Bioinformatics and Computational Biology
A.Y. 2023/2024
Learning objectives
The course will introduce computational approaches recently developed for studying biological systems with a focus on biotechnological applications: the identification of essential genes (Tn-Seq and network analysis), or genes that are involved in interesting processes (Tn-Seq) together with methods to study gene regulation (ChIP-Seq, small RNAs). On these premises we will then discuss how to engineer eco-systems (community engineering) and how metabolic optimization can be achieved (model-guided metabolic engineering).
An introduction to computational methods for the characterization of protein structures, with biochemical basis.
An introduction to computational methods for the characterization of protein structures, with biochemical basis.
Expected learning outcomes
The course will introduce students to the computational techniques that are at the basis of the identification of important genes in Tn-seq datasets and to the structural analysis of networks with the aim of identifying genes that can be manipulated for specific objectives. Techniques to study gene regulation will also be discussed (ChIP-Seq, sRNA).
In the part of the course relating to the computational study of proteins, the student will learn the biochemical and biophysical bases on which the secondary and tertiary structure prediction algorithms, structural disorder and protein dynamics are based. The student will also directly perform a series of prediction tests by learning to use structural analysis and prediction programs.
In the part of the course relating to the computational study of proteins, the student will learn the biochemical and biophysical bases on which the secondary and tertiary structure prediction algorithms, structural disorder and protein dynamics are based. The student will also directly perform a series of prediction tests by learning to use structural analysis and prediction programs.
Lesson period: year
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Responsible
Lesson period
year
Prerequisites for admission
Basic knowledge of genetics, molecular biology and biochemistry.
Assessment methods and Criteria
Bioinformatics: students will perform bioinformatics analyses, describing methods employed and results obtained in a lab notebook. At the exam, students will discuss the notebook with the teacher, and the grade will depend on their understanding of the methods employed as well as the results obtained.
Computational biology: written and oral test.
Computational biology: written and oral test.
Bioinformatics
Course syllabus
Introduction: definition and aims of Bioinformatics. Genome projects and next-generation sequencing. Gene and genome annotations. A bioinformatic view of the structure of protein-coding genes: exons, introns, promoters, and alternative splicing. The structure of mature eukaryotic mRNAs. Primary and specialized biological databases. Genome browsers. Definition of sequence similarity, homology, orthology, and paralogy. Global and local alignments. Scoring matrices for nucleotide and amino acid sequence alignments (PAM and BLOSUM). BLAST sequence similarity search: algorithm and usage. Multiple sequence alignments. Expression data and RNA-Seq. Functional gene annotation and gene ontology.
Teaching methods
Theoretical lectures will be alternated with practical exercises with the PC.
Teaching Resources
Slides and handouts will be shared with students.
Reference textbook (suggested):
M. Helmer Citterich, F. Ferrè, G. Pavesi, C. Romualdi, G. Pesole, Fondamenti di bioinformatica, Zanichelli editore 2018
Reference textbook (suggested):
M. Helmer Citterich, F. Ferrè, G. Pavesi, C. Romualdi, G. Pesole, Fondamenti di bioinformatica, Zanichelli editore 2018
Computational Biology
Course syllabus
1. Exploring function and regulation
a. Transposon insertion mutagenesis for the discovery of essential or otherwise important genes
b. ChIP-Seq, transcription factor binding sites and gene regulatory networks;
c. Metagenomics and metatranscriptomics
2. Small RNAs in Bacteria, mechanisms of action, function and dynamical behavior of small genetic circuits implementing sRNA-mediated regulations;
3. Introduction to network theory with applications in Biology (+Practice in R).
4. Protein Structure and its analysis
a. The main chemical and geometrical properties of protein structures will be shown: secondary (alpha helix, beta sheets and coil) and tertiary structures. TIM barrel will be used as an example of protein fold ductility.
b. Covalent and non-covalent bonds are fundamental for protein folding: peptide bond, salt bridges, van der Waals interactions and hydrogen bonds. The role of water in protein folding.
c. Computer analysis of protein structures to verify several of the protein properties discussed during course.
d. The evolution of the structure of globular proteins, of membrane proteins and of intrinsically disordered proteins will be accompanied by test of protein structure predictions.
e. Structure prediction by homology modelling
f. Molecular dynamics simulations
g. Structure prediction and refinement by molecular dynamics
a. Transposon insertion mutagenesis for the discovery of essential or otherwise important genes
b. ChIP-Seq, transcription factor binding sites and gene regulatory networks;
c. Metagenomics and metatranscriptomics
2. Small RNAs in Bacteria, mechanisms of action, function and dynamical behavior of small genetic circuits implementing sRNA-mediated regulations;
3. Introduction to network theory with applications in Biology (+Practice in R).
4. Protein Structure and its analysis
a. The main chemical and geometrical properties of protein structures will be shown: secondary (alpha helix, beta sheets and coil) and tertiary structures. TIM barrel will be used as an example of protein fold ductility.
b. Covalent and non-covalent bonds are fundamental for protein folding: peptide bond, salt bridges, van der Waals interactions and hydrogen bonds. The role of water in protein folding.
c. Computer analysis of protein structures to verify several of the protein properties discussed during course.
d. The evolution of the structure of globular proteins, of membrane proteins and of intrinsically disordered proteins will be accompanied by test of protein structure predictions.
e. Structure prediction by homology modelling
f. Molecular dynamics simulations
g. Structure prediction and refinement by molecular dynamics
Teaching methods
Lessons supported by projected material plus interactive lessons at the computer. Students will be stimulated to participate actively to the lesson/discussion to improve their skills by analysing the cited literature. We strongly suggest to attend all the lessons.
Teaching Resources
For the exam we will mainly refer to the slides that will be available for download after each lesson on the ariel website.
The following is a list of articles/books that can be used by students to explore more in detail some of the issues discussed in the lessons.
Alouev A, Johnson DS, Sidow Sundquist A, Medina C, Anton E, Batzoglou S, Myers RM, Anton Valouev, David S Ds David S Johnson, Andreas Sundquist, Catherine Medina, Elizabeth Anton, Serafim Batzoglou, Richard M Myers, Arend Sidow, Elizabeth Anton, Serafim Batzoglou, Richard M Myers, and Arend Sidow. Genome-wide analysis of transcription factor binding sites based on ChIP-Seq data. Nature Methods, 5(9):829-834, 2008. ISSN 1548-7091. doi: 10.1038/nmeth.1246.Genome-Wide. URL http://www.nature.com/nmeth/journal/vaop/ncurrent/full/nmeth.1246.html.
Sumeet Agarwal, Charlotte M Deane, Mason a. Porter, and Nick S Jones. Revisiting Date and Party Hubs: Novel Approaches to Role Assignment in Protein Interaction Networks. PLoS Computational Biology, 6(6):e1000817, jun 2010. ISSN 1553-7358. doi: 10.1371/journal.pcbi.1000817. URL http://dx.plos.org/10.1371/journal.pcbi.1000817.
R 'eka Albert. General Network Theory R 'eka Albert. Technical report, 2006.
R 'eka Albert, H Jeong, and Albert-Laszlo Barabasi. Error and attack tolerance of complex networks. Nature, 406(6794): 378-382, jul 2000. ISSN 1476-4687. doi: 10.1038/35019019. URL http://www.ncbi.nlm.nih.gov/pubmed/10935628.
Manuel Allhoff, Kristin Ser 'e, Heike Chauvistr 'e, Qiong Lin, Martin Zenke, Ivan G. Costa, H. Chauvistre, Ivan G. Costa, Qiong Lin, Manuel Allhoff, and K. Sere. Detecting differential peaks in ChIP-seq signals with ODIN. Bioinformatics, 30(24):3467-3475, 2014. ISSN 14602059. doi: 10.1093/bioinformatics/btu722.
Albert-Laszlo Barabasi. Linked: The New Science of Everything, 2002.
Albert-Laszlo Barabasi and Zolt 'an N Oltvai. Network biology: understanding the cell's functional organization. Nature Reviews Genetics, 5(2):101-113, feb 2004. ISSN 1471-0056. doi: 10.1038/nrg1272. URL http://www.ncbi.nlm.nih.gov/ pubmed/14735121.
Michael A. DeJesus, Subhalaxmi Nambi, Clare M. Smith, Richard E. Baker, Christopher M. Sassetti, and Thomas R. Ioerger. Statistical analysis of genetic interactions in Tn-Seq data. Nucleic Acids Research, 45(11):1-11, 2017. ISSN 13624962. doi: 10.1093/nar/gkx128.
Diana Ekman, Sara Light, Asa K Bj ̈orklund, and Arne Elofsson. What properties characterize the hub proteins of the protein-protein interaction network of Saccharomyces cerevisiae? Genome Biology, 7(6):R45, jan 2006. ISSN 1465-6914. doi: 10.1186/gb-2006-7-6-r45. URL http://www.ncbi.nlm.nih.gov/pubmed/16780599.
Santo Fortunato and Marc Barthelemy. Resolution limit in community detection. Proceedings of the Na- tional Academy of Sciences, 104(1):36-41, jan 2007. ISSN 0027-8424. doi: 10.1073/pnas.0605965104. URL http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1765466{\&}tool=pmcentrez{\&}rendertype=abstracthttp:// www.pnas.org/cgi/doi/10.1073/pnas.0605965104.
Bratati Kahali, Shandar Ahmad, and Tapash Chandra Ghosh. Exploring the evolutionary rate differences of party hub and date hub proteins in Saccharomyces cerevisiae protein-protein interaction network. Gene, 429(1-2):18-22, jan 2009. ISSN 1879-0038. doi: 10.1016/j.gene.2008.09.032. URL http://dx.doi.org/10.1016/j.gene.2008.09.032 http://www.ncbi.nlm.nih.gov/pubmed/18973798.
Byoungjin Kim, Robert Binkley, Hyun Uk Kim, and Sang Yup Lee. Metabolic engineering of Escherichia coli for the enhanced production of l-tyrosine. Biotechnology and Bioengineering, 115(10):2554-2564, 2018. ISSN 10970290. doi: 10.1002/bit.26797.
C. Klein, A. Marino, M.-F. Sagot, P.V. Milreu, and M. Brilli. Structural and dynamical analysis of biological networks. Briefings in Functional Genomics, 11(6), 2012. ISSN 20412649. doi: 10.1093/bfgp/els030.
Young Min Kwon, Steven C. Ricke, and Rabindra K. Mandal. Transposon sequencing: methods and expanding applica- tions. Applied Microbiology and Biotechnology, 100(1):31-43, 2016. ISSN 14320614. doi: 10.1007/s00253-015-7037-8.
Shmoolik Mangan and Uri Alon. Structure and function of the feed-forward loop network motif. Proceedings of the national Academy of Sciences, 100(21):11980-11985, oct 2003. ISSN 0027-8424. doi: 10.1073/pnas.2133841100. URL http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=218699{\&}tool=pmcentrez{\&}rendertype=abstract.
Shmoolik Mangan, A Zaslaver, and Uri Alon. The Coherent Feedforward Loop Serves as a Sign- sensitive Delay Element in Transcription Networks. Journal of molecular biology, 334:197-204, 2003. doi: 10.1016/j.jmb.2003.09.049. URL http://www.ncbi.nlm.nih.gov/pubmed/14607112.
Jason E McDermott, Ronald C Taylor, Hyunjin Yoon, and Fred Heffron. Bottlenecks and hubs in inferred networks are important for virulence in Salmonella typhimurium. Journal of Computational Biology, 16(2):169-180, feb 2009. ISSN 1557-8666. doi: 10.1089/cmb.2008.04TT. URL http://www.ncbi.nlm.nih.gov/pubmed/19178137http://www.liebertpub.com/ doi/10.1089/cmb.2008.04TT.
Ben O. Oyserman, Marnix H. Medema, and Jos M. Raaijmakers. Road MAPs to engineer host microbiomes. Current Opinion in Microbiology, 43:46-54, 2018. ISSN 18790364. doi: 10.1016/j.mib.2017.11.023. URL https://doi.org/10.1016/j.mib.2017.11.023
Maria Concetta Palumbo, Sara Zenoni, Marianna Fasoli, M 'elanie Massonnet, Lorenzo Farina, Filippo Castiglione, Mario Pezzotti, and Paola Paci. Integrated network analysis identifies fight-club nodes as a class of hubs encompassing key putative switch genes that induce ma jor transcriptome reprogramming during grapevine development. The Plant Cell, 26(12):4617-4635, 2014. ISSN 1532-298X. doi: 10.1105/tpc.114.133710.
Seon Young Park, Robert M. Binkley, Won Jun Kim, Mun Hee Lee, and Sang Yup Lee. Metabolic engineering of Escherichia coli for high-level astaxanthin production with high productivity. Metabolic Engineering, 49(August):105-115, 2018. ISSN 10967184. doi: 10.1016/j.ymben.2018.08.002. URL https://doi.org/10.1016/j.ymben.2018.08.002.
Romualdo Pastor-Satorras and Alessandro Vespignani. Epidemic Spreading in Scale-Free Networks. Physical Review Letters, 86(14):3200-3203, apr 2001. ISSN 0031-9007. doi: 10.1103/PhysRevLett.86.3200. URL http://link.aps.org/ doi/10.1103/PhysRevLett.86.3200.
Carlotta Ronda, Sway P. Chen, Vitor Cabral, Stephanie J. Yaung, and Harris H. Wang. Metagenomic engineer- ing of the mammalian gut microbiome in situ. Nature Methods, 16(2):167-170, 2019. ISSN 15487105. doi: 10.1038/s41592-018-0301-y. URL http://dx.doi.org/10.1038/s41592-018-0301-y.
Shai S Shen-Orr, Ron Milo, Shmoolik Mangan, and Uri Alon. Network motifs in the transcriptional regulation network of Escherichia coli. Nature Genetics, 31(1):64-68, may 2002. ISSN 1061-4036. doi: 10.1038/ng881. URL http: //www.ncbi.nlm.nih.gov/pubmed/11967538.
Tim Van Opijnen and Andrew Camilli. Transposon insertion sequencing: A new tool for systems-level analysis of mi- croorganisms. Nature Reviews Microbiology, 11(7):435-442, 2013. ISSN 17401526. doi: 10.1038/nrmicro3033. URL http://dx.doi.org/10.1038/nrmicro3033.
Tim van Opijnen, David W. Lazinski, and Andrew Camilli. Genome-wide fitness and genetic interactions determined by Tn-seq, a high-throughput massively parallel sequencing method for microorganisms. Current Protocols in Microbiology, 2015(April):1E.3.1-1E.3.24, 2015. ISSN 19348533. doi: 10.1002/9780471729259.mc01e03s36.
Haiyuan Yu, Philip M Kim, Emmett Sprecher, Valery Trifonov, and Mark B Gerstein. The importance of bottlenecks in protein networks: correlation with gene essentiality and expression dynamics. PLoS Computational Biology, 3(4):e59, apr 2007. ISSN 1553-7358. doi: 10.1371/journal.pcbi.0030059. URL http://www.ncbi.nlm.nih.gov/pubmed/17447836.
Tao Yu, Yasaman Dabirian, Quanli Liu, Verena Siewers, and Jens Nielsen. Strategies and challenges for metabolic rewiring. Current Opinion in Systems Biology, 15:30-38, 2019. ISSN 24523100. doi: 10.1016/j.coisb.2019.03.004. URL https://doi.org/10.1016/j.coisb.2019.03.004.
The following is a list of articles/books that can be used by students to explore more in detail some of the issues discussed in the lessons.
Alouev A, Johnson DS, Sidow Sundquist A, Medina C, Anton E, Batzoglou S, Myers RM, Anton Valouev, David S Ds David S Johnson, Andreas Sundquist, Catherine Medina, Elizabeth Anton, Serafim Batzoglou, Richard M Myers, Arend Sidow, Elizabeth Anton, Serafim Batzoglou, Richard M Myers, and Arend Sidow. Genome-wide analysis of transcription factor binding sites based on ChIP-Seq data. Nature Methods, 5(9):829-834, 2008. ISSN 1548-7091. doi: 10.1038/nmeth.1246.Genome-Wide. URL http://www.nature.com/nmeth/journal/vaop/ncurrent/full/nmeth.1246.html.
Sumeet Agarwal, Charlotte M Deane, Mason a. Porter, and Nick S Jones. Revisiting Date and Party Hubs: Novel Approaches to Role Assignment in Protein Interaction Networks. PLoS Computational Biology, 6(6):e1000817, jun 2010. ISSN 1553-7358. doi: 10.1371/journal.pcbi.1000817. URL http://dx.plos.org/10.1371/journal.pcbi.1000817.
R 'eka Albert. General Network Theory R 'eka Albert. Technical report, 2006.
R 'eka Albert, H Jeong, and Albert-Laszlo Barabasi. Error and attack tolerance of complex networks. Nature, 406(6794): 378-382, jul 2000. ISSN 1476-4687. doi: 10.1038/35019019. URL http://www.ncbi.nlm.nih.gov/pubmed/10935628.
Manuel Allhoff, Kristin Ser 'e, Heike Chauvistr 'e, Qiong Lin, Martin Zenke, Ivan G. Costa, H. Chauvistre, Ivan G. Costa, Qiong Lin, Manuel Allhoff, and K. Sere. Detecting differential peaks in ChIP-seq signals with ODIN. Bioinformatics, 30(24):3467-3475, 2014. ISSN 14602059. doi: 10.1093/bioinformatics/btu722.
Albert-Laszlo Barabasi. Linked: The New Science of Everything, 2002.
Albert-Laszlo Barabasi and Zolt 'an N Oltvai. Network biology: understanding the cell's functional organization. Nature Reviews Genetics, 5(2):101-113, feb 2004. ISSN 1471-0056. doi: 10.1038/nrg1272. URL http://www.ncbi.nlm.nih.gov/ pubmed/14735121.
Michael A. DeJesus, Subhalaxmi Nambi, Clare M. Smith, Richard E. Baker, Christopher M. Sassetti, and Thomas R. Ioerger. Statistical analysis of genetic interactions in Tn-Seq data. Nucleic Acids Research, 45(11):1-11, 2017. ISSN 13624962. doi: 10.1093/nar/gkx128.
Diana Ekman, Sara Light, Asa K Bj ̈orklund, and Arne Elofsson. What properties characterize the hub proteins of the protein-protein interaction network of Saccharomyces cerevisiae? Genome Biology, 7(6):R45, jan 2006. ISSN 1465-6914. doi: 10.1186/gb-2006-7-6-r45. URL http://www.ncbi.nlm.nih.gov/pubmed/16780599.
Santo Fortunato and Marc Barthelemy. Resolution limit in community detection. Proceedings of the Na- tional Academy of Sciences, 104(1):36-41, jan 2007. ISSN 0027-8424. doi: 10.1073/pnas.0605965104. URL http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1765466{\&}tool=pmcentrez{\&}rendertype=abstracthttp:// www.pnas.org/cgi/doi/10.1073/pnas.0605965104.
Bratati Kahali, Shandar Ahmad, and Tapash Chandra Ghosh. Exploring the evolutionary rate differences of party hub and date hub proteins in Saccharomyces cerevisiae protein-protein interaction network. Gene, 429(1-2):18-22, jan 2009. ISSN 1879-0038. doi: 10.1016/j.gene.2008.09.032. URL http://dx.doi.org/10.1016/j.gene.2008.09.032 http://www.ncbi.nlm.nih.gov/pubmed/18973798.
Byoungjin Kim, Robert Binkley, Hyun Uk Kim, and Sang Yup Lee. Metabolic engineering of Escherichia coli for the enhanced production of l-tyrosine. Biotechnology and Bioengineering, 115(10):2554-2564, 2018. ISSN 10970290. doi: 10.1002/bit.26797.
C. Klein, A. Marino, M.-F. Sagot, P.V. Milreu, and M. Brilli. Structural and dynamical analysis of biological networks. Briefings in Functional Genomics, 11(6), 2012. ISSN 20412649. doi: 10.1093/bfgp/els030.
Young Min Kwon, Steven C. Ricke, and Rabindra K. Mandal. Transposon sequencing: methods and expanding applica- tions. Applied Microbiology and Biotechnology, 100(1):31-43, 2016. ISSN 14320614. doi: 10.1007/s00253-015-7037-8.
Shmoolik Mangan and Uri Alon. Structure and function of the feed-forward loop network motif. Proceedings of the national Academy of Sciences, 100(21):11980-11985, oct 2003. ISSN 0027-8424. doi: 10.1073/pnas.2133841100. URL http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=218699{\&}tool=pmcentrez{\&}rendertype=abstract.
Shmoolik Mangan, A Zaslaver, and Uri Alon. The Coherent Feedforward Loop Serves as a Sign- sensitive Delay Element in Transcription Networks. Journal of molecular biology, 334:197-204, 2003. doi: 10.1016/j.jmb.2003.09.049. URL http://www.ncbi.nlm.nih.gov/pubmed/14607112.
Jason E McDermott, Ronald C Taylor, Hyunjin Yoon, and Fred Heffron. Bottlenecks and hubs in inferred networks are important for virulence in Salmonella typhimurium. Journal of Computational Biology, 16(2):169-180, feb 2009. ISSN 1557-8666. doi: 10.1089/cmb.2008.04TT. URL http://www.ncbi.nlm.nih.gov/pubmed/19178137http://www.liebertpub.com/ doi/10.1089/cmb.2008.04TT.
Ben O. Oyserman, Marnix H. Medema, and Jos M. Raaijmakers. Road MAPs to engineer host microbiomes. Current Opinion in Microbiology, 43:46-54, 2018. ISSN 18790364. doi: 10.1016/j.mib.2017.11.023. URL https://doi.org/10.1016/j.mib.2017.11.023
Maria Concetta Palumbo, Sara Zenoni, Marianna Fasoli, M 'elanie Massonnet, Lorenzo Farina, Filippo Castiglione, Mario Pezzotti, and Paola Paci. Integrated network analysis identifies fight-club nodes as a class of hubs encompassing key putative switch genes that induce ma jor transcriptome reprogramming during grapevine development. The Plant Cell, 26(12):4617-4635, 2014. ISSN 1532-298X. doi: 10.1105/tpc.114.133710.
Seon Young Park, Robert M. Binkley, Won Jun Kim, Mun Hee Lee, and Sang Yup Lee. Metabolic engineering of Escherichia coli for high-level astaxanthin production with high productivity. Metabolic Engineering, 49(August):105-115, 2018. ISSN 10967184. doi: 10.1016/j.ymben.2018.08.002. URL https://doi.org/10.1016/j.ymben.2018.08.002.
Romualdo Pastor-Satorras and Alessandro Vespignani. Epidemic Spreading in Scale-Free Networks. Physical Review Letters, 86(14):3200-3203, apr 2001. ISSN 0031-9007. doi: 10.1103/PhysRevLett.86.3200. URL http://link.aps.org/ doi/10.1103/PhysRevLett.86.3200.
Carlotta Ronda, Sway P. Chen, Vitor Cabral, Stephanie J. Yaung, and Harris H. Wang. Metagenomic engineer- ing of the mammalian gut microbiome in situ. Nature Methods, 16(2):167-170, 2019. ISSN 15487105. doi: 10.1038/s41592-018-0301-y. URL http://dx.doi.org/10.1038/s41592-018-0301-y.
Shai S Shen-Orr, Ron Milo, Shmoolik Mangan, and Uri Alon. Network motifs in the transcriptional regulation network of Escherichia coli. Nature Genetics, 31(1):64-68, may 2002. ISSN 1061-4036. doi: 10.1038/ng881. URL http: //www.ncbi.nlm.nih.gov/pubmed/11967538.
Tim Van Opijnen and Andrew Camilli. Transposon insertion sequencing: A new tool for systems-level analysis of mi- croorganisms. Nature Reviews Microbiology, 11(7):435-442, 2013. ISSN 17401526. doi: 10.1038/nrmicro3033. URL http://dx.doi.org/10.1038/nrmicro3033.
Tim van Opijnen, David W. Lazinski, and Andrew Camilli. Genome-wide fitness and genetic interactions determined by Tn-seq, a high-throughput massively parallel sequencing method for microorganisms. Current Protocols in Microbiology, 2015(April):1E.3.1-1E.3.24, 2015. ISSN 19348533. doi: 10.1002/9780471729259.mc01e03s36.
Haiyuan Yu, Philip M Kim, Emmett Sprecher, Valery Trifonov, and Mark B Gerstein. The importance of bottlenecks in protein networks: correlation with gene essentiality and expression dynamics. PLoS Computational Biology, 3(4):e59, apr 2007. ISSN 1553-7358. doi: 10.1371/journal.pcbi.0030059. URL http://www.ncbi.nlm.nih.gov/pubmed/17447836.
Tao Yu, Yasaman Dabirian, Quanli Liu, Verena Siewers, and Jens Nielsen. Strategies and challenges for metabolic rewiring. Current Opinion in Systems Biology, 15:30-38, 2019. ISSN 24523100. doi: 10.1016/j.coisb.2019.03.004. URL https://doi.org/10.1016/j.coisb.2019.03.004.
Bioinformatics
INF/01 - INFORMATICS - University credits: 6
Lessons: 48 hours
Professor:
Pavesi Giulio
Computational Biology
BIO/10 - BIOCHEMISTRY
BIO/11 - MOLECULAR BIOLOGY
INF/01 - INFORMATICS
BIO/11 - MOLECULAR BIOLOGY
INF/01 - INFORMATICS
Lessons: 48 hours
Educational website(s)
Professor(s)
Reception:
Tuesday or Friday, h. 15.00- 17.00
Via Celoria 26 (Department of Biosciences)/Online