SSBSS 2016

Recent advances in DNA synthesis have increased our ability to build biological systems. Synthetic Biology aims at streamlining the design and synthesis of robust and predictable biological systems using engineering design principles. Designing biological systems requires a deep understanding of how genes and proteins are organized and interact in living cells: Systems Biology aims at elucidating the cellular organization at gene, protein and network level using computational and biochemical methods.

The Synthetic and Systems Biology Summer School (SSBSS) is a full-immersion five-day residential summer school at the Volterra Learning Center (Pisa - Tuscany, Italy) on cutting-edge advances in systems and synthetic biology with lectures delivered by world-renowned experts. The school provides a stimulating environment for students (from Master students to PhD students), Post-Docs, early career researches, academics and industry leaders. Participants will also have the chance to present their results, and to interact with their peers, in a friendly and constructive environment.

• Genetic Engineering
• Metabolic Engineering
• Reading and Writing Genomes
• Synthetic Genomes
• Synthetic Circuits and Cells
• Artificial Tissues and Organs
• Genomically Recoded Organisms
• Genome Design
• Pathway Design
• Biological Design Automation and Biological CAD

• Genome Engineering
• Cellular Systems Biology
• Experimental Synthetic Biology
• Computational Synthetic Biology
• Stochastic Gene Regulation
• Gene Signaling
• Quantitative Molecular Biology
• High-throughput Techniques
• Biological Engineering
• Industrial Synthetic and Systems Biology

Speakers and Lectures

• Yaakov (Kobi) Benenson, Synthetic Biology Group@Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
Lecture 1: "The Practice of Mammalian Synthetic Biology"
Lecture 2: "Mammalian Cell Classifiers"


• Leonidas Bleris, Bioengineering Department, The University of Texas at Dallas, USA
Lecture 1: "Genome Editing Technologies and Therapeutic Modalities"
Lecture 2: "Benchmark Circuits and Topological Properties"


• Domitilla Del Vecchio, Department of Mechanical Engineering, MIT, USA
Lecture 1: "Modularity in genetic circuits: Dream versus Reality"
Lecture 2: "Engineering Modularity in Genetic Circuits"


• Diego Di Bernardo, Dept of Chemical Materials and Industrial Production Engineering University of Naples "Federico II", Naples, Italy
Lecture 1: "Engineering and Control of Biological Circuits in Yeast"
Lecture 2: "Engineering and Control of Biological Circuits in Mammalian Cells"


• Barbara Di Ventura, Synthetic Biology Group - BioQuant/DKFZ, Heidelberg, Germany
Lecture 1: "Using Blue Light to Control Protein Localization in Living Mammalian Cells"
Lecture 2: "Using Split Inteins for Protein Engineering in Living Cells"


• J. Gootenberg, Feng Zhang and Aviv Regev Groups, Department of Systems Biology, Harvard Medical School, Harvard University, USA
Lecture 1: "Discovering and Characterizing CRISPR effectors"
Lecture 2: "Probing Biology with CRISPR Screening"


• Markus Herrgard, Technical University of Denmark - Biosustain, Novo Nordisk Foundation Center for Biosustainability, Denmark
Lecture 1: "Developing an Integrated Cell Factory Design Tool"
Lecture 2: "Using Automated Laboratory Evolution to Optimize Cell Factories"


• Shalev Itzkovitz, Department of Molecular Cell Biology, Weizmann Institute of Science, Israel
Lecture 1: "Single Molecule Approaches for Studying Gene Expression in Intact Mammalian Tissues"
Lecture 2: "Systems Biology of Stem Cell-Maintained Tissues"


• Francesco Ricci, Dipartimento di Scienze e Tecnologie Chimiche, University of Rome Tor Vergata, Rome, Italy
Lecture 1: "DNA Nanotechnology Tools and Reactions for Synthetic Biology"
Lecture 2: "Nature-inspired DNA-based Nanodevices"

Industrial Panel

• John Cumbers, Founder SynBioBeta, Mountain View, CA, USA
Lecture: "TBA"

Next Generation Sequencing Workshop

• Lecture: "How Fast can we Align Sequences?", Mario Guarracino, CNR, Italy

  Sequence alignment consists in the comparison of two or more strings to find similarities between the sequences. Each symbol of a string is assigned to at most one (maybe none) symbol in another string. These similarities are particularly important when the sequences represent biological molecules, such as DNA, RNA and proteins, because we assume that similar sequences hold a similar function, structure, and there might be an evolutionary relationships. In this lecture, we will address the issue of sequence similarity. We will introduce the concept of sequence alignment, and the concept of sequence similarity in terms of distance between symbols. Given a matrix of similarities between all possible pairs of symbols, the similarity of the alignment is the sum of the similarities between the aligned symbols. The problem is to find the alignment between two sequences having the maximum similarity (minimum distance). A global search in the space of all possible alignments is computationally impractical, and we will see how dynamic programming efficiently solves the problem. We will devote some time to the problem of finding local and local alignments between sequences. Finally we will focus on algorithms where short sequences need to be compared to a reference. This is often the case with next generation sequencing technologies, in which ten of millions of short strings need to be locally matched on a long one. A wide variety of alignment algorithms have been developed over the past few years and we will discuss their properties and applications on different types of experimental data.

• Tutorial: "Detection and Analysis of Contaminating Sequences in NGS Sequencing Data", Ilaria Granata, CNR, Italy
  

  Time duration: 2 hours (45 minutes Presentation + 1hour 15minutes Practical tutorial)
  
  Reads alignment is an essential step of NGS data analyses. One challenging issue is represented by unmapped reads that are usually discarded and considered as not informative. However, it is important to fully understand the source of those reads, to assess the quality of the whole experiment. Moreover, is of interest to get some insights on possible contamination from organisms other than the one under investigation that might be present in the sequenced samples. Contamination may take place during the experimental procedures leading to sequencing, or be due to the presence of microorganisms infecting the sampled tissues. We will present a new pipeline aimed to the detection of viral, bacterial and fungi contamination in human sequenced data. The contaminating sequences can be filtered out from total reads in fastq or fasta formats and detected after the alignment to better understand their origin and correlation to the object of study. To this extend, data are sorted by organism and classified by taxonomic group.
  Description:
  1) Theoretical explanation of the topic and the state of art (Presentation);
  2) Illustration of the new pipeline and workflow (Practical);
  3) Application of the pipeline to a case study and discussion of the results (Practical).
  



• Tutorial: "Detection and Interpretation of Circular RNAs in RNA-seq Experiments", Parijat Tripathi, CNR, Italy
  

  Time duration: 2 hours (45 minutes presentation + 1 hour 15minutes: Practical tutorial)
  
  Circular RNAs are a large class of animal RNAs with regulatory potency. In the few unambiguously validated circRNAs in animals, the spliceosome seems to link the 5' and downstream 3' ends of exons within the same transcript. There are number of tools available to detect circular RNA such as CIRI, segemehl, find_circ and CIRCexplorer. In this tutorial, we focus on CIRCexplorer tool to identify circular RNA. Basically CIRCexplorer based on combined strategy to identify junction reads from back splices exons and introns lariats. At present it is only a circular RNA annotating tool, and it parses fusion junction information from mapping results of other aligners. In the tutorial, we try to identify these junction reads from unmapped reads after alignment is carried out for normal reads. CIRCexplorer need number of prerequisite steps which should be further carried out before running this tools. We have developed a computational pipeline using CIRCexplorer and all the other dependencies to identify Circular RNA in a given sample of RNA-seq data. We also added some more in house-built script to compare two different samples, for example disease vs. normal condition with respect to the expression of circular RNA and also try to understand the functional importance of Circular RNA harboring regions.
  
  Description:
  1) Introduction to mapping strategy to obtain junction reads for circular RNAs;
  2) Running in house built wrapper pipeline using CIRCexplorer to obtain the results;
  3) Parsing the results for functional annotation and comparative studies between different samples.