Computational Systems Biology

Computational Systems Biology is a multidisciplinary domain which focuses on the holistic study of complex biological systems by using and developing efficient data structures, algorithms, visualization and modelling tools to understand the dynamics of system. Cancer is one such systems level disease, having a huge number of underlying factors associated with its onset; therefore, this research pivots around unveiling the relation between these factors and hallmarks of cancer at the genetic level and establishing its connection with the systemic level dynamics which will help to analyse and understand the control feedback mechanism in detail. We are using computational modeling paradigms like Petri nets, automota and control theory etc. to model the molecular level. On the other hand side, machine learning is to be used to develop pipelines and work flows for efficient preprocessing and analysis of omics data to identify cancer biomarkers which can yield important pathways crucial to cancer progression. In addition to this, generative adversarial networks and convolutional neural networks are to be utilized to infer regulatory regions i.e. enhancers/promoters and termination sequences etc., from a given DNA sequence.

• Identification of Differentially Expressed Genes (DEGs) for Glioblastoma using Deep Neural Networks (DNNs).
• Identification of Glioblastoma critical pathways upon subjecting DEGs to gene and pathway ontology.
• Interpretation of control and decision making at genomic level in stimulation of cancer pathways.
• To divulge the regulatory regions using DNN approaches to minimize the use of ATAC-seq and CHIP-seq experiments for costly analyses at the genetic level.
• Use of existing genetic data sets to map the features of the known DNA on the black or junk DNA.
• Understanding of biological pathways associated with cell cycle and cell proliferation using computational methods.
  
  

Mutation prediction in the SARS-CoV-2 genome using attention-based neural machine translation
Mathematical Biosciences and Engineering, 2024. DOI
D. M. Quddusi,  S.A. Hiremath,  N. Bajcinca

Identification of genomic biomarkers and their pathway crosstalks for deciphering mechanistic links in glioblastoma
IET Systems Biology, 2023. DOI
D. M. Quddusi, N. Bajcinca

Glioblastoma
System modelling
Machine learning
Computational Systems Biology
Differentially Expressed Genes
Generative Adversarial Networks

M.Sc. Darrak Moin Quddusi
Gottlieb-Daimler-Str. 42
67663, Kaiserslautern
Phone: +49 (0)631/205-3398
Fax: +49 (0)631/205-4201
darrak.quddusi(at)mv.uni-kl.de
 

State of Rhineland-Palatinate

Since 2019

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