Multimodal Networks: Structure and Operations

A multimodal network (MMN) is a novel graph-theoretic formalism designed to capture the structure of biological networks and to represent relationships derived from multiple biological databases. MMNs generalize the standard notions of graphs and hypergraphs, which are the bases of current diagrammatic representations of biological phenomena and incorporate the concept of mode. Each vertex of an MMN is a biological entity, a biot, while each modal hyperedge is a typed relationship, where the type is given by the mode of the hyperedge. The current paper defines MMNs and concentrates on the structural aspects of MMNs. A companion paper develops MMNs as a representation of the semantics of biological networks and discusses applications of the MMNs in managing complex biological data. The MMN model has been implemented in a database system containing multiple kinds of biological networks.     

Mismatch negativity in school-age children to speech stimuli that are just perceptibly different

The mismatch negativity event-related potential (MMN) was elicited in normal school-age children in response to just perceptibly different variants of the speech phoneme /da/. A significant MMN was measured in each subject tested. Child and adult MMNs were similar with respect to peak latency and duration. Measures of MMN magnitude (peak-to-peak amplitude and area) were significantly larger in children than in adults. The results of the present study indicate that the MMN can be elicited in response to minimal acoustic stimulus differences in complex speech signals in school-age children. The results support the feasibility of using the MMN as a tool in the study of deficient auditory perception in children.     

Theory of interface: Category theory,directed networks and evolution of biological networks

Biological networks have two modes. The first mode is static: a network is a passage on which something flows. The second mode is dynamic: a network is a pattern constructed by gluing functions of entities constituting the network. In this paper, first we discuss that these two modes can be associated with the category theoretic duality (adjunction) and derive a natural network structure (a path notion) for each mode by appealing to the category theoretic universality. The path notion corresponding to the static mode is just the usual directed path. The path notion for the dynamic mode is called lateral path which is the alternating path considered on the set of arcs. Their general functionalities in a network are transport and coherence, respectively. Second, we introduce a betweenness centrality of arcs for each mode and see how the two modes are embedded in various real biological network data. We find that there is a trade-off relationship between the two centralities: if the value of one is large then the value of the other is small. This can be seen as a kind of division of labor in a network into transport on the network and coherence of the network. Finally, we propose an optimization model of networks based on a quality function involving intensities of the two modes in order to see how networks with the above trade-off relationship can emerge through evolution. We show that the trade-off relationship can be observed in the evolved networks only when the dynamic mode is dominant in the quality function by numerical simulations. We also show that the evolved networks have features qualitatively similar to real biological networks by standard complex network analysis.     

New strategy for the representation and the integration of biomolecular knowledge at a cellular scale

The combination of sequencing and post-sequencing experimental approaches produces huge collections of data that are highly heterogeneous both in structure and in semantics. We propose a new strategy for the integration of such data. This strategy uses structured sets of sequences as a unified representation of biological information and defines a probabilistic measure of similarity between the sets. Sets can be composed of sequences that are known to have a biological relationship (e.g. proteins involved in a complex or a pathway) or that share similar values for a particular attribute (e.g. expression profile). We have developed a software, BlastSets, which implements this strategy. It exploits a database where the sets derived from diverse biological information can be deposited using a standard XML format. For a given query set, BlastSets returns target sets found in the database whose similarity to the query is statistically significant. The tool allowed us to automatically identify verified relationships between correlated expression profiles and biological pathways using publicly available data for Saccharomyces cerevisiae. It was also used to retrieve the members of a complex (ribosome) based on the mining of expression profiles. These first results validate the relevance of the strategy and demonstrate the promising potential of BlastSets.     

Integration of consonant and pitch processing as revealed by the absence of additivity in mismatch negativity

Consonants, unlike vowels, are thought to be speech specific and therefore no interactions would be expected between consonants and pitch, a basic element for musical tones. The present study used an electrophysiological approach to investigate whether, contrary to this view, there is integrative processing of consonants and pitch by measuring additivity of changes in the mismatch negativity (MMN) of evoked potentials. The MMN is elicited by discriminable variations occurring in a sequence of repetitive, homogeneous sounds. In the experiment, event-related potentials (ERPs) were recorded while participants heard frequently sung consonant-vowel syllables and rare stimuli deviating in either consonant identity only, pitch only, or in both dimensions. Every type of deviation elicited a reliable MMN. As expected, the two single-deviant MMNs had similar amplitudes, but that of the double-deviant MMN was also not significantly different from them. This absence of additivity in the double-deviant MMN suggests that consonant and pitch variations are processed, at least at a pre-attentive level, in an integrated rather than independent way. Domain-specificity of consonants may depend on higher-level processes in the hierarchy of speech perception.     

Differences in Mismatch Responses to Vowels and Musical Intervals: MEG Evidence

We investigated the electrophysiological response to matched two-formant vowels and two-note musical intervals, with the goal of examining whether music is processed differently from language in early cortical responses. Using magnetoencephalography (MEG), we compared the mismatch-response (MMN/MMF, an early, pre-attentive difference-detector occurring approximately 200 ms post-onset) to musical intervals and vowels composed of matched frequencies. Participants heard blocks of two stimuli in a passive oddball paradigm in one of three conditions: sine waves, piano tones and vowels. In each condition, participants heard two-formant vowels or musical intervals whose frequencies were 11, 12, or 24 semitones apart. In music, 12 semitones and 24 semitones are perceived as highly similar intervals (one and two octaves, respectively), while in speech 12 semitones and 11 semitones formant separations are perceived as highly similar (both variants of the vowel in ‘cut’). Our results indicate that the MMN response mirrors the perceptual one: larger MMNs were elicited for the 12–11 pairing in the music conditions than in the language condition; conversely, larger MMNs were elicited to the 12–24 pairing in the language condition that in the music conditions, suggesting that within 250 ms of hearing complex auditory stimuli, the neural computation of similarity, just as the behavioral one, differs significantly depending on whether the context is music or speech.     

Design of a network with state stability

Designing a network with given functions or reconstruct a network based on its dynamical behavior is an important problem in the study of complex systems. In this paper, we put forward certain principles in constructing a network with state stability. We show that a necessary and sufficient condition to design networks with a global fixed point is that active nodes inhibit inactive nodes, while the latter activate the former directly or indirectly. We also designed networks based on basic modules, where each basic module consists a sub-network, they communicate through the inhibition link from each activator in lower module to the inhibitor of upper module. We found that long activation links, i.e. indirect activation links are important to the formation of convergence trajectory. We believe that these principles may help us to understand the topology of biological networks.     

A visualization method for multi-relation in dataset

To express the multi-relation in complex dataset, proposed a fast visualization method based on the continuous Catmull–Rom curve. The method expresses the entity data and the taxonomic relation in dataset using the hypergraph. The nodes are designed to represent the entity data in dataset. The hyperedge is introduced to link all the relative nodes in one multi-relation. The hyperedge can be visualized by the curve style or the regional style according to different requirements. The nodes in one hyperedge are linked by a continuous curve in curve style, while all the nodes are surrounded by one closed region in regional style. The Catmull–Rom algorithm is adopted to produce the continuous curve between the adjacent nodes in curve style. The nodes are regarded as the control points in the interpolating processing of Catmull–Rom algorithm. The peripheral points are obtained by extending along the tendency line of the hyperedge in regional style. The closed curve connecting these peripheral points forms a closed region expressing the hyperedge. The color selected to stain the hyperedge based on color wheel. The selected colors for each hyperedge can maximize the visual differentiation in the processing of coloring each hyperedge. The experimental results denoted that the method can achieve the intuitive and accurate expression for the multi-relation in complex dataset. The method can visualize the common scale dataset the in real time.     

The modularity of biological regulatory networks

A useful approach to complex regulatory networks consists of modeling their elements and interactions by Boolean equations. In this context, feedback circuits (i.e. circular sequences of interactions) have been shown to play key dynamical roles: whereas positive circuits are able to generate multistationarity, negative circuits may generate oscillatory behavior. In this paper, we principally focus on the case of gene networks. These are represented by fully connected Boolean networks where each element interacts with all elements including itself. Flexibility in network design is introduced by the use of Boolean parameters, one associated with each interaction or group of interactions affecting a given element. Within this formalism, a feedback circuit will generate its typical dynamical behavior (i.e. multistationarity or oscillations) only for appropriate values of some of the logical parameters. Whenever it does, we say that the circuit is 'functional'. More interestingly, this formalism allows the computation of the constraints on the logical parameters to have any feedback circuit functional in a network. Using this methodology, we found that the fraction of the total number of consistent combinations of parameter values that make a circuit functional decreases geometrically with the circuit length. From a biological point of view, this suggests that regulatory networks could be decomposed into small and relatively independent feedback circuits or 'regulatory modules'.     

Liposome shrinkage and swelling under osmotic-diffusional stress: evaluation of kinetic parameters from spectrophotometric measurements.

Analyses of spectrophotometric measurements of shrinkage and swelling of liposomes under osmotic-diffusional stress tacitly assume an 'empirical' inverse relationship between the absorbance (A) and the liposomal volume (v). In this paper, we have proposed an alternate more explicit relationship: A = EcL e - alpha Lv/1-Lv where L is liposomal concentration, Ec the extinction coefficient and alpha a dimensionless parameter. The exponential term, in essence, defines the partitioning of aqueous volume into intra- and extra-liposomal compartments. Experimental data obtained with glycerol as model compound are used to test the validity and internal consistency of the proposed formalism.     

Semiotes: a semantics for sharing

MOTIVATION: Reliable, automated communication of biological information requires methods to declare the information's semantics. In this paper I describe an approach to semantic declaration intended to permit independent, distributed databases, algorithms, and servers to exchange and process requests for information and computations without requiring coordination or agreement among them on universe of discourse, data model, schema, or implementation. RESULTS: This approach uses Glossa, a formal language defining the semantics of biological ideas, information, and algorithms, to executably define the semantics of complex ideas and computations by constructs of semiotes, terms which axiomatically define very simple notions. A database or algorithm wishing to exchange information or computations maintains a set of mappings between its particular notions and semiotes, and a parser to translate between its indigenous ideas and implementation and the semiotes. Requests from other databases or algorithms are issued as semiotic messages, locally interpreted and processed, and the results returned as semiotes to the requesting entity. Thus, semiotes serve as a shared, abstract layer of definitions which can be computably combined by each database or algorithm according to its own needs and ideas. By combining the explicit declaration of semantics with the computation of the semantics of complex ideas, Glossa and its semiotes permit independent computational entities to lightly federate their capabilities as desired while maintaining their unique perspectives on both scientific and technical questions.     

A Markovian approach to the control of genetic regulatory networks

This paper presents an approach for controlling gene networks based on a Markov chain model, where the state of a gene network is represented as a probability distribution, while state transitions are considered to be probabilistic. An algorithm is proposed to determine a sequence of control actions that drives (without state feedback) the state of a given network to within a desired state set with a prescribed minimum or maximum probability. A heuristic is proposed and shown to improve the efficiency of the algorithm for a class of genetic networks.     

Tellegen's theorem for bond graphs its relevance to chemical networks

Amongst the available graph theories the one making use of “bond graphs” has been considered the most suitable in network thermodynamics for representing a wide range of physico-chemical processes in the form of networks.In this article a complete representation of chemical reactions, both far from- and near-equilibrium, by bond graphs, is proposed. In addition, a new proof of the Tellegen theorem is given, derived directly from the properties of bond graphs. A new insight into the general meaning of Tellegen's theorem in variable networks and its relevance to biological networks is thus provided. The structure of the network being represented by new elements — i.e. the junctions — in bond graphs, time variations of these elements have the meaning of changes in the structure itself, to that the Tellegen theorem appears as an invariance relation in networks where the structure is allowed to change.     

Intervention in a family of Boolean networks

MOTIVATION: Intervention in a gene regulatory network is used to avoid undesirable states, such as those associated with a disease. Several types of intervention have been studied in the framework of a probabilistic Boolean network (PBN), which is a collection of Boolean networks in which the gene state vector transitions according to the rules of one of the constituent networks and where network choice is governed by a selection distribution. The theory of automatic control has been applied to find optimal strategies for manipulating external control variables that affect the transition probabilities to desirably affect dynamic evolution over a finite time horizon. In this paper we treat a case in which we lack the governing probability structure for Boolean network selection, so we simply have a family of Boolean networks, but where these networks possess a common attractor structure. This corresponds to the situation in which network construction is treated as an ill-posed inverse problem in which there are many Boolean networks created from the data under the constraint that they all possess attractor structures matching the data states, which are assumed to arise from sampling the steady state of the real biological network. RESULTS: Given a family of Boolean networks possessing a common attractor structure composed of singleton attractors, a control algorithm is derived by minimizing a composite finite-horizon cost function that is a weighted average over all the individual networks, the idea being that we desire a control policy that on average suits the networks because these are viewed as equivalent relative to the data. The weighting for each network at any time point is taken to be proportional to the instantaneous estimated probability of that network being the underlying network governing the state transition. The results are applied to a family of Boolean networks derived from gene-expression data collected in a study of metastatic melanoma, the intent being to devise a control strategy that reduces the WNT5A gene's action in affecting biological regulation. AVAILABILITY: The software is available on request. SUPPLEMENTARY INFORMATION: The supplementary Information is available at http://ee.tamu.edu/~edward/tree     

Sequential grouping of tone sequence as reflected by the mismatch negativity

The human sequential grouping that organizes parts of tones into a group was examined by the mismatch negativity (MMN), a component of event-related potentials that reveals the sensory memory process. The sequential grouping is accomplished by the combinations of some factors, e.g., temporal and frequency proximity principles. In this study, auditory oddball stimuli in which each of the stimuli consisted of series of tone bursts, were applied to the subjects, and the MMN elicited by the deviation of the frequency of the last tone in the stimulus was investigated. The relationship between the expected phenomena of sequential grouping of tones and observed magnitudes of MMN was evaluated. It was shown that the magnitudes of MMN changed according to the configuration (number of tones, frequency) of tone sequence to be stored. This result suggested that the sequential grouping of presented tones was achieved on the preattentive auditory sensory memory process. It was also shown that the relative change of MMN magnitudes corresponded to the conditions of sequential grouping, which had been proposed by the auditory psychophysical studies. The investigation of MMN properties could reveal the nature of auditory sequential grouping.This study was approved by the Ethics Committee on Clinical Investigation, Graduate School of Engineering, Tohoku University and was carried out in accordance with the policy of the Declaration of Helsinki.     

Stability and looping in connectionist models with asymmetric weights

Recently, researchers in artificial intelligence have been actively investigating various connectionist models of computation. The model that is often studied is that of an asynchronous symmetric network, in which a global energy measure can be established and used to prove that the network totally stabilizes. In this paper, we discuss asymmetric networks that might admit infinite activated computations. Within this framework, we define an operational semantics under a synchronous activation rule and similarly under a fair asynchronous rule. Using this semantics, we analyze flow properties of a circle-network with respect to a specification that characterizes oscillation. We further explore the complexity of the decidable question of whether or not a given asymmetric network totally stabilizes. It is shown that this problem is NP-hard, and is in PSPACE.We thank the Xerox Corporation University Grants Program for providing equipment used in the preparation of this paper. This work was supported by the Air Force Systems Command, Rome Air Development Center, Griffiss Air Force Base, New York 13441-5700, and the Air Force Office of Scientific Research, Boiling AFB, DC 20332 under Contract No. F30602-85-C-0008. This contract supports the Northeast Artificial Intelligence Consortium (NAIC). A preliminary version of this paper was presented at the Ninth European Meeting on Cybernetics and Systems Research, Vienna, Austria, April, 1988     

A sparse neural code for some speech sounds but not for others

The precise neural mechanisms underlying speech sound representations are still a matter of debate. Proponents of 'sparse representations' assume that on the level of speech sounds, only contrastive or otherwise not predictable information is stored in long-term memory. Here, in a passive oddball paradigm, we challenge the neural foundations of such a 'sparse' representation; we use words that differ only in their penultimate consonant ("coronal" [t] vs. "dorsal" [k] place of articulation) and for example distinguish between the German nouns Latz ([lats]; bib) and Lachs ([laks]; salmon). Changes from standard [t] to deviant [k] and vice versa elicited a discernible Mismatch Negativity (MMN) response. Crucially, however, the MMN for the deviant [lats] was stronger than the MMN for the deviant [laks]. Source localization showed this difference to be due to enhanced brain activity in right superior temporal cortex. These findings reflect a difference in phonological 'sparsity': Coronal [t] segments, but not dorsal [k] segments, are based on more sparse representations and elicit less specific neural predictions; sensory deviations from this prediction are more readily 'tolerated' and accordingly trigger weaker MMNs. The results foster the neurocomputational reality of 'representationally sparse' models of speech perception that are compatible with more general predictive mechanisms in auditory perception.     

Representation and Processing of Lexical Tone and Tonal Variants: Evidence from the Mismatch Negativity

Pronunciation variation is ubiquitous in the speech signal. Different models of lexical representation have been put forward to deal with speech variability, which differ in the level as well as the nature of mental representation. We present the first mismatch negativity (MMN) study investigating the effect of allophonic variation on the mental representation and neural processing of lexical tones. Native speakers of Standard Chinese (SC) participated in an oddball electroencephalography (EEG) experiment. All stimuli have the same segments (ma) but different lexical tones: level [T1], rising [T2], and dipping [T3]. In connected speech with a T3T3 sequence, the first T3 may undergo allophonic change and is produced with a rising pitch contour (T3V), similar to the lexical T2 pitch contour. Four oddball conditions were constructed (T1/T3, T3/T1, T2/T3, T3/T2; standard/deviant). All four conditions elicited MMN effects, with the T1–T3 pair eliciting comparable MMNs, but the T2–T3 pair asymmetrical MMN effects. There were significantly greater and earlier MMN effects in the T2/T3 condition than that in the reversed T3/T2 condition. Furthermore, the T3/T2 condition showed more rightward MMN effects than the T2/T3 condition and the T1–T3 pair. Such asymmetries suggest co-activation of long-term memory representations of both T3 and T3V when T3 serves as the standard. The acoustic similarity between the activated T3V (by the standard T3) and the incoming deviant stimulus T2 induces acoustic processing of the tonal contrast in the T3/T2 condition, similar to that of within-category lexical tone processing, which is in contrast to the processing of between-category lexical tones observed in the T2/T3, T1/T3, and T3/T1 conditions.     

Proteomic analyser with applications to diagnostics and vaccines

This paper describes a method for proteomic analysis with applications to diagnostics and vaccines. A panel of N (> or = 1) reagents called X(j), with j = 1 to N, is used. The binding strength of each of the X(j) reagents to each other is measured, for example by an ELISA assay, giving an N x N matrix K. The matrix K is used to define another set of N reagents called Y(j), with j = 1 to N, each of which is a linear combination of the X(j) reagents and each of which is tailored to be complementary to one of the X(j) reagents. Each of the N pairs of reagents X(j) and Y(j) defines an axis in an N-dimensional shape space. The definition of these axes facilitates proteomic analysis of diverse biological samples, for example, mixtures of proteins such as serum samples or T cell extracts. A method for defining and measuring similarity between pairs of biological samples and between sets of biological samples in the context of the set of N reagent pairs is described. This leads to methods for using the N reagent pairs in the diagnosis of diseases and in the formulation of preventive and therapeutic vaccines. The relationship of this work to previous research on shape space is discussed.