presenting a new intracellular mediator of non‐canonical signaling in Xenopus1 and (2) implicating the existence of an additional non‐canonical Wnt signaling pathway in flies.2 BioEssays 24:881–884, 2002. © 2002 Wiley Periodicals, Inc. (shrink)

The mitogenic action of cytokines such as epidermal growth factor (EGF)d̊ or platelet dericed growth factor (PDGF) involves the stimulation of a signal cascade controlled by a small G protein called Ras. Mutations of Ras can cause its constitutive activation and, as a consequence, bypass the regulation of cell growth by cytokines. Both growth factor‐induced and oncogenic activation of Ras involve the conversion of Ras from the GDP‐bound (D‐Ras) to the GTP‐bound (T‐Ras) forms. T‐Ras activates a network of protein (...) kinases including c‐Mos, c‐Raf‐1 and MAP kinase. Eventually the activation of MAP kinase leads to the activation of the elongation factor 4E and several transcription factors such as c‐Jun, c‐Myc and c‐Fos. There are several modulators of Ras activity, such as the GTPase activating proteins (GAP1 and NF1), which stimulate the coversion of T‐Ras to D‐Ras. A series of small NF1 fragments, which bind T‐Ras, as well as truncated forms or derivatives of c‐Raf‐1, c‐Jun and c‐Myc, are capable of blocking the T‐Ras‐activated mitogenesis in a competitive manner. These agents offer a unique opportunity to control the proliferation of T‐Ras‐associated tumors, which represent more than 30% of total human carcinomas. (shrink)

Prostate cancer is the most frequently diagnosed non‐skin cancer and the third leading cause of cancer mortality in men. In the initial stages, prostate cancer is dependent on androgens for growth, which is the basis for androgen ablation therapy. However, in most cases, prostate cancer progresses to a hormone refractory phenotype for which there is no effective therapy available at present. The androgen receptor (AR) is required for prostate cancer growth in all stages, including the relapsed, “androgen‐independent” tumors in the (...) presence of very low levels of androgens. This review focuses on AR function and AR‐target genes and summarizes the major signaling pathways implicated in prostate cancer progression, their crosstalk with each other and with AR signaling. This complex network of interactions is providing a deeper insight into prostate carcinogenesis and may form the basis for novel diagnostic and therapeutic strategies. BioEssays 29:1227–1238, 2007. © 2007 Wiley Periodicals, Inc. (shrink)

To depict the mechanisms that have enabled the emergence of semantic conventions, philosophers and researchers particularly access a game-theoretic model: the signaling game. In this article I argue that this model is also quite appropriate to analyze not only the emergence of a semantic convention, but also its change. I delineate how the application of signaling games helps to reproduce and depict mechanisms of semantic change. For that purpose I present a model that combines a signaling game with innovative reinforcement (...) learning; in simulation runs I conduct this game repeatedly within a multi-agent setup, where agents are arranged in social network structures. The results of these runs are contrasted with an attested theory from sociolinguistics: the ‘weak tie’ theory. Analyses of the produced data target a deeper understanding of the role of environmental variables for the promotion of semantic change or solidity of semantic conventions. (shrink)

The Motin family proteins (Motins) are a class of scaffolding proteins consisting of Angiomotin (AMOT), AMOT‐like protein 1 (AMOTL1), and AMOT‐like protein 2 (AMOTL2). Motins play a pivotal role in angiogenesis, tumorigenesis, and neurogenesis by modulating multiple cellular signaling pathways. Recent findings indicate that Motins are components of the Hippo pathway, a signaling cascade involved in development and cancer. This review discusses how Motins are integrated into the Hippo signaling network, as either upstream regulators or downstream effectors, to modulate (...) cell proliferation and migration. The repression of YAP/TAZ by Motins contributes to growth inhibition, whereas subcellular localization of Motins and their interactions with actin fibers are critical in regulating cell migration. The net effect of Motins on cell proliferation and migration may contribute to their diverse biological functions. (shrink)

The Liver kinase B1 with its downstream target AMP activated protein kinase (LKB1‐AMPK), and the key nutrient sensor mammalian target of rapamycin complex 1 (mTORC1) form two signaling systems that coordinate metabolic and cellular activity with changes in the environment in order to preserve homeostasis. For example, nutritional fluctuations rapidly feed back on these signaling systems and thereby affect cell‐specific functions. Recent studies have started to reveal important roles of these strategic metabolic regulators in Schwann cells for the trophic support (...) and myelination of axons. Because aberrant intermediate metabolism along with mitochondrial dysfunction in Schwann cells is mechanistically linked to nerve abnormalities found in acquired and inherited peripheral neuropathies, manipulation of the LKB1‐AMPK, and mTORC1 signaling hubs may be a worthwhile therapeutic target to mitigate nerve damage in disease. Here, recent advances in our understanding of LKB1‐AMPK and mTORC1 functions in Schwann cells are covered, and future research areas for this key metabolic signaling network are proposed. (shrink)

Embryonic development and adult tissue homeostasis are controlled through activation of intracellular signal transduction pathways by extracellular growth factors. In the past, signal transduction has largely been regarded as a linear process. However, more recent data from large‐scale and high‐throughput experiments indicate that there is extensive cross‐talk between individual signaling cascades leading to the notion of a signaling network. The behavior of such complex networks cannot be predicted by simple intuitive approaches but requires sophisticated models and computational simulations. The (...) purpose of such models is to generate experimentally testable hypotheses and to find explanations for unexpected experimental results. Here, we discuss the need for, and the future impact of, mathematical models for exploring signal transduction in different biological contexts such as for example development. BioEssays 30:1110–1125, 2008. © 2008 Wiley Periodicals, Inc. (shrink)

In species capable of learning, including our own, individuals can modify their behavior by some adaptive process. Important classes of behavior - mating, predation, coalitions, trade, signaling, and division of labor - involve interactions between individuals. The agents involved learn two things: with whom to interact and how to act. That is to say that adaptive dynamics operates both on structure and strategy.

It was meaningful to predict the customers' decision-making behavior in the field of market. However, due to individual differences and complex, non-linear natures of the electroencephalogram signals, it was hard to classify the EEG signals and to predict customers' decisions by using traditional classification methods. To solve the aforementioned problems, a recurrent t-distributed stochastic neighbor embedding neural network was proposed in current study to classify the EEG signals in the designed brand extension paradigm and to predict the participants' decisions. (...) The recurrent t-SNE neural network contained two steps. In the first step, t-SNE algorithm was performed to extract features from EEG signals. Second, a recurrent neural network with long short-term memory layer, fully connected layer, and SoftMax layer was established to train the features, classify the EEG signals, as well as predict the cognitive performance. The proposed network could give a good prediction with accuracy around 87%. Its superior in prediction accuracy as compared to a recurrent principal component analysis network, a recurrent independent component correlation algorithm [independent component analysis ] network, a t-SNE support vector machine network, a t-SNE back propagation neural network, a deep LSTM neural network, and a convolutional neural network were also demonstrated. Moreover, the performance of the proposed network with different activated channels were also investigated and compared. The results showed that the proposed network could make a relatively good prediction with only 16 channels. The proposed network would become a potentially useful tool to help a company in making marketing decisions and to help uncover the neural mechanisms behind individuals' decision-making behavior with low cost and high efficiency. (shrink)

Although Caenorhabditis and Drosophila proved invaluable in unraveling the molecular mechanisms of apoptosis, it is now clear that these animals are of limited value for understanding the evolution of apoptotic systems. Whereas data from these invertebrates led to the assumption that the extrinsic apoptotic pathway is restricted to vertebrates, recent data from cnidarians and sponges indicate that this pathway predates bilaterian origins. Here we review the phylogenetic distribution of caspase‐8, the initiator caspase of the extrinsic apoptotic pathway, its paralogs and (...) other components of the network. The ancestral caspase‐8 gave rise to four paralogs early in vertebrate evolution, and these have been maintained in many tetrapods. However, eutherians have lost caspase‐18 and myomorph rodents have lost caspase‐10, these losses suggesting functional redundancy amongst caspase‐8 paralogs. The apoptotic network of the eumetazoan ancestor appears to have been complex and vertebrate like, and is only now being revealed by studying simple animals. (shrink)

Recent work by Brian Skyrms offers a very general way to think about how information flows and evolves in biological networks—from the way monkeys in a troop communicate to the way cells in a body coordinate their actions. A central feature of his account is a way to formally measure the quantity of information contained in the signals in these networks. In this article, we argue there is a tension between how Skyrms talks of signalling networks and his formal (...) measure of information. Although Skyrms refers to both how information flows through networks and that signals carry information, we show that his formal measure only captures the latter. We then suggest that to capture the notion of flow in signalling networks, we need to treat them as causal networks. This provides the formal tools to define a measure that does capture flow, and we do so by drawing on recent work defining causal specificity. Finally, we suggest that this new measure is crucial if we wish to explain how evolution creates information. For signals to play a role in explaining their own origins and stability, they can’t just carry information about acts; they must be difference-makers for acts. _1_ Signalling, Evolution, and Information _2_ Skyrms’s Measure of Information _3_ Carrying Information versus Information Flow _3.1_ Example 1 _3.2_ Example 2 _3.3_ Example 3 _4_ Signalling Networks Are Causal Networks _4.1_ Causal specificity _4.2_ Formalizing causal specificity _5_ Information Flow as Causal Control _5.1_ Example 1 _5.2_ Examples 2 and 3 _5.3_ Average control implicitly ‘holds fixed’ other pathways _6_ How Does Evolution Create Information? _7_ Conclusion Appendix >. (shrink)

As a result of the time‐ and context‐dependency of gene expression, gene regulatory and signaling pathways undergo dynamic changes during development. Creating a model of the dynamics of molecular interaction networks offers enormous potential for understanding how a genome orchestrates the developmental processes of an organism. The dynamic nature of pathway topology calls for new modeling strategies that can capture transient molecular links at the runtime. The aim of this paper is to present a brief and informative, but not all‐inclusive, (...) viewpoint on the computational aspects of modeling and simulation of a non‐static molecular network. BioEssays 26:68–72, 2004. © 2003 Wiley Periodicals, Inc. (shrink)

Neuronal signalling involves multiple neuropeptides that are diverse in structure and function. Complex patterns of tissue‐specific expression arise from alternate RNA splicing of neuropeptide‐encoding gene transcripts. The pattern of expression and its role in cell signalling is diffecult to study at the level of single neurons in the complex vertebrate brain. However, in the model molluscan system, Lymnaea, it is possible to show that alternate mRNA expression of the FMRFamide gene is specific to single identified neurons. Two different (...) transcripts are expressed in a mutually exclusive manner in different neurons. Post‐translational processing of the two precursor proteins leads to completely distinct sets of neuropeptide transmitters. The function of these transmitter cocktails, resulting from alternate mRNA splicing, was studied physiologically in identified neurons forming part of a behaviourally important network regulating heartbeat. (shrink)

Brian Skyrms offers a fascinating demonstration of how fundamental signals are to our world. He uses various scientific tools to investigate how meaning and communication develop. Signals operate in networks of senders and receivers at all levels of life, transmitting and processing information. That is how humans and animals think and interact.

Hindbrain development is orchestrated by a vertebrate gene regulatory network that generates segmental patterning along the anterior–posterior axis via Hox genes. Here, we review analyses of vertebrate and invertebrate chordate models that inform upon the evolutionary origin and diversification of this network. Evidence from the sea lamprey reveals that the hindbrain regulatory network generates rhombomeric compartments with segmental Hox expression and an underlying Hox code. We infer that this basal feature was present in ancestral vertebrates and, as (...) an evolutionarily constrained developmental state, is fundamentally important for patterning of the vertebrate hindbrain across diverse lineages. Despite the common ground plan, vertebrates exhibit neuroanatomical diversity in lineage‐specific patterns, with different vertebrates revealing variations of Hox expression in the hindbrain that could underlie this diversification. Invertebrate chordates lack hindbrain segmentation but exhibit some conserved aspects of this network, with retinoic acid signaling playing a role in establishing nested domains of Hox expression. (shrink)

Recent work by Brian Skyrms offers a very general way to think about how information flows and evolves in biological networks — from the way monkeys in a troop communicate, to the way cells in a body coordinate their actions. A central feature of his account is a way to formally measure the quantity of information contained in the signals in these networks. In this paper, we argue there is a tension between how Skyrms talks of signalling networks and (...) his formal measure of information. Although Skyrms refers to both how information flows through networks and that signals carry information, we show that his formal measure only captures the latter. We then suggest that to capture the notion of flow in signalling networks, we need to treat them as causal networks. This provides the formal tools to define a measure that does capture flow, and we do so by drawing on recent work defining causal specificity. Finally, we suggest that this new measure is crucial if we wish to explain how evolution creates information. For signals to play a role in explaining their own origins and stability, they can’t just carry information about acts: they must be difference-makers for acts. (shrink)

When imposing traffic congestion pricing around downtown commercial centers, there is a concern that commercial activities will have to consider relocating due to reduced demand, at a cost to merchants. Concerns like these were important in the debates before the introductions of congestion charges in both London and Stockholm and influenced the final policy design choices. This study introduces a sequential experimental game to study reactions to congestion pricing in the commercial sector. In the game, merchants first make location choices. (...) Consumers, who drive to do their shopping, subsequently choose where to shop. Initial responses to the introduction of congestion pricing and equilibrium selection adjustments over time are observed. These observations are compared to responses and adjustments in a condition where congestion pricing is reduced from an initially high level. Payoffs are non-linear and non-transparent, making it less than obvious that the efficient equilibrium will be selected, and introducing possibilities that participants need to discover their preferences and anchor on past experiences. We find that initial responses reflect standard inverse price–demand relations, and that adjustments over time rely on signaling by consumers leading to the efficient equilibrium. There is also evidence that priming from initial experiences influence play somewhat. We confirm that commercial activities relocate following the introduction of congestion pricing and that the adjustment process is costly to merchants. (shrink)

Accurate and rapid defect identification based on pulsed eddy current testing plays an important role in the structural integrity and health monitoring of in-service equipment in the renewable energy system. However, in conventional data-driven defect identification methods, the signal feature extraction is time consuming and requires expert experience. To avoid the difficulty of manual feature extraction and overcome the shortcomings of the classic deep convolutional network, such as large memory and high computational cost, an intelligent defect recognition pipeline based (...) on the general Warblet transform method and optimized two-dimensional DCNN is proposed. The GWT method is used to convert the one-dimensional PECT signal to a 2D grayscale image used as the input of 2D DCNN. A compound method is proposed to optimize the baseline VGG16, a well-known DCNN, from four aspects including reducing the input size, adding batch normalization layer after every convolutional layer and fully connection layer, simplifying the FCs, and removing unimportant filters in Convs so as to reduce memory and computational costs while improving accuracy. Through a pulsed eddy current testing experiment considering interference factors including liftoff and noise, the following conclusion can be obtained. The time-frequency representation obtained by the GWT method not only has excellent ability in terms of the transient component analysis but also is less affected by the reduction of image size; the proposed optimized DCNN can accurately identify defect types without manual feature extraction. And compared to the baseline VGG16, the accuracy obtained by the optimized DCNN is improved by 7%, to about 99.58%, and the memory and computational cost are reduced by 98%. Moreover, compared with other well-known DCNNs, such as GoogLeNet, Inception V3, ResNet50, and AlexNet, the optimized network has significant advantages in terms of accuracy and computational cost, too. (shrink)

Improving the resilience of urban road networks suffering from various disruptions has been a central focus for urban emergence management. However, to date the effective methods which may mitigate the negative impacts caused by the disruptions, such as road accidents and natural disasters, on urban road networks is highly insufficient. This study proposes a novel adaptive signal control strategy based on a doubly dynamic learning framework, which consists of deep reinforcement learning and day-to-day traffic dynamic learning, to improve the (...) class='Hi'>network performance by adjusting red/green time split. In this study, red time split is regarded as extra traffic flow to discourage drivers to use affected roads, so as to reduce congestion and improve the resilience when urban road networks are subject to different levels of disruptions. In addition, we utilize the convolution neural network as Q-network to approximate Q values, link flow distribution and link capacity are regarded as the state space, and actions are denoted as red/green time split. A small network is utilized as a numerical example, and a fixed time signal control and other two adaptive signal controls are employed for the comparisons with the proposed one. The results show that the proposed adaptive signal control based on deep reinforcement learning can achieve better resilience in most of the cases, particularly in the scenarios of moderate and severe disruptions. This study may shed light on the advantages of the proposed adaptive signal control dealing with major emergencies compared to others. (shrink)

Dragon boat sport is a traditional activity in China. In recent years, dragon boat sport has become more and more popular around the world. In order to face more challenges, it is urgent for athletes to enhance their own strength. Scientific training methods are particularly important for athletes, and accurate training data are the basis to support scientific training. Traditional mathematical statistic methods neither can sample signals accurately nor can they do real-time analysis and feedback the characteristics to each athlete. (...) In this paper, we use the wearable device with a triaxial accelerometer and heart rate sensor builtin to sample the speed signals and heart rate signals of athletes in various stages of men’s 1000m straight race. Based on the complex network theory, we regard the 23 dragon boat athletes in the dragon boat race as 23 nodes so as to establish a network with 23 nodes and reflect the importance of nodes by measuring the impact of node deletion on the results of the race. The neural network multilayer perceptron model is used for training to obtain the optimal combined value with speed and heart rate for each race stage. The optimal value will be used in the simulated race as the target value to verify if it can help to improve the training efficiency. Experimental results show that the optimal value obtained by this method has a positive effect on the results of the dragon boat race which is beneficial to sports training and tactics planning. (shrink)

Bioactive peptides are a group of diverse intercellular signalling molecules. Almost half a century of research on this topic has resulted in an enormous amount of data. In this essay, a general perspective to interpret all these data will be given. In classical endocrinology, neuropeptides were thought of as simple signalling molecules that each elicit one response. However, the fact that the total bioactive peptide signal is far from simple puts this view under pressure. Cells and tissues express (...) many different bioactive peptides and they are also able to respond to many different bioactive peptides, indicating that multiple receptors and signal transduction pathways are present in a single cell. Therefore, the authors suggest that the bioactive peptide signalling system should be regarded in the context of network and systems biology. Bioactive peptides can best be viewed as an extension of the protein interaction network that allows regulating and fine‐tuning the metabolism of the different cells and tissues in the body. The cell thus responds to the ‘peptidome’ instead of to a single peptide. The intracellular part of this signalling network consists of the various signalling transduction cascades. Recently, new systems biology approaches have emerged for the modelling of cell signalling. The network and systems biology approach is also able to shed new light on the evolution of intercellular signalling. (shrink)

This paper considers how surprise (or its lack) can be cast as a metacognitive signal with an adaptive function in learning new knowledge and revising belief networks. It reviews the phenomena that may hinder this signal (e.g., hindsight bias) and argues for its extrinsic exploitation in instructional and educational contexts by educators, journalists and parents, who might train learners to internalize the use of surprise to drive explanation‐based learning.

Deep neural networks (DNNs) have had extraordinary successes in classifying photographic images of objects and are often described as the best models of biological vision. This conclusion is largely based on three sets of findings: (1) DNNs are more accurate than any other model in classifying images taken from various datasets, (2) DNNs do the best job in predicting the pattern of human errors in classifying objects taken from various behavioral datasets, and (3) DNNs do the best job in predicting (...) brain signals in response to images taken from various brain datasets (e.g., single cell responses or fMRI data). However, these behavioral and brain datasets do not test hypotheses regarding what features are contributing to good predictions and we show that the predictions may be mediated by DNNs that share little overlap with biological vision. More problematically, we show that DNNs account for almost no results from psychological research. This contradicts the common claim that DNNs are good, let alone the best, models of human object recognition. We argue that theorists interested in developing biologically plausible models of human vision need to direct their attention to explaining psychological findings. More generally, theorists need to build models that explain the results of experiments that manipulate independent variables designed to test hypotheses rather than compete on making the best predictions. We conclude by briefly summarizing various promising modeling approaches that focus on psychological data. (shrink)

Low frequency oscillation is an important attribute of human brain activity, and the amplitude of low frequency fluctuation is an effective method to reflect the characteristics of low frequency oscillation, which has been widely used in the treatment of brain diseases and other fields. However, due to the low accuracy of the current analysis methods for low frequency signal extraction of ALFF, we propose the Fourier-based synchrosqueezing transform, which is often used in the field of signal processing to extract the (...) ALFF of the low frequency power spectrum of the whole-time dimension. The low frequency characteristics of the extracted signal are compared with those of FSST and fast Fourier transform through the resting-state data. It is clear that the signal extracted by FSST has more low frequency characteristics, which is significantly different from FFT. (shrink)

Adaptors are proteins of multi‐modular structure without enzymatic activity. Their capacity to organise large, temporary protein complexes by linking proteins together in a regulated and selective fashion makes them of outstanding importance in the establishment and maintenance of specificity and efficiency in all known signal transduction pathways. This review focuses on the structural and functional characterisation of adaptors involved in tyrosine kinase (TK) signalling. TK‐linked adaptors can be distinguished by their domain composition and binding specificities. However, such structural classifications (...) have proven inadequate as indicators of functional roles. A better way to understand the logic of signalling networks might be to look at functional aspects of adaptor proteins such as signalling specificity, negative versus positive contribution to signal propagation, or their position in the signalling hierarchy. All of these functions are dynamic, suggesting that adaptors have important regulatory roles rather than acting only as stable linkers in signal transduction. BioEssays 28: 465–479, 2006. © 2006 Wiley Periodicals, Inc. (shrink)

We propose that interaction rules derived from polyamine exchange in connected cells may explain the spatio‐temporal organization of gap junctions observed during tissue regeneration and tumorigenesis. We also hypothesize that polyamine exchange can be considered as signal that allows cells to sense the proliferation status of their neighbors. Polyamines (putrescine, spermidine, and spermine) are indeed small aliphatic polycations that serve as fuels to sustain elevated proliferation rates of the order observed in cancer cells. Based on recent reports, we consider here (...) that polyamines can be exchanged through gap junction channels between mammalian cells. Such intercellular exchange of polyamines has critical consequences on the local control of growth. In line with this hypothesis, the complex protein network that keeps polyamine levels finely tuned in mammalian cells can translate polyamine efflux or influx into integrated signals controlling transcription, translation, and cell communications. (shrink)

Mechanical stimulation has a critical role in the development and maintenance of the skeleton. This function requires the perception of extracellular stimuli as well as their conversion into intracellular biochemical responses. This process is called mechanotransduction and is mediated by a plethora of molecular events that regulate bone metabolism. Indeed, mechanoreceptors, such as integrins, G protein‐coupled receptors, receptor protein tyrosine kinases, and stretch‐activated Ca2+ channels, together with their downstream effectors coordinate the transmission of load‐induced signals to the nucleus and the (...) expression of bone‐related genes. During the past decade, scientists have gained increasing insight into the molecular networks implicated in bone mechanotransduction. In the present paper, we consider the major signaling cascades and transcription factors that control bone and cartilage mechanobiology and discuss the influence of the mechanical microenvironment on the determination of skeletal morphology. (shrink)

Engineers use neural networks to control systems too complex for conventional engineering solutions. To examine the behavior of individual hidden units would defeat the purpose of this approach because it would be largely uninterpretable. Yet neurophysiologists spend their careers doing just that! Hidden units contain bits and scraps of signals that yield only arcane hints about network function and no information about how its individual units process signals. Most literature on single-unit recordings attests to this grim fact. On the (...) other hand, knowing a system's function and describing it with elegant mathematics tell one very little about what to expect of interneuronal behavior. Examples of simple networks based on neurophysiology are taken from the oculomotor literature to suggest how single-unit interpretability might decrease with increasing task complexity. It is argued that trying to explain how any real neural network works on a cell-by-cell, reductionist basis is futile and we may have to be content with trying to understand the brain at higher levels of organization. (shrink)

Design patterns are generalized solutions to frequently recurring problems. They were initially developed by architects and computer scientists to create a higher level of abstraction for their designs. Here, we extend these concepts to cell biology to lend a new perspective on the evolved designs of cells' underlying reaction networks. We present a catalog of 21 design patterns divided into three categories: creational patterns describe processes that build the cell, structural patterns describe the layouts of reaction networks, and behavioral patterns (...) describe reaction network function. Applying this pattern language to the E. coli central metabolic reaction network, the yeast pheromone response signaling network, and other examples lends new insights into these systems. (shrink)

In this study, an adaptive output feedback fault tolerant control scheme is proposed for a class of multi-input and multioutput nonlinear systems with multiple constraints. The neural network is adopted to handle the unknown nonlinearity by means of its superior approximation capability. Based on it, the state observer is designed to estimate the unmeasured states, and the nonlinear disturbance observer is constructed to tackle the external disturbances. In addition, the Nussbaum function is utilized to cope with the actuator faults, (...) which are coupled with the unknown control directions. Combining with the Lyapunov theory, a NN-based output feedback FTC law is developed for the MIMO nonlinear systems, and the boundedness of all closed-loop system error signals is proved. Simulation results on the unmanned helicopter are performed to demonstrate the effectiveness of the proposed controller. (shrink)

Many abiotic and other signals are transduced in eukaryotic cells by changes in the level of free calcium via pumps, channels and stores. We suggest here that ion condensation should also be taken into account. Calcium, like other counterions, is condensed onto linear polymers at a critical value of the charge density. Such condensation resembles a phase transition and has a topological basis in that it is promoted by linear as opposed to spherical assemblies of charges. Condensed counterions are delocalised (...) and can diffuse in the so‐called near region along the polymers. It is generally admitted that cytoskeletal filaments, proteins colocalised with these filaments, protein filaments distinct from cytoskeletal filaments, and filamentous assemblies of other macromolecules, constitute an intracellular macromolecular network. Here we draw attention to the fact that this network has physicochemical characteristics that enable counterion condensation. We then propose a model in which the feedback relationships between the condensation/decondensation of calcium and the activation of calcium‐dependent kinases and phosphatases control the charge density of the filaments of the intracellular macromolecular network. We show how condensation might help mediate free levels of calcium both locally and globally. In this model, calcium condensation/decondensation on the macromolecular network creates coherent patterns of protein phosphorylation that integrate signals. This leads us to hypothesize that the process of ion condensation operates in signal transduction, that it can have an integrative role and that the macromolecular network serves as an integrative receptor. BioEssays 26:549–557, 2004. © 2004 Wiley Periodicals, Inc. (shrink)

Atypical brain function in attention-deficit/hyperactivity disorder (ADHD) has been identified using both task-activation and functional connectivity fMRI approaches. Recent work highlights the potential for another measure derived from functional neuroimaging data, brain signal variability, to reveal insights into clinical conditions. Higher brain signal variability has previously been linked with optimal behavioral performance. At present, little is known regarding the relationship between resting-state brain signal variability and ADHD symptom severity. The current study examined the relationship between a measure of moment-to-moment brain (...) signal variability called mean-square successive difference (MSSD) and ADHD symptomatology in a group of children (7–12 years old) with ( n = 40) and without ( n = 30) a formal diagnosis of ADHD. A categorical analysis comparing subjects with and without a clinical diagnosis of ADHD showed no differences in MSSD between groups. A dimensional analysis revealed a positive relationship between MSSD and overall ADHD symptom severity and inattention across children with and without an ADHD diagnosis. Specifically, this positive relationship was found in medial prefrontal areas comprising the default mode network. These results demonstrate a link between intrinsic brain signal variability and ADHD symptom severity that cuts across diagnostic categories, and point to a locus of dysfunction consistent with previous neuroimaging literature. (shrink)

Reactive oxygen species (ROS) are known as toxic metabolic products in plants and other aerobic organisms. An elaborate and highly redundant plant ROS network, composed of antioxidant enzymes, antioxidants and ROS-producing enzymes, is responsible for maintaining ROS levels under tight control. This allows ROS to serve as signaling molecules that coordinate an astonishing range of diverse plant processes. The specificity of the biological response to ROS depends on the chemical identity of ROS, intensity of the signal, sites of production, (...) plant developmental stage, previous stresses encountered and interactions with other signaling molecules such as nitric oxide, lipid messengers and plant hormones. Although many components of the ROS signaling network have recently been identified, the challenge remains to understand how ROS-derived signals are integrated to eventually regulate such biological processes as plant growth, development, stress adaptation and programmed cell death. (shrink)