PLoS Computational Biology: New Articles

Questioning the Ubiquity of Neofunctionalization

2009-01-02T08:00:00Z

by Todd A. Gibson, Debra S. Goldberg

Author Summary

Molecular evolution studies have shown that the redundancy intrinsic to gene duplication may allow one gene duplicate to acquire a new function (neofunctionalization) or for both duplicates to each assume a subset of the ancestral gene's functions (subfunctionalization). Studies of networks of interacting proteins and models of evolving protein interaction networks have shown that both subfunctionalization and neofunctionalization are widespread in protein evolution. Here, we present evidence that shows that the methods and models that have established neofunctionalization as a ubiquitous force in protein interaction network evolution are flawed and under reexamination support subfunctionalization, not neofunctionalization. We start by reviewing the methods and models that engender prolific subfunctionalization and neofunctionalization in evolution. We then critically approach neofunctionalization. We show that biases in protein interaction assays, failure to consider concurrent and subsequent gene duplications in evolutionary inferences, and an inability of theoretical models to reproduce empirical clustering have all led to neofunctionalization being erroneously identified as a pervasive force in evolution.

Polyamine Sharing between Tubulin Dimers Favours Microtubule Nucleation and Elongation via Facilitated Diffusion

2009-01-02T08:00:00Z

by Alain Mechulam, Konstantin G. Chernov, Elodie Mucher, Loic Hamon, Patrick A. Curmi, David Pastré

Author Summary

Interactions between biomolecules (DNA, proteins, sugar, etc.) represent the link between genome information and function of living organisms. For effective competition between organisms and adaptation to environmental changes, these interactions have to take place at very high rates. As such interactions require successive associations and dissociations between biomolecules, most of the time could be spent in between interactions when biomolecules diffuse freely in the intracellular space until the next interaction occurs. To reduce this waste of time, cells have developed adaptive mechanisms. First, the concentration of biomolecules in the intracellular medium is very high, which increases their frequency of interactions. Second, the possibility for biomolecules to diffuse along linear polymers, a process named “facilitated diffusion,” increases the probability for biomolecules to find their targets. This mechanism was first described to understand how DNA-binding proteins could find their specific targets among thousands of putative binding sites. We show here that facilitated diffusion could also play a significant role in promoting the assembly of cytoskeleton proteins that are involved in critical cell functions (e.g., division or neuron architecture). Alteration of this mechanism may be of particular interest for cancer and neuropathologies.

Shape, Size, and Robustness: Feasible Regions in the Parameter Space of Biochemical Networks

2009-01-02T08:00:00Z

by Adel Dayarian, Madalena Chaves, Eduardo D. Sontag, Anirvan M. Sengupta

Author Summary

Developing models with a large number of parameters for describing the dynamics of a biochemical network is a common exercise today. The dependence of predictions of such a network model on the choice of parameters is important to understand for two reasons. For the purpose of fitting biological data and making predictions, we need to know which combinations of parameters are strongly constrained by observations and also which combinations seriously affect a particular prediction. In addition, we expect naturally evolved networks to be somewhat robust to parameter changes. If the functioning of the network requires fine-tuning in many parameters, then mutations causing changes in regulatory interactions could quickly make the network dysfunctional. For predictions involving gene products being ON or OFF, we found a method that facilitates the study parameter dependence. As an example, we analyzed several competing models of the segment polarity network in Drosophila. We explicitly describe the region in the parameter space where the wild-type expression pattern of key genes becomes feasible for each model. We also study how random walks in the parameter space exit from the feasible region of a network model, allowing us to compare the relative robustness of the alternative models.

Predicting Cellular Growth from Gene Expression Signatures

2009-01-02T08:00:00Z

by Edoardo M. Airoldi, Curtis Huttenhower, David Gresham, Charles Lu, Amy A. Caudy, Maitreya J. Dunham, James R. Broach, David Botstein, Olga G. Troyanskaya

Author Summary

A major challenge for living organisms is the regulation of cellular growth in a fluctuating environment. Sudden changes in nutrient availability or the presence of stress factors typically require rapid adjustments of cellular growth. The misregulation of growth control in higher organisms is a major factor in the development of cancer. A statistical characterization of cellular growth based on gene expression levels provides a quantitative perspective to understand the regulatory network that controls growth. We develop a model of cellular growth in the yeast Saccharomyces cerevisiae, grounded in the expression levels of a small set of genes. The model is able to predict the growth rate of new cellular cultures from expression data and is robust to changing biological conditions, experimental methods, and technological platforms. The predictions are informative about changes in growth at very short time scales, which direct experimental methods cannot generally access. The model also predicts growth rates in Saccharomyces bayanus and in Schizosaccharomyces pombe, a yeast diverged by approximately a billion years of evolution. Our findings suggest that the model describes fundamental characteristics of the unicellular eukaryotic growth regulatory program. A case study explores the role of nutrient sensing in the yeast growth regulatory system.

PLoS Computational Biology Issue Image | Vol. 4(12) December 2008

2008-12-26T08:00:00Z

A depiction of a reconstructed HSN neuron from the fly rendered with ray-tracing program POV-Ray.

In "Ceci n'est pas une pipe," René Magritte faithfully depicts a pipe but all the same proclaims that it isn't a pipe: it is a painting of a pipe. We allude to Magritte's painting here, showing that by rebuilding a neuron's anatomy, we create something that is not a neuron: it is the model of a neuron (see Cuntz et al., doi:10.1371/journal.pcbi.1000251).

Image Credit: Hermann Cuntz (University College London).

Mechanics and Dynamics of X-Chromosome Pairing at X Inactivation

2008-12-26T08:00:00Z

by Antonio Scialdone, Mario Nicodemi

Author Summary

Some important cellular processes involve homologous chromosome recognition and pairing. A prominent example is the colocalization of X chromosomes occurring at the onset of X chromosome inactivation, the vital process whereby female mammalian cells silence one of their two X chromosomes to equalize the dosage of X products with respect to males (having just one X). The crucial question on how the Xs recognize each other and come together is, however, still open. Starting from important recent experimental discoveries, we propose a quantitative model, from statistical mechanics, which elucidates the mechanical basis of such phenomena. We demonstrate that a set of soluble molecules binding specific DNA sequences are sufficient to induce recognition and colocalization. This is possible, however, only when their binding energy/concentration exceeds a threshold value, and this suggests how the cell could regulate colocalization. The pairing mechanism that we propose is grounded in general thermodynamic principles, so it could apply to other DNA pairing processes. While we also explore the kinetics of X colocalization, we compare our results to available experimental data and produce testable predictions.

A Mathematical Framework for the Selection of an Optimal Set of Peptides for Epitope-Based Vaccines

2008-12-26T08:00:00Z

by Nora C. Toussaint, Pierre Dönnes, Oliver Kohlbacher

Author Summary

Over the last decade the design of tailor-made vaccines for prophylactic applications (e.g., prevention of infection) and therapeutic applications (e.g., cancer therapy) has attracted significant interest. Epitope-based vaccines are good candidates for such tailor-made approaches. They trigger an immune response by confronting the immune system with immunogenic peptides derived from, e.g., viral- or cancer-specific proteins. These peptides bind to major histocompatibility complex (MHC) molecules in a specific manner. The resulting complex is crucial for immune system activation. However, there are many allelic variants of MHC molecules, meaning that different patients typically bind different repertoires of peptides. Nevertheless, due to economical and regulatory issues one cannot simply add all immunogenic peptides to such a peptide mix. Hence, it is crucial to identify the optimal set of peptides for a vaccine, given constraints such as MHC allele frequencies in the target population, peptide mutation rates, and maximum number of selected peptides. In this work we formalize this problem, and variants thereof, in a mathematical framework. The resulting optimization problem can be solved efficiently and yields a provably optimal peptide combination. We can show that the method performs better than existing solutions. Furthermore, the framework is highly flexible and can easily handle additional criteria.

I Am Not a Scientist, I Am a Number

2008-12-26T08:00:00Z

by Philip E. Bourne, J. Lynn Fink

Tag-Trigger-Consolidation: A Model of Early and Late Long-Term-Potentiation and Depression

2008-12-26T08:00:00Z

by Claudia Clopath, Lorric Ziegler, Eleni Vasilaki, Lars Büsing, Wulfram Gerstner

Author Summary

Humans and animals learn by changing the strength of connections between neurons, a phenomenon called synaptic plasticity. These changes can be induced by rather short stimuli (lasting sometimes only a few seconds) but should then be stable for months or years in order to be useful for long-term memory. Experimentalists have shown that synapses undergo a sequence of steps that transforms the rapid change during the early phase of synaptic plasticity into a stable memory trace in the late phase. In this paper we introduce a model with a small number of equations that can describe the phenomena of induction of synaptic changes during the early phase of synaptic plasticity, the trigger process for protein synthesis, and the final stabilization. The model covers a broad range of experimental phenomena known as tagging experiments and makes testable predictions. The ability to model the stabilization of synapses is crucial to understand learning and memory processes in animals and humans and a necessary ingredient for any large-scale model of the brain.

Search Algorithms as a Framework for the Optimization of Drug Combinations

2008-12-26T08:00:00Z

by Diego Calzolari, Stefania Bruschi, Laurence Coquin, Jennifer Schofield, Jacob D. Feala, John C. Reed, Andrew D. McCulloch, Giovanni Paternostro

Author Summary

This work describes methods that identify drug combinations that might alleviate the suffering caused by complex diseases. Our biological model systems are: physiological decline associated with aging, and selective killing of cancer cells. The novelty of this approach is based on a new application of methods from digital communications theory, which becomes useful when the number of possible combinations is large and a complete set of measurements cannot be obtained. This limit is reached easily, given the many drugs and doses available for complex diseases. We are not simply using computer models but are using search algorithms implemented with biological measurements, built to integrate information from different sources, including simulations. This might be considered parallel biological computation and differs from the classic systems biology approach by having search algorithms rather than explicit quantitative models as the central element. Because variation is an essential component of biology, this approach might be more appropriate for combined drug interventions, which can be considered a form of biological control. Search algorithms are used in many fields in physics and engineering. We hope that this paper will generate interest in a new application of importance to human health from practitioners of diverse computational disciplines.

Identification of Mechanosensitive Genes during Embryonic Bone Formation

2008-12-26T08:00:00Z

by Niamh C. Nowlan, Patrick J. Prendergast, Paula Murphy

Author Summary

While mechanical forces are known to be critical to adult bone maintenance and repair, the importance of mechanobiology in embryonic bone formation is less widely accepted. The influence of mechanical forces on cells is thought to be mediated by “mechanosensitive genes,” genes which respond to mechanical stimulation. In this research, we examined the situation in the developing embryo. Using finite element analysis, we simulated the biophysical stimuli in the developing bone resulting from spontaneous muscle contractions, incorporating detailed morphology of the developing chick limb. We compared patterns of stimuli with expression patterns of a number of genes involved in bone formation and demonstrated a clear colocalisation in the case of two genes (Ihh and ColX). We then altered the mechanical environment of the growing chick embryo by blocking muscle contractions and demonstrated changes in the magnitudes and patterns of biophysical stimuli and in the expression patterns of both Ihh and ColX. We have demonstrated the value of combining computational techniques with in vivo gene expression analysis to identify genes that may play a mechanoregulatory role and have identified genes that respond to mechanical stimulation during bone formation in vivo.

The Morphological Identity of Insect Dendrites

2008-12-26T08:00:00Z

by Hermann Cuntz, Friedrich Forstner, Juergen Haag, Alexander Borst

Author Summary

Neural computation has been shown to be heavily dependent not only on the connectivity of single neurons but also on their specific dendritic shape—often used as a key feature for their classification. Still, very little is known about the constraints determining a neuron's morphological identity. In particular, one would like to understand what cells with the same or similar function share anatomically, what renders them different from others, and whether one can formalize this difference objectively. A large number of approaches have been proposed, trying to put dendritic morphology in a parametric frame. A central problem lies in the wide variety and variability of dendritic branching and function even within one narrow cell class. We addressed this problem by investigating functionally and anatomically highly conserved neurons in the fly brain, where each neuron can easily be individually identified in different animals. Our analysis shows that the pattern of dendritic branching is not unique in any particular cell, only the features of the area that the dendrites cover allow a clear classification. This leads to the conclusion that all fly dendrites share the same growth program but a neuron's dendritic field shape, its “anatomical receptive field”, is key to its specific identity.

A Common Cortical Circuit Mechanism for Perceptual Categorical Discrimination and Veridical Judgment

2008-12-26T08:00:00Z

by Feng Liu, Xiao-Jing Wang

Author Summary

In daily life, we constantly face two types of perceptual decisions: to identify an object feature (what is the speed of that car?) or to discriminate the same feature among two or more possible categories (is that car going faster than the speed limit?). These decision processes appear to involve very different computations: while identification relies on an analog judgment, categorical discrimination is based on a comparison of the object feature with discrete options. Do they engage entirely separate brain mechanisms? In this work, we showed that these two types of decision making can be instantiated by a single cortical circuit. We used a continuous recurrent network model to simulate two monkey experiments in which subjects were required to make either a two-alternative choice or a veridical judgment about the direction of random-dot motion. The model reproduced salient experimental observations and makes testable predictions. The results demonstrate that a common cortical circuit can perform both categorical discrimination and veridical judgment. Conceptually, this work supports the notion that a cortical circuit endowed with reverberatory dynamics can fulfill multiple cognitive functions such as working memory and decision making.

Game Theory of Mind

2008-12-26T08:00:00Z

by Wako Yoshida, Ray J. Dolan, Karl J. Friston

Author Summary

The ability to work out what other people are thinking is essential for effective social interactions, be they cooperative or competitive. A widely used example is cooperative hunting: large prey is difficult to catch alone, but we can circumvent this by cooperating with others. However, hunting can pit private goals to catch smaller prey that can be caught alone against mutually beneficial goals that require cooperation. Understanding how we work out optimal strategies that balance cooperation and competition has remained a central puzzle in game theory. Exploiting insights from computer science and behavioural economics, we suggest a model of ‘theory of mind’ using ‘recursive sophistication’ in which my model of your goals includes a model of your model of my goals, and so on ad infinitum. By studying experimental data in which people played a computer-based group hunting game, we show that the model offers a good account of individual decisions in this context, suggesting that such a formal ‘theory of mind’ model can cast light on how people build internal representations of other people in social interactions.

Disordered Flanks Prevent Peptide Aggregation

2008-12-19T08:00:00Z

by Sanne Abeln, Daan Frenkel

Author Summary

In their natural cellular environment proteins are dissolved in a concentrated aqueous solution of biomolecules. Even under such crowded conditions, proteins must not clump together or aggregate; otherwise their biological functions may be compromised, and the cell could die. Diseases such as Parkinson and Alzheimer are thought to be caused by aggregation of specific proteins. Evolutionary pressure generally ensures that proteins do not aggregate in their natural biochemical environment. A well-known mechanism to prevent aggregation is the folding of proteins, where the hydrophobic (attractive) part of the protein is buried inside the protein. Here we report a different mechanism that can prevent the aggregation of proteins. Recently, it was discovered that many proteins contain regions that are disordered (not folded) in their natural environment. We show with coarse-grained simulations that aggregation of small hydrophobic binding motifs can be prevented by embedding the motifs in disordered regions: the disordered regions of different proteins obstruct or sterically hinder the formation of aggregates. Moreover, our simulations show that the disordered regions have no adverse effect on the biological function of the binding motifs, because they do not obstruct the binding and folding of the binding motif on its specific substrate.

Dependence of Bacterial Chemotaxis on Gradient Shape and Adaptation Rate

2008-12-19T08:00:00Z

by Nikita Vladimirov, Linda Løvdok, Dirk Lebiedz, Victor Sourjik

Author Summary

Chemotaxis plays an important role in bacterial lifestyle, providing bacteria with the ability to actively search for an optimal growth environment. The chemotaxis system is likely to be highly optimized, because on the evolutionary time scale even a modest enhancement of its efficiency can give cells a large competitive advantage. In this study, we use up-to-date experimental and modeling information to construct a new computational model of chemotactic E. coli and implement it in a computationally efficient way to simulate large bacterial populations. Our simulations are performed in a new type of attractant gradient that ensures a constant level of chemotactic excitation at any position. We show that optimal chemotactic movement in a gradient results from a fine balance between excitation and adaptation. As a consequence, steeper gradients require higher optimal rates of adaptation. Simulations demonstrate that the observed intercellular variability of adaptation times, which is caused by gene expression noise, may play a positive role for the bacterial population as a whole, by allowing its subpopulations to be optimally tactic in gradients of different strengths. We further show that optimal chemotactic properties in a porous medium (agar) are different from those in a liquid.

Malleable Machines in Transcription Regulation: The Mediator Complex

2008-12-19T08:00:00Z

by Ágnes Tóth-Petróczy, Christopher J. Oldfield, István Simon, Yuichiro Takagi, A. Keith Dunker, Vladimir N. Uversky, Monika Fuxreiter

Author Summary

Intrinsically disordered proteins/regions do not adopt well-defined three dimensional structures; instead, they function as conformational ensembles. They are distinguished in molecular recognition and involved in various regulatory processes. Several components in the transcription machinery–for example, the transactivator domains of transcription factors–are disordered. Mediator, which is a large complex that transduces regulatory information from activators/repressors to the core apparatus, was found to contain a preponderance of intrinsically disordered regions in its various subunits. Such disordered regions are commonly involved in conformational changes coupled to functional transitions, in protein–protein interactions, or in posttranslational modifications. Several such predicted recognition sites were in good agreement with experimental data. Intrinsically disordered regions illuminate a novel aspect of Mediator's regulation and could explain its versatility and specificity in handling transcriptional signals. Their integral role in Mediator function is further underscored by the conserved arrangements of ordered/disordered segments and of the embedded interaction sites.

Association Rate Constants of Ras-Effector Interactions Are Evolutionarily Conserved

2008-12-19T08:00:00Z

by Christina Kiel, Dorothee Aydin, Luis Serrano

Author Summary

Cellular signal transductions processes are based on protein interactions. Proteins can either associate transiently with each other or form stable complexes, and the strength of the interaction is described by the affinity (the affinity is the ratio between the rate of dissociation and association). Protein complexes with similar affinities can bind and dissociate with different rates, and these rates describe the kinetic properties of protein binding. These kinetic rates are important for signaling; however, to what extent individual changes in such rate constants are biologically important or whether the affinity is more crucial might be different in different signaling processes. In this study we analyze whether association rates are conserved during evolution, because evolutionary conservation of protein biochemical properties is usually a valuable indication of its importance. We analyzed the binding of Ras proteins to effector domains, which are central proteins in many signal transduction pathways, in different organisms. On the basis of homology modeling and energy calculations we find that association rates are conserved, although the sequence similarity decreases compared to the human protein. Our finding should encourage further analysis of the importance of kinetics for cellular signal transduction.

Encoding of Naturalistic Stimuli by Local Field Potential Spectra in Networks of Excitatory and Inhibitory Neurons

2008-12-12T08:00:00Z

by Alberto Mazzoni, Stefano Panzeri, Nikos K. Logothetis, Nicolas Brunel

Author Summary

The brain displays rhythmic activity in almost all areas and over a wide range of frequencies and amplitudes. However, the role of these rhythms in the processing of sensory information is still unclear. To study the interplay between visual stimuli and ongoing oscillations in the brain, we developed a model of a local circuit of the visual cortex. We injected into the network the signal recorded in the thalamus of an anesthetized monkey watching a movie, to mimic the effect of a naturalistic stimulus arriving at the visual cortex. Our results are in striking agreement with recordings from the visual cortex. Furthermore, through manipulations of the signal and information analysis, we found that two specific frequency bands of the neurons' activity are used to encode independent stimuli features. These results describe how sensory stimuli can modulate frequency and amplitude of ongoing neural activity and how these modulations can be used to convey sensory information through the different layers of the brain.

Dynamic Correlation between Intrahost HIV-1 Quasispecies Evolution and Disease Progression

2008-12-12T08:00:00Z

by Ha Youn Lee, Alan S. Perelson, Su-Chan Park, Thomas Leitner

Author Summary

Saturation of sequence divergence and a decline of diversity in later stages of infection have been commonly observed during HIV-1 infection, although the length of the time to acquired immunodeficiency syndrome (AIDS) is highly variable among patients. To explain this common feature, we developed a simple sequence evolution model with two main components: (i) fitness, the number of offspring produced, and (ii) the proportion of offspring that are mutants. Assuming a decrease in the proportion of offspring that are mutants as virus variants evolve further from the founder strain, we were able to fit the universal trends of divergence and diversity. In contrast, neither the model with gradual increase of fitness nor the model with rapid emergence of virus variants with greater fitness explained the dynamics of divergence and diversity. The prediction of the model was confirmed in the majority of longitudinally followed patients; the rate of HIV-1 evolution was stationary before disease progresses; however, the rate slowed down at a rate correlated with the rate of immune cell decline. Deciphering dynamic correlation between the rate of HIV-1 evolution and the kinetics of immune cell level united previous conflicting observations of the relationships between the rate of HIV-1 evolution and disease progression.