Results for correlation features based in CMA models: EERs at varying correlation coefficient threshold values (θ) with the corresponding projection dimension (dim)
\r\n\tIn sum, the book presents a reflective analysis of the pedagogical hubs for a changing world, considering the most fundamental areas of the current contingencies in education.
",isbn:"978-1-83968-793-8",printIsbn:"978-1-83968-792-1",pdfIsbn:"978-1-83968-794-5",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"b01f9136149277b7e4cbc1e52bce78ec",bookSignature:"Dr. María Jose Hernandez-Serrano",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10229.jpg",keywords:"Teacher Digital Competences, Flipped Learning, Online Resources Design, Neuroscientific Literacy (Myths), Emotions and Learning, Multisensory Stimulation, Citizen Skills, Violence Prevention, Moral Development, Universal Design for Learning, Sensitizing on Diversity, Supportive Strategies",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"September 14th 2020",dateEndSecondStepPublish:"October 12th 2020",dateEndThirdStepPublish:"December 11th 2020",dateEndFourthStepPublish:"March 1st 2021",dateEndFifthStepPublish:"April 30th 2021",remainingDaysToSecondStep:"3 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Dr. Phil. Maria Jose Hernandez Serrano is a tenured lecturer in the Department of Theory and History of Education at the University of Salamanca, where she currently teaches on Teacher Education. She graduated in Social Education (2000) and Psycho-Pedagogy (2003) at the University of Salamanca. Then, she obtained her European Ph.D. in Education and Training in Virtual Environments by research with the University of Manchester, UK (2009).",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"187893",title:"Dr.",name:"María Jose",middleName:null,surname:"Hernandez-Serrano",slug:"maria-jose-hernandez-serrano",fullName:"María Jose Hernandez-Serrano",profilePictureURL:"https://mts.intechopen.com/storage/users/187893/images/system/187893.jpg",biography:"DPhil Maria Jose Hernandez Serrano is a tenured Lecturer in the Department of Theory and History of Education at the University of Salamanca (Spain), where she currently teaches on Teacher Education. She graduated in Social Education (2000) and Psycho-Pedagogy (2003) at the University of Salamanca. Then, she obtained her European Ph.D. on Education and Training in Virtual Environments by research with the University of Manchester, UK (2009). She obtained a Visiting Scholar Postdoctoral Grant (of the British Academy, UK) at the Oxford Internet Institute of the University of Oxford (2011) and was granted with a postdoctoral research (in 2021) at London Birbeck University.\n \nShe is author of more than 20 research papers, and more than 35 book chapters (H Index 10). She is interested in the study of the educational process and the analysis of cognitive and affective processes in the context of neuroeducation and neurotechnologies, along with the study of social contingencies affecting the educational institutions and requiring new skills for educators.\n\nHer publications are mainly of the educational process mediated by technologies and digital competences. Currently, her new research interests are: the transdisciplinary application of the brain-based research to the educational context and virtual environments, and the neuropedagogical implications of the technologies on the development of the brain in younger students. Also, she is interested in the promotion of creative and critical uses of digital technologies, the emerging uses of social media and transmedia, and the informal learning through technologies.\n\nShe is a member of several research Networks and Scientific Committees in international journals on Educational Technologies and Educommunication, and collaborates as a reviewer in several prestigious journals (see public profile in Publons).\n\nUntil March 2010 she was in charge of the Adult University of Salamanca, by coordinating teaching activities of more than a thousand adult students. She currently is, since 2014, the Secretary of the Department of Theory and History of Education. Since 2015 she collaborates with the Council Educational Program by training teachers and families in the translation of advances from educational neuroscience.",institutionString:"University of Salamanca",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"University of Salamanca",institutionURL:null,country:{name:"Spain"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"23",title:"Social Sciences",slug:"social-sciences"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"301331",firstName:"Mia",lastName:"Vulovic",middleName:null,title:"Mrs.",imageUrl:"https://mts.intechopen.com/storage/users/301331/images/8498_n.jpg",email:"mia.v@intechopen.com",biography:"As an Author Service Manager, my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. Whether that be identifying an exceptional author and proposing an editorship collaboration, or contacting researchers who would like the opportunity to work with IntechOpen, I establish and help manage author and editor acquisition and contact."}},relatedBooks:[{type:"book",id:"6942",title:"Global Social Work",subtitle:"Cutting Edge Issues and Critical Reflections",isOpenForSubmission:!1,hash:"222c8a66edfc7a4a6537af7565bcb3de",slug:"global-social-work-cutting-edge-issues-and-critical-reflections",bookSignature:"Bala Raju Nikku",coverURL:"https://cdn.intechopen.com/books/images_new/6942.jpg",editedByType:"Edited by",editors:[{id:"263576",title:"Dr.",name:"Bala",surname:"Nikku",slug:"bala-nikku",fullName:"Bala Nikku"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophanides",surname:"Theophile",slug:"theophanides-theophile",fullName:"Theophanides Theophile"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1373",title:"Ionic Liquids",subtitle:"Applications and Perspectives",isOpenForSubmission:!1,hash:"5e9ae5ae9167cde4b344e499a792c41c",slug:"ionic-liquids-applications-and-perspectives",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/1373.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"57",title:"Physics and Applications of Graphene",subtitle:"Experiments",isOpenForSubmission:!1,hash:"0e6622a71cf4f02f45bfdd5691e1189a",slug:"physics-and-applications-of-graphene-experiments",bookSignature:"Sergey Mikhailov",coverURL:"https://cdn.intechopen.com/books/images_new/57.jpg",editedByType:"Edited by",editors:[{id:"16042",title:"Dr.",name:"Sergey",surname:"Mikhailov",slug:"sergey-mikhailov",fullName:"Sergey Mikhailov"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"371",title:"Abiotic Stress in Plants",subtitle:"Mechanisms and Adaptations",isOpenForSubmission:!1,hash:"588466f487e307619849d72389178a74",slug:"abiotic-stress-in-plants-mechanisms-and-adaptations",bookSignature:"Arun Shanker and B. Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4816",title:"Face Recognition",subtitle:null,isOpenForSubmission:!1,hash:"146063b5359146b7718ea86bad47c8eb",slug:"face_recognition",bookSignature:"Kresimir Delac and Mislav Grgic",coverURL:"https://cdn.intechopen.com/books/images_new/4816.jpg",editedByType:"Edited by",editors:[{id:"528",title:"Dr.",name:"Kresimir",surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"17738",title:"Multimodal Fusion for Robust Identity Authentication: Role of Liveness Checks",doi:"10.5772/18131",slug:"multimodal-fusion-for-robust-identity-authentication-role-of-liveness-checks",body:'\n\t\tMost of the current biometric identity authentication systems currently deployed are based on modeling the identity of a person based on unimodal information, i.e. face, voice, or fingerprint features. Also, many current interactive civilian remote human computer interaction applications are based on speech based voice features, which achieve significantly lower performance for operating environments with low signal-to-noise ratios (SNR). For a long time, use of acoustic information alone has been a great success for several automatic speech processing applications such as automatic speech transcription or speaker authentication, while face identification systems based visual information alone from faces also proved to be of equally successful. However, in adverse operating environments, performance of either of these systems could be suboptimal. Use of both visual and audio information can lead to better robustness, as they can provide complementary secondary clues that can help in the analysis of the primary biometric signals (Potamianos et al (2004)). The joint analysis of acoustic and visual speech can improve the robustness of automatic speech recognition systems (Liu et al (2002), Gurbuz et al (2002).
\n\t\t\tThere have been several systems proposed on use of joint face-voice information for improving the performance of current identity authentication systems. However, most of these state-of-the-art authentication approaches are based on independently processing the voice and face information and then fusing the scores – the score fusion (Chibelushi et al (2002), Pan et al (2000), Chaudari et. al.(2003)). A major weakness of these systems is that they do not take into account fraudulent replay attack scenarios into consideration, leaving them vulnerable to spoofing by recording the voice of the target in advance and replaying it in front of the microphone, or simply placing a still picture of the target’s face in front of the camera. This problem can be addressed with liveness verification, which ensures that biometric cues are acquired from a live person who is actually present at the time of capture for authenticating the identity. With the diffusion of Internet based authentication systems for day-to-day civilian scenarios at a astronomical pace (Chetty and Wagner (2008)), it is high time to think about the vulnerability of traditional biometric authentication approaches and consider inclusion of liveness checks for next generation biometric systems. Though there is some work in finger print based liveness checking techniques (Goecke and Millar (2003), Molhom et al (2002)), there is hardly any work in liveness checks based on user-friendly biometric identifiers (face and voice), which enjoy more acceptability for civilian Internet based applications requiring person identity authentication.
\n\t\t\tA significant progress however, has been made in independent processing of face only or voice only based authentication approaches (Chibelushi et al (2002), Pan et al (2000), Chaudari et. al.(2003)), in which until now, inherent coupling between jointly occurring primary biometric identifiers were not taken into consideration. Some preliminary approaches such as the ones described in (Chetty and Wagner (2008), Goecke and Millar (2003)), address liveness checking problem by using the traditional acoustic and visual speech features for testing liveness. Both these approaches, neither considered an inherent coupling between speech and orafacial articulators (lips, jaw and chin) during speech production, nor used a solid pattern recogntion based evaluation framework for the validating the performance of co-inertia features.
\n\t\t\tIn this Chapter we propose a novel approach for extraction of audio-visual correlation features based on cross-modal association models, and formulate a hybrid fusion framework for modelling liveness information in the identity authentication approach. Further, we develop a sound evaluation approach based on Bayesian framework for assessing the vulnerability of system at different levels of replay attack complexity. The rest of the Chapter is organized as follows. Section 2 describes the motivation for using the proposed approach, and the details the cross-modal association models are described in Section 3. Section 4 describes the hybrid fusion approach for combining the correlation features with loosely couple and mutually independent face-speech components. The data corpora used and the experimental setup for evaluation of the proposed features is described in Section 5. The experimental results, evaluating proposed correlation features and hybrid fusion technique is discussed in Section 6. Finally, Section 7 summarises the conclusions drawn from this work and plans for further research.
\n\t\tThe motivation to use cross-modal association models is based on the following two observations: The first observation is in relation to any video event, for example a speaking face video, where the content usually consists of the co-occurring audio and the visual elements. Both the elements carry their contribution to the highest level semantics, and the presence of one has usually a “priming” effect on the other: when hearing a dog barking we expect the image of a dog, seeing a talking face we expect the presence of her voice, images of a waterfall usually bring the sound of running water etc. A series of psychological experiments on the cross-modal influences (Molhom et al (2002), MacDonald and McGurk (1978)) have proved the importance of synergistic fusion of the multiple modalities in the human perception system. A typical example of this kind is the well-known McGurk effect (MacDonald and McGurk (1978)). Several independent studies by cognitive psychologists suggest that the type of multi-sensory interaction between acoustic and orafacial articulators occurring in the McGurk effect involves both the early and late stages of integration processing (MacDonald and McGurk (1978)). It is likely that a human brain uses a hybrid form of fusion that depends on the availability and quality of different sensory cues.
\n\t\t\tYet, in audiovisual speech and speaker verification systems, the analysis is usually performed separately on different modalities, and the results are brought together using different fusion methods. However, in this process of separation of modalities, we lose valuable cross-modal information about the whole event or the object we are trying to analyse and detect. There is an inherent association between the two modalities and the analysis should take advantage of the synchronised appearance of the relationship between the audio and the visual signal. The second observation relates to different types of fusion techniques used for joint processing of audiovisual speech signals. The late-fusion strategy, which comprises decision or the score fusion, is effective especially in case the contributing modalities are uncorrelated and thus the resulting partial decisions are statistically independent. Feature level fusion techniques, on the other hand, can be favoured (only) if a couple of modalities are highly correlated. However, jointly occurring face and voice dynamics in speaking face video sequences, is neither highly correlated (mutually dependent) nor loosely correlated nor totally independent (mutually independent). A complex and nonlinear spatiotemporal coupling consisting of highly coupled, loosely coupled and mutually independent components may exist between co-occurring acoustic and visual speech signals in speaking face video sequences (Jiang et al(2002), Yehia et al (1999)). The compelling and extensive findings by authors in Jiang et al (2002), validate such complex relationship between external face movements, tongue movements, and speech acoustics when tested for consonant vowel (CV) syllables and sentences spoken by male and female talkers with different visual intelligibility ratings. They proved that the there is a higher correlation between speech and lip motion for C/a/ syllables than for C/i/ and C/u/ syllables. Further, the degree of correlation differs across different places of articulation, where lingual places have higher correlation than bilabial and glottal places. Also, mutual coupling can vary from talker to talker; depending on the gender of the talker, vowel context, place of articulation, voicing, and manner of articulation and the size of the face. Their findings also suggest that male speakers show higher correlations than female speakers. Further, the authors in Yehia et al (1999), also validate the complex, spatiotemporal and non-linear nature of the coupling between the vocal-tract and the facial articulators during speech production, governed by human physiology and language-specific phonetics. They also state that most likely connection between the tongue and the face is indirectly by way of the jaw. Other than the biomechanical coupling, another source of coupling is the control strategy between the tongue and cheeks. For example, when the vocal tract is shortened the tongue does not get retracted.
\n\t\t\tDue to such a complex nonlinear spatiotemporal coupling between speech and lip motion, this could be an ideal candidate for detecting and verifying liveness, and modelling the speaking faces by capturing this information can make the biometric authentication systems less vulnerable to spoof and fraudulent replay attacks, as it would be almost impossible to spoof a system which can accurately distinguish the artificially manufactured or synthesized speaking face video sequences from the live video sequences. Next section briefly describes the proposed cross modal association models based on cross-modal association models.
\n\t\tIn this section we describe the details of extracting audio-visual features based on cross-modal association models, which capture the nonlinear correlation components between the audio and lip modalities during speech production. This section is organised as follows: The details of proposed audio-visual correlation features based on different cross modal association techniques: Latent Semantic Analysis (LSA) technique, Cross-modal Factor Analysis (CFA) and Canonical Correlation Analysis (CCA) technique is described next.
\n\t\t\tLatent semantic analysis (LSA) is used as a powerful tool in text information retrieval to discover underlying semantic relationships between different textual units e.g. keywords and paragraphs (Li et al(2003), Li et al(2001)). It is possible to detect the semantic correlation between visual faces and their associated speech based on the LSA technique. The method consists of three major steps: the construction of a joint multimodal feature space, the normalization, the singular value decomposition (SVD), and the semantic association measurement.
\n\t\t\t\tGiven n visual features and m audio features at each of the t video frames, the joint feature space can be expressed as:
\n\t\t\t\twhere
\n\t\t\t\tVarious visual and audio features can have quite different variations. Normalization of each feature in the joint space according to its maximum elements (or certain other statistical measurements) is thus needed and can be expressed as:
\n\t\t\t\tAfter normalisation, all elements in the normalised matrix \n\t\t\t\t\t\t
where S and D are matrices composed of left and right singular vectors and V is the diagonal matrix of singular values in descending order.
\n\t\t\t\tKeeping only the first k singular vectors in S and D, we can derive an optimal approximation of with reduced feature dimensions, where the semantic correlation information between visual and audio features is mostly preserved. Traditional Pearson correlation or mutual information calculation (Li et al (2003), Hershey and Movellan (1999), Fisher et al(2000)) can then be used to effectively identify and measure semantic associations between different modalities. Experiments in Li et al(2003), have shown the effectiveness of LSA and its advantages over the direct use of traditional correlation calculation.
\n\t\t\t\tThe above optimization of \n\t\t\t\t\t\t
Where \n\t\t\t\t\t\t
The selection of an appropriate value for k is still an open issue in the literature. In general, k has to be large enough to keep most of the semantic structures. Eqn. 6 is not applicable for applications using off-line training since the optimization has to be performed on the fly directly based on the input data. However, due to the orthogonal property of singular vectors, we can rewrite Eqn. 6 in a new form as follows:
\n\t\t\t\tNow we only need the \n\t\t\t\t\t\t
LSA does not distinguish features from different modalities in the joint space. The optimal solution based on the overall distribution, which LSA models, may not best represent the semantic relationships between the features of different modalities, since distribution patterns among features from the same modality will also greatly impact the results of the LSA.
\n\t\t\t\tA solution to the above problem is to treat the features from different modalities as two separate subsets and focus only on the semantic patterns between these two subsets. Under the linear correlation model, the problem now is to find the optimal transformations that can best represent or identify the coupled patterns between the features of the two different subsets. We adopt the following optimization criterion to obtain the optimal transformations:
\n\t\t\t\tGiven two mean-centred matrices X and Y, which consist of row-by-row coupled samples from two subsets of features, we want orthogonal transformation matrices A and B that can minimise the expression:
\n\t\t\t\twhere
\n\t\t\t\t\n\t\t\t\t\tIn other words, A and B define two orthogonal transformation spaces where coupled data in X and Y can be projected as close to each other as possible.
\n\t\t\t\tSince we have:
\n\t\t\t\twhere the trace of a matrix is defined to be the sum of the diagonal elements. We can easily see from above that matrices A and B which maximise trace (XAB\n\t\t\t\t\t\n\t\t\t\t\t\tT\n\t\t\t\t\t\n\t\t\t\t\tY\n\t\t\t\t\t\n\t\t\t\t\t\tT\n\t\t\t\t\t) will minimise (10). It can be shown (Li et al(2003)), that such matrices are given by:
\n\t\t\t\tWith the optimal transformation matrices A and B, we can calculate the transformed version of X and Y as follows:
\n\t\t\t\tCorresponding vectors in \n\t\t\t\t\t\t
In addition to feature dimension reduction, feature selection capability is another advantage of CFA. The weights in A and B automatically reflect the significance of individual features, clearly demonstrating the great feature selection capability of CFA, which makes it a promising tool for different multimedia applications including audiovisual speaker identity verification.
\n\t\t\tFollowing the development of the previous section, we can adopt a different optimization criterion: Instead of minimizing the projected distance, we attempt to find transformation matrices A and B that maximise the correlation between X\n\t\t\t\t\t\n\t\t\t\t\t\tA\n\t\t\t\t\t and Y\n\t\t\t\t\t\n\t\t\t\t\t\tB\n\t\t\t\t\t. This can be described more specifically using the following mathematical formulations:
\n\t\t\t\tGiven two mean centered matrices X and Y as defined in the previous section, we seek matrices A and B such that
\n\t\t\t\twhere \n\t\t\t\t\t\t
The CCA is described in further details in Hotelling (1936) and Hardoon et al(2004). The optimization criteria used for all three cross modal associations CFA, CCA and LSA exhibit a high degree of noise tolerance. Hence the correlation features extracted perform better as compared to normal correlation analysis against noisy environmental conditions.
\n\t\t\tIn this Section, we describe the fusion approach used for combing the extracted audio-lip correlated components with mutually independent audio and visual speech features.
\n\t\t\tThe algorithm for fusion of audiovisual feature extracted using the cross modal association (CMA) models (a common term being used here to represent LSA, CFA or CCA analysis methods) can be described as follows:
\n\t\t\t\tLet f\n\t\t\t\t\t\n\t\t\t\t\t\tA\n\t\t\t\t\t and f\n\t\t\t\t\t\n\t\t\t\t\t\tL\n\t\t\t\t\t represent the audio MFCC and lip-region eigenlip features respectively. A and B represent the CMA transformation matrices (LSA, CFA or CMA matrices). One can apply CMA to find two new feature sets \n\t\t\t\t\t\t
Here \n\t\t\t\t\t\t
In the Bayesian framework, late fusion can be performed using the product rule assuming statistically independent modalities, and various methods have been proposed in the literature as alternatives to the product rule such as max rule, min rule and the reliability-based weighted summation rule (Nefian et al(2002), Movellan and Mineiro(1997)). In fact, the most generic way of computing the joint scores can be expressed as a weighted summation
\n\t\t\t\twhere \n\t\t\t\t\t\t
The hybrid audiovisual fusion vector in this Chapter was obtained by late fusion of feature fused correlated components (\n\t\t\t\t\t\t
For the RWS rule, the fusion weights are chosen empirically, whereas for the automatic weight adaptation, a mapping needs to be developed between modality reliability estimate and the modality weightings. The late fusion scores can be fused via sum rule or product rule. Both methods were evaluated for empirically chosen weights, and it was found that the results achieved for both were similar. However, sum rule for fusion has been shown to be more robust to classifier errors in literature (Jain et al (2005), Sanderson (2008)), and should perform better when the fusion weights are automatically, rather than empirically determined. Hence the results for additive fusion only, are presented here. Prior to late fusion, all scores were normalised to fall into the range of (0, 1), using min-max normalisation.
\n\t\t\t\twhere
\n\t\t\t\twhere, x\n\t\t\t\t\t\n\t\t\t\t\t\tA\n\t\t\t\t\t and x\n\t\t\t\t\t\n\t\t\t\t\t\tV\n\t\t\t\t\t refer to the audio test utterance and visual test sequence/image respectively.
\n\t\t\t\tTo carry out automatic fusion, that adapts to varying acoustic SNR conditions, a single parameter c, the fusion parameter, was used to define the weightings; the audio weight α and the visual weight β, i.e., both α and β dependent on c. Higher values of c (>0) place more emphasis on the audio module whereas lower values (<0) place more emphasis on the visual module. For c ≥ 1, α = 1 and β = 0, hence the audiovisual fused decision is based entirely on the audio likelihood score, whereas, for c ≤ -1, α = 0 and β = 1, the decision is based entirely on the visual score. So in order to account for varying acoustic conditions, only c has to be adapted.
\n\t\t\t\tThe reliability measure was the audio log-likelihood score\n\t\t\t\t\t\t
A sigmoid function was employed to provide a mapping between the c\n\t\t\t\t\t\n\t\t\t\t\t\topt\n\t\t\t\t\t and the ρ\n\t\t\t\t\t\tmean\n\t\t\t\t\t values, where c\n\t\t\t\t\t\n\t\t\t\t\t\tos\n\t\t\t\t\t and ρos represent the offsets of the fusion parameter and reliability estimate respectively; h captures the range of the fusion parameter; and d determines the steepness of the sigmoid curve. The sigmoidal parameters were determined empirically to give the best performance. Once the parameters have been determined, automatic fusion can be carried out. For each set of N test scores, the ρ value was calculated and mapped to c, using c = c(ρ), and hence, α and β can be determined. This fusion approach is similar to that used in (Sanderson(2008)) to perform speech recognition. The method can also be considered to be a secondary classifier, where the measured ρ value arising from the primary audio classifier is classified to a suitable c value; also, the secondary classifier is trained by determining the parameters of the sigmoid mapping.
\n\t\t\t\tSystem Overview of Hybrid Fusion Method
The described method can be employed to combine any two modules. It can also be adapted to include a third module. We assume here that only the audio signal is degraded when testing, and that the video signal is of fixed quality. The third module we use here is an audio-lip correlation module, which involves a cross modal transformation of feature fused audio-lip features based on CCA, CFA or LSA cross modal analysis as described in Section 3.
\n\t\t\t\tAn overview of the fusion method described is given in Figure 1. It can be seen that the reliability measure, ρ, depends only on the audio module scores. Following the sigmoidal mapping of ρ, the fusion parameter c is passed into the fusion module along with the three scores arising from the three modules; fusion takes place to give the audiovisual decision.
\n\t\t\tA experimental evaluation of proposed correlation features based on cross-modal association models and their subsequent hybrid usion was carried out with two different audio-visual speaking face video corpora VidTIMIT (Sanderson(2008)) and (DaFEx (Battocchi et al (2004), Mana et al (2006)). Figure 2 show some images from the two corpora. The details of the two corpora are given in VidTIMIT (Sanderson(2008), DaFEx (Battocchi et al (2004), Mana et al (2006)).
\n\t\t\tThe pattern recognition experiments with the data from the two corpora and the correlation features extracted from the data involved two phases, the training phase and the testing phase. In the training phase a 10-mixture Gaussian mixture model λ of a client’s audiovisual feature vectors was built, reflecting the probability densities for the combined phonemes and visemes (lip shapes) in the audiovisual feature space. In the testing phase, the clients’ live test recordings were first evaluated against the client’s model λ by determining the log likelihoods log p(X|λ) of the time sequences X of audiovisual feature vectors under the usual assumption of statistical independence of successive feature vectors.
\n\t\t\tFor testing replay attacks, we used a two level testing, a different approach from traditional impostor attacks testing used in identity verification experiments. Here the impostor attack is a surreptitious replay of previously recorded data and such an attack can be simulated by synthetic data. Two different types of replay attacks with increasing level of sophistication and complexity were simulated: the “static” replay attacks and the “dynamic” replay attacks.
\n\t\t\tSample Images from VidTIMIT and DaFeX corpus; a) VidTIMIT corpus; b) DaFeX corpus
For testing “static” replay attacks, a number of “fake” or synthetic recordings were constructed by combining the sequence of audio feature vectors from each test utterance with ONE visual feature vector chosen from the sequence of visual feature vectors and keeping that visual feature vector constant throughout the utterance. Such a synthetic sequence represents an attack on the authentication system, carried out by replaying an audio recording of a client’s utterance while presenting a still photograph to the camera. Four such fake audiovisual sequences were constructed from different still frames of each client test recording. Log-likelihoods log p(X’|λ) were computed for the fake sequences X’ of audiovisual feature vectors against the client model λ. In order to obtain suitable thresholds to distinguish live recordings from fake recordings, detection error trade-off (DET) curves and equal error rates (EER) were determined.
\n\t\t\tFor testing “dynamic” replay attacks, an efficient photo-realistic audio-driven facial animation technique with near-perfect lip-synching of the audio and several image key-frames of the speaking face video sequence was done to create a artificial speaking character for each person (Chetty and Wagner(2008), Sanderson(2008).
\n\t\t\tIn Bayesian framework, the liveness verification task can be essentially considered as a two class decision task, distinguishing the test data as a genuine client or an impostor. The impostor here is a fraudulent replay of client specific biometric data. For such a two-class decision task, the system can make two types of errors. The first type of error is a False Acceptance Error (FA), where an impostor (fraudulent replay attacker) is accepted. The second error is a False Rejection (FR), where a true claimant (genuine client) is rejected. Thus, the performance can be measured in terms of False Acceptance Rate (FAR) and False Reject Rate (FRR), as defined as (Eqn. 23):
\n\t\t\twhere I\n\t\t\t\t\n\t\t\t\t\tA\n\t\t\t\t is the number of impostors classified as true claimants, I\n\t\t\t\t\n\t\t\t\t\tT\n\t\t\t\t is the total number of impostor classification tests, C\n\t\t\t\t\n\t\t\t\t\tR\n\t\t\t\t is the number of true claimants classified as impostors, and C\n\t\t\t\t\n\t\t\t\t\tT\n\t\t\t\t is the total number of true claimant classification tests. The implications of this is minimizing the FAR increases the FRR and vice versa, since the errors are related. The trade-off between FAR and FRR is adjusted using the threshold θ, an experimentally determined speaker-independent global threshold from the training/enrolment data. The trade-off between FAR and FRR can be graphically represented by a Receiver Operating Characteristics (ROC) plot or a Detection Error Trade-off (DET) plot. The ROC plot is on a linear scale, while the DET plot is on a normal-deviate logarithmic scale. For DET plot, the FRR is plotted as a function of FAR. To quantify the performance into a single number, the Equal Error Rate (EER) is often used. Here the system is configured with a threshold, set to an operating point when FAR % = FRR %.
\n\t\t\tIt must be noted that the threshold θ can also be adjusted to obtain a desired performance on test data (data unseen by the system up to this point). Such a threshold is known as the aposteriori threshold. However, if the threshold is fixed before finding the performance, the threshold is known as the apriori threshold. The apriori threshold can be found via experimental means using training/enrolment or evaluation data, data which has also been unseen by the system up to this point, but is separate from test data.
\n\t\t\tPractically, the a priori threshold is more realistic. However, it is often difficult to find a reliable apriori threshold. The test section of a database is often divided into two sets: evaluation data and test data. If the evaluation data is not representative of the test data, then the apriori threshold will achieve significantly different results on evaluation and test data. Moreover, such a database division reduces the number of verification tests, thus decreasing the statistical significance of the results. For these reasons, many researchers prefer to use the aposteriori and interpret the performance obtained as the expected performance.
\n\t\t\tDifferent subsets of data from the VidTIMIT and DaFeX were used. The gender-specific universal background models (UBMs) were developed using the training data from two sessions, Session 1 and Session 2, of the VidTIMIT corpus, and for testing Session 3 was used. Due to the type of data available (test session sentences differ from training session sentences), only text-independent speaker verification experiments could be performed with VidTIMIT. This gave 1536 (2*8*24*4) seconds of training data for the male UBM and 576 (2*8*19*4) seconds of training data for the female UBM. The GMM topology with 10 Gaussian mixtures was used for all the experiments. The number of Gaussian mixtures was determined empirically to give the best performance. For the DaFeX database, similar gender-specific universal background models (UBMs) were obtained using training data from the text-dependent subsets corresponding to neutral expression. Ten sessions of the male and female speaking face data from these subsets were used for training and 5 sessions for testing.
\n\t\t\tFor all the experiments, the global threshold was set using test data. For the male only subset of the VidTIMIT database, there were 48 client trials (24 male speakers x 2 test utterances in Session 3) and 1104 impostor trials (24 male speakers x 2 test utterances in Session 3 x 23 impostors/client), and for the female VidTIMIT subset, there were 38 client trials (19 male speakers x 2 test utterances in Session 3) and 684 impostor trials (19 male speakers x 2 test utterances in Session 3 x 18 impostors/client). For the male only subset for DaFeX database, there were 25 client trials (5 male speakers x 5 test utterances in each subset) and 100 impostor trials (5 male speakers x 5 test utterances x 4 impostors/client), and for the female DaFeX subset, there were similar numbers of the client and impostor trials as in the male subset as we used 5 male and 5 female speakers from different subsets.
\n\t\t\tDifferent sets of experiments were conducted to evaluate the performance of the proposed correlation features based on cross modal association models (LSA, CCA and CMA), and their subsequent fusion in terms of DET curves and equal error rates (EER). Next Section discusses the results from different experiments.
\n\t\t\n\t\t\t\tFigure 3 plots the maximised diagonal terms of the between class correlation coefficient matrix after the LSA, CCA and CFA analysis of audio MFCC and lip-texture (\n\t\t\t\t\t
\n\t\t\t\tTable 1 presents the EER performance of the feature fusion of correlated audio-lip fusion features (cross modal features) for varying correlation coefficient threshold θ. Note that, when all the 40 transformed coefficients are used, the EER performance is 6.8%. The EER performance is observed to have a minimum around 4.7% for threshold values from 0.1 to 0.4. The optimal threshold that minimises the EER performance and the feature dimension is found to be 0.4.
\n\t\t\t\n\t\t\t\t\t\t | EER(%) at (θ, dim) | \n\t\t\t\t\t||||||
Θ | \n\t\t\t\t\t\t0.0 | \n\t\t\t\t\t\t0.1 | \n\t\t\t\t\t\t0.2 | \n\t\t\t\t\t\t0.3 | \n\t\t\t\t\t\t0.4 | \n\t\t\t\t\t\t0.5 | \n\t\t\t\t\t\t0.6 | \n\t\t\t\t\t
Dim | \n\t\t\t\t\t\t40 | \n\t\t\t\t\t\t15 | \n\t\t\t\t\t\t12 | \n\t\t\t\t\t\t10 | \n\t\t\t\t\t\t8 | \n\t\t\t\t\t\t6 | \n\t\t\t\t\t\t4 | \n\t\t\t\t\t
\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t6.8 | \n\t\t\t\t\t\t4.7 | \n\t\t\t\t\t\t5.3 | \n\t\t\t\t\t\t5.0 | \n\t\t\t\t\t\t4.7 | \n\t\t\t\t\t\t7.4 | \n\t\t\t\t\t\t10.3 | \n\t\t\t\t\t
\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t7.5 | \n\t\t\t\t\t\t5.18 | \n\t\t\t\t\t\t5.84 | \n\t\t\t\t\t\t5.5 | \n\t\t\t\t\t\t5.18 | \n\t\t\t\t\t\t8.16 | \n\t\t\t\t\t\t11.36 | \n\t\t\t\t\t
\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t11.7 | \n\t\t\t\t\t\t8.09 | \n\t\t\t\t\t\t9.12 | \n\t\t\t\t\t\t8.6 | \n\t\t\t\t\t\t8.09 | \n\t\t\t\t\t\t12.74 | \n\t\t\t\t\t\t17.74 | \n\t\t\t\t\t
Results for correlation features based in CMA models: EERs at varying correlation coefficient threshold values (θ) with the corresponding projection dimension (dim)
Sorted correlation coefficient plot for audio and lip texture cross modal analysis
As can seen in Table 2 and Figure 4, for static replay attack scenarios (from the last four rows in Table 2), the nonlinear correlation components between acoustic and orafacial articulators during speech production is more efficiently captured by hybrid fusion scheme involving late fusion of audio \n\t\t\t\t\t
\n\t\t\t\t\t\t | VidTIMIT male subset | \n\t\t\t\t\t\tDaFeX male subset | \n\t\t\t\t\t||||
Modality | \n\t\t\t\t\t\tCFA EER (%) | \n\t\t\t\t\t\tCCA EER (%) | \n\t\t\t\t\t\tLSA EER (%) | \n\t\t\t\t\t\tCFA EER (%) | \n\t\t\t\t\t\tCCA EER (%) | \n\t\t\t\t\t\tLSA EER (%) | \n\t\t\t\t\t
\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t4.88 | \n\t\t\t\t\t\t4.88 | \n\t\t\t\t\t\t4.88 | \n\t\t\t\t\t\t5. 7 | \n\t\t\t\t\t\t5. 7 | \n\t\t\t\t\t\t5. 7 | \n\t\t\t\t\t
\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t6.2 | \n\t\t\t\t\t\t6.2 | \n\t\t\t\t\t\t6.2 | \n\t\t\t\t\t\t7.64 | \n\t\t\t\t\t\t7.64 | \n\t\t\t\t\t\t7.64 | \n\t\t\t\t\t
\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t7.87 | \n\t\t\t\t\t\t7.87 | \n\t\t\t\t\t\t7.87 | \n\t\t\t\t\t\t9.63 | \n\t\t\t\t\t\t9.63 | \n\t\t\t\t\t\t9.63 | \n\t\t\t\t\t
\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t3.78 | \n\t\t\t\t\t\t2.3 | \n\t\t\t\t\t\t2.76 | \n\t\t\t\t\t\t4.15 | \n\t\t\t\t\t\t2.89 | \n\t\t\t\t\t\t3.14 | \n\t\t\t\t\t
\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t2.97 | \n\t\t\t\t\t\t2.97 | \n\t\t\t\t\t\t2.97 | \n\t\t\t\t\t\t3.01 | \n\t\t\t\t\t\t3.01 | \n\t\t\t\t\t\t3.01 | \n\t\t\t\t\t
\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t0.56 | \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t0.31\n\t\t\t\t\t\t | \n\t\t\t\t\t\t0.42 | \n\t\t\t\t\t\t0.58 | \n\t\t\t\t\t\t0.38 | \n\t\t\t\t\t\t0.57 | \n\t\t\t\t\t
\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t6.68 | \n\t\t\t\t\t\t6.68 | \n\t\t\t\t\t\t6.68 | \n\t\t\t\t\t\t7.75 | \n\t\t\t\t\t\t7.75 | \n\t\t\t\t\t\t7.75 | \n\t\t\t\t\t
\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t0.92 | \n\t\t\t\t\t\t0.72 | \n\t\t\t\t\t\t0.81 | \n\t\t\t\t\t\t0.85 | \n\t\t\t\t\t\t0.78 | \n\t\t\t\t\t\t0.83 | \n\t\t\t\t\t
EER performance for static replay attack scenario with late fusion of correlated components with mutually independent components: (+) represents RWS rule for late fusion, (-) represents feature level fusion)
Though all correlation features performed well, the CCA features appear to be the best performer for static attack scenario, with an EER of 0.31%. This was the case for all the subsets of data shown in Table 2. Also, the EERs for hybrid fusion experiments with \n\t\t\t\t\t
The EER table in Table 3 shows the evaluation of hybrid fusion of correlated audio-lip features based on cross modal analysis (CFA, CCA and LSA) for dynamic replay attack scenario. As can be seen, the CMA optimized correlation features perform better as compared to uncorrelated audio-lip features for complex dynamic attacks. Further, for the VidTIMIT male subset, it was possible to achieve the best EER of l0.06% for \n\t\t\t\t\t
In this Chapter, we have proposed liveness verification for enhancing the robustness of biometric person authentication systems against impostor attacks involving fraudulent replay of client data. Several correlation features based on novel cross-modal association models have been proposed as an effective countermeasure against such attacks. These new
\n\t\t\tDET curves for hybrid fusion of correlated audio-lip features and mutually independent audio-lip features for static replay attack scenario
correlation measures model the nonlinear acoustic-labial temporal correlations for the speaking faces during speech production, and can enhance the system robustness against replay attacks.
\n\t\t\tFurther, a systematic evaluation methodology was developed, involving increasing level of difficulty in attacking the system – moderate and simple static replay attacks, and, sophisticated and complex dynamic replay attacks, allowing a better assessment of system vulnerability against attacks of increasing complexity and sophistication. For both static and dynamic replay attacks, the EER results were very promising for the proposed correlation features, and their hybrid fusion with loosely coupled (feature-fusion) and mutually independent (late fusion) components, as compared to fusion of uncorrelated features. This suggests that it is possible to perform liveness verification in authentication paradigm. and thwart replay attacks on the system. Further, this study shows that, it is difficult to beat the system, if underlying modelling approach involves efficient feature extraction and feature selection techniques, that can capture intrinsic biomechanical properties accurately.
\n\t\t\t\n\t\t\t\t\t\t | VidTIMIT male subset | \n\t\t\t\t\t\tDaFeX male subset | \n\t\t\t\t\t||||
Modality | \n\t\t\t\t\t\tCFA EER (%) | \n\t\t\t\t\t\tCCA EER (%) | \n\t\t\t\t\t\tLSA EER (%) | \n\t\t\t\t\t\tCFA EER (%) | \n\t\t\t\t\t\tCCA EER (%) | \n\t\t\t\t\t\tLSA EER (%) | \n\t\t\t\t\t
\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t36.58 | \n\t\t\t\t\t\t36. 58 | \n\t\t\t\t\t\t36. 58 | \n\t\t\t\t\t\t37.51 | \n\t\t\t\t\t\t37. 51 | \n\t\t\t\t\t\t37. 51 | \n\t\t\t\t\t
\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t27.68 | \n\t\t\t\t\t\t27.68 | \n\t\t\t\t\t\t27.68 | \n\t\t\t\t\t\t28.88 | \n\t\t\t\t\t\t28.88 | \n\t\t\t\t\t\t28.88 | \n\t\t\t\t\t
\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t24.48 | \n\t\t\t\t\t\t22.36 | \n\t\t\t\t\t\t23.78 | \n\t\t\t\t\t\t26.43 | \n\t\t\t\t\t\t24.67 | \n\t\t\t\t\t\t25.89 | \n\t\t\t\t\t
\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t22.45 | \n\t\t\t\t\t\t22.45 | \n\t\t\t\t\t\t22.45 | \n\t\t\t\t\t\t23.67 | \n\t\t\t\t\t\t23.67 | \n\t\t\t\t\t\t23.67 | \n\t\t\t\t\t
\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t17.89 | \n\t\t\t\t\t\t16.44 | \n\t\t\t\t\t\t19.48 | \n\t\t\t\t\t\t18.46 | \n\t\t\t\t\t\t17.43 | \n\t\t\t\t\t\t20.11 | \n\t\t\t\t\t
\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t21.67 | \n\t\t\t\t\t\t21.67 | \n\t\t\t\t\t\t21.67 | \n\t\t\t\t\t\t25.42 | \n\t\t\t\t\t\t25.42 | \n\t\t\t\t\t\t25.42 | \n\t\t\t\t\t
\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t14.23 | \n\t\t\t\t\t\t10.06 | \n\t\t\t\t\t\t12.27 | \n\t\t\t\t\t\t16.68 | \n\t\t\t\t\t\t12.36 | \n\t\t\t\t\t\t13.88 | \n\t\t\t\t\t
EER performance for dynamic replay attack scenario with late fusion of correlated components with mutually independent components
However, though the EER performance appeared to be very promising for static replay attack scenarios (EER of 0.31 % for CCA features), the deterioration in performance for more sophisticated - dynamic replay attack scenario (EER of 10.06 % for CCA features), suggests that, there is an urgent need to investigate more robust feature extraction, feature selection, and classifier approaches, as well as sophisticated replay attack modelling techniques. Further research will focus on these two aspects.
\n\t\tGlycans are long chains of carbohydrate-based polymers composed of repeating units of monosaccharide monomers bound together by glycosidic linkages. Complex and diverse glycans appear to be ever-present macromolecules in all cells in nature, and essential to all biological systems. Glycans play physical, structural, and metabolic roles in living organisms [1]. In the last century, knowledge on the biochemistry and biology of nucleic acids and proteins rapidly increased. Nevertheless, it has been much more difficult to understand the biology of glycans, which are main component of the cell surface [2]. The biosynthesis mechanism of glycans is totally different from those of nucleic acids and proteins. Biological mechanism of glycans is complex, which makes analysis of them extremely difficult and limits our understanding of mechanisms responsible for biological functions of glycans [3]. After the genomics revolution and development of high-throughput technologies, scientific interests increased to understand the characterization, function, and interaction of other significant biomolecules (e.g., DNA transcripts, proteins, lipids, and glycans) for the cell. These interests resulted in emergence of other omic types such as transcriptomics, proteomics, metabolomics, lipidomics and glycomics [4]. From the perspective of evolutionary conservation, conservation decreased in the order genomics, transcriptomics, proteomics, metabolomics, lipidomics, and glycomics. On the other hand, reverse order is present for informational diversity of these fields of omics (Figure 1) [5].
\nThe degree of evolutionary conservation and informational diversity for the omics fields.
With the progress in high-throughput technologies, studies on glycobiology increased to screen cells quickly and generate huge glycomics data sets. Moreover, advanced analytical techniques and tools for data analysis provide possibility to improve high-throughput techniques for screening glycans as a marker of diseases and to classify structure of glycans in therapeutic proteins [6].
\nGlycans are linear or branched sugar macromolecules composed of repeating monosaccharides linked glycosidically. Beside nucleic acids and protein, glycans are known as the third dimension in molecular biology [7, 8]. These macromolecules can be found in the form of heteropolysaccharides or homopolysaccharides. Furthermore, glycoconjugates (glycolipid, glycoprotein and proteoglycan), can be also considered as glycan despite the fact that the carbohydrate part of glycoconjugates are only oligosaccharides [9]. In glycoproteins, oligosaccharides and proteins can be linked in different forms, namely N-linked glycans and O-linked glycans. N-acetylglucosamine is linked to the amide side chain of asparagine in N-linked glycans. C-1 of N-acetylgalactosamine is linked to the hydroxyl function of serine or threonine in O-linked glycans [10].
\nWith the increasing researches in glycoscience, many different roles of glycans in biological systems have been revealed in the last decades. Significant functions of glycans have been determined in numerous research areas such as immunity, development and differentiation, biopharmaceuticals, cancer, fertilization, blood types, infectious diseases, etc. Glycans are called as “cloths of cells” since they are present on the surface of the cell and responsible for the signaling and communications between cells. Glycans can be classified in several ways. Varki divided the biological roles of glycans into four main categories: (1) structural and modulatory roles, (2) extrinsic (interspecies) recognition of glycans, (3) intrinsic (intraspecies) recognition of glycans, and (4) molecular mimicry of host glycans. A total of 50 distinct roles are defined under these main categories [1].
\nGlycans perform huge range of biological function due to the diversity of them, and they have significant roles in several physiological and pathological events, such as cell growth, cell signaling, cell-cell interactions, differentiation, and tumor growth [11, 12, 13]. In biological systems, information is carried by glycans, which are significant biomarker candidates for many diseases such as cardiovascular diseases, deficiencies of immune system, genetically inherited disorders, several cancer types, and neurodegenerative diseases [14, 15, 16]. Alteration of glycan expression is observed during the development and progression of these diseases, which is caused by misregulated enzymes such as glycosyltransferases and glycosidases. As a result, altered glycan structures have potential use for the identification of these diseases at an early stage. Besides significant role of glycans in diagnosis and management of disease, they can be used as therapeutics, markers for identification and isolation of special cell types, and targets in discovery of drugs [17, 18, 19]. Moreover, glycans can be considered as an ideal target for vaccines due to the presence of them on the surface of several different pathogens and malignant cells. High affinity and exquisite specificity of other molecules to recognize glycans are a vital point of developments in the research of glycans and related diagnostics and therapeutic applications.
\nGlycosylation plays significant roles in many biological processes including growth and development of cell, tumor growth and metastasis, immune recognition and response, intercommunication of cells, and microbial pathogenesis. As a result, glycosylation of proteins is the one of the most common and significant posttranslational modifications of proteins [20, 21]. Furthermore, more than half of proteins undergo glycosylation [6]. Many issues such as genetic factors, nucleotide levels of monosaccharides, cytokines, metabolites, hormones, and ecological factors can affect and change glycosylation process [20, 21, 22, 23, 24]. Thus, integration of omics approaches (e.g., proteomics, genomics, transcriptomics, and metabolomics) to the field of glycobiology is essential to view the big picture of the whole biological system [20, 21, 25]. Furthermore, for the analysis of glycans and glycosylation pathways, many glycoinformatics tools and databases are now accessible [6].
\nGlycomics is one of the most recent types of omics area which is responsible for the structure and function evaluations of glycans in bio-systems [26]. Integrating glycomics to other fields of omics provides new system-scale insights in integrative biology [27].
\nMoreover, glycomics informs other crucial scholarships such as systems glycobiology and personalized glycomedicine that collectively aim to explain the role of glycans in person-to-person and between population variations in disease susceptibility and response to health interventions such as drugs, nutrition, and vaccines. Glycosylation is present in both normal and diseased individuals [1]. Abnormal glycosylation is observed in a variety of diseases. Difference between glycosylation patterns of healthy and diseased individuals can be used as glycobiomarkers in personalized medicine [28]. As a result, many new medical implications will be enabled by glycobiology and glycopathology [29]. Development of glycomedicine can be contributed by holistic approach of functional and structural glycomics, which have applications in therapy development, fine-tuning immunological responses and the performance of therapeutic antibodies and boosting immune responses [28, 30]. Many applications of glycan arrays are present in many fields, from basic biochemical research to biomedical applications [31]. In addition to shotgun glycan microarrays [32], cell-based array resource has been developed [33]. These developments enable deeper understanding of the many biological roles of the glycome. Nevertheless, multiplatform and multiomics technologies are expected to further extend the knowledge of molecular mechanisms of glycans.
\nMonosaccharides represent four free hydroxyl groups for the linkage of another monosaccharide. As a result of this, glycans have more complex structure compared to structure of peptides and nucleic acids. It is known that glycans are more than the sequential monosaccharides; monomer types, modifications, the position of modifications around the ring of sugar, glycopolymer branching, and linkages chirality are the factors that are responsible for the complexity. As a result, sequencing techniques used for peptides or DNA (Sanger or Edman sequencing) are not appropriate for glycans. Moreover, most of the glycans are present as a part of a glycoconjugate. Therefore, glycan part should be released from lipid or protein part, by the use of enzymatic or chemical methods and isolated for analysis.
\nIn the last decades, a number of techniques developed and applied to determine structure of the glycans with different degrees of detail [34]. A traditional method is to label the glycoconjugates radioactively and then apply anionic exchange, gel filtration, or paper chromatographic analyses prior and subsequent to enzymatic or chemical treatments. Still, it is difficult to figure out the definition of the actual structure; in consequence, in earlier studies, if adequate amounts were present, gas chromatography together with mass spectrometry (GC-MS) and/or nuclear magnetic resonance (NMR) studies were performed. However, these analyses involve special expertise to perform the research and interpret the results, particularly if standards were unavailable to compare with results.
\nHPLC and UPLC have superseded simple chromatography systems in recent years, and radioactive labeling has been replaced by fluorescent labeling. Nowadays, variable columns such as graphitized carbon, reversed-phase (RP), anion exchange, normal phase, or hydrophilic interaction resins can be used along with suitable enzymatic/chemical treatments. A less used alternative is to analyze glycans at elevated pH. As a result of this, the hydroxyl side chain deprotonation occurs, that enables the usage of anion exchange together with amperometric detection (HPAEC-PAD). On the other hand, glycan structure cannot be defined only by HPLC retention times, and for the unknown structure, analyses in the absence of standards should be interpreted with attention [35].
\nWith the improvements in the types and the sensitivity, contribution of mass spectrometer to studies of glycans and glycoconjugates has increased in the last decades [36, 37]. At first, for the analysis of variable types of glycopolymers from different sources, researchers used fast atom bombardment mass spectrometers (FAB-MS). For the analyses with FAB-MS, chemical modifications such as methylation and acetylation were required. As an alternative method, matrix-assisted laser-desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) was developed and analysis of both permethylated and native glycans can be performed with MALDI-TOF MS. Furthermore, current numerous electrospray techniques with many detector types have significance in glycomics. Mainly, a significant point in MS-based analysis is the capability to obtain glycan fragments. Besides, the preparation and separation techniques are of great importance to obtain the best results. As a consequence, liquid chromatography-mass spectrometry (LC-MS) in a number of forms is in general necessary since glycans with low abundancy or poor ionization capacity can be suppressed in the case of whole glycome examination. Moreover, reanalysis after the treatment of a chemical and an enzyme results in maximization of the ability to obtain clear results from the existing data.
\nGlycan is generally a part of the glycoconjugate; thus, glycoproteomics and glycolipidomics that consider both peptide/lipid and glycan parts are significant fields. At this point, mass spectrometry technique comes into prominence [38]. Both glycan and polypeptide/lipid parts can be studied with this technique. On the other hand, glycan parts of glycoproteins and glycolipids can be in various forms even if the polypeptide/lipid part is same, defined as microheterogeneity. The nature of glycan modifications is non-template driven and that leads to mentioned microheterogeneity [39].
\nBlotting technique can be used for simple screens. Reagents such as lectins and anti-carbohydrate antibodies with low specificity are often used for this technique; as a result, misleading results are often obtained [40]. Still, lectins, and antiserums have significance for immune responses in animals. New array-based systems can provide essential clues on proteins bounded to glycans [41].
\nDevelopments in integrative informatics and systems biology of glycans based on a holistic approach can make available a more comprehensive analysis. It elucidates annotation of glycans, enzyme levels, abundances of glycans biosynthesis pathways, and other omics data sets which are complementary. Though, several tools are developed for proteomics and genomics data sets and standard bioinformatics approaches are used in these tools, the complex relationships between diverse components (such as glycans, enzymes, transporters, and sugar nucleotides) of the glycosylation process are not considered by most of the existing bioinformatics tools. Consequently, the use of these tools for glycomics data sets has some limitations. The genome does not encode glycans directly and unlike proteins, interconnected action of many enzymes provides assembly of glycans. Due to mentioned limitations, developments in glycan analysis tools and methods have been delayed and most of the present glycoinformatics tools are special for single type data analysis [42, 43, 44, 45]. For instance, database matching between obtained MS results and specific glycans in a glycan library is used as a mutual method for MS-based glycoprofiling for the purpose of individual peak annotation [46, 47]. If the complexity of glycosylation is wanted to be considered, enzymes of the organism which synthesize the studied glycans should generate glycan structures used for the annotation of the spectrum [48]. Due to this alignment, activities of enzyme and those structures assigned to each peak in the same spectrum will be consistent.
\nAlthough many omics approaches have significant progress in the last decades, existing techniques of bioinformatics are still unsatisfactory for the integration of varied data sets [49, 50, 51]. For instance, relations between expression levels of gene and specific glycan linkages abundance are investigated by statistical database-driven approaches, and these approaches could not predict quantity of detailed glycan distributions [50, 51]. This indicates the necessity of glycoinformatics and systems biology tools integration for the identification of glycan structure and these should be also linked to the information of gene expression responsible for glycosylation enzymes which synthesize these glycans. In order to understand levels of mRNA which is related with the distribution and quantity of glycans present within healthy and diseased cells, mathematical modeling of glycosylation is considered as a promising method [48, 49, 52].
\nVariability in the platform of analytical high-throughput experiments can be reduced by data integration approach. Increased confidence of biomarker predictions and recommendations can be obtained if different data from experiments such as glycogenes expression information or mass spectra profile confirm the results from integrative glycoinformatics and systems glycobiology tools. Although integrated glycoinformatics tools have limitations in analytical sensitivities, analysis and comparison of various results with various platforms are enabled by these tools [6].
\nThe integration of glycomics with other various omics data is promising for further innovation in diagnosis and treatment of diseases [30]. The start point of multiomics data integration is to sort the data based on the omics level. In the following part, association between glycomics and other omics levels will be represented.
\nIntegration of glycomics with genetic sequence can be occur in a number of ways. For instance, glycosylation site can be gained or lost with the variation of sequence. A single-nucleotide polymorphism (SNP) affects glycosylation of prostate-specific antigen (PSA) and an altered function of it increases the risk of prostate cancer. Functional analysis indicated that the stability and structural conformation of PSA are affected by missense variant rs61752561, which causes an additional extra glycosylation site [53]. Furthermore, computational studies revealed that variations in cancer somatic cells have potential to cause gain or loss of glycosylation. In addition to SNP, variations in structure and abnormalities in cytogenetics could be integrated with glycomics. Cytogenetic abnormalities have been associated with glycome expression [54]. A particular glycosyltransferase can glycosylate numerous proteins, so genetic variants of it have extraordinary significance because function of many glycoproteins can be affected by a single difference in activity of enzyme. Several downstream pathways and cell metabolism can be affected by a genetic or epigenetic variant that is called pleiotropic effect of genetic or epigenetic variant on glycosylation [55].
\nMost of the glycomics research have been done at the level of transcriptome, which can be performed either at a particular locus or with a technology of microarray. In colorectal cancer (CRC), glycosyltransferase ST6GAL1 is associated with cancer, and altered ST6GAL1 expression was found by The Cancer Genome Atlas (TCGA) mining [56]. Moreover, in order to identify differential expression of glycosylation-related genes Saravanan et al. [57] used GLYCOv2 glycogene microarray technology. In the further studies, myeloma was compared with normal plasma cell samples and 60 upregulated and 20 downregulated genes were found among 243 genes in glycan-biosynthesis pathway [54]. A novel molecular signature that is enriched for enzymes of glycosylation was revealed by meta-analysis performed for gene expression of prostate cancer [58]. Additionally, hepatocellular carcinoma was investigated by reviewing gene expressions that are related with core fucosylation of the disease [59]. More systematic reviews and meta-analyses are required to develop reliable biomarkers.
\nStudies on glycoproteomics include peptide structures, glycan structures, and sites of glycosylation [30]. Single site on the peptide chain can be glycosylated by different glycans, and by this way, glycans can modulate function of the protein [60]. In the literature, diverse techniques were associated with different phenotypes, for instance, breast cancer, colon cancer, liver cancer, skin cancer, ovary cancer, bladder cancer, and neurodegenerative diseases, and additionally, a number of structural variations including sialylation, fucosylation, degree of branching, and specific glycosyltransferases expression [61, 62, 63]. For instance, cerebrospinal fluid N-glycoproteomics is of significant importance in early diagnosis of Alzheimer’s disease. Glycosylation patterns were assessed in patients and therapeutics targets such as glycoenzymes were suggested [63]. For the diagnosis of pancreatic cancer, specific glycoforms together with protein levels should be measured to improve potential for diagnosis [64]. Glycoproteins constitute the majority of protein tumor markers approved by Food and Drug Administration (FDA), and they are also used currently in clinical practice. Many of these glycoproteins have alterations of glycosylation in cancer [60]. MUC-1 (CA15-3/CA27.29) [65] and plasminogen activator inhibitor (PAI-1) [66] are biomarkers of breast cancer; beta-human chorionic gonadotropin (Beta-hCG) [67] is biomarker of colorectal cancer; alpha-fetoprotein (AFP) [68] is a biomarker of liver cancer and germ cell tumors; chromogranin A (CgA) [69] is a biomarker of neuroendocrine tumors; MUC16 (CA-125) [70] and HE4 [71] are biomarkers of ovarian cancer; and many other biomarkers are present for a variety type of cancer. Most of the results in the existing publications are heterogeneous; thus, systematic integrative reviews of the literature are required for further development of glycoproteomics.
\nMetabolomics is the large-scale study of the small molecule substrates that investigates variations in the metabolites within cells, biofluids, tissues, or organism. Metabolomics and glycomics were investigated in the research of post-traumatic stress [72]. According to the researchers of this study, these biomarkers together with omics markers should be integrated to understand the biological differences responsible for this stress. For discovery of liver cancer biomarker, proteomics, glycomics, and metabolomics were integrated and this integration enhanced performance when compared to separate omics data [73]. Physiological and pathological conditions are reflected by metabolomic and glycomic data in individuals. Similar to metabolites, small glycans can be quantified easily [74]. Human Metabolome Database (HMDB) is the most inclusive metabolite source that offers significant resource for the discovery of biomarkers in glycomics [75].
\nGlycolipidomics is a scientific field that identifies and quantifies glycolipids. For the determination of physiological and pathological conditions of individual, glycolipids can be used as a specific biomarker. They take role in development of neurological and neurodegenerative diseases, such as Lewy body dementia, Alzheimer’s disease, Parkinson’s disease, and frontotemporal dementia [30]. Furthermore, glycosphingolipids are associated with cancer and they are promising molecules for diagnosis as biomarkers and for malignant tumor immunotherapy as target [76]. More recently, Dehelean et al. [77] reviewed trends in the discovery of glycolipid biomarker by MS.
\nInteractomics is the research field that investigates whole set of interactions between molecules including glycans. Interaction of glycans with glycan-binding proteins (GBPs) is of significant importance in immune response, signaling, cell recognition, infections, neurodegenerative diseases, and cancer. High-throughput technologies ease studies also on interactomics [78]. UniLectin3D is a database that catalog lectins that are most studied GBPs. Database consists of curated information on 3D structures and interacting ligands [79]. Lectin-glycan interaction on surface of the cell is a significant factor for the regulation in corneal biology (i.e., corneal infection) and pathophysiology (i.e., inflammation) [80]. The whole protein-glycan interactome information has not been obtained yet [41]. For future studies, estimated number of interactions is of importance. GenProBiS is a bioinformatics tool that analyzes binding sites between peptide-peptide, peptide-nucleic acid, and peptide-compound and also sites of glycosylation and other posttranslational modifications. Furthermore, it provides maps between sequence variations and structure of protein. More developments of bioinformatics tools analyzing huge data will prioritize the objections for experimental verification and provide contribution to interactomics development.
\nIn future studies, many other omics fields should be associated with glycomics such as comparative genomics, epigenomics, regulomics, NcRNomics, MiRNomics, LncRNomics, etc. Although glycomics is the significant field related with molecular interactions, information about how these complex processes controlled by regulatory network is still inadequate. In addition to classic omics fields, omics applications such as iatromics, environmental omics, pharmacogenomics, and nutrigenomics should also be reviewed.
\nGlycoinformatics combines bioinformatics tools with glycome. Glycomics data is collected by the tools and databases to investigate, reveal, and associate with other repository of related data of proteomics, genomics, and interactomics. Commonly used tools and databases are summarized in Table 1.
\n\n | Name | \nDescription | \nLink | \n
---|---|---|---|
Databases | \nCAZY | \nDescribes the families of structurally related catalytic and carbohydrate binding modules (or functional domains) of enzymes that degrade, modify, or create glycosidic bonds | \n\n | \n
KEGG GLYCAN | \nThe KEGG GLYCAN structure database is a collection of experimentally determined glycan structures. It contains all unique structures taken from CarbBank, structures entered from recent publications, and structures present in KEGG | \n\n | \n|
Glycan Library | \nA list of approximately 830 lipid-linked sequence-defined glycan probes derived from diverse natural sources or chemically synthesized | \n\n | \n|
GlycoMob | \nAn ion mobility-mass spectrometry collision cross-section database for glycomics | \n\n | \n|
GlycoBase 3.2 | \nA database of over 650 N- and O-linked glycan structures of HPLC, UPLC, exoglycosidase sequencing, and mass spectrometry (MALDI-MS, ESI-MS, ESI-MS/MS, LC-MS, LC-ESI-MS/MS) data | \n\n | \n|
Glyco3D | \nA portal of 3D structures of mono-, di-, oligo-, and polysaccharides and carbohydrate recognizing proteins (lectins, monoclonal antibodies, glycosyltransferases) and glycosaminoglycan binding proteins | \n\n | \n|
GlyMAP | \nAn online resource mapping of the variational landscape of glycoactive enzymes | \n\n | \n|
Glycosciences.de | \nA collection of databases and bioinformatics tools for glycobiology and glycomics | \n\n | \n|
UniProtKB | \nThe universal protein sequence database with information on glycosylated proteins | \n\n | \n|
UniCarbKB | \nUniCarbKB is a curated and annotated glycan database which curates information from the scientific literature on glycoprotein-derived glycan structures. It includes data previously available from GlycoSuiteDB | \nhttp://www.unicarbkb.org/ | \n|
UniCarbDB | \nUniCarbDB is a platform for presenting glycan structures and fragment data characterized by LC-MS/MS strategies. The database is annotated with high-quality datasets and is designed to extend and reinforce those standards and ontologies developed by existing glycomics databases | \n\n | \n|
UniPep | \nA database for human N-linked glycosites: a resource for biomarker discovery | \n\n | \n|
SugarBindDB | \nSugarBindDB provides a collection of known carbohydrate sequences to which pathogenic organisms specifically adhere via lectins or adhesins. The data were compiled through an exhaustive search of literature published over the past 30 years by glycobiologists, microbiologists, and medical histologists | \nhttp://sugarbind.expasy.org/ | \n|
Consortium for Functional Glycomics (CFG) | \nThe CFG serves to combine the expertise and glycomics resources to reveal functions of glycans and glycan-binding proteins (GBPs) that impact human health and disease. The CFG offers resources to the community, including glycan array screening services, a reagent bank, and access to a large glycomics database and data analysis tools | \n\n | \n|
GLYCONAVI | \nA Website for carbohydrate research. It consists of the “GlycoNAVI database” for molecular information of carbohydrates, and chemical reactions of carbohydrate synthesis, the “Route Searching System for Glycan Synthesis,” and “GlycoNAVI tools” for editing two-dimensional molecular structure of carbohydrates | \n\n | \n|
GlycoGeneDataBase (GGDB) | \nGlycogene includes genes associated with glycan synthesis such as glycosyltransferase, sugar nucleotide synthases, sugar-nucleotide transporters, sulfotransferases, etc. | \n\n | \n|
Carbohydrate Structure Data Base (CSDB) | \nCSDB covers information on structures and taxonomy of natural carbohydrates published in the literature and mostly resolved by nuclear magnetic resonance (NMR). CSDB is composed of two parts: Bacterial and Archeal (BCSDB) and Plant and Fungal (PFCSDB) | \n\n | \n|
EXPASy | \nThis section of the ExPASy server gathers a toolbox for processing data as well as simulating, predicting, or visualizing information, relative to glycans, glycoproteins, and glycan-binding proteins | \nhttp://www.expasy.org/glycomics | \n|
TOOLS | \nCASPER | \nA tool for calculating NMR chemical shifts of oligo- and polysaccharides | \n\n | \n
Glycan Builder | \nA software library and set of tools to allow the rapid drawing of glycan structures with support for all of the most common symbolic notation formats | \n\n | \n|
GlycoDomainViewer | \nAn online resource to study site glycosylation with respect to protein context and conservation | \n\n | \n|
Glynsight | \nGlynsight offers visualization and interactive comparison of glycan expression profiles. The tool was initially developed with a focus on IgG N-glycan profiles but it was extended to usage with any experiment, which produces N- or O-linked glycan expression data | \n\n | \n|
GlycoMinestruct | \nA new bioinformatics tool for highly accurate mapping of the human N-linked and O-linked glycoproteomes by incorporating structural features | \n\n | \n|
GlyMAP | \nAn online resource mapping out the variational landscape of glycoactive enzymes | \n\n | \n|
GlycoMod | \nAn online tool to predict oligosaccharide structures on proteins from experimentally determined masses | \n\n | \n|
GlycoMiner/GlycoPattern | \nSoftware tools designed to detect, characterize, and perform relative quantitation of N-glycopeptides based on LC-MS runs | \n\n | \n|
Glycosciences.de | \nA collection of databases and bioinformatics tools for glycobiology and glycomics | \n\n | \n|
RINGS | \nA Web resource providing algorithmic and data mining tools to aid glycobiology research | \n\n | \n|
MonosaccharideDB | \nA comprehensive reference source for monosaccharide notation | \n\n | \n|
NetOGlyc | \nNext generation prediction of O-glycosylation sites on proteins | \n\n | \n|
GlycoSpectrumScan | \nA Web-based bioinformatic tool designed to link glycomics and proteomics analyses for the characterization of glycopeptides. GlycoSpectrumScan is a MS platform which is independent, freely accessible, and profiles glycopeptide MS data using beforehand separately acquired released glycan and proteomics information. Both N- and O-glycosylated peptides as well as multiply glycosylated peptides can be analyzed | \n\n | \n|
SimGlycan | \nA predictive carbohydrate analysis tool for MS/MS data | \n\n | \n|
SugarQb | \nSugarQb enables genome-wide insights into protein glycosylation and glycan modifications in complex biological systems. This is a collection of software tools (Nodes) which enable the automated identification of intact glycopeptides from HCD‐MS/MS data sets, using commonly use peptide-centric MS/MS search engines | \n\n | \n|
GlycoDigest | \nGlycoDigest is a tool that simulates exoglycosidase digestion based on controlled rules acquired from expert knowledge and experimental evidence available in GlycoBase | \n\n | \n|
Virtual Glycome | \nThis Website is focused on presenting selected computational tools and experimental resources that can be used to better understand the processes regulating cellular glycosylation at multiple levels | \n\n | \n|
SweetUnityMol | \nSoftware to display 3-D structures of carbohydrates, polysaccharides, and glycoconjugates | \n\n | \n
Tools and databases used in glycoinformatics.
System-based analyses applied smoothly to network of signaling, metabolic processes, and physiological modeling; however, applications in systems glycobiology still have problems in computational and analytical studies and this situation arises from prominent bottlenecks [81]: (i) there is no accepted standard for model building; (ii) glycoinformatics databases are underdeveloped; (iii) and insufficient quantitative data are from glycoproteomics experiments.
\nIn recent years, many systems based models have been developed to simulate biosynthesis of glycans. Nevertheless, difficulty in the incorporation of glycan structure and specificity data of enzymes related with glycosylation into mathematical models. As a result of this difficulty, systematic model building is still not present in this field. Moreover, limited number of the current models is available in Systems Biology Markup Language (SBML) format [82], which is the obstacle to develop, share, and validate computational models.
\nIn the last decades, many databases related to glycoscience have emerged. Nevertheless, functional information is limited when compared to glycan structure and taxonomy data. In the future, relation of glycan structure to specific enzymes that synthesize them, the rates of their synthesis, and also their function are required in order to build model.
\nFor the measurement of glycome, two main approaches are common. In the first approach, enzymes or mild hydrolysis is used to separate the glycans from the peptide backbone. Next, to obtain information about the composition and relative abundance of the carbohydrate structures, permethylation of glycans and MS analysis are used [83]. The bottleneck is the lack of well-developed software. For the data analysis of glycoproteomics and correspondingly acceleration of system-based model building and validation, more sophisticated computational tools are required.
\nMathematical models of glycosylation are developed in three main stages: (i) biological information gathering; (ii) model formulation; (iii) and simulation and postsimulation analysis. First step includes definition of components (enzyme, substrate, and product) crucial for the model. All of the components present in the biochemical network and connections between them are cataloged in this step. The process relies on information of biochemistry and cell biology, and analytical tools. In the next step, behavior in the steady state of the system is investigated by using simple linear algebra and principles of optimization. If time is a variable, the computer model can incorporate ordinary differential equations (ODEs) or Boolean networks. Proper kinetic/thermodynamic/stochastic/optimization parameters are collated depending on the formulation nature of the model and processes which are specified by enzymatically/nonenzymatically. The last step is performed to simulate the experimental system in the computer and to define unknown parameters of model by the help of fitting experimental data [81]. Visualization of multidimensional results is significant because numerous diverse models may attempt to fit one data set obtained from time labor and concentration-dependent experiments. As a result, consolidation of the findings obtained by simulations of complex reaction network and generation of hypotheses that can be tested experimentally require network analysis strategies.
\nGlycomics is a very comprehensive research area of science and interacts with several different omics fields. As many other omics types, it consists of a huge number of genomics components. In the future, techniques in high-throughput analysis and bioinformatics will be developed and enable the integration of all available data of glycomics into a particular diagram and by this way, it will be possible to develop biomarker and identify potential new therapeutic targets. Moreover, progresses in the field reveal that integrative multiomics approach should include glycomics in order to develop new biomarkers for robust diseases. One of the specific fields of systems biology is the systems glycobiology. It is based on a holistic approach that indicates process of complex glycosylation and associations between its constituents. A more complete glycome overview is targeted by using enzyme levels, abundances of glycans, pathways for biosynthesis, glycan annotation, and related omics data sets.
\nAn approach of systems glycobiology is constructed in combination of various data sets of glycomics with that of other omics fields by the use of glycoinformatics tools to clarify understanding on process of glycosylation from various data sets. With the presented chapter, main aspects of glycobiology, glycomics, and systems glycobiology are summarized. However, these fields are still developing and further developments provide more insight to this specific research area.
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