Kusumastuti, Ari (2026) Analisis Kompleksitas Penyakit Degenerative Melalui Pendekatan Sistem Persamaan Aktifasi Protein-Protein Regulator (Studi Kasus Diabetes Mellitus Tipe 2). Doctoral thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Diabetes Mellitus Type 2 (T2DM) merupakan penyakit degeneratif yang berkaitan erat dengan disfungsi pensignalan insulin dan resistensi insulin, khususnya pada jaringan otot. Meskipun demikian, analisis pada tingkat metabolic networks dan protein-protein interactions (PPIs), beserta dokumentasi dalam Systems Biology Markup Language (SBML), masih relatif terbatas. SBML menyediakan representasi komprehensif dari mekanisme kompleks sistem biologi pada T2DM dan berfungsi sebagai benchmark dalam pengembangan computational pipeline untuk investigasi T2DM secara in silico. Model matematika yang terenkapsulasi dalam SBML berperan dalam mendeskripsikan reaksi kinetik, baik dalam metabolic networks maupun PPIs. Selain itu model tersebut juga memuat berbagai skenario yang dapat dijadikan acuan dalam penyusunan protokol eksperimen in vitro untuk memperoleh data primer yang spesifik terkait aktifitas protein maupun metabolit yang terlibat dalam sistem. Data primer yang diperoleh selanjutnya digunakan dalam tahap validasi model matematika. Model yang telah tervalidasi bersama dengan data primer, kemudian didokumentasikan kembali dalam format SBML. Format ini bersifat dinamis dan memenuhi prinsip FAIR (Findability, Accessibility, Interoperability, and Reusability), sehingga mendukung keterbukaan data dan memungkinkan pengembangan riset berkelanjutan terkait dinamika dan regulasi penyakit T2DM. Berdasarkan hal tersebut, disertasi ini mengusulkan suatu kerangka investigasi T2DM berbasis SBML yang mengintegrasikan model matematika pada pensignalan dan resistensi insulin, serta data primer dari metabolic networks dan PPIs pada sel otot sehat. Investigasi metabolic networks dilakukan secara komputasi (in silico) menggunakan algoritma Met2Graph yang diterapkan pada data sampel darah pasien dari basis data The Genotype-Tissue Expression (GTEx) portal serta jaringan T2DM dari metabolic atlas untuk mengidentifikasi reaksi kinetik antar metabolites dan memformulasikan model matematika beserta representasi SBMLnya. Di sisi lain, formulasi model matematika, data primer, dan SBML untuk PPIs difokuskan pada insulin signaling cascade, yang diperoleh dari basis data the Kyoto Encyclopedia of Genes and Genome (KEGG) dan STRING.db. SBML untuk metabolic networks dan PPIs selanjutnya digunakan dalam penelitian ini sebagai basis pengembangan pipeline komputasi untuk optimasi dengan metode Flux Balance Analysis (FBA), untuk mendapatkan deskripsi uptake fluks energi dan sekresi metabolites atau protein yang diperlukan di dalam sistem normal dan sistem yang resisten terhadap insulin. Analisis dengan FBA yang terintegrasi dengan SBML memberikan peluang analisis masa depan seperti design kandidat obat, treatment, dan design protokol uji di tingkat invivo, pra-klinis, dan klinis. Hasil penelitian ini telah menghasilkan SBML yang termodifikasi untuk metabolic networks dan PPIs serta deskripsi FBA untuk metabolic networks dan PPIs. Data primer yang diperoleh telah didokumentasikan dalam format open akses zenodo sehingga dapat digunakan untuk analisis berkelanjutan yang relevan.
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Type 2 diabetes mellitus (T2DM) is a degeneratif disease closely associated with impired insulin signaling and insulin resistance, particularly in muscle cell tissues. However, analyzes at the level of metabolic networks and protein-protein interactions (PPIs), along with their documentations in System Biology Markup Language (SBML), remain relatively unexplored.SBML provides a comprehensive representation of the complex mechanisms underlying biological systems in T2DM and serves a benchmark for developing computational pipelines for in silico investigations. Mathematical models encoded in SBML describe the underlying kinetic reactions within both metabolic networks and PPIs. Furthermore, the models define experimental scenarios that can guide the development of in vitro protocols to obtain primary data on the activity of metabolites and proteins involved in the system. The primary data obtained are subsequently used for model validation. Validated mathematical models together with their associated datasets, are then documented in SBML format. This format is dynamic and adheres to the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) thereby facilitating data sharing and enabling sustainable research on the dynamics and regulation of T2DM. Based on this framework, this disertation proposeses an SBML-based approach for investigating T2DM by integrating mathematical models of insulin signaling and insulin resistance with primary data derived from metabolic networks and PPIs in healthy muscle cell. The investigation of metabolic networks is conducted in silico using the Met2Graph algorithm, applied to blood samples data from The Genotype-Tissue Expression (GTEx) Portal and T2DM networks data from the Metabolic Atlas. This approach enables the identifications of kinetic reactions among metabolites and the formulations of corresponding mathematical models and SBML representations. In paralel, the formulation of mathematical models, primary data, and SBML for PPIs is specifically focused on the insulin signaling cascade, using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and STRING databases. The resulting SBMLmodels for both metabolic networks and PPIs are subsequently utilized for foundation for developing computational pipelines based on Flux Balance Analysis (FBA). This approach aims to characterize energy uptake fluxes and the secretion of metabolites or proteins under both normal and nsulin-resistance conditions. The integration of SBML and FBA provides an opportunities for advances analyses, including drug candidate design, treatment strategy development, and the design of experimental protocols at in vivo, preclinical, and clinical levels. This study produces modified SBML for metabolic networks and PPIs, aong with FBA-based characterizations of both systems. Additionally, the generated primary datasets are documented in an open-access repository (Zenodo), enabling their reuse for future research.
| Item Type: | Thesis (Doctoral) |
|---|---|
| Uncontrolled Keywords: | Model Matematika, T2DM-Disease, Metabolic Networks, PPIs, SBML, FBA. ============================================================ Mathematical Model, T2DM-Disease, Metabolic Networks, PPIs, SBML, FBA. |
| Subjects: | Q Science > QA Mathematics > QA401 Mathematical models. R Medicine > RB Pathology |
| Divisions: | Faculty of Science and Data Analytics (SCIENTICS) > Mathematics > 44002-(S3) PhD Thesis |
| Depositing User: | Ari Kusumastuti |
| Date Deposited: | 03 Aug 2026 08:55 |
| Last Modified: | 03 Aug 2026 08:56 |
| URI: | http://repository.its.ac.id/id/eprint/141288 |
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