Structural and Computational Biology
Welcome to the frontier where advanced data science converges with complex biology. Within this domain, the Faculty of Life Sciences at Bar-Ilan University approaches living systems as dynamic networks waiting to be deciphered. By integrating computational power, synthetic biology, and advanced biochemistry, our researchers investigate life across three interconnected dimensions:
The Code: Applying intelligent algorithms to track genetic variations and uncover new pathways to combat disease.
The Environment: Profiling metabolic chemistry and viral landscapes to understand how inner ecosystems dictate human health.
The Structure: Combining deep neural networks with high-resolution imaging to capture molecular mechanics in motion.
We look past isolated biological components to build the predictive frameworks that translate complex datasets into precise, targeted clinical solutions.
- Prof. Efroni Sol
The T-cell Repertoire
Single-cell RNA sequencing
Networks in Cancer Genomics
Predicting drug response through cancer genomics
- Dr. Knisbacher Binyamin
Computational Biology, Cancer Genomics and Personalized Medicine
How can a computer use big data to help a doctor choose a more precise cancer treatment? By translating genomes into decisions.
Research focus: Every cancer patient has a unique genetic story, the challenge is turning massive datasets into one actionable clinical choice. The lab integrates multi-omic sequencing data (DNA, RNA & epigenetics), clinical information and computational modeling (AI, ML & statistics) to detect patterns that explain what goes wrong in each patient and how it opens opportunities for therapy and precision medicine.
Highlighted takeaway: The future of oncology is integrating clinical and molecular data for personalized medicine - treatment plans tailored to the person.
Methods: Computational cancer genomics · Machine learning · Big data · Single-cell sequencing · Clinical data integration
Hobbies: Hiking, running and telling my kids dad jokes.
- Prof. Levanon Erez
Computational genomics
RNA editing by ADAR proteins
DNA editing by APOBEC proteins
Transcriptome complexity
Mobile elements in the genome
RNA editing in diseases
- Prof. Opatowsky Yarden
Structural biology - X-ray crystallography
Drug design
Axon guidance - the Slit-Robo signaling system
Molecular basis for human brain evolution
- Prof. Orenstein Yaron
Computational and Structural Bioinformatics
What can algorithms reveal about biology? Even abstract computational models can uncover how DNA and RNA encode regulation, how proteins recognize their targets, and how genetic variants reshape the molecular rules of life.
Research focus: The lab focuses on computational biology and bioinformatics, including deep learning and algorithm development to model protein-DNA and protein-RNA interactions, predict DNA and RNA structures, and analyze high-throughput sequencing data. Research also includes designing algorithms to infer the effects of genetic variants, to interpret deep neural networks in genomics, and to construct optimized sequence libraries, bridging large data analysis and biological insight.
Highlighted takeaway: Computational and machine learning methods are essential for understanding gene regulation and the molecular rules that govern life.
Methods: Bioinformatics · Machine learning · Deep learning · Predictive modeling · Sequence and structure prediction · Data structures and algorithms · High-throughput data analysis · DNA/RNA interaction modeling · Variant effect prediction.
Hobbies: Running, Cycling, Swimming, Triathlon, Basketball
- Dr. Pinto Yishay
Microbiome Virology and Computational Biology
Not only bacteria: who are the viruses living inside us? The human virome is still largely unknown.
Research focus: The lab studies bacteriophages, viruses that infect bacteria and can reshape microbiome composition and human health. Research examines how phages influence bacterial communities, inflammation, and responses to drug treatments. By combining genomic sequencing, computational approaches, and clinical datasets, the lab maps a hidden viral world with medical relevance.
Highlighted takeaway: Viruses in the body may affect disease, treatment response, and microbiome stability, understanding them opens new paths for personalized medicine.
Methods: Metagenomics · Genomic sequencing · Language models · Machine learning · Synthetic biology · Clinical data analysis · Computational Biology
Hobbies: Hiking, Tabletop games
- Dr. Roichman Asael
Nutrition, Microbiome, and Metabolites in Cancer and Health
How does what we eat influence cancer? Not just calories - chemistry and microbes.
Research focus: The lab studies how diet interacts with gut bacteria to produce metabolites that affect physiology and disease. We identify bioactive food-derived molecules, track how microbes modify them, and test their effects on liver function, systemic metabolism, and cancer development. A key focus is uncovering hidden nutrition chemistry that may explain why diet influences health and treatment response.
Highlighted takeaway: Nutrition and the microbiome are central to personalized medicine. Understanding active metabolites can reshape disease prevention, diagnosis, and therapy.
Methods: High-resolution metabolomics · HPLC separations · Multi-omics · Cellular & mouse models · Gut microbiology · Advanced computational tools
Hobbies: Hiking, Music, Reading, Having good time with family and friends
- Prof. Unger Ron
Computational Biology and Bioinformatics
How can computation uncover the hidden logic of life? By modeling biological systems and molecular structures through algorithms, patterns, and data-driven thinking.
Research focus: The lab explores fundamental questions in computational biology, including protein structure and folding, non-coding RNA function, biological robustness, and using AI methods to gain insights from large scale medical records. Research bridges theory and application, using computational models to analyze biological complexity and large-scale biological and medical data.
Highlighted takeaway: Computational approaches are key to understanding and predicting biological and medical structure, function, and complexity.
Methods: Bioinformatics · Computational modeling · Machine learning · Genetic algorithms · Systems biology · Protein structure analysis · Sequence analysis · Data mining
Hobbies: Reading, Collecting clocks and watches, Rockets building and flying
