Which departments use this laboratory?

This laboratory is primarily used by students and researchers in the undergraduate and graduate programs in Molecular Biology and Genetics (English/Turkish) and Biomedical Engineering within the Faculty of Engineering and Natural Sciences. In addition, other programs conducting interdisciplinary computational projects—such as computer engineering, software engineering, pharmacy, and chemistry—can also make use of the laboratory.

Are experiments conducted on actual patients in the laboratory?

The laboratory conducts protein-ligand molecular docking analyses, protein structure prediction and homology modeling tests, molecular dynamics (MD) stability simulations, genomic sequence alignment processes, and biostatistical analyses of large biological datasets.

Are experiments conducted with living organisms in the laboratory?

The laboratory conducts protein-ligand molecular docking analyses, protein structure prediction and homology modeling tests, molecular dynamics (MD) stability simulations, genomic sequence alignment, and biostatistical analyses of large biological datasets.

Is the laboratory open to external industry partnerships?

Yes, the laboratory has an infrastructure open to industry collaborations, including computer-aided drug discovery, genetic data analytics prototyping, and computational biology projects to be carried out jointly with pharmaceutical companies, biotechnology startups, and clinical research centers.

Can students use the lab for individual practice outside of class hours?

Yes, students may use the laboratory’s computational infrastructure and software outside of class hours as part of their undergraduate thesis projects, TÜBİTAK student research projects, or graduate thesis work, subject to approval by the laboratory coordinators, the processor load schedule for workstations, and under the direct supervision of an expert.

In Silico Research Laboratory | Üsküdar University

Üsküdar University In Silico Research Laboratory is an advanced research center established for the purpose of modeling and analyzing biological, chemical, and medical processes in a computer environment (in silico) through computer simulations, mathematical models, and advanced algorithms.

Main Purpose: To enable the processing of complex data in the fields of molecular biology, genetics, bioengineering, and chemistry using high-performance computing methods; and to digitally test potential biomolecules, drug candidates, and genetic mutations before conducting wet-lab (in vitro and in vivo) studies, thereby optimizing time and cost.
Academic Contribution: The laboratory transforms abstract data sets and biological sequences into meaningful functional models. It supports evidence-based computational scientific research at the academic level by providing students with data mining and biostatistical analysis skills in the fields of systems biology, structural bioinformatics, and evolutionary genetics.

Equipment and Technical Infrastructure Available in the Laboratory

The laboratory infrastructure consists of high-capacity hardware for intensive data processing and molecular modeling software. The technical equipment and software tools available in the laboratory under the Key Features section are listed below:

High-Performance Workstations: Computer systems equipped with high numbers of CPU cores and advanced graphics processing units (GPUs), enabling the uninterrupted execution of complex molecular dynamics simulations and big data analyses.
Molecular Modeling and Visualization Software (PyMOL, Chimera, etc.): Visualization tools used to examine the three-dimensional structures of proteins, nucleic acids, and small chemical ligands at the atomic level, and to map surface charges and hydrophobic interactions.
Molecular Docking Protocols and Tools: Software algorithms that calculate the binding affinities, free binding energies, and active-site interactions of potential drug molecules with target receptor proteins.
Alignment and Genomic Analysis Tools (BLAST, ClustalW, etc.): Bioinformatics database interfaces used to compare DNA, RNA, and amino acid sequences, construct phylogenetic trees, and perform mutation analyses.
Data Analytics and Programming Environments: Programming languages (Python, R, etc.) and command-line libraries used for the statistical analysis, filtering, and visualization of large biological datasets.

Research Areas and Fields of Application

The applied curricula, R&D projects, and postgraduate thesis studies conducted in the laboratory cover the most critical layers of computational biology:
Computer-Aided Drug Design (CADD): Screening new chemical compounds through molecular docking methods (virtual screening) to identify those that may exhibit the highest efficacy and lowest toxicity against target protein structures associated with specific diseases.
Structural Bioinformatics and Homology Modeling: Predicting and validating the three-dimensional (3D) structures of proteins whose structures have not yet been experimentally determined, using mathematical algorithms based on known homologous sequences.
Genomic and Proteomic Data Mining: Processing raw genetic data obtained from next-generation sequencing (NGS) systems, identifying disease-associated single nucleotide polymorphisms (SNPs), and analyzing protein expression profiles.
Molecular Dynamics (MD) Simulations: Simulating the time-dependent atomic movements and structural stability of protein-ligand or protein-protein complexes over a specific period under physiological temperature and pressure conditions.

Contributions to Students and the Industry

The In Silico Research Laboratory prepares students for the global biotechnology and pharmaceutical market by developing analytical engineering skills aligned with the requirements of the digital age.
Cost and Time Management Competency: Instead of testing millions of molecules individually in a wet laboratory, students learn to narrow down targets using in silico methods and advance only the candidates with the highest potential for success to the laboratory, thereby developing an optimization-oriented approach to R&D processes.
Industry Employability: Graduates who successfully use the bioinformatics tools, data analysis methods, and modeling software available in this laboratory can be rapidly employed in the design and preclinical R&D departments of pharmaceutical companies, biotechnology start-ups, personalized medicine and genetic diagnostics centers, and data analytics companies as Bioinformatics Specialists, Computational Biologists, Data Analysts, or R&D Researchers.