Functional Modularity Research Articles

The proteins of intra-nuclear bodies: a data-driven analysis of sequence, interaction and expression

BackgroundCajal bodies, nucleoli, PML nuclear bodies, and nuclear speckles are morpohologically distinct intra-nuclear structures that dynamically respond to cellular cues. Such nuclear bodies are hypothesized to play important regulatory roles, e.g. by sequestering and releasing transcription factors in a timely manner. While the nucleolus and nuclear speckles have received more attention experimentally, the PML nuclear body and the Cajal body are still incompletely characterized in terms of their roles and protein complement.ResultsBy collating recent experimentally verified data, we find that almost 1000 proteins in the mouse nuclear proteome are known to associate with one or more of the nuclear bodies. Their gene ontology terms highlight their regulatory roles: splicing is confirmed to be a core activity of speckles and PML nuclear bodies house a range of proteins involved in DNA repair. We train support-vector machines to show that nuclear proteins contain discriminative sequence features that can be used to identify their intra-nuclear body associations. Prediction accuracy is highest for nucleoli and nuclear speckles. The trained models are also used to estimate the full protein complement of each nuclear body. Protein interactions are found primarily to link proteins in the nuclear speckles with proteins from other compartments. Cell cycle expression data provide support for increased activity in nucleoli, nuclear speckles and PML nuclear bodies especially during S and G2 phases.ConclusionsThe large-scale analysis of the mouse nuclear proteome sheds light on the functional organization of physically embodied intra-nuclear compartments. We observe partial support for the hypothesis that the physical organization of the nucleus mirrors functional modularity. However, we are unable to unambiguously identify proteins' intra-nuclear destination, suggesting that critical drivers behind of intra-nuclear translocation are yet to be identified.

The beak of the other finch: coevolution of genetic covariance structure and developmental modularity during adaptive evolution

The link between adaptation and evolutionary change remains the most central and least understood evolutionary problem. Rapid evolution and diversification of avian beaks is a textbook example of such a link, yet the mechanisms that enable beak's precise adaptation and extensive adaptability are poorly understood. Often observed rapid evolutionary change in beaks is particularly puzzling in light of the neo-Darwinian model that necessitates coordinated changes in developmentally distinct precursors and correspondence between functional and genetic modularity, which should preclude evolutionary diversification. I show that during first 19 generations after colonization of a novel environment, house finches (Carpodacus mexicanus) express an array of distinct, but adaptively equivalent beak morphologies-a result of compensatory developmental interactions between beak length and width in accommodating microevolutionary change in beak depth. Directional selection was largely confined to the elimination of extremes formed by these developmental interactions, while long-term stabilizing selection along a single axis-beak depth-was mirrored in the structure of beak's additive genetic covariance. These results emphasize three principal points. First, additive genetic covariance structure may represent a historical record of the most recurrent developmental and functional interactions. Second, adaptive equivalence of beak configurations shields genetic and developmental variation in individual components from depletion by natural selection. Third, compensatory developmental interactions among beak components can generate rapid reorganization of beak morphology under novel conditions and thus greatly facilitate both the evolution of precise adaptation and extensive diversification, thereby linking adaptation and adaptability in this classic example of Darwinian evolution.

An Active-Site Phenylalanine Directs Substrate Binding and C−H Cleavage in the α-Ketoglutarate-Dependent Dioxygenase TauD

Enzymes that cleave C-H bonds are often found to depend on well-packed hydrophobic cores that influence the distance between the hydrogen donor and acceptor. Residue F159 in taurine alpha-ketoglutarate dioxygenase (TauD) is demonstrated to play an important role in the binding and orientation of its substrate, which undergoes a hydrogen atom transfer to the active site Fe(IV)=O. Mutation of F159 to smaller hydrophobic side chains (L, V, A) leads to substantially reduced rates for substrate binding and for C-H bond cleavage, as well as increased contribution of the chemical step to k(cat) under steady-state turnover conditions. The greater sensitivity of these substrate-dependent processes to mutation at position 159 than observed for the oxygen activation process supports a previous conclusion of modularity of function within the active site of TauD (McCusker, K. P.; Klinman, J. P. Proc. Natl. Acad. Sci. U.S.A. 2009, 106, 19791-19795). Extraction of intrinsic deuterium kinetic isotope effects (KIEs) using single turnover transients shows 2- to 4-fold increase in the size of the KIE for F159V in relation to wild-type and F159L. It appears that there is a break in behavior following removal of a single methylene from the side chain of F159L to generate F159V, whereby the protein active site loses its ability to restore the internuclear distance between substrate and Fe(IV)=O that supports optimal hydrogenic wave function overlap.

Functional Modularity of Background Activities in Normal and Epileptic Brain Networks

We analyze the connectivity structure of weighted brain networks extracted from spontaneous magnetoencephalographic signals of healthy subjects and epileptic patients (suffering from absence seizures) recorded at rest. We find that, for the activities in the 5-14 Hz range, healthy brains exhibit a sparse connectivity, whereas the brain networks of patients display a rich connectivity with a clear modular structure. Our results suggest that modularity plays a key role in the functional organization of brain areas during normal and pathological neural activities at rest.

Functional Diversity of Robo Receptor Immunoglobulin Domains Promotes Distinct Axon Guidance Decisions

Recognition molecules of the immunoglobulin (Ig) superfamily control axon guidance in the developing nervous system. Ig-like domains are among the most widely represented protein domains in the human genome, and the number of Ig superfamily proteins is strongly correlated with cellular complexity. In Drosophila, three Roundabout (Robo) Ig superfamily receptors respond to their common Slit ligand to regulate axon guidance at the midline: Robo and Robo2 mediate midline repulsion, Robo2 and Robo3 control longitudinal pathway selection, and Robo2 can promote midline crossing. How these closely related receptors mediate distinct guidance functions is not understood. We report that the differential functions of Robo2 and Robo3 are specified by their ectodomains and do not reflect differences in cytoplasmic signaling. Functional modularity of Robo2's ectodomain facilitates multiple guidance decisions: Ig1 and Ig3 of Robo2 confer lateral positioning activity, whereas Ig2 confers promidline crossing activity. Robo2's distinct functions are not dependent on greater Slit affinity but are instead due in part to differences in multimerization and receptor-ligand stoichiometry conferred by Robo2's Ig domains. Together, our findings suggest that diverse responses to the Slit guidance cue are imparted by intrinsic structural differences encoded in the extracellular Ig domains of the Robo receptors.

Protein Interaction Networks—More Than Mere Modules

It is widely believed that the modular organization of cellular function is reflected in a modular structure of molecular networks. A common view is that a “module” in a network is a cohesively linked group of nodes, densely connected internally and sparsely interacting with the rest of the network. Many algorithms try to identify functional modules in protein-interaction networks (PIN) by searching for such cohesive groups of proteins. Here, we present an alternative approach independent of any prior definition of what actually constitutes a “module”. In a self-consistent manner, proteins are grouped into “functional roles” if they interact in similar ways with other proteins according to their functional roles. Such grouping may well result in cohesive modules again, but only if the network structure actually supports this. We applied our method to the PIN from the Human Protein Reference Database (HPRD) and found that a representation of the network in terms of cohesive modules, at least on a global scale, does not optimally represent the network's structure because it focuses on finding independent groups of proteins. In contrast, a decomposition into functional roles is able to depict the structure much better as it also takes into account the interdependencies between roles and even allows groupings based on the absence of interactions between proteins in the same functional role. This, for example, is the case for transmembrane proteins, which could never be recognized as a cohesive group of nodes in a PIN. When mapping experimental methods onto the groups, we identified profound differences in the coverage suggesting that our method is able to capture experimental bias in the data, too. For example yeast-two-hybrid data were highly overrepresented in one particular group. Thus, there is more structure in protein-interaction networks than cohesive modules alone and we believe this finding can significantly improve automated function prediction algorithms.

Functional characterization and topological modularity of molecular interaction networks

BackgroundAnalyzing interaction networks for functional characterization poses significant challenges arising from the noisy, incomplete, and generic nature of both the interaction data as well as functional annotation of molecules. Network-based methods focus on interacting molecules (pairs or sets) occurring in close proximity to infer functional associations.ResultsIn this paper we perform a formal comparative investigation of the relationship between functional coherence and topological proximity in networks. We investigate the problem of assessing the coherence of sets of biomolecules (or segments thereof) taking into account functional specificity as well as the distribution of functional attributes across entity groups. We also propose novel measures of topological proximity that are more robust to noisy and incomplete interaction data.ConclusionWe derive the following results in this paper: (i) there exists strong correlation between functional similarity and topological proximity in various network abstractions, with domain interaction networks (DDIs) demonstrating higher correlation than protein interaction networks (PPIs); (ii) measures that quantify coherence among entire sets of proteins are superior to aggregates of known pair-wise measures; and (iii) random-walk based measures of topological proximity are better suited to existing interaction data. We validate our methods on diverse data, including experimentally and computationally derived PPIs and DDIs, as well as on sets of known biologically related groups of molecules.

Functional modularity of nuclear hormone receptors in a Caenorhabditis elegans metabolic gene regulatory network.

Gene regulatory networks (GRNs) provide insights into the mechanisms of differential gene expression at a systems level. GRNs that relate to metazoan development have been studied extensively. However, little is still known about the design principles, organization and functionality of GRNs that control physiological processes such as metabolism, homeostasis and responses to environmental cues. In this study, we report the first experimentally mapped metazoan GRN of Caenorhabditis elegans metabolic genes. This network is enriched for nuclear hormone receptors (NHRs). The NHR family has greatly expanded in nematodes: humans have 48 NHRs, but C. elegans has 284, most of which are uncharacterized. We find that the C. elegans metabolic GRN is highly modular and that two GRN modules predominantly consist of NHRs. Network modularity has been proposed to facilitate a rapid response to different cues. As NHRs are metabolic sensors that are poised to respond to ligands, this suggests that C. elegans GRNs evolved to enable rapid and adaptive responses to different cues by a concurrence of NHR family expansion and modular GRN wiring.

Modularity and anti-modularity in networks with arbitrary degree distribution.

BackgroundMuch work in systems biology, but also in the analysis of social network and communication and transport infrastructure, involves an in-depth analysis of local and global properties of those networks, and how these properties relate to the function of the network within the integrated system. Most often, systematic controls for such networks are difficult to obtain, because the features of the network under study are thought to be germane to that function. In most such cases, a surrogate network that carries any or all of the features under consideration, while created artificially and in the absence of any selective pressure relating to the function of the network being studied, would be of considerable interest.ResultsHere, we present an algorithmic model for growing networks with a broad range of biologically and technologically relevant degree distributions using only a small set of parameters. Specifying network connectivity via an assortativity matrix allows us to grow networks with arbitrary degree distributions and arbitrary modularity. We show that the degree distribution is controlled mainly by the ratio of node to edge addition probabilities, and the probability for node duplication. We compare topological and functional modularity measures, study their dependence on the number and strength of modules, and introduce the concept of anti-modularity: a property of networks in which nodes from one functional group preferentially do not attach to other nodes of that group. We also investigate global properties of networks as a function of the network's growth parameters, such as smallest path length, correlation coefficient, small-world-ness, and the nature of the percolation phase transition. We search the space of networks for those that are most like some well-known biological examples, and analyze the biological significance of the parameters that gave rise to them.ConclusionsGrowing networks with specified characters (degree distribution and modularity) provides the opportunity to create surrogates for biological and technological networks, and to test hypotheses about the processes that gave rise to them. We find that many celebrated network properties may be a consequence of the way in which these networks grew, rather than a necessary consequence of how they work or function.ReviewersThis article was reviewed by Erik van Nimwegen, Teresa Przytycka (nominated by Claus Wilke), and Leonid Mirny. For the full reviews, please go to the Reviewer's Comments section.

Structural and functional modularity of proteins in the de novo purine biosynthetic pathway.

It is generally accepted that naturally existing functional domains can serve as building blocks for complex protein structures, and that novel functions can arise from assembly of different combinations of these functional domains. To inform our understanding of protein evolution and explore the modular nature of protein structure, two model enzymes were chosen for study, purT-encoded glycinamide ribonucleotide formyltransferase (PurT) and purK-encoded N(5)-carboxylaminoimidazole ribonucleotide synthetase (PurK). Both enzymes are found in the de novo purine biosynthetic pathway of Escherichia coli. In spite of their low sequence identity, PurT and PurK share significant similarity in terms of tertiary structure, active site organization, and reaction mechanism. Their characteristic three domain structures categorize both PurT and PurK as members of the ATP-grasp protein superfamily. In this study, we investigate the exchangeability of individual protein domains between these two enzymes and the in vivo and in vitro functional properties of the resulting hybrids. Six domain-swapped hybrids were unable to catalyze full wild-type reactions, but each hybrid protein could catalyze partial reactions. Notably, an additional loop replacement in one of the domain-swapped hybrid proteins was able to restore near wild-type PurK activity. Therefore, in this model system, domain-swapped proteins retained the ability to catalyze partial reactions, but further modifications were required to efficiently couple the reaction intermediates and achieve catalysis of the full reaction. Implications for understanding the role of domain swapping in protein evolution are discussed.

Proposal of an improved design of IFMIF Test Cell components for enhanced handling and reliability

An adequate design of components to be manipulated by remote handling is a key factor in the success of any activated facility, having a decisive impact on availability, prompt and safe maintenance, occupational exposures and flexibility of the facility. Such components should satisfy at least the basic remote handling requirements of simplicity, accessibility, modularity, standardization and assembling adequacy. Highly activated components in the IFMIF facility are found in the Test Cell, a pit closed by stepped shielding plugs. The Test Cell confines the Test Modules which contain the samples and experiments. The present reference design of the IFMIF Test Cell shows some drawbacks, in particular the jamming tendency of the shielding plugs, slow and complex access to the Backplate, a low lifetime component, and difficult positioning of the Test Modules. This paper summarises several modifications aiming at improving, under such remote handling requirements, the present reference design of the Test Cell shielding plugs and aspects of the geometrical structure of the Test Cell. A functional modularization of the present shielding plugs has been carried out and positioning guides for the Test Modules have been devised.

Dynamically reconfigurable hardware–software architecture for partitioning networking functions on the SoC platform

We present an issue of the dynamically reconfigurable hardware–software architecture which allows for partitioning networking functions on a SoC (System on Chip) platform. We address this issue as a partition problem of implementing network protocol functions into dynamically reconfigurable hardware and software modules. Such a partitioning technique can improve the co-design productivity of hardware and software modules. Practically, the proposed partitioning technique, which is called the ITC (Inter-Task Communication) technique incorporating the RT-IJC 2 (Real-Time Inter-Job Communication Channel), makes it possible to resolve the issue of partitioning networking functions into hardware and software modules on the SoC platform. Additionally, the proposed partitioning technique can support the modularity and reuse of complex network protocol functions, enabling a higher level of abstraction of future network protocol specifications onto the SoC platform. Especially, the RT-IJC 2 allows for more complex data transfers between hardware and software tasks as well as provides real-time data processing simultaneously for given application-specific real-time requirements. We conduct a variety of experiments to illustrate the application and efficiency of the proposed technique after implementing it on a commercial SoC platform based on the Altera’s Excalibur including the ARM922T core and up to 1 million gates of programmable logic.

Revisiting the modularity of modular polyketide synthases

Modularity is a highly sought after feature in engineering design. A modular catalyst is a multi-component system whose parts can be predictably interchanged for functional flexibility and variety. Nearly two decades after the discovery of the first modular polyketide synthase (PKS), we critically assess PKS modularity in the face of a growing body of atomic structural and in vitro biochemical investigations. Both the architectural modularity and the functional modularity of this family of enzymatic assembly lines are reviewed, and the fundamental challenges that lie ahead for the rational exploitation of their full biosynthetic potential are discussed.

A columnar model of bottom-up and top-down processing in the neocortex

Event Abstract Back to Event A columnar model of bottom-up and top-down processing in the neocortex Sven Schrader1*, Marc-Oliver Gewaltig1, 2, Ursula Körner1 and Edgar Körner1 1 Honda Research Institute Europe GmbH, Germany 2 Bernstein Center for Computational Neuroscience, Germany Thorpe et al. (1996) demonstrated that our brains are able to process visual stimuli within the first 150 ms, without considering all possible interpretations. It is therefore likely that a first coarse hypothesis, which captures the most relevant features of the stimulus, is made in a pure feed-forward manner. Details and less relevant features are postponed to a later, feedback-mediated stage. Based on our assumptions (Körner et al., 1999), we present a columnar model of cortical processing that demonstrates the formation of a fast initial hypothesis and its subsequent disambiguation by inter-columnar communication. Neural representation occurs by forming coherent spike waves (volleys) as local decisions. The model consists of three areas, each representing more abstract features of the stimulus hierarchy. The areas are connected with converging bottom-up projections that propagate activity to the next higher level. During this forward propagation, the initial hypothesis is generated. Top-down feedback is mediated by modulatory connections that amplify the correct representation and suppress the incorrect ones, until only the most compact representation of the object remains active. Our model foots on three postulates that interpret the cortical architecture in terms of the algorithm it implements. First, we argue that the columnar modularization reflects a functional modularization. We interpret columns as computational units that use the same set of powerful processing strategies over and over again. Second, each cortical column hosts the circuitry of two processing streams, a fast feed-forward "A-", and a slower modulatory "B-" system that refines the decision taken in the A-system by mixing experience with the afferent stimulus stream (predictive coding). Third, time is too short to estimate the reliability of a neuron's response in a rate-coded manner. We therefore argue that cortical neurons code reliability in their relative response latencies. By receiving the fastest response, a target neuron automatically picks up the most reliable one. At first, our model generates a sequence of spike volleys, each being a possible representation of the stimulus. These candidates comprise about one percent of all 300 learned objects. The correctness of a response is directly expressed in its latency: the better a representation matches the stimulus, the earlier the response occurs. The B-system implements top-down predictive coding: Based on the stored knowledge, responses are modified until the set of candidates is on average reduced to one. Thus, the network makes a unique decision on the stimulus. It is correct in 95% of the trials, even with degraded stimuli. We analyze the spike volleys in terms of their occurrence times and precision, and give a functional interpretation to rhythmic activity such as gamma oscillations. Our model has been simulated with the simulation tool NEST (Gewaltig and Diesmann, 2007).

Crosscutting Concern Identification at Requirements Level

An unresolved problem faced by software developers is the failure to identify and modularize certain artefacts that compose the software. It is difficult to modularize these artefacts because they are dispersed among other artefacts in the software properties. Aspects Oriented Requirements Engineering is showing encouraging results in improving identification, modularization and composition of crosscutting concerns. Identifying and documenting crosscutting concerns at the requirements-level is crucial. It avoids coupling between requirements, improves traceability among requirements, eases function modularization, reduces software complexity, enhances the correctness of the software design and most importantly it saves the cost. Although the research area is still in its infancy, several techniques for crosscutting concern identification have already been developed. However, all of the techniques reviewed are based on semi-automated way whereby human intervention is required to achieve the desired results. Therefore, in this paper, a fully automated technique based on Natural Language Processing (NLP) is proposed to identify crosscutting concern at the requirements level.

Hierarchical functional modularity in the resting-state human brain.

Functional magnetic resonance imaging (fMRI) studies have shown that anatomically distinct brain regions are functionally connected during the resting state. Basic topological properties in the brain functional connectivity (BFC) map have highlighted the BFC's small-world topology. Modularity, a more advanced topological property, has been hypothesized to be evolutionary advantageous, contributing to adaptive aspects of anatomical and functional brain connectivity. However, current definitions of modularity for complex networks focus on nonoverlapping clusters, and are seriously limited by disregarding inclusive relationships. Therefore, BFC's modularity has been mainly qualitatively investigated. Here, we introduce a new definition of modularity, based on a recently improved clustering measurement, which overcomes limitations of previous definitions, and apply it to the study of BFC in resting state fMRI of 53 healthy subjects. Results show hierarchical functional modularity in the brain.

On the explicit construction of higher deformations of partition statistics

The modularity of the partition-generating function has many important consequences: for example, asymptotics and congruences for p(n). In a pair of articles, Bringmann and Ono [11], [12] connected the rank, a partition statistic introduced by Dyson [18], to weak Maass forms, a new class of functions that are related to modular forms and that were first considered in [14]. Here, we take a further step toward understanding how weak Maass forms arise from interesting partition statistics by placing certain 2-marked Durfee symbols introduced by Andrews [1] into the framework of weak Maass forms. To do this, we construct a new class of functions that we call quasi-weak Maass forms because they have quasi-modular forms as components. As an application, we prove two conjectures of Andrews [1, Conjectures 11, 13]. It seems that this new class of functions will play an important role in better understanding weak Maass forms of higher weight themselves and also their derivatives. As a side product, we introduce a new method that enables us to prove transformation laws for generating functions over incomplete lattices

Functional coherence in domain interaction networks

Extracting functional information from protein-protein interactions (PPI) poses significant challenges arising from the noisy, incomplete, generic and static nature of data obtained from high-throughput screening. Typical proteins are composed of multiple domains, often regarded as their primary functional and structural units. Motivated by these considerations, domain-domain interactions (DDI) for network-based analyses have received significant recent attention. This article performs a formal comparative investigation of the relationship between functional coherence and topological proximity in PPI and DDI networks. Our investigation provides the necessary basis for continued and focused investigation of DDIs as abstractions for functional characterization and modularization of networks. We investigate the problem of assessing the functional coherence of two biomolecules (or segments thereof) in a formal framework. We establish essential attributes of admissible measures of functional coherence, and demonstrate that existing, well-accepted measures are ill-suited to comparative analyses involving different entities (i.e. domains versus proteins). We propose a statistically motivated functional similarity measure that takes into account functional specificity as well as the distribution of functional attributes across entity groups to assess functional similarity in a statistically meaningful and biologically interpretable manner. Results on diverse data, including high-throughput and computationally predicted PPIs, as well as structural and computationally inferred DDIs for different organisms show that: (i) the relationship between functional similarity and network proximity is captured in a much more (biologically) intuitive manner by our measure, compared to existing measures and (ii) network proximity and functional similarity are significantly more correlated in DDI networks than in PPI networks, and that structurally determined DDIs provide better functional relevance as compared to computationally inferred DDIs.

A type system for the automatic distribution of higher-order synchronous dataflow programs

We address the design of distributed systems with synchronous dataflow programming languages. As modular design entails handling both architectural and functional modularity, our first contribution is to extend an existing synchronous dataflow programming language with primitives allowing the description of a distributed architecture and the localization of some expressions onto some processors. We also present a distributed semantics to formalize the distributed execution of synchronous programs. Our second contribution is to provide a type system, in order to infer the localization of non-annotated values by means of type inference and to ensure, at compilation time, the consistency of the distribution. Our third contribution is to provide a type-directed projection operation to obtain automatically, from a centralized typed program, the local program to be executed by each computing resource. The type system as well as the automatic distribution mechanism has been fully implemented in the compiler of an existing synchronous data-flow programming language.

Modular Functional Descriptions

The construction of reactive systems often requires the combination of different individual functionalities, thus leading to a complex overall behavior. To achieve an efficient construction of reliable systems, a structured approach to the definition of the behavior is needed. Here, functional modularization supports a separation of the overall functionality into individual functions as well as their combination to construct the intended behavior, by using functional modules as basic paradigm together with conjunctive and disjunctive modular composition.