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Synthetic biology combines engineering, programming, and the life sciences to create innovative biological systems adapted to humans and the environment. The M.Sc. program in Computer Science with a specialization in Computational and Synthetic Biology, the first of its kind in Israel, offers comprehensive training and a strong advantage for careers in biotechnology and the pharmaceutical industry, providing advanced knowledge in the life sciences and cutting-edge computer science tools.
Head of Specialization
Prof. Yosi Shacham-Diamand
Scojen Institute, Reichman University
Academic Advisor
Prof. Farren Isaacs
Yale University
The specialization is delivered in active collaboration with the Scojen Institute for Synthetic Biology
This advanced course is designed primarily for graduate students. The course surveys a range of topics in algorithm design and analysis and develops computational problem-solving skills. Subjects include approximation algorithms for NP-hard problems, linear programming, randomized algorithms, online algorithms, parameterized complexity, and additional topics.
Every cell in our body contains our genetic material - DNA. This DNA serves as the blueprint that shapes our health, appearance, personality, and nearly every aspect of who we are. Although a DNA molecule is often extraordinarily large, it is made up of only four basic nucleotides, known as A, G, T, and C. The rise of sophisticated technologies, such as DNA microarrays, has enabled scientists to treat the DNA code as plain text. Leveraging and applying computer science algorithms to these long texts has given rise to a new scientific discipline: bioinformatics. In this course, no prior knowledge of biology is required. We will begin by learning the fundamental biological concepts relevant to the field. We will continue with a theoretical overview of the principles underlying the tools we will be using, and then apply these methods in practice using open-source software. We will explore relatively simple evolutionary questions, the genetic code, and gene expression in disease, offering a glimpse into the vast scope of this field. By the end of the course, students will be able to interpret genome-sequencing data formats, predict protein and RNA structures, and more.
Over the past two decades, biological research has undergone a revolution and is now driven primarily by large-scale experiments that generate vast amounts of data. Computational biology is a field that specializes in the development of algorithms and statistical methods to analyze this data and extract meaningful insights from it. The course will cover foundational topics in computational biology, such as DNA sequence alignment and evolutionary tree construction, as well as more general methods for processing large, noisy data, such as probabilistic models and hidden Markov models (HMMs). The course will focus on theory and algorithm design, while also highlighting the practical applications of these algorithms and their contribution to biological research. The course requires basic knowledge of algorithms and probability and does not require any prior knowledge of biology.
Scientific and technological progress - from economics and computer science to molecular biology to the development of learning health systems - revolves around information and data. Statistics is the scientific discipline that focuses on the efficient and controlled analysis of data and the drawing of conclusions from it. In many cases, the data analyzed is “noisy” and incomplete. Organizing, presenting, and interacting with the data are also integral parts of the analytical process. Efficient algorithmic methods are necessary to handle extremely high volumes of data. In this course, we will explore all the aspects of statistics and data analysis listed above. We will develop a conceptual understanding of the topics while emphasizing practical tools and solutions. This course assumes prior knowledge of probability, combinatorics, linear algebra, and advanced programming ability in at least one efficient language (C-based languages, JAVA, MATLAB, etc.). We will start by reviewing the basics of probability theory. From there, we will turn to hypothesis testing and confidence intervals. Building on these foundations, we will move on to statistical inference techniques and tools such as linear regression, principal component analysis, and distribution comparison. The course will include examples and exercises in which we will apply these methods to analyze real scientific and industrial datasets.
This course aims to provide a broad introduction to biosensor technologies (biologically based sensing devices) and to present the working principles and fundamentals of biosensors. The course will cover:
(i) classification of biosensors;
(ii) biological recognition mechanisms in biosensors;
(iii) integration of engineering and biology in biosensor design; and
(iv) design considerations for specific and functional sensors, including optical and electrochemical biosensors.
The course will also cover concepts and algorithms in signal processing, data storage, and communication methods. The course is accompanied by laboratory experiments that demonstrate key concepts and provide deeper insights. This course is offered in alternating years.
The course will cover concepts and methods in protein engineering, protein expression, and protein structure. Students will review instruments and techniques for protein characterization and learn how to create libraries for high-throughput screening. The course will also provide an introduction to research methods in nanobiotechnology. Upon successful completion of this course, students will be able to understand the different approaches used in protein engineering; become familiar with computational and physical tools for protein and glycan design and characterization; and gain exposure to the field of glycobiology and learn how glycans and biological sugars can be applied in bio-design. This course is offered to second-year students of the track and is contingent upon the completion of the courses Introduction to Biology and Bioinformatics with a passing grade.
The term microbiome refers to the microscopic ecosystems made up of all microorganisms living in or on a host body. A microbiome is a collection of bacteria, viruses, and fungi that naturally live in a community on and within larger organisms, playing an important role in health and disease. This course offers a systems-level and computational approach to understanding host-associated microbes. It introduces the basic concepts and principles in the field of microbial community research. Through hands-on experience, it provides students with the tools to analyse the human microbiome and other microbiome communities, focusing on population composition and function. Topics include experimental design, sampling and data collection, bioinformatics tools for data analysis, statistics, and visualization techniques. This subject is relevant to a wide range of disciplines, including medicine, anthropology, sociology, biology, ecology, and systems design. The course is offered in alternating years.
This interdisciplinary course explores how artificial intelligence is revolutionizing the field of synthetic biology. Students will learn how machine learning, data modelling, and automation are used to design, simulate, and optimize biological systems. The seminar emphasizes practical applications in biological engineering, therapeutics, and biotechnology innovation.
Synthetic biology is a field of research that seeks to decipher the fundamental mechanisms of life through a constructive approach - using biology, programming, and engineering to build entirely new systems from existing components. The course will demonstrate how increasingly complex systems are created by rebuilding computational and biological systems, from molecular building blocks and cellular systems to tissues, organisms, and functional communities. The course will include specialization in computational tools for designing biological systems and biological logic circuits, in which no prior knowledge is required. The course emphasizes practical applications in biological engineering, therapeutics, and biotechnology innovation. The course is offered in alternating years.
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