All eukaryotes have the ability to detect and respond to environmental and hormonal signals. In many cases these signals evoke cellular changes that are incompatible and must therefore be orchestrated by the responding cell. In the yeast Saccharomyces cerevisiae, hyperosmotic stress and mating pheromones initiate signaling cascades that each terminate with a MAP kinase, Hog1 and Fus3, respectively. Despite sharing components, these pathways are initiated by distinct inputs and produce distinct cellular behaviors. To understand how these responses are coordinated, we monitored the pheromone response during hyperosmotic conditions. We show that hyperosmotic stress limits pheromone signaling in at least three ways. First, stress delays the expression of pheromone-induced genes. Second, stress promotes the phosphorylation of a protein kinase, Rck2, and thereby inhibits pheromone-induced protein translation. Third, stress promotes the phosphorylation of a shared pathway component, Ste50, and thereby dampens pheromone-induced MAPK activation. Whereas all three mechanisms are dependent on an increase in osmolarity, only the phosphorylation events require Hog1. These findings reveal how an environmental stress signal is able to postpone responsiveness to a competing differentiation signal, by acting on multiple pathway components, in a coordinated manner.
All cells can detect and respond to signals in their environment. The ability to interpret these signals with accuracy is needed for proper growth and differentiation. Moreover, cells must prioritize responses when confronted with competing signals. However the molecular mechanisms that govern signal prioritization are poorly understood. To address this question, we studied two signaling pathways in the genetic model organism budding yeast. Specifically we focused on the pheromone mating (differentiation) pathway and the high osmolarity glycerol (stress response) pathway. These pathways respond differently to each stimulus despite sharing pathway components. We find that cells must first adapt to stress before they can mate. At early times, the stress response cross-inhibits and dampens the pheromone response to suspend mating differentiation. Once cells adapt, the stress response ends and the differentiation program resumes. All signaling pathways that regulate cell fate decisions are interconnected to varying degrees. Our study highlights the importance of proper signal coordination in cell fate decisions, and it reveals new mechanisms that govern signal coordination within complex signaling networks.
Molecular machines are examples of “pre-established” nanotechnology, driving the basic biochemistry of living cells. They encompass an enormous range of function, including fuel generation for chemical processes, transport of molecular components within the cell, cellular mobility, signal transduction and the replication of the genetic code, amongst many others. Much of our understanding of such nanometer length scale machines has come from in vitro studies performed in isolated, artificial conditions. Researchers are now tackling the challenges of studying nanomachines in their native environments. In this review, we outline recent in vivo investigations on nanomachines in model bacterial systems using state-of-the-art genetics technology combined with cutting-edge single-molecule and super-resolution fluorescence microscopy. We conclude that single-molecule and super-resolution fluorescence imaging provide powerful tools for the biochemical, structural and functional characterization of biological nanomachines. The integrative spatial, temporal, and single-molecule data obtained simultaneously from fluorescence imaging open an avenue for systems-level single-molecule cellular biophysics and in vivo biochemistry.
fluorescence microscopy; fluorescent protein; in vivo imaging; molecular machine; nanomachine; photobleach; single molecule; slimfield; super-resolution; total internal reflection
One of the most exciting developments in signal transduction research has been the proliferation of studies in which a biological discovery was initiated by computational modeling. Here we review the major efforts that enable such studies. First, we describe the experimental technologies that are generally used to identify the molecular components and interactions in, and dynamic behavior exhibited by, a network of interest. Next, we review the mathematical approaches that are used to model signaling network behavior. Finally, we focus on three specific instances of “model-driven discovery”: cases in which computational modeling of a signaling network has led to new insights which have been verified experimentally.
Signal transduction networks are the bridge between the extraordinarily complex extracellular environment and a carefully orchestrated cellular response. These networks are largely composed of proteins which can interact, move to specific cellular locations, or be modified or degraded. The integration of these events often leads to the activation or inactivation of transcription factors, which then induce or repress the expression of thousands of genes.
Because of this critical role in translating environmental cues to cellular behaviors, malfunctioning signaling networks can lead to a variety of pathologies. One example is cancer, in which many of the key genes found to be involved in cancer onset and development are components of signaling pathways [1, 2]. A detailed understanding of the cellular signaling networks underlying such diseases would likely be extremely useful in developing new treatments.
However, the complexity of signaling networks is such that their integrated functions cannot be determined without computational simulation. In recent years, mathematical modeling of signal transduction has led to some exciting new findings and biological discoveries. Here, we review the work that has enabled computational modeling of mammalian signaling networks, as well as the demonstrated value of such modeling. We begin by reviewing the experimental techniques commonly associated with model-building efforts, in terms of mapping network interactions as well as determining the dynamic network response to perturbation. We then discuss modeling strategies, and finally focus on three cases that dramatically illustrate the power of models to discover new biology.
Signaling pathways; Mathematical models; Intra-cellular signaling; Regression models; Transcription factor networks
Iron is essential for all known life due to its redox properties; however, these same properties can also lead to its toxicity in overload through the production of reactive oxygen species. Robust systemic and cellular control are required to maintain safe levels of iron, and the liver seems to be where this regulation is mainly located. Iron misregulation is implicated in many diseases, and as our understanding of iron metabolism improves, the list of iron-related disorders grows. Recent developments have resulted in greater knowledge of the fate of iron in the body and have led to a detailed map of its metabolism; however, a quantitative understanding at the systems level of how its components interact to produce tight regulation remains elusive. A mechanistic computational model of human liver iron metabolism, which includes the core regulatory components, is presented here. It was constructed based on known mechanisms of regulation and on their kinetic properties, obtained from several publications. The model was then quantitatively validated by comparing its results with previously published physiological data, and it is able to reproduce multiple experimental findings. A time course simulation following an oral dose of iron was compared to a clinical time course study and the simulation was found to recreate the dynamics and time scale of the systems response to iron challenge. A disease state simulation of haemochromatosis was created by altering a single reaction parameter that mimics a human haemochromatosis gene (HFE) mutation. The simulation provides a quantitative understanding of the liver iron overload that arises in this disease. This model supports and supplements understanding of the role of the liver as an iron sensor and provides a framework for further modelling, including simulations to identify valuable drug targets and design of experiments to improve further our knowledge of this system.
Iron is an essential nutrient required for healthy life but, in excess, is the cause of debilitating and even fatal conditions. The most common genetic disorder in humans caused by a mutation, haemochromatosis, results in an iron overload in the liver. Indeed, the liver plays a central role in the regulation of iron. Recently, an increasing amount of detail has been discovered about molecules related to iron metabolism, but an understanding of how they work together and regulate iron levels (in healthy people) or fail to do it (in disease) is still missing. We present a mathematical model of the regulation of liver iron metabolism that provides explanations of its dynamics and allows further hypotheses to be formulated and later tested in experiments. Importantly, the model reproduces accurately the healthy liver iron homeostasis and simulates haemochromatosis, showing how the causative mutation leads to iron overload. We investigate how best to control iron regulation and identified reactions that can be targets of new medicines to treat iron overload. The model provides a virtual laboratory for investigating iron metabolism and improves understanding of the method by which the liver senses and controls iron levels.
Across species, the brain evolved to respond to natural rewards such as food and sex. These physiological responses are important for survival, reproduction and evolutionary processes. It is no surprise, therefore, that many of the neural circuits and signaling pathways supporting reward processes are conserved from Caenorhabditis elegans to Drosophilae, to rats, monkeys and humans. The central role of dopamine (DA) in encoding reward and in attaching salience to external environmental cues is well recognized. Less widely recognized is the role of reporters of the “internal environment”, particularly insulin, in the modulation of reward. Insulin has traditionally been considered an important signaling molecule in regulating energy homeostasis and feeding behavior rather than a major component of neural reward circuits. However, research over recent decades has revealed that DA and insulin systems do not operate in isolation from each other, but instead, work together to orchestrate both the motivation to engage in consummatory behavior and to calibrate the associated level of reward. Insulin signaling has been found to regulate DA neurotransmission and to affect the ability of drugs that target the DA system to exert their neurochemical and behavioral effects. Given that many abused drugs target the DA system, the elucidation of how dopaminergic, as well as other brain reward systems, are regulated by insulin will create opportunities to develop therapies for drug and potentially food addiction. Moreover, a more complete understanding of the relationship between DA neurotransmission and insulin may help to uncover etiological bases for “food addiction” and the growing epidemic of obesity. This review focuses on the role of insulin signaling in regulating DA homeostasis and DA signaling, and the potential impact of impaired insulin signaling in obesity and psychostimulant abuse.
Insulin; Dopamine; Dopamine transporter; Amphetamine; Obesity
Biological systems are increasingly being studied in a holistic manner, using omics approaches, to provide quantitative and qualitative descriptions of the diverse collection of cellular components. Among the omics approaches, metabolomics, which deals with the quantitative global profiling of small molecules or metabolites, is being used extensively to explore the dynamic response of living systems, such as organelles, cells, tissues, organs and whole organisms, under diverse physiological and pathological conditions. This technology is now used routinely in a number of applications, including basic and clinical research, agriculture, microbiology, food science, nutrition, pharmaceutical research, environmental science and the development of biofuels. Of the multiple analytical platforms available to perform such analyses, nuclear magnetic resonance and mass spectrometry have come to dominate, owing to the high resolution and large datasets that can be generated with these techniques. The large multidimensional datasets that result from such studies must be processed and analyzed to render this data meaningful. Thus, bioinformatics tools are essential for the efficient processing of huge datasets, the characterization of the detected signals, and to align multiple datasets and their features. This paper provides a state-of-the-art overview of the data processing tools available, and reviews a collection of recent reports on the topic. Data conversion, pre-processing, alignment, normalization and statistical analysis are introduced, with their advantages and disadvantages, and comparisons are made to guide the reader.
Bioinformatics; mass spectrometry; metabolome; metabolomics; software development; statistical analysis; systems biology.
The last four years have brought exciting progress in membrane protein research. Finally those many efforts that have been put into expression of eukaryotic membrane proteins are coming to fruition and enable to solve an ever-growing number of high resolution structures. In the past, many skilful optimization steps were required to achieve sufficient expression of functional membrane proteins. Optimization was performed individually for every membrane protein, but provided insight about commonly encountered bottlenecks and, more importantly, general guidelines how to alleviate cellular limitations during microbial membrane protein expression. Lately, system-wide analyses are emerging as powerful means to decipher cellular bottlenecks during heterologous protein production and their use in microbial membrane protein expression has grown in popularity during the past months.
This review covers the most prominent solutions and pitfalls in expression of eukaryotic membrane proteins using microbial hosts (prokaryotes, yeasts), highlights skilful applications of our basic understanding to improve membrane protein production. Omics technologies provide new concepts to engineer microbial hosts for membrane protein production.
Assembly of the mitotic spindle is a classic example of macromolecular self-organization. During spindle assembly, microtubules (MTs) accumulate around chromatin. In centrosomal spindles, centrosomes at the spindle poles are the dominating source of MT production. However, many systems assemble anastral spindles, i.e., spindles without centrosomes at the poles. How anastral spindles produce and maintain a high concentration of MTs in the absence of centrosome-catalyzed MT production is unknown. With a combined biochemistry-computer simulation approach, we show that the concerted activity of three components can efficiently concentrate microtubules (MTs) at chromatin: (1) an external stimulus in form of a RanGTP gradient centered on chromatin, (2) a feed-back loop where MTs induce production of new MTs, and (3) continuous re-organization of MT structures by dynamic instability. The mechanism proposed here can generate and maintain a dissipative MT super-structure within a RanGTP gradient.
Historically, much of biology was studied by physicists and mathematicians. With the advent of modern molecular biology, a wave of researchers became trained in a new scientific discipline filled with the language of genes, mutants, and the central dogma. These new molecular approaches have provided volumes of information on biomolecules and molecular pathways from the cellular to the organismal level. The challenge now is to determine how this seemingly endless list of components works together to promote the healthy function of complex living systems. This effort requires an interdisciplinary approach by investigators from both the biological and the physical sciences.
Since its discovery in the early 1990’s, cortactin has emerged as a key signaling protein in many cellular processes, including cell adhesion, migration, endocytosis, and tumor invasion. While the list of cellular functions influenced by cortactin grows, the ability of cortactin to interact with and alter the cortical actin network is central to its role in regulating these processes. Recently, several advances have been made in our understanding of the interaction between actin and cortactin, providing insight into how these two proteins work together to provide a framework for normal and altered cellular function. This review examines how regulation of cortactin through post-translational modifications and interactions with multiple binding partners elicits changes in cortical actin cytoskeletal organization, impacting the regulation and formation of actin-rich motility structures.
cortactin; actin; Arp2/3; N-WASp; motility
This article is a summary of a lecture on cellular mechanotransduction that was presented at a symposium on “Cardiac Mechano-Electric Feedback and Arrhythmias” that convened at Oxford, England in April 2007. Although critical mechanosensitive molecules and cellular components, such as integrins, stretch-activated ion channels, and cytoskeletal filaments, have been shown to contribute to the response by which cells convert mechanical signals into a biochemical response, little is known about how they function in the structural context of living cells, tissues and organs to produce orchestrated changes in cell behavior in response to stress. Here, studies are reviewed that suggest our bodies use structural hierarchies (systems within systems) composed of interconnected extracellular matrix and cytoskeletal networks that span from the macroscale to the nanoscale to focus stresses on specific mechanotransducer molecules. A key feature of these networks is that they are in a state of isometric tension (i.e., experience a tensile prestress), which ensures that various molecular-scale mechanochemical transduction mechanisms proceed simultaneously and produce a concerted response. These features of living architecture are the same principles that govern tensegrity (tensional integrity) architecture, and mathematical models based on tensegrity are beginning to provide new and useful descriptions of living materials, including mammalian cells. This article reviews how the use of tensegrity at multiple size scales in our bodies guides mechanical force transfer from the macro to the micro, as well as how it facilitates conversion of mechanical signals into changes in ion flux, molecular binding kinetics, signal transduction, gene transcription, cell fate switching and developmental patterning.
Although the roots of Ras sprouted from the rich history of retrovirus research, it was the discovery of mutationally activated RAS genes in human cancer in 1982 that stimulated an intensive research effort to understand Ras protein structure, biochemistry and biology. While the ultimate goal has been developing anti-Ras drugs for cancer treatment, discoveries from Ras have laid the foundation for three broad areas of science. First, they focused studies on the origins of cancer to the molecular level, with the subsequent discovery of genes mutated in cancer that now number in the thousands. Second, elucidation of the biochemical mechanisms by which Ras facilitates signal transduction established many of our fundamental concepts of how a normal cell orchestrates responses to extracellular cues. Third, Ras proteins are also founding members of a large superfamily of small GTPases that regulate all key cellular processes and established the versatile role of small GTP-binding proteins in biology. We highlight some of the key findings of the last 28 years.
small GTPase; GTPase activating protein; guanine nucleotide exchange factor; Raf; PI3-K; Ral; Rho; cancer; targeted therapy; developmental disorders
The 1990s, the “decade of the brain,” witnessed major advances in the study of
visual perception, cognition, and consciousness. Impressive techniques in
neurophysiology, neuroanatomy, neuropsychology, electrophysiology, psychophysics
and brain-imaging were developed to address how the nervous system transforms
and represents visual inputs. Many of these advances have dealt with the
steady-state properties of processing. To complement this “steady-state
approach,” more recent research emphasized the importance of dynamic aspects of
visual processing. Visual masking has been a paradigm of choice for more than a
century when it comes to the study of dynamic vision. A recent workshop
(http://lpsy.epfl.ch/VMworkshop/), held in Delmenhorst, Germany,
brought together an international group of researchers to present
state-of-the-art research on dynamic visual processing with a focus on visual
masking. This special issue presents peer-reviewed contributions by the workshop
participants and provides a contemporary synthesis of how visual masking can
inform the dynamics of human perception, cognition, and consciousness.
backward masking; dynamic vision; modeling
The endoplasmic reticulum (ER) is the site of folding of membrane and secreted proteins in the cell. Physiological or pathological processes that disturb protein folding in the endoplasmic reticulum cause ER stress and activate a set of signaling pathways termed the Unfolded Protein Response (UPR). The UPR can promote cellular repair and sustained survival by reducing the load of unfolded proteins through upregulation of chaperones and global attenuation of protein synthesis. Research into ER stress and the UPR continues to grow at a rapid rate as many new investigators are entering the field. There are also many researchers not working directly on ER stress, but who wish to determine whether this response is activated in the system they are studying: thus, it is important to list a standard set of criteria for monitoring UPR in different model systems. Here, we discuss approaches that can be used by researchers to plan and interpret experiments aimed at evaluating whether the UPR and related processes are activated. We would like to emphasize that no individual assay is guaranteed to be the most appropriate one in every situation and strongly recommend the use of multiple assays to verify UPR activation.
Recent studies in hematopoietic cells have led to a growing appreciation of the diverse modes of molecular and functional cross-talk between canonical signaling pathways. However, these intersections represent only the tip of the iceberg. Emerging global analytical methods are providing an even richer and more complete picture of the many components that measurably interact in a network manner to produce cellular responses. Here we highlight the pieces in this Focus, emphasize the limitations of the present canonical pathway paradigm, and discuss the value of a systems biology approach using more global, quantitative experimental design and data analysis strategies. Lastly, we urge caution about overly facile interpretation of genome- and proteome-level studies.
Calcium ions generate ubiquitous cellular signals. Calcium signals play an important role in development. The most obvious example is fertilization, where calcium signals and calcium waves are triggered by the sperm and are responsible for activating the egg from dormancy and cell cycle arrest. Calcium signals also appear to contribute to cell cycle progression during the rapid cell cycles of early embryos. There is increasing evidence that calcium signals are an essential component of the signalling systems that specify developmental patterning and cell fate. This issue arises from a Discussion Meeting that brought together developmental biologists studying calcium signals with those looking at other patterning signals and events. This short introduction provides some background to the papers in this issue, setting out the emerging view that calcium signals are central to dorsoventral axis formation, gastrulation movements, neural specification and neuronal cell fate.
calcium signals; development; patterning; axis formation; cell fate; organogenesis
In the past decade, the interaction between prions and nucleic acids has garnered significant attention from the scientific community. For many years, the participation of RNA and/or DNA in prion pathology has been largely ruled out by the “protein-only” hypothesis, but this is now being reconsidered. Experimental data now indicate that nucleic acids (particularly RNA), besides being carriers of genetic information, function as important key components during development, physiological responsiveness and cellular signaling. This revelation has brought a new perspective to prion pathology. Here we discuss the role of RNA molecules in prion protein aggregation and the resulting cellular toxicity. We combine our most recent findings with existing literature to shed new light on this exciting field of research.
prion; RNA; nucleic acid; catalyzed conversion; transmissible spongiform encephalopathies; structural biology; protein folding
Yeast cells are able to tolerate and adapt to a variety of environmental stresses. An essential aspect of stress adaptation is the regulation of monovalent ion concentrations. Ion regulation determines many fundamental physiological parameters, such as cell volume, membrane potential, and intracellular pH. It is achieved through the concerted activities of multiple cellular components, including ion transporters and signaling molecules, on both short and long time scales. Although each component has been studied in detail previously, it remains unclear how the physiological parameters are maintained and regulated by the concerted action of all components under a diverse range of stress conditions. In this study, we have constructed an integrated mathematical model of ion regulation in Saccharomyces cerevisiae to understand this coordinated adaptation process. Using this model, we first predict that the interaction between phosphorylated Hog1p and Tok1p at the plasma membrane inhibits Tok1p activity and consequently reduces Na+ influx under NaCl stress. We further characterize the impacts of NaCl, sorbitol, KCl and alkaline pH stresses on the cellular physiology and the differences between the cellular responses to these stresses. We predict that the calcineurin pathway is essential for maintaining a non-toxic level of intracellular Na+ in the long-term adaptation to NaCl stress, but that its activation is not required for maintaining a low level of Na+ under other stresses investigated. We provide evidence that, in addition to extrusion of toxic ions, Ena1p plays an important role, in some cases alongside Nha1p, in re-establishing membrane potential after stress perturbation. To conclude, this model serves as a powerful tool for both understanding the complex system-level properties of the highly coordinated adaptation process and generating further hypotheses for experimental investigation.
Ion regulation is fundamental to cell physiology. The concentrations of monovalent ions, such as H+, K+ and Na+, determine many physiological parameters such as cell volume, plasma membrane potential and intracellular pH. In yeast cells, these parameters are maintained within a narrow range during the adaptation to external perturbations, including ionic, osmotic and alkaline pH stress. This is achieved by the remarkably coordinated activities of ion transporters, regulatory molecules and signaling pathways. The response characteristics of individual components in adaptation have been studied extensively. However, a coherent understanding of the coordinated adaptation process is lacking. In this study, we address this gap by constructing a mathematical model that integrates the characteristics of the ion transporters, regulatory molecules, signaling pathways and changes in cell volume. Using this model, we characterize the impact of ionic, osmotic and alkaline pH stresses on cellular physiology and analyze the role that individual components play in the cellular adaptation processes. Our results also reveal system level properties achieved by the concerted regulatory responses. Therefore, this integrated model serves as a suitable tool to understand the coordinated processes of ion regulation in response to environmental stresses, and to make predictions that are experimentally testable.
Cellular glucose availability is crucial for the functioning of most biological processes. Our understanding of the glucose regulatory system has been greatly advanced by studying the model organism Saccharomyces cerevisiae, but many aspects of this system remain elusive. To understand the organisation of the glucose regulatory system, we analysed 91 deletion mutants of the different glucose signalling and metabolic pathways in Saccharomyces cerevisiae using DNA microarrays.
In general, the mutations do not induce pathway-specific transcriptional responses. Instead, one main transcriptional response is discerned, which varies in direction to mimic either a high or a low glucose response. Detailed analysis uncovers established and new relationships within and between individual pathways and their members. In contrast to signalling components, metabolic components of the glucose regulatory system are transcriptionally more frequently affected. A new network approach is applied that exposes the hierarchical organisation of the glucose regulatory system.
The tight interconnection between the different pathways of the glucose regulatory system is reflected by the main transcriptional response observed. Tps2 and Tsl1, two enzymes involved in the biosynthesis of the storage carbohydrate trehalose, are predicted to be the most downstream transcriptional components. Epistasis analysis of tps2Δ double mutants supports this prediction. Although based on transcriptional changes only, these results suggest that all changes in perceived glucose levels ultimately lead to a shift in trehalose biosynthesis.
Regulatory networks; Glucose signalling; Trehalose biosynthesis; Gene expression profiling; Saccharomyces cerevisiae
Developmental biology, like many other areas of biology, has undergone a dramatic shift in the perspective from which developmental processes are viewed. Instead of focusing on the actions of a handful of genes or functional RNAs, we now consider the interactions of large functional gene networks and study how these complex systems orchestrate the unfolding of an organism, from gametes to adult. Developmental biologists are beginning to realize that understanding ontogeny on this scale requires the utilization of computational methods to capture, store and represent the knowledge we have about the underlying processes. Here we review the use of the Gene Ontology (GO) to study developmental biology. We describe the organization and structure of the GO and illustrate some of the ways we use it to capture the current understanding of many common developmental processes. We also discuss ways in which gene product annotations using the GO have been used to ask and answer developmental questions in a variety of model developmental systems. We provide suggestions as to how the GO might be used in more powerful ways to address questions about development. Our goal is to provide developmental biologists with enough background about the GO that they can begin to think about how they might use the ontology efficiently and in the most powerful ways possible.
The study of chemotaxis describes the cellular processes that control the movement of organisms toward favorable environments. In bacteria and archaea, motility is controlled by a two-component system involving a histidine kinase that senses the environment and a response regulator, a very common type of signal transduction in prokaryotes. Most insights into the processes involved have come from studies of Escherichia coli over the last three decades. However, in the last 10 years, with the sequencing of many prokaryotic genomes, it has become clear that E. coli represents a streamlined example of bacterial chemotaxis. While general features of excitation remain conserved among bacteria and archaea, specific features, such as adaptational processes and hydrolysis of the intracellular signal CheY-P, are quite diverse. The Bacillus subtilis chemotaxis system is considerably more complex and appears to be similar to the one that existed when the bacteria and archaea separated during evolution, so that understanding this mechanism should provide insight into the variety of mechanisms used today by the broad sweep of chemotactic bacteria and archaea. However, processes even beyond those used in E. coli and B. subtilis have been discovered in other organisms. This review emphasizes those used by B. subtilis and these other organisms but also gives an account of the mechanism in E. coli.
The Molecular Biology Database Collection is a public resource listing key databases of value to the biologist, including those featured in this issue of Nucleic Acids Research, and other high-quality databases. All databases included in this Collection are freely available to the public. This listing aims to serve as a convenient starting point for searching the web for reliable information on various aspects of molecular biology, biochemistry and genetics. This year’s update includes 548 databases, 162 more than the previous one. The databases are organized in a hierarchical classification that should simplify finding the right database for each given task. Each database in the list comes with a recently updated brief description. The database list and the database descriptions can be accessed online at the Nucleic Acids Research web site http://nar.oupjournals.org/. The great challenge in biological research today is how to turn data into knowledge. I have met people who think data is knowledge but these people are then striving for a means of turning knowledge into understanding.Sydney Brenner. The Scientist 16:12, March 18, 2002
Actin is the essential force-generating component of the microfilament system, which powers numerous motile processes in eukaryotic cells and undergoes dynamic remodeling in response to different internal and external signaling. The ability of actin to polymerize into asymmetric filaments is the inherent property behind the site-directed force-generating capacity that operates during various intracellular movements and in surface protrusions. Not surprisingly, a broad variety of signaling pathways and components are involved in controlling and coordinating the activities of the actin microfilament system in a myriad of different interactions. The characterization of these processes has stimulated cell biologists for decades and has, as a consequence, resulted in a huge body of data. The purpose here is to present a cellular perspective on recent advances in our understanding of the microfilament system with respect to actin polymerization, filament structure and specific folding requirements.
Cell motility; Actin dynamics; Actin-binding proteins; Actin folding; Molecular chaperone; CCT
Biomolecular networks dynamically respond to stimuli and implement cellular function. Understanding these dynamic changes is the key challenge for cell biologists. As biomolecular networks grow in size and complexity, the model of a biomolecular network must become more rigorous to keep track of all the components and their interactions. In general this presents the need for computer simulation to manipulate and understand the biomolecular network model.
In this paper, we present a novel method to model the regulatory system which executes a cellular function and can be represented as a biomolecular network. Our method consists of two steps. First, a novel scale-free network clustering approach is applied to the large-scale biomolecular network to obtain various sub-networks. Second, a state-space model is generated for the sub-networks and simulated to predict their behavior in the cellular context. The modeling results represent hypotheses that are tested against high-throughput data sets (microarrays and/or genetic screens) for both the natural system and perturbations. Notably, the dynamic modeling component of this method depends on the automated network structure generation of the first component and the sub-network clustering, which are both essential to make the solution tractable.
Experimental results on time series gene expression data for the human cell cycle indicate our approach is promising for sub-network mining and simulation from large-scale biomolecular network.
Massive gene expression changes in different cellular states measured by microarrays, in fact, reflect just an "echo" of real molecular processes in the cells. Transcription factors constitute a class of the regulatory molecules that typically require posttranscriptional modifications or ligand binding in order to exert their function. Therefore, such important functional changes of transcription factors are not directly visible in the microarray experiments.
We developed a novel approach to find key transcription factors that may explain concerted expression changes of specific components of the signal transduction network. The approach aims at revealing evidence of positive feedback loops in the signal transduction circuits through activation of pathway-specific transcription factors. We demonstrate that promoters of genes encoding components of many known signal transduction pathways are enriched by binding sites of those transcription factors that are endpoints of the considered pathways. Application of the approach to the microarray gene expression data on TNF-alpha stimulated primary human endothelial cells helped to reveal novel key transcription factors potentially involved in the regulation of the signal transduction pathways of the cells.
We developed a novel computational approach for revealing key transcription factors by knowledge-based analysis of gene expression data with the help of databases on gene regulatory networks (TRANSFAC® and TRANSPATH®). The corresponding software and databases are available at .