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Mechanistic and Data-Driven Antibody Response Modeling Strategies
Mechanistic and Data-Driven Antibody Response Modeling Strategies
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211152837
- ISBN
- 9798384087106
- DDC
- 574
- 저자명
- Tan, Cyrillus.
- 서명/저자
- Mechanistic and Data-Driven Antibody Response Modeling Strategies
- 발행사항
- [Sl] : University of California, Los Angeles, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 193 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-03, Section: B.
- 주기사항
- Advisor: Meyer, Aaron S.
- 학위논문주기
- Thesis (Ph.D.)--University of California, Los Angeles, 2024.
- 초록/해제
- 요약Antibodies are essential to adaptive immunity and therapeutic development. IgG antibodies coordinate immune effector responses by selectively binding to target antigens and interacting with various effector cells via Fcγ receptors. In this study, I explore two computational strategies for modeling antibody responses. First, I extend and employ a mechanistic model to analyze mixed Fc IgG binding measurements. This multivalent binding model efficiently predicts interactions between mixtures of multiple multivalent ligands and multiple cell surface receptors. Applied to experimental data, this model quantitatively matches mixed FcγR binding measurements, refines affinity estimates, and predicts antibody-mediated immune effector cell responses. Notably, it highlights IgG2's binding capabilities to FcγRI, contrary to previous nonbinding estimations. Second, I adopt a data-driven approach using tensor-based methods to deconvolute systems serology data. Given the complexity of recent biological research characterized by measurements in multiple degrees of variation, I provide an overview of applying tensor methods to high-throughput biological datasets. Applied these principles to HIV- and SARS-CoV-2- infected patients' serum sample data, tensor methods reveal consistent patterns and outperform traditional methods in data reduction and prediction accuracy, emphasizing their efficacy in identifying immune functional responses and disease status. Overall, this study demonstrates how mechanistic and data-driven approaches can be effectively applied to analyze antibody-mediated immunity, showcasing their distinct roles in computational biology.
- 일반주제명
- Bioinformatics
- 일반주제명
- Cellular biology
- 일반주제명
- Immunology
- 키워드
- Antibodies
- 기타저자
- University of California, Los Angeles Bioinformatics 025F
- 기본자료저록
- Dissertations Abstracts International. 86-03B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798384087106
■035 ▼a(MiAaPQ)AAI31561876
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a574
■1001 ▼aTan, Cyrillus.
■24510▼aMechanistic and Data-Driven Antibody Response Modeling Strategies
■260 ▼a[Sl]▼bUniversity of California, Los Angeles▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a193 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-03, Section: B.
■500 ▼aAdvisor: Meyer, Aaron S.
■5021 ▼aThesis (Ph.D.)--University of California, Los Angeles, 2024.
■520 ▼aAntibodies are essential to adaptive immunity and therapeutic development. IgG antibodies coordinate immune effector responses by selectively binding to target antigens and interacting with various effector cells via Fcγ receptors. In this study, I explore two computational strategies for modeling antibody responses. First, I extend and employ a mechanistic model to analyze mixed Fc IgG binding measurements. This multivalent binding model efficiently predicts interactions between mixtures of multiple multivalent ligands and multiple cell surface receptors. Applied to experimental data, this model quantitatively matches mixed FcγR binding measurements, refines affinity estimates, and predicts antibody-mediated immune effector cell responses. Notably, it highlights IgG2's binding capabilities to FcγRI, contrary to previous nonbinding estimations. Second, I adopt a data-driven approach using tensor-based methods to deconvolute systems serology data. Given the complexity of recent biological research characterized by measurements in multiple degrees of variation, I provide an overview of applying tensor methods to high-throughput biological datasets. Applied these principles to HIV- and SARS-CoV-2- infected patients' serum sample data, tensor methods reveal consistent patterns and outperform traditional methods in data reduction and prediction accuracy, emphasizing their efficacy in identifying immune functional responses and disease status. Overall, this study demonstrates how mechanistic and data-driven approaches can be effectively applied to analyze antibody-mediated immunity, showcasing their distinct roles in computational biology.
■590 ▼aSchool code: 0031.
■650 4▼aBioinformatics
■650 4▼aCellular biology
■650 4▼aImmunology
■653 ▼aAntibodies
■653 ▼aImmune effector responses
■653 ▼aAntibody-mediated immunity
■690 ▼a0715
■690 ▼a0379
■690 ▼a0982
■71020▼aUniversity of California, Los Angeles▼bBioinformatics 025F.
■7730 ▼tDissertations Abstracts International▼g86-03B.
■790 ▼a0031
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17164149▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
