PLoS Computational Biology: New Articles

Tumor Growth Rate Determines the Timing of Optimal Chronomodulated Treatment Schedules

2010-03-19T07:00:00Z

Author Summary

Chronotherapy of cancers aims at exploiting daily physiological rhythms to improve anti-cancer efficacy and tolerance to drugs by administering treatments at a specific time of the day. Recent clinical trials have shown that chronotherapy can be beneficial in improving quality of life and median life span in patients, but that it can also have negative effects if the timing is wrong. A theoretical basis for the rational development of individualized therapy schedules is still lacking. Here, we use a simple cell population model to show how biological rhythms and the cell cycle interact to modulate the response to cancer therapy. In particular, we show that the proliferation rate of cancer cells determines when treatments are most effective. We provide a simple formulation of the problem that can be used to compute an objective response function based on the drug sensitivity and the proliferation rate of tumor cells. Finally, we show that in some cases, treating at a different time every day may be more appropriate than standard daily chronotherapy. These results constitute an important step in designing individualized chronotherapy treatments, and point out to ways to design better clinical trials.

Estimating the Stoichiometry of HIV Neutralization

2010-03-19T07:00:00Z

Author Summary

A large part of the research on the Human Immunodeficiency Virus focuses on how virus particles attach and enter their target cells, and how entry can be inhibited by antibodies or antiretroviral drugs. Because virus particles are too small to be observed in action the inference of the details of HIV entry has to be indirect—involving the genetic manipulation of virions, and often mathematical modeling. It is known that virus particles establish contact to their target cells with spikes on their surface, and antibodies binding to these spikes can inhibit virus entry. It is not known, however, how many antibodies are needed to neutralize a spike. In this article, we develop a mathematical framework to estimate this number, called the stoichiometry of neutralization, from data obtained in experiments with genetically engineered virions. An estimate of the stoichiometry of neutralization for different antibodies is important, as it will allow us to calculate the amount of antibodies required to abrogate virus replication.

Predicting Transmembrane Helix Packing Arrangements using Residue Contacts and a Force-Directed Algorithm

2010-03-19T07:00:00Z

Author Summary

Alpha-helical transmembrane proteins constitute a significant proportion of the proteins encoded in a typical genome and are involved in a wide variety of important biological processes. Many common diseases including diabetes, hypertension and epilepsy have been related to transmembrane protein dysfunction, therefore they represent one of the most important classes of protein for pharmaceutical intervention. However, due to the experimental difficulties of structure determination, this class of protein is severely under-represented in structural databases. Here, we present a novel approach that is able to predict lipid exposure, residue contacts, helix-helix interactions and finally the optimal helical packing arrangement of a transmembrane protein. Under stringent cross-validation, our approach demonstrates a significant improvement in prediction over existing software. This method can be used to gain insights into transmembrane protein folding and enhance the quality of ab initio modelling, while providing testable hypotheses for a variety of studies including protein design, mutagenesis and thermostability experiments.

Patient Referral Patterns and the Spread of Hospital-Acquired Infections through National Health Care Networks

2010-03-19T07:00:00Z

Author Summary

The prevalence of hospital acquired infections is widely believed to reflect the quality of health care in individual hospitals, and is therefore often used as a benchmark. Intuitively, the idea is that infections spread more easily in hospitals with a poor quality of health care. This assumes that the rate at which admitted patients introduce new infections is the same for all hospitals. In this article, we show that this assumption is unlikely to be correct. Using national data on patient admissions, we are able to reconstruct the entire hospital network consisting of patients referred between hospitals. This network reveals that university hospitals admit more patients that recently stayed in other hospitals. Consequently, they are more likely to admit patients that still carry pathogens acquired during their previous hospital stay. Therefore, the prevalence of infections does not only reflect the quality of health care but also the connectedness to hospitals from which patients are referred. This phenomenon is missed at the single hospital level; our study is the first to address the connectedness between hospitals in explaining the prevalence of hospital acquired infections. Our findings imply that interventions should focus on hospitals that are central in the network of patient referrals.

Emergence of Spatial Structure in Cell Groups and the Evolution of Cooperation

2010-03-19T07:00:00Z

Author Summary

Cooperation is a fundamental and widespread phenomenon in nature, yet explaining the evolution of cooperation is difficult. Natural selection typically favors individuals that maximize their own reproduction, so how is it that many diverse organisms, from bacteria to humans, have evolved to help others at a cost to themselves? Research has shown that cooperation can most readily evolve when cooperative individuals preferentially help each other, but this leaves open another critical question: How do cooperators achieve selective interaction with one another? We focus on this question in the context of unicellular organisms, such as bacteria, which exhibit simple forms of cooperation that play roles in nutrient acquisition and pathogenesis. We use a realistic simulation framework to model large cell groups, and observe that cell lines can spontaneously segregate from each other in space as the group expands. Finally, we demonstrate that lineage segregation allows cooperative cell types to preferentially benefit each other, thereby favoring the evolution of cooperation.

Predicted Auxiliary Navigation Mechanism of Peritrichously Flagellated Chemotactic Bacteria

2010-03-19T07:00:00Z

Author Summary

Chemotaxis of bacteria plays an important role in their life, providing them with the ability to actively search for an optimal growth environment. The chemotaxis system is supposed to be highly optimized, because on the evolutionary time scale even a modest enhancement of its efficiency can give cells a large competitive advantage. For a long time it was believed that the only navigation mechanism of bacteria is increasing the run length toward the preferred direction. The tumble was assumed to be a purely random change of direction between runs. We analysed recently published experimental data that demonstrate a dependence of tumbling angle on the number of CW-switched motors. We introduced such a dependence into our model of chemotactic E. coli, and simulated it under different conditions. Our simulations show that this dependence is an important additional mechanism of bacterial navigation, which was previously unrecognized because it lays below the experimental errors of conventional single-cell tracking. We show that such a fine tuning of tumbling significantly improves efficiency of chemotaxis, and represents a new level of evolutionary optimization of bacteria.

Quantitative Comparison of Catalytic Mechanisms and Overall Reactions in Convergently Evolved Enzymes: Implications for Classification of Enzyme Function

2010-03-12T08:00:00Z

Author Summary

When species evolve, their genes duplicate and diverge to allow for adaptation of their functional repertoires to the changing environment. In this scenario, unrelated genes can convergently evolve to produce proteins with the same molecular function, termed “functionally analogous.” A quantitative determination of the reaction similarities among functionally analogous enzymes could provide insight about the different structural solutions nature has used to evolve similar catalysts. Bond changes between substrates and products, and between successive reaction intermediates, were used to compare the reactions catalyzed and the mechanisms of catalysis for 95 pairs of functionally analogous enzymes. Less than half of the reactions catalyzed by unrelated enzymes, but defined as similar by the Enzyme Commission (EC) classification, are similar in terms of bond changes, suggesting that this classification often fails to capture quantitative differences between many enzyme reactions. Furthermore, we addressed for the first time whether the chemical mechanisms by which similar overall reactions are achieved in functional analogs are also similar. We conclude that convergence of reaction is often accompanied by convergence of chemical mechanism. These results will be useful for classifying enzymes, guiding functional annotation of newly determined enzyme sequences and structures and for informing the engineering of enzymes with new functions.

Non-Linear Neuronal Responses as an Emergent Property of Afferent Networks: A Case Study of the Locust Lobula Giant Movement Detector

2010-03-12T08:00:00Z

Author Summary

The tiny brains of insects of about 1mm³ smoothly control a flying platform while avoiding obstacles, regulating its distance to objects and search for objects of interest. This is largely achieved through a complex hierarchical processing of signals from the multitude of ommatidia in their eye to a set of highly specialized neurons that are optimized to respond to specific properties of the visual world. One of these neurons, the Lobula Giant Movement Detector (LGMD) of the locust, has been recently shown to perform a functional multiplication of its synaptic inputs. If true, that would make the LGMD neuron a unique and highly sophisticated neuron that raises questions about the non-linear operations other neurons in other neuronal systems would be able to perform. Hence it is crucial to understand its properties, its role in behaviour and to evaluate whether its responses can be explained in simpler terms. Our results emphasize the role of network architecture and distributed computation as opposed to local complex non-linear computation. We show that our model reliably reproduces the known properties of the LGMD and can be used to control a high-speed robot.

Effects of Transcriptional Pausing on Gene Expression Dynamics

2010-03-12T08:00:00Z

Author Summary

Investigation on how phenotypic diversity of genetically identical organisms is generated and regulated has focused on noise in gene expression. It is unknown to what extent noise in gene expression and genetic networks is evolvable, and by which mechanisms it evolves. The noise has several sources, e.g., noise in transcription initiation and during elongation. We focus on RNA polymerase (RNAP) pausing and show that it can regulate, to some extent, noise in gene expression. RNAP frequently pauses during elongation. The pausing frequency and average duration are sequence-specific, thus evolvable. The dependency of pause propensity on regulatory molecules makes pausing a mechanism adaptable to rapidly changing environments. We study, in a stochastic model of bacterial transcription at the single nucleotide level that includes the promoter open complex formation, pausing, arrest, misincorporation and editing, pyrophosphorolysis, and premature termination, how pausing affects the dynamics of gene expression and gene networks. In a model of a genetic clock, with periodic dynamics, pauses affect the period length but do not disrupt the periodicity. We conclude that RNAP pausing is an important evolvable feature of gene regulatory networks, that can be used by organisms to adapt to changing environments and regulate phenotypic diversity.

Detailed Simulations of Cell Biology with Smoldyn 2.1

2010-03-12T08:00:00Z

Author Summary

We developed a general-purpose biochemical simulation program, called Smoldyn. It represents proteins and other molecules of interest with point-like particles that diffuse, interact with surfaces, and react, all in continuous space. This high level of detail allows users to investigate spatial organization within cells and natural stochastic variability. Although similar to the MCell and ChemCell programs, Smoldyn is more accurate and runs faster. Smoldyn also supports many unique features, such as commands that a “virtual experimenter” can execute during simulations and automatic reaction network expansion for simulating protein complexes. We illustrate Smoldyn's capabilities with a model of signaling between yeast cells of opposite mating type. It investigates the role of the secreted protease Bar1, which inactivates mating pheromone. Intuitively, it might seem that inactivating most of the pheromone would make a cell less able to detect the local pheromone concentration gradient. In contrast, we found that Bar1 secretion improves pheromone gradient detectability: the local gradient is sharpened because pheromone is progressively inactivated as it diffuses through a cloud of Bar1. This result helps interpret experiments that showed that Bar1 secretion helped cells distinguish between potential mates, and suggests that Bar1 helps yeast cells identify the fittest mating partners.

A Multiscale Model to Investigate Circadian Rhythmicity of Pacemaker Neurons in the Suprachiasmatic Nucleus

2010-03-12T08:00:00Z

Author Summary

Circadian rhythms are ~24 hour cycles in biochemical, physiological and behavioral processes observed in a diverse range of organisms including Cyanobacteria, Neurospora, Drosophila, mice and humans. In mammals, the dominant circadian pacemaker that drives daily rhythms is located in the suprachiasmatic nucleus (SCN) of the hypothalamus. The SCN is composed of a highly connected network of ~20,000 neurons. Within each individual SCN neuron core clock genes and proteins interact through intertwined regulatory loops to generate circadian oscillations on the molecular level. These neurons express daily rhythmicity in their firing frequency and other electrophysiological properties. The mechanisms by which the core clock produces synchronized rhythms in neural firing and gene expression are postulated to involve intracellular calcium, a second messenger that regulates many cellular processes. The interaction between the various clock components however remains unknown. In this paper, we present a single cell model that incorporates the circadian gene regulatory pathway, cellular electrophysiological properties, and cytosolic calcium dynamics. Our results suggest a possible system architecture that accounts for the robustness of the circadian clock at the single cell level. Our simulations predict a dual role for intracellular pathways instigated by intracellular calcium and VIP: maintaining the periodicity and amplitude of the core clock genes as well as the firing frequency oscillations.

Modeling Co-Expression across Species for Complex Traits: Insights to the Difference of Human and Mouse Embryonic Stem Cells

2010-03-12T08:00:00Z

Author Summary

A major goal in biology is to understand the evolution of complex traits, such as the development of multicellular body plans. To a certain extent, complex traits are governed by regulated gene expression. The comparison expression data between species requires extra considerations than sequence comparison, because gene expression is not static and the level of expression is influenced by external conditions. Considering that co-expression patterns are often comparable across species, we developed a statistical model for cross-species clustering analysis. The model allows each species to create its own clusters of the genes but also encourages the species to borrow strength from each others' clusters of orthologous genes. The result is a pairing of clusters, one from each species, where the paired clusters share many but not necessarily all orthologous genes. The model-based approach not only reduces subjective influence but also enables effective use of evolutionary dependence. Applying this model to analyze human and mouse embryonic stem (ES) cell data, we identified the transcription factors and the signaling proteins that are specifically expressed in either human or mouse ES cells. These results suggest that the pluripotent cell identity can be established and maintained through more than one gene regulatory network.

Slower Visuomotor Corrections with Unchanged Latency are Consistent with Optimal Adaptation to Increased Endogenous Noise in the Elderly

2010-03-12T08:00:00Z

Author Summary

In a hand-eye coordination task that requires continuous movement to correct for a disturbance, it turns out that signs of response to the disturbance appear no later in the elderly than in the young. The elderly motion is noisy and less efficient, however, and once movements in response to a disturbance begin, they are at a lower speed. One can model subject response by assuming that it results from combining noise and a response that is mathematically optimal given this noise, delay, and a least-squares sort of control objective. This modeling approach is appropriate for young and most elderly subjects. The model holds that increased noise should lead to no change in delay until response gets underway, but should make the response itself proceed at a slower speed. This is consistent with the data and with a causal link from the observed noise and disorder in elderly motor function to the observed age-related slowing.

Comparing Families of Dynamic Causal Models

2010-03-12T08:00:00Z

Author Summary

Bayesian model comparison provides a formal method for evaluating different computational models in the biological sciences. Emerging application domains include dynamical models of neuronal and biochemical networks based on differential equations. Much previous work in this area has focussed on selecting the single best model. This approach is useful but can become brittle if there are a large number of models to compare and if different subjects use different models. This paper shows that these problems can be overcome with the use of Family Level Inference and Bayesian Model Averaging within model families.

Interplay between Pleiotropy and Secondary Selection Determines Rise and Fall of Mutators in Stress Response

2010-03-12T08:00:00Z

Author Summary

The dramatic rise of mutators has been found to accompany adaptation of bacteria in response to many kinds of stress. Two views on the evolutionary origin of this phenomenon emerged: the pleiotropic hypothesis positing that it is a byproduct of environmental stress or other specific stress response mechanisms and the second order selection which states that mutators hitchhike to fixation with unrelated beneficial alleles. Conventional population genetics models could not fully resolve this controversy because they are based on certain assumptions about fitness landscape. Here we address this problem using a microscopic multiscale model, which couples physically realistic molecular descriptions of proteins and their interactions with population genetics of carrier organisms without assuming any a priori mutational effect on fitness landscape. We found that both pleiotropy and second order selection play a crucial role at different stages of adaptation: the supply of mutators is provided through destabilization of error correction complexes or, alternatively, fluctuations of production levels of prototypic mismatch repair proteins (pleiotropic effects), while the rise and fixation of mutators occurs when there is a sufficient supply of beneficial mutations in replication-controlling genes. This general mechanism assures a robust and reliable adaptation of organisms to unforeseen challenges. This study highlights physical principles underlying biological mechanisms of stress response and adaptation.

Neocortical Axon Arbors Trade-off Material and Conduction Delay Conservation

2010-03-12T08:00:00Z

Author Summary

Within the grey matter of cerebral cortex is a complex network formed by a dense tangle of individual branching axons mostly of cortical origin. Yet remarkably when presented with a barrage of complex, noisy sensory stimuli this convoluted network architecture computes accurately and rapidly. How does such a highly interconnected though jumbled forest of axonal trees process vital information so quickly? Pioneering neuroscientist Ramón y Cajal thought the size and shape of individual neurons was governed by simple rules to save cellular material and to reduce signal conduction delay. In this study, we investigated how these rules applied to whole axonal trees in neocortex by comparing their 3D structure to equivalent artificial arbors optimized for these rules. We discovered that neocortical axonal trees achieve a balance between these two rules so that a little more cellular material than necessary was used to substantially reduce conduction delays. Importantly, we suggest the nature of arbor branching balances time and material so that neocortical axons may communicate with a high degree of temporal precision, enabling accurate and rapid computation within local cortical networks. This approach could be applied to other neural structures to better understand the functional principles of brain design.

Molecular Structures of Quiescently Grown and Brain-Derived Polymorphic Fibrils of the Alzheimer Amyloid Aβ9-40 Peptide: A Comparison to Agitated Fibrils

2010-03-05T08:00:00Z

Author Summary

Amyloid diseases are characterized by the presence of amyloid fibrils on organs and tissue in the body. Alzheimer's disease, Parkinson's diseases and Type II Diabetes are all examples of amyloid diseases. Determining the structure of amyloid fibrils is critical for understanding the mechanism of fibril formation as well as for the design of inhibitor molecules that can prevent aggregation. In the case of the Alzheimer Amyloid-β (Aβ) peptide, the structure of fibrils grown under conditions of mechanical agitation has been elucidated from a combination of simulation and experiments. However, the structures of the asymmetric quiescent Aβ fibrils (grown under conditions akin to physiological conditions) and of Alzheimer's brain–derived fibrils are not known. In this paper, we propose the first atomically detailed structures of these two fibrils, using molecular dynamics simulations combined with data from previously published experiments. In additions, we suggest a unifying lateral growth mechanism that explains the increased toxicity of quiescent Aβ fibrils, the effects of external perturbations on fibril lateral architecture and the inhibition mechanism of the small molecule inhibitors on fibril formation.

Diffusion, Crowding & Protein Stability in a Dynamic Molecular Model of the Bacterial Cytoplasm

2010-03-05T08:00:00Z

Author Summary

The interior of a typical bacterial cell is a highly crowded place in which molecules must jostle and compete with each other in order to carry out their biological functions. The conditions under which such molecules are typically studied in vitro, however, are usually quite different: one or a few different types of molecules are studied as they freely diffuse in a dilute, aqueous solution. There is therefore a significant disconnect between the conditions under which molecules can be most usefully studied and the conditions under which such molecules usually “live”, and developing ways to bridge this gap is likely to be important for properly understanding molecular behavior in vivo. Toward this end, we show in this work that computer simulations can be used to model the interior of bacterial cells at a near atomic level of detail: the rates of diffusion of proteins are matched to known experimental values, and their thermodynamic stabilities are found to be in good agreement with the few measurements that have so far been performed in vivo. While the simulation approach is certainly not free of assumptions, it offers a potentially important complement to experimental techniques and provides a vivid illustration of molecular behavior inside a biological cell that is likely to be of significant educational value.

Designing Focused Chemical Libraries Enriched in Protein-Protein Interaction Inhibitors using Machine-Learning Methods

2010-03-05T08:00:00Z

Author Summary

Protein-protein interactions (PPIs) are essential to life and various diseases states are associated with aberrant PPIs. Therefore significant efforts are dedicated to this new class of therapeutic targets. Even though it might not be possible to modulate the estimated 650,000 PPIs that regulate human life with drug-like compounds, a sizeable number of PPI should be druggable. Only 10-15% of the human genome is thought to be druggable with around 1000-3000 druggable protein targets. A hypothetical similar ratio for PPIs would bring the number of druggable PPIs to about 65,000, although no data can yet support such a hypothesis. PPI have been historically intricate to tackle with standard experimental and virtual screening techniques, possibly because of the shift in the chemical space between today's chemical libraries and PPI physico-chemical requirements. Therefore, one possible avenue to circumvent this conundrum is to design focused libraries enriched in putative PPI inhibitors. Here, we show how chemoinformatics can assist library design by learning physico-chemical rules from a data set of known PPI inhibitors and their comparison with regular drugs. Our study shows the importance of specific molecular shapes and a privileged number of aromatic bonds.

Parameter Estimation and Model Selection in Computational Biology

2010-03-05T08:00:00Z

Author Summary

Parameter estimation is a key issue in systems biology, as it represents the crucial step to obtaining predictions from computational models of biological systems. This issue is usually addressed by “fitting” the model simulations to the observed experimental data. Such approach does not take the measurement noise into full consideration. We introduce a new method built on the combination of Kalman filtering, statistical tests, and optimization techniques. The filter is well-known in control and estimation theory and has found application in a wide range of fields, such as inertial guidance systems, weather forecasting, and economics. We show how the statistics of the measurement noise can be optimally exploited and directly incorporated into the design of the estimation algorithm in order to achieve more accurate results, and to validate/invalidate the computed estimates. We also show that a significant advantage of our estimator is that it offers a powerful tool for model selection, allowing rejection or acceptance of competing models based on the available noisy measurements. These results are of immediate practical application in computational biology, and while we demonstrate their use for two specific examples, they can in fact be used to study a wide class of biological systems.

Within- and Cross-Modal Distance Information Disambiguate Visual Size-Change Perception

2010-03-05T08:00:00Z

Author Summary

To perceive your surroundings your brain must distinguish between different possible scenes, each of which is more or less likely. In order to disambiguate interpretations that are equally likely given sensory input, the brain aggregates multiple sensations to form an interpretation of the world consistent with each. For instance, when you judge the size of an object you are viewing, its distance influences its image size that projects to your eyes. To estimate its true size, your brain must use extra information to disambiguate whether it is a small, near object, or large, far object. If you touch the object your brain could use the felt distance to scale the apparent size of the object. Cognitive scientists do not fully understand the computations that make perceptual disambiguation possible. Here we investigate how people disambiguate an object's size from its distance by measuring participants' size judgments when we provide different types of distance sensations. We find that distance sensations provided by viewing objects with both eyes open, and by touching the object, are both effective for disambiguating its size. We provide a general probabilistic framework to explain these results, which provides a unifying account of sensory fusion in the presence of ambiguity.

Systematic Planning of Genome-Scale Experiments in Poorly Studied Species

2010-03-05T08:00:00Z

Author Summary

Microarray expression experiments allow fast functional profiling of an organism's entire genome and significant efforts are devoted to analyzing the resulting data. Available genome sequences are also increasing quickly. However, it is unexplored how to use available functional genomics data to direct large-scale experiments in newly sequenced but poorly studied species. In this paper, we propose a strategy to systematically plan experimental treatments in the poorly studied species based on their model organism relatives. We consider both the accuracy of the datasets in capturing different biological processes and the redundancy between datasets. Quantifying the above information allows us to recommend a list of experimental treatments. We demonstrate the efficacy of this approach by designing, performing and evaluating S. bayanus microarray experiments using an available S. cerevisiae data repository. We show that this systematic planning process could reduce the labor in doing microarray experiments by 10 fold and achieve similar functional coverage.

Estimating the Stochastic Bifurcation Structure of Cellular Networks

2010-03-05T08:00:00Z

Author Summary

Decades ago, Waddington, and later Kauffman, likened the dynamics of a differentiating cell to a marble rolling downhill on bumpy terrain—the epigenetic landscape. In this metaphor, the valleys of the landscape represent the paths that cells can follow towards a stable cell type, and the fate of the cell is determined by the constant modulation of the epigenetic landscape by internal and external signals. With new technologies for measuring single-cell gene expression, it is increasingly feasible to map out these valleys and how external variables influence cellular responses. Moreover, it is possible to quantify population level effects, such as what fraction of a population of cells arrives at one valley or another, and variability at the cellular level, such as how individual cells bounce around within, and possibly between, valleys due to the stochasticity of cellular biochemistry. In this paper, we discuss which characteristics of the epigenetic landscape can readily be extracted from single-cell gene expression data, and describe computational methods for doing so.

Mathematical Modelling of Cell-Fate Decision in Response to Death Receptor Engagement

2010-03-05T08:00:00Z

Author Summary

Activation of death receptors (TNFR and Fas) can trigger either survival or cell death according to the cell type and the cellular conditions. In other words, the same signal can have antagonist responses. On one hand, the cell can survive by activating the NFκB signalling pathway. On the other hand, it can die by apoptosis or necrosis. Apoptosis is a suicide mechanism, i.e., an orchestrated way to disrupt cellular components and pack them into specialized vesicles that can be easily removed from the environment, whereas necrosis is a type of death that involves release of intracellular components in the surrounding tissues, possibly causing inflammatory response and severe injury. We, biologists and theoreticians, have recapitulated and integrated known biological data from the literature into an influence diagram describing the molecular events leading to each possible outcome. The diagram has been translated into a dynamical Boolean model. Simulations of wild type, mutant cells and drug treatments qualitatively match current data, and predict several novel mutant phenotypes, along with general characteristics of the cell fate decision mechanism: transient activation of some key proteins in necrosis, mutual inhibitory cross-talks between the three pathways. Our model can further be used to assess contradictory data and address specific biological questions through in silico experiments.

PLoS Computational Biology Issue Image | Vol. 6(2) February 2010

2010-02-26T08:00:00Z

Computational complementation.

Autoregulation of nodulation (AON) is a long-distance, shoot-root signalling system for regulating nodule formation in legume plants. This visualisation, taken from a computational complementation experiment, demonstrates the possible allocation of an unidentified signal for inhibition of nodulation in soybean root. In this approach, an empirical model of a loss-of-function (non-AON) mutant is complemented with hypothetical AON mechanisms. If the resulting nodulation phenotype matches the wild-type plant, the hypotheses would be supported as reasonable. The first application of computational complementation predicted that soybean cotyledons participate in AON, which was subsequently confirmed by a real-plant experiment (see Han et al., doi:10.1371/journal.pcbi.1000685).

Image Credit: Liqi Han (University of Queensland).

Ten Simple Rules for Organizing a Virtual Conference—Anywhere

2010-02-26T08:00:00Z

How to Understand the Cell by Breaking It: Network Analysis of Gene Perturbation Screens

2010-02-26T08:00:00Z

A Primer on Metagenomics

2010-02-26T08:00:00Z

Metagenomics is a discipline that enables the genomic study of uncultured microorganisms. Faster, cheaper sequencing technologies and the ability to sequence uncultured microbes sampled directly from their habitats are expanding and transforming our view of the microbial world. Distilling meaningful information from the millions of new genomic sequences presents a serious challenge to bioinformaticians. In cultured microbes, the genomic data come from a single clone, making sequence assembly and annotation tractable. In metagenomics, the data come from heterogeneous microbial communities, sometimes containing more than 10,000 species, with the sequence data being noisy and partial. From sampling, to assembly, to gene calling and function prediction, bioinformatics faces new demands in interpreting voluminous, noisy, and often partial sequence data. Although metagenomics is a relative newcomer to science, the past few years have seen an explosion in computational methods applied to metagenomic-based research. It is therefore not within the scope of this article to provide an exhaustive review. Rather, we provide here a concise yet comprehensive introduction to the current computational requirements presented by metagenomics, and review the recent progress made. We also note whether there is software that implements any of the methods presented here, and briefly review its utility. Nevertheless, it would be useful if readers of this article would avail themselves of the comment section provided by this journal, and relate their own experiences. Finally, the last section of this article provides a few representative studies illustrating different facets of recent scientific discoveries made using metagenomics.

Will Widgets and Semantic Tagging Change Computational Biology?

2010-02-26T08:00:00Z

Molecular Predictors of 3D Morphogenesis by Breast Cancer Cell Lines in 3D Culture

2010-02-26T08:00:00Z

Author Summary

Cell culture models are an important vehicle for understanding biological processes and evaluation of therapeutic reagents. More importantly, the literature suggests that tumor cells grown in 3D exhibit pronounced drug and radiation resistances that are remarkably similar to that of tumors in vivo. Therefore, the needs for quantifying 3D assays continue to grow. In this paper, we develop robust computational methods to integrate morphometric and molecular information for a panel of breast cancer cell lines that are grown in 3D. Specifically, morphometric traits are imaged through microscopy, and then quantified computationally. We then show that these morphometric traits can identify subtypes within this panel of breast cancer cell lines, and that the subtypes are clinically relevant in terms of being ERBB2 positive or triple negative. These subtypes and their representations are then associated with their molecular data to reveal PPARG as an important marker for triple-negative breast cancer. Finally, we design two independent experiments to show the validity of this marker in both 3D cell culture models and human breast cancer tissue.

Computational Complementation: A Modelling Approach to Study Signalling Mechanisms during Legume Autoregulation of Nodulation

2010-02-26T08:00:00Z

Author Summary

Endogenous signals, such as phytohormones, play a vital role in plant development and function, controlling processes such as flowering, branching, disease response, and nodulation. However, the signalling mechanisms are so subtle and so complex that details about them remain largely unknown. In this study, we develop a “Computational Complementation” approach for the investigation of long-distance signalling networks during legume autoregulation of nodulation (AON). The key idea is to use computational modelling to complement the deficiency of an empirical model of an AON deficient mutant with hypothesised AON components. If the complementation restores a wild-type nodulation phenotype, the modelled hypotheses would be supported as reasonable. To evaluate the feasibility of this approach, we tested whether wild-type soybean cotyledons participate in AON, commonly controlled by “real” leaves. The test gave an affirmative result (i.e., cotyledons do have AON activity), which was subsequently confirmed by a graft experiment on real plants. Future applications of this approach may be to test candidate AON signals such as auxins, flavones, and CLE peptides, and other plant signalling networks.

Spatial Simulations of Myxobacterial Development

2010-02-26T08:00:00Z

Author Summary

Understanding how relatively simple, single cell bacteria can communicate and coordinate their actions is important for explaining how complex multicellular behaviour can emerge without a central controller. Myxobacteria are particularly interesting in this respect because cells undergo multiple phases of coordinated behaviour during their life-cycle. One of the most fascinating and complex phases is the formation of fruiting bodies—large multicellular aggregates of cells formed in response to starvation. In this article we use evidence from the latest experimental data to construct a computational model explaining how cells can form fruiting bodies. Both in our model and in nature, cells move together in dense swarms, which collide to form aggregation centres. In particular, we show that it is possible for aggregates to form spontaneously where previous models require artificially induced aggregates to start the fruiting process.

A Review of 2009 for PLoS Computational Biology

2010-02-26T08:00:00Z

Stochastic Model of Integrin-Mediated Signaling and Adhesion Dynamics at the Leading Edges of Migrating Cells

2010-02-26T08:00:00Z

Author Summary

Cell migration is fundamental to human physiology and a phenomenon of long-standing interest in cell biology. It requires the concerted regulation of several dynamic processes that mediate physical anchorage of the cell and productive generation of protrusion and traction forces that propel the cell forward. In this work, we have developed a mathematical model that describes this interplay, cast at the level of biochemical signaling pathways activated at the front of a moving cell. Based on our analysis of the model and experimental confirmation of its basic predictions, we assert that coupled, counteracting feedback loops constitute a functional switch between maintenance and stalling of the cell protrusion speed. Our model successfully explains the dependence of this switch on the abundance of adhesive molecules in the cell's immediate surroundings and sheds light on how non-muscle myosin shapes that dependence.

Unfolding Simulations Reveal the Mechanism of Extreme Unfolding Cooperativity in the Kinetically Stable α-Lytic Protease

2010-02-26T08:00:00Z

Author Summary

Proteins, synthesized as linear polymers of amino acids, fold up into compact native states, burying their hydrophobic amino acids into their interiors. Protein folding minimizes the non-specific interactions that unfolded protein chains can make, which include aggregation with other proteins and degradation by proteases. Unfortunately, even in the native state, proteins can partially unfold, opening up regions of their structure and making these adverse events possible. Some proteins, particularly those in harsh environments full of proteases, have evolved to virtually eliminate partial unfolding, significantly reducing their rate of degradation. This elimination of partial unfolding is termed “cooperative,” because unfolding is an all-or-none process. One class of proteins has diverged into two families, one bacterial and highly cooperative and the other animal and non-cooperative. We have used detailed simulations of unfolding for members of each family, α-lytic protease (bacterial) and trypsin (animal) to understand the unfolding pathways of each and the mechanism for the differential unfolding cooperativity. Our results explain prior biochemical experiments, reproduce the large difference in unfolding cooperativity between the families, and point to the interface between α-lytic protease's two domains as essential to establishing unfolding cooperativity. As seen in an unrelated protein family, generation of a cooperative domain interface may be a common evolutionary response for ensuring the highest protein stability.

Decoupling Environment-Dependent and Independent Genetic Robustness across Bacterial Species

2010-02-26T08:00:00Z

Author Summary

When a species is grown under optimal conditions the single-knockout of most of its genes is not likely to affect its viability. The resilience of biological systems to mutations is termed genetic robustness and its extent across different species has not yet been systematically described. Since the deletion of a gene can have varying consequences depending on the environmental conditions, the extent of species' genetic robustness reflects both the range of conditions (or environments) in which it can survive as well as the availability of alternative cellular routes (compensating for a gene's loss of function). Here, we developed a computational model for estimating the essentiality of metabolic reactions across natural-like environments and applied it to chart species' level of genetic robustness, providing the first systematic description of genetic robustness across species. Studying robustness across a wide collection of natural-like environments enables one to stratify, for each species individually, the extent of environmental-dependant and independent robustness and hence advances our understanding of its evolutionary origins. Our main finding is that the level of environmental dependent robustness is associated with the lifestyle of a species (i.e., specialists versus generalist), whereas the level of environmental-independent robustness is associated with its metabolic production capacities.

Temporal Sensitivity of Protein Kinase A Activation in Late-Phase Long Term Potentiation

2010-02-26T08:00:00Z

Author Summary

The hippocampus is a part of the cerebral cortex intimately involved in learning and memory behavior. A common cellular model of learning is a long lasting form of long term potentiation (L-LTP) in the hippocampus, because it shares several characteristics with learning. For example, both learning and long term potentiation exhibit sensitivity to temporal patterns of synaptic inputs and share common intracellular events such as activation of specific intracellular signaling pathways. Therefore, understanding the pivotal molecules in the intracellular signaling pathways underlying temporal sensitivity of L-LTP in the hippocampus may illuminate mechanisms underlying learning. We developed a computational model to evaluate whether the signaling pathways leading to activation of the two critical enzymes: protein kinase A and calcium-calmodulin-dependent kinase II are sufficient to explain the experimentally observed temporal sensitivity. Indeed, the simulations demonstrate that these enzymes exhibit different temporal sensitivities, and make a key experimental prediction, that L-LTP is dependent on protein kinase A for intervals larger than 60 sec. Measurements of hippocampal L-LTP confirm this prediction, demonstrating the value of a systems biology approach to computational neuroscience.

Interpreting Metabolomic Profiles using Unbiased Pathway Models

2010-02-26T08:00:00Z

Author Summary

Human disease is complex, arising from the interaction of many genetic and environmental factors. Efforts to personalize treatment have been thwarted by “phenotypic heterogeneity”, the apparent similarity of disease states with diverse underlying causes. One approach to resolve this heterogeneity is to redefine diseases on the basis of abnormal physiologic activities, which should allow grouping patients into categories with similar treatment response and prognosis. Physiologic activities can be identified and assessed through quantitative measurements of biomolecules—proteins, mRNAs, metabolites—in individual patient samples. The field of metabolomics involves the analysis of a broad array of metabolite levels from clinical fluid samples such as blood or urine and can be used to evaluate disease states. Because metabolic profiles are complex, we have taken an integrative network-based approach to understand them in terms of abnormal activities of enzymes and small molecule transporters. We have focused on the oral glucose tolerance test, used to diagnose diabetes, and have found that multiple transporters play an important role in the normal response to ingesting sugar. Many of these transporter activities are abnormal in individuals with impaired glucose tolerance and differing activities among them may reflect the diverse underlying causes and variable clinical courses of such patients.

Amplification of Asynchronous Inhibition-Mediated Synchronization by Feedback in Recurrent Networks

2010-02-19T08:00:00Z

Author Summary

Neurons in many parts of the brain fire spikes rhythmically and synchronously in many behaviorally and functionally relevant contexts. There are many mechanisms for producing oscillatory synchronization between populations of biological oscillators. One way to produce synchrony is that the population of oscillators receives common correlated input. In this paper, we study a population of oscillating neurons (mitral cells) that are not directly coupled to each other but receive broadband correlated input from a second population of neurons (granule cells). The granule cell population, in turn, receives inputs from the mitral cells; hence, the mitral and granule cells are reciprocally connected. Correlated input to the oscillating mitral cells produces tighter synchrony in the activity of the mitral cell population. We hypothesize that this increased mitral cell synchrony will evoke greater activity in specific groups of granule cells and that these specific granule cells, in turn, become the source of the correlated input to the mitral cells. That is, the synchronous input from the mitral cells increases the fraction of correlated feedback. Thus, we close the correlation loop. We show through analysis and simulations that this feedback mechanism can lead to the spontaneous appearance of highly synchronous activity within the mitral cells. We show that there is good experimental support for this mechanism in the circuitry of the olfactory bulb. We speculate that such mechanisms could also arise in other parts of the brain.

Self versus Environment Motion in Postural Control

2010-02-19T08:00:00Z

Author Summary

Visual cues typically provide ambiguous information about the orientation of our body in space. When we perceive relative motion between ourselves and the environment, it could have been caused by our movement within the environment, or the movement of the environment around us, or the simultaneous movements of both our body and the environment. The nervous system must resolve this ambiguity for efficient control of our body posture during stance. Here, we show that the nervous system could solve this problem by optimally combining visual signals with physical motion cues. Sensory ambiguity is a central problem during cue combination. Our results thus have implications on how the nervous system could resolve sensory ambiguity in other cue combination tasks.

Identifying the Rules of Engagement Enabling Leukocyte Rolling, Activation, and Adhesion

2010-02-19T08:00:00Z

Author Summary

To gain access to sites of inflammation, leukocytes must first adhere to the blood vessel wall using integrin molecules. It has been hypothesized that integrin clustering is essential for sustaining adhesion prior to transmigration into the inflamed tissue. We cannot challenge such hypotheses directly because it is infeasible to measure molecular level events during the leukocyte adhesion process. At best correlative relationships have been made. The alternative approach undertaken was to experimentally challenge the hypothesized mechanisms in silico. We used object-oriented, software engineering methods to build and execute multi-level, multi-attribute analogues of leukocytes and binding surfaces. The simulated leukocytes contained diffusible objects (representing integrins) on their surface that were allowed to interact with binding partners on simulated endothelial surfaces. Validation was achieved across different experimental conditions, in vitro, ex vivo, and in vivo, at both the individual cell and population levels. Consequently, the finalized virtual mechanisms stand as a concrete, working theory about detailed events occurring at the leukocyte-surface interface during adhesion. We challenged mechanistic hypotheses by conducting experiments in which the consequences of multiple mechanistic events were tracked. We discovered that integrin clustering was not necessary to achieve adhesion as long as integrin and binding partner object densities were above a critical level. Importantly, at low densities integrin clustering enabled adhesion that exhibited measurable, cell level positive cooperativity.

Numerical Modelling Of The V-J Combinations Of The T Cell Receptor TRA/TRD Locus

2010-02-19T08:00:00Z

Author Summary

Lymphocytes of the immune system ensure the body defense by the expression of receptors which are specific of targets, termed antigens. Each lymphocyte, deriving from the same original clone, expresses the same unique receptor. To achieve the production of receptors covering the wide variety of antigens, lymphocytes use a specialized genetic mechanism consisting of gene rearrangements. For instance, the genes encoding the receptor of the alpha chain of the T lymphocyte receptor (TRA) spread over a 1500 Kb genetic region which includes around 100 V genes, 60 J genes, and a single C gene. To constitute a functional alpha chain, one of the V and one of the J genes rearrange together to form a single exon. The precise definition of these V-J combinations is essential to understand the repertoire of TRA. We have developed a numerical model simulating all of the V-J combinations of TRA, fitting the available experimental observations obtained from the analysis of TRA in T lymphocytes of the thymus and the blood. Our model gives new insights on the rules controlling the use of V and J genes in providing a dynamic estimation of the total V-J combinations.

A Bayesian Approach to Quantifying the Effects of Mass Poultry Vaccination upon the Spatial and Temporal Dynamics of H5N1 in Northern Vietnam

2010-02-19T08:00:00Z

Author Summary

Highly pathogenic avian influenza H5N1 continues to spread rapidly between flocks of poultry in many parts of the world including areas in Southeast Asia and Africa where infection has become endemic. Meanwhile the number of human cases and fatalities are steadily accumulating. As a result, the control of outbreaks in poultry remains both a key public and animal health priority. In Vietnam control policies have evolved from a policy of reliance upon drastic “stamping out” measures to regular mass vaccination campaigns. Using Bayesian data augmentation techniques in order to take into account the unobserved infection times, we found that this has led to a significant reduction in the daily probability of transmission between communes but that the time taken to detect outbreaks has increased. As a result it is still possible for sustained transmission to occur, albeit at a slower rate. Through an analysis of the reconstructed epidemic tree, we found that any measures that have the effect of restoring detection capacity to that estimated for the “stamping out” policy would have a large effect upon the size and scale of any future outbreak wave and may be effective at preventing sustained transmission between communes.