Protein complexes are central in the yeast genetic landscape

If perturbing two genes together has a stronger or weaker effect than expected, they are said to genetically interact. Genetic interactions are important because they help map gene function, and functionally related genes have similar genetic interaction patterns. Mapping quantitative (positive and negative) genetic interactions on a global scale has recently become possible. This data clearly shows groups of genes connected by predominantly positive or negative interactions, termed monochromatic groups. These groups often correspond to functional modules, like biological processes or complexes, or connections between modules. However it is not yet known how these patterns globally relate to known functional modules. Here we systematically study the monochromatic nature of known biological processes using the largest quantitative genetic interaction data set available, which includes fitness measurements for ～5.4 million gene pairs in the yeast Saccharomyces cerevisiae. We find that only 10% of biological processes, as defined by Gene Ontology annotations, and less than 1% of inter-process connections are monochromatic. Further, we show that protein complexes are responsible for a surprisingly large fraction of these patterns. This suggests that complexes play a central role in shaping the monochromatic landscape of biological processes. Altogether this work shows that both positive and negative monochromatic patterns are found in known biological processes and in their connections and that protein complexes play an important role in these patterns. The monochromatic processes, complexes and connections we find chart a hierarchical and modular map of sensitive and redundant biological systems in the yeast cell that will be useful for gene function prediction and comparison across phenotypes and organisms. Furthermore the analysis methods we develop are applicable to other species for which genetic interactions will progressively become more available.     

Biological activity and the Earth's surface evolution: Insights from carbon,sulfur, nitrogen and iron stable isotopes in the rock record

The search for early Earth biological activity is hindered by the scarcity of the rock record. The very few exposed sedimentary rocks have all been affected by secondary processes such as metamorphism and weathering, which might have distorted morphological microfossils and biogenic minerals beyond recognition and have altered organic matter to kerogen. The search for biological activity in such rocks therefore relies entirely on chemical, molecular or isotopic indicators. A powerful tool used for this purpose is the stable isotope signature of elements related to life (C, N, S, Fe). It provides key informations not only on the metabolic pathways operating at the time of the sediment deposition, but more globally on the biogeochemical cycling of these elements and thus on the Earth's surface evolution. Here, we review the basis of stable isotope biogeochemistry for these isotopic systems. Rather than an exhaustive approach, we address some examples to illustrate how they can be used as biosignatures of early life and as proxies for its environment, while keeping in mind what their limitations are. We then focus on the covariations among these isotopic systems during the Archean time period to show that they convey important information both on the evolution of the redox state of the terrestrial surface reservoirs and on co-occurring ecosystems in the Archean.     

Pathways and Networks-Based Analysis of Candidate Genes Associated with Nicotine Addiction

Nicotine is the addictive substance in tobacco and it has a broad impact on both the central and peripheral nervous systems. Over the past decades, an increasing number of genes potentially involved in nicotine addiction have been identified by different technical approaches. However, the molecular mechanisms underlying nicotine addiction remain largely unclear. Under such situation, a comprehensive analysis focusing on the overall functional characteristics of these genes, as well as how they interact with each other will provide us valuable information to understand nicotine addiction. In this study, we presented a systematic analysis on nicotine addiction-related genes to identify the major underlying biological themes. Functional analysis revealed that biological processes and biochemical pathways related to neurodevelopment, immune system and metabolism were significantly enriched in the nicotine addiction-related genes. By extracting the nicotine addiction-specific subnetwork, a number of novel genes associated with addiction were identified. Moreover, we constructed a schematic molecular network for nicotine addiction via integrating the pathways and network, providing an intuitional view to understand the development of nicotine addiction. Pathway and network analysis indicated that the biological processes related to nicotine addiction were complex. Results from our work may have important implications for understanding the molecular mechanism underlying nicotine addiction.     

Unfolding single RNA molecules: bridging the gap between equilibrium and non-equilibrium statistical thermodynamics

During the last 15 years, scientists have developed methods that permit the direct mechanical manipulation of individual molecules. Using this approach, they have begun to investigate the effect of force and torque in chemical and biochemical reactions. These studies span from the study of the mechanical properties of macromolecules, to the characterization of molecular motors, to the mechanical unfolding of individual proteins and RNA. Here I present a review of some of our most recent results using mechanical force to unfold individual molecules of RNA. These studies make it possible to follow in real time the trajectory of each molecule as it unfolds and characterize the various intermediates of the reaction. Moreover, if the process takes place reversibly it is possible to extract both kinetic and thermodynamic information from these experiments at the same time that we characterize the forces that maintain the three-dimensional structure of the molecule in solution. These studies bring us closer to the biological unfolding processes in the cell as they simulate in vitro, the mechanical unfolding of RNAs carried out in the cell by helicases. If the unfolding process occurs irreversibly, I show here that single-molecule experiments can still provide equilibrium, thermodynamic information from non-equilibrium data by using recently discovered fluctuation theorems. Such theorems represent a bridge between equilibrium and non-equilibrium statistical mechanics. In fact, first derived in 1997, the first experimental demonstration of the validity of fluctuation theorems was obtained by unfolding mechanically a single molecule of RNA. It is perhaps a sign of the times that important physical results are these days used to extract information about biological systems and that biological systems are being used to test and confirm fundamental new laws in physics.     

Biologically valid linear factor models of gene expression

MOTIVATION: The identification of physiological processes underlying and generating the expression pattern observed in microarray experiments is a major challenge. Principal component analysis (PCA) is a linear multivariate statistical method that is regularly employed for that purpose as it provides a reduced-dimensional representation for subsequent study of possible biological processes responding to the particular experimental conditions. Making explicit the data assumptions underlying PCA highlights their lack of biological validity thus making biological interpretation of the principal components problematic. A microarray data representation which enables clear biological interpretation is a desirable analysis tool. RESULTS: We address this issue by employing the probabilistic interpretation of PCA and proposing alternative linear factor models which are based on refined biological assumptions. A practical study on two well-understood microarray datasets highlights the weakness of PCA and the greater biological interpretability of the linear models we have developed.     

Representing and analysing molecular and cellular function using the computer

Determining the biological function of a myriad of genes, and understanding how they interact to yield a living cell, is the major challenge of the post genome-sequencing era. The complexity of biological systems is such that this cannot be envisaged without the help of powerful computer systems capable of representing and analysing the intricate networks of physical and functional interactions between the different cellular components. In this review we try to provide the reader with an appreciation of where we stand in this regard. We discuss some of the inherent problems in describing the different facets of biological function, give an overview of how information on function is currently represented in the major biological databases, and describe different systems for organising and categorising the functions of gene products. In a second part, we present a new general data model, currently under development, which describes information on molecular function and cellular processes in a rigorous manner. The model is capable of representing a large variety of biochemical processes, including metabolic pathways, regulation of gene expression and signal transduction. It also incorporates taxonomies for categorising molecular entities, interactions and processes, and it offers means of viewing the information at different levels of resolution, and dealing with incomplete knowledge. The data model has been implemented in the database on protein function and cellular processes 'aMAZE' (http://www.ebi.ac.uk/research/pfbp/), which presently covers metabolic pathways and their regulation. Several tools for querying, displaying, and performing analyses on such pathways are briefly described in order to illustrate the practical applications enabled by the model.     

Left/right,up/down: the role of endogenous electrical fields as directional signals in development,repair and invasion

A fundamental aspect of biological systems is their spatial organization. In development, regeneration and repair, directional signals are necessary for the proper placement of the components of the organism. Likewise, pathogens that invade other organisms rely on directional signals to target vulnerable areas. It is widely understood that chemical gradients are important directional signals in living systems. Less well recognized are electrical fields, which can also provide directional information. Small, steady electrical fields can directly guide cell movement and growth and can generate chemical gradients of charged macromolecules against the leveling action of diffusion. At the site of a lesion in an ion-transporting epithelium, for example, a substantial electrical field is instantly generated and may extend over many cell diameters. There are numerous other situations in which relatively long-range electrical fields have been shown to exist naturally. Recently, there has been substantial progress in identifying specific processes that are controlled, to some extent, by these endogenous electrical fields. This review highlights these recent data and discusses possible mechanisms by which the fields might affect biological processes.     

Adaptability and the integration of computer-based information processing into the dynamics of organizations

The structure of a system influences its adaptability. An important result of adaptability theory is that subsystem independence increases adaptability [Conrad, M., 1983. Adaptability. Plenum Press, New York]. Adaptability is essential in systems that face an uncertain environment such as biological systems and organizations. Modern organizations are the product of human design. And so it is their structure and the effect that it has on their adaptability. In this paper we explore the potential effects of computer-based information processing on the adaptability of organizations. The integration of computer-based processes into the dynamics of the functions they support and the effect it has on subsystem independence are especially relevant to our analysis.     

Addressing high excitation conditions in time-resolved X-ray diffraction experiments and issues of biological relevance

One of the most important fundamental questions connecting chemistry to biology is how chemistry scales in complexity up to biological systems where there are innumerable possible pathways and competing processes. With the development of ultrabright electron and x-ray sources, it has been possible to literally light up atomic motions to directly observe the reduction in dimensionality in the barrier crossing region to a few key reaction modes. How do these chemical processes further couple to the surrounding protein or macromolecular assembly to drive biological functions? Optical methods to trigger photoactive biological processes are needed to probe this issue on the relevant timescales. However, the excitation conditions have been in the highly nonlinear regime, which questions the biological relevance of the observed structural dynamics.     

Surface plasmon resonance spectroscopy: an emerging tool for the study of peptide-membrane interactions

The interactions between peptides and membranes mediate a wide variety of biological processes, and characterization of the molecular details of these interactions is central to our understanding of cellular events such as protein trafficking, cellular signaling and ion-channel formation. A wide variety of biophysical techniques have been combined with the use of model membrane systems to study peptide-membrane interactions, and have provided important information on the relationship between membrane-active peptide structure and their biological function. However, what has generally not been reported is a detailed analysis of the affinity of peptide for different membrane systems, which has largely been due to the difficulty in obtaining this information. To address this issue, surface plasmon resonance (SPR) spectroscopy has recently been applied to the study of biomembrane-based systems using both planar mono- or bilayers or liposomes. This article provides an overview of these recent applications that demonstrate the potential of SPR to enhance our molecular understanding of membrane-mediated peptide function.     

Necessity of chance: biological roulettes and biodiversity

Chance plays an important role in the dynamics of biodiversity. It is largely responsible for the spontaneous processes leading to biological diversification. The mechanisms behind chance belong to two categories: on the one hand, those outside of biological systems, and thus belonging to their environment, on the other hand, those endogenous to these systems. These last mechanisms are present at all levels of the hierarchical organization of the living world, from genes to ecosystems. We propose calling them 'biological roulettes'. Like roulettes in casinos, they could be deterministic processes functioning in chaotic domains and producing results that look as though they had been generated by random processes. The spontaneous appearance and natural selection of these roulettes have led to living systems potentially adapted to new environmental conditions not encountered before. They may even have permitted some of them to survive major upheavals. Moreover, palaeontological data show that the rate of biological diversification accelerates and that living systems become more and more complex over time. That may also increase their resilience. It can be also the consequence of the appearance and the selection of 'biological roulettes' and of chance they generate. They are at the same time products and engines of the evolution. Without them, life would have disappeared from the Earth a long time ago. Thus, they are of primary importance.     

Applying Molecular Crowding Models to Simulations of Virus Capsid Assembly In Vitro

Virus capsid assembly has been widely studied as a biophysical system, both for its biological and medical significance and as an important model for complex self-assembly processes. No current technology can monitor assembly in detail and what information we have on assembly kinetics comes exclusively from in vitro studies. There are many differences between the intracellular environment and that of an in vitro assembly assay, however, that might be expected to alter assembly pathways. Here, we explore one specific feature characteristic of the intracellular environment and known to have large effects on macromolecular assembly processes: molecular crowding. We combine prior particle simulation methods for estimating crowding effects with coarse-grained stochastic models of capsid assembly, using the crowding models to adjust kinetics of capsid simulations to examine possible effects of crowding on assembly pathways. Simulations suggest a striking difference depending on whether or not a system uses nucleation-limited assembly, with crowding tending to promote off-pathway growth in a nonnucleation-limited model but often enhancing assembly efficiency at high crowding levels even while impeding it at lower crowding levels in a nucleation-limited model. These models may help us understand how complicated assembly systems may have evolved to function with high efficiency and fidelity in the densely crowded environment of the cell.     

Depression, antidepressants, and peripheral blood components

The biological attributes of affective disorders and factors which are able to predict a response to treatment with antidepressants have not been identified sufficiently. A number of biochemical variables in peripheral blood constituents have been tested for this purpose, as a consequence of the lack of availability of human brain tissue. At first, the biological attributes of mental disorders were sought at the level of concentrations of neurotransmitters and their metabolites or precursors. Later on, attention shifted to receptor systems. Since the 1990s, intracellular processes influenced by an illness or its treatment with psychopharmaceuticals have been at the forefront of interest. Interest in biological predictors of treatment with antidepressants has reappeared in recent years, thanks to new laboratory techniques which make it possible to monitor cellular processes associated with the transmission of nerve signals in the brain. These processes can also be studied in plasma and blood elements, especially lymphocytes and platelets. The selection of the qualities to which attention is paid can be derived from today's most widely discussed biochemical hypotheses of affective disorders, especially the monoamine hypothesis and the molecular and cellular theory of depression. Mitochondrial enzymes can also play an important role in the pathophysiology of depression and the effects of antidepressants. In this paper, we sum up the cellular, neurochemical, neuroendocrine, genetic, and neuroimmunological qualities which can be measured in peripheral blood and which appear to be indicators of affective disorders, or parameters which make it possible to predict therapeutic responses to antidepressant administration.     

Single-molecule studies of complex systems: the replisome

A complete, system-level understanding of biological processes requires comprehensive information on the kinetics and thermodynamics of the underlying biochemical reactions. A wide variety of structural, biochemical, and molecular biological techniques have led to a quantitative understanding of the molecular properties and mechanisms essential to the processes of life. Yet, the ensemble averaging inherent to these techniques limits us in understanding the dynamic behavior of the molecular participants. Recent advances in imaging and molecular manipulation techniques have made it possible to observe the activity of individual enzymes and record "molecular movies" that provide insight into their dynamics and reaction mechanisms. An important future goal is extending the applicability of single-molecule techniques to the study of larger, more complex multi-protein systems. In this review, the DNA replication machinery will be used as an example to illustrate recent progress in the development of various single-molecule techniques and its contribution to our understanding of the orchestration of multiple enzymatic processes in large biomolecular systems.     

The nature of the root clock at single cell resolution: Principles of communication and similarities with plant and animal pulsatile and circadian mechanisms

Oscillatory mechanisms are present in most life forms and regulate biological processes periodically. In multicellular organisms where more than one oscillatory mechanism is present, they are organized forming a hierarchical coordinated system even at the cellular level. Here, we focus on the Root Clock, an oscillatory mechanism located at the tip of roots that patterns the spacing of lateral organs through oscillating gene expression. We present a series of recent findings and hypotheses about the cellular mechanisms driving the oscillations, how oscillatory information is transmitted within this clock and similarities with other oscillatory systems. Next, we review principles of communication in other pulsatile mechanisms such as circadian rhythms in plants and mammals, and address the possible communication between plant circadian rhythms and the Root Clock. Finally, we advocate for the use of single-cell approaches to address cell communication, synchronization and integration of external outputs into the Root Clock system.     

Multiscale computational models can guide experimentation and targeted measurements for crop improvement

Computational models of plants have identified gaps in our understanding of biological systems, and have revealed ways to optimize cellular processes or organ‐level architecture to increase productivity. Thus, computational models are learning tools that help direct experimentation and measurements. Models are simplifications of complex systems, and often simulate specific processes at single scales (e.g. temporal, spatial, organizational, etc.). Consequently, single‐scale models are unable to capture the critical cross‐scale interactions that result in emergent properties of the system. In this perspective article, we contend that to accurately predict how a plant will respond in an untested environment, it is necessary to integrate mathematical models across biological scales. Computationally mimicking the flow of biological information from the genome to the phenome is an important step in discovering new experimental strategies to improve crops. A key challenge is to connect models across biological, temporal and computational (e.g. CPU versus GPU) scales, and then to visualize and interpret integrated model outputs. We address this challenge by describing the efforts of the international Crops in silico consortium.