Structural and Computational Biology

עברית
ResearcherResearch Focus
Prof. Efroni Sol
  1. The T-cell Repertoire
  2. Single-cell RNA sequencing
  3. Networks in Cancer Genomics
  4. Predicting drug response through cancer genomics
Dr. Knisbacher Binyamin
  1. RNA aberrations that drive cancer
  2. Neoantigens derived from RNA aberrations for immunotherapy applications
  3. Integrating sequencing and drug screen data for personalized medicine
  4. Identifying cancer cell vulnerabilities
  5. Cancer and immune heterogeneity via single-cell RNA-seq analysis
Prof. Levanon Erez
  1. Computational genomics
  2. RNA editing by ADAR proteins
  3. DNA editing by APOBEC proteins
  4. Transcriptome complexity
  5. Mobile elements in the genome
  6. RNA editing in diseases
Prof. Opatowsky Yarden
  1. Structural biology - X-ray crystallography
  2. Drug design
  3. Axon guidance - the Slit-Robo signaling system
  4. Molecular basis for human brain evolution
Prof. Orenstein Yaron
  1. Deep learning in computational biology
  2. Computational models of protein-DNA/RNA binding and gene expression regulation
  3. Structure prediction of DNA and RNA molecules
  4. Predicting the effects of genetic variants on protein function using high-throughput data
  5. Algorithms for designing DNA, RNA, and amino acid sequences
  6. Data structures and algorithms for efficient analysis of high-throughput sequencing data
Dr. Pinto Yishay 
  1. Genomics, metagenomics, and analyses of sequencing data
  2. Development of algorithms for detection and quantification of bacteria and viruses in the human microbiome
  3. Disease prediction using the human microbiome
  4. Use of language models for analysis of sequence data
  5. Building networks to study virus–bacteria–human interactions
  6. Development of genomic tools for classification of the viral life cycle in the human body
Dr. Roichman Asael
  1. Metabolite discovery using state-of-the-art HPLC and high-resolution LC-MS platforms coupled with advanced computational pipelines
  2. Identifying bioactive metabolites formed at the diet–gut microbiota–host interface, with a focus on metabolites found in plant-based foods (phytochemicals)
  3. Understanding how diet and the microbiome shape liver function
  4. Revealing mechanisms by which diet–microbiome interactions modulate cancer development and therapeutic response
Prof. Unger Ron
  1. Understanding protein folding
  2. Computational analysis of ncRNA in Trypanosomes
  3. Studying the genetic components of Human diseases
  4. Using Machine learning for medical data mining

 

 

Dr. Binyamin Knisbacher

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.

Dr. Yishay Pinto

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. Asael Roichman

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. Yaron Orenstein

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