B.Sc. in Bioinformatics (Three Years)

Total Page:16

File Type:pdf, Size:1020Kb

B.Sc. in Bioinformatics (Three Years) MAULANA ABUL KALAM AZAD UNIVERSITY OF TECHNOLOGY, WEST BENGAL NH-12 (Old NH-34), Simhat, Haringhata, Nadia -741249 Department of Bioinformatics B.Sc. in Bioinformatics Choice Based Credit System 144 Credits for 3-Year UG MAKAUT Framework w.e.f. AY 2020-21 MODEL CURRICULUM For B.Sc. in Bioinformatics MAULANA ABUL KALAM AZAD UNIVERSITY OF TECHNOLOGY, WEST BENGAL NH-12 (Old NH-34), Simhat, Haringhata, Nadia -741249 Department of Bioinformatics B.Sc. in Bioinformatics Course Title: Course Name: B.Sc. in Bioinformatics Formal Abbreviation: BSBIN Organizational Arrangements: Managing Faculty: Faculty from the Department of Bioinformatics Collaborating Faculties: Professionals from the Department of Biotechnology, Mathematics, English, Chemistry, Physics etc. External Partners: To be decided Nature of Development: - a new course. Objective: B.Sc.in Bioinformatics (BSBIN) is one of the most sought after career oriented professional programs offered at the bachelor’s level. This degree course opens up innumerable career options and opportunities to the aspiring bioinformatics professionals both in India and abroad. This program offers to gain knowledge about multidisciplinary science with triple major combination of biology, mathematics and computer science. The course is MAULANA ABUL KALAM AZAD UNIVERSITY OF TECHNOLOGY, WEST BENGAL NH-12 (Old NH-34), Simhat, Haringhata, Nadia -741249 Department of Bioinformatics B.Sc. in Bioinformatics closely attached with analytical and numerical methods of applied biological, mathematical and computational problems to address and solve pressing challenges in all areas of science and engineering. Subjects are taught in this program with hands on experience in Bioinformatics, drug discovery, genomics and proteomics, Molecular Biology and Genetics, Microbiology and Biochemsitry, Computer programming, Biostatistics, Mathematical Modelling etc. Course: - Three – Year full-time B.Sc. in Bioinformatics (Six –Semester). - Minimum number of class room-contact teaching for B.Sc. in Bioinformatics programme should be 134 credits (one credit equals 10 hours), Seminar on Emerging Area of Bioinformatics should be 02 credits, Final year Viva voice should be 02 credits and Internship / Project should be 06 credits i.e., Total 134 + 2 + 2 + 6 = 144 credits. - Specialization: Students can opt for anyone from two Specialization; DSE 1-4 shall contribute to programme specific objectives and evaluated as project and comprehensive VIVA VOCE. Reasons for Introduction of Course: B.Sc. in Bioinformatics is a 3 years’ professional course in in combination of Biology and Computer science domain. This is a three years’ full time undergraduate programme and is can also be recommended as an another course like B.Sc. in Biotechnology. This proposed course is divided in six semesters and each semester will have different papers according to CBCS format. One can join the course after passing common entrance test (CET) conducted by MAKAUT, WB. This course focuses on teaching students how to prosper in Bioinformatics field as a professional. MAULANA ABUL KALAM AZAD UNIVERSITY OF TECHNOLOGY, WEST BENGAL NH-12 (Old NH-34), Simhat, Haringhata, Nadia -741249 Department of Bioinformatics B.Sc. in Bioinformatics Eligibility Criteria: Interested aspirants for the course are required to fulfil the below-mentioned eligibility criteria. - A candidate should have cleared class 12 (10+2 or equivalent) examination with English and Biology. Notification for admission to the B.Sc. in Bioinformatics programme will be published and classes will start around the commencement of the academic session. Admission Process: Through CET Exam Course Structure: Subject type Abbreviation Number Credit Total Credit Mode of Point Credit Distribution of delivery courses Core Course CC All 14 6 84 (Theory, Prac) Online/Offline/ Blended Discipline Specific DSE All 4 6 24 (Theory, Prac) Online/Offline/ Elective Blended Skill Enhancement SEC All 2 2 4 (Theory) Online/Offline/ Course Blended Generic Elective or GE All 4 6 24 (Theory, Prac) Online/Offline/ Interdisciplinary Blended Ability Enhancement AECC All 4 2 8 Theory Online/Offline/ Compulsory Courses Blended Grant Total 144 MAULANA ABUL KALAM AZAD UNIVERSITY OF TECHNOLOGY, WEST BENGAL NH-12 (Old NH-34), Simhat, Haringhata, Nadia -741249 Department of Bioinformatics B.Sc. in Bioinformatics Subject Type Semester I Semester II Semester III Semester IV Semester V Semester VI CC CC1, CC2 CC3, CC4, CC5 CC6, CC7, CC8 CC9, CC10 CC11, CC12 CC13, CC14 DSE DSE1, DSE2 DSE3, DSE4 GE GE1 GE2 GE3 GE4 AECC AECC1 AECC2 SEC SEC1 SEC2 SEC3 SEC4 4 (20) 5 (26) 5 (26) 4 (20) 5 (26) 5 (26) CURRICULUM STRACTURE: Semester I Sl. No. CBCS Category Course Code Course Name L T P Credits Theory + Practical 1 CC-1 BSBINC 101 Cell Biology 4 0 4 6 BSBINC 191 Cell Biology Lab 2 CC-2 BSBINC 102 Introduction to Fundamental Computer 4 0 4 6 BSBINC 192 Lab Fundamental Computer 3 AECC-1 BSBINA 101 English Communication Skill Development 2 0 0 2 4 GE-1 BSBING 101 Any One from the List of 4 0 4 6 Generic Elective / / / / Interdisciplinary Courses 5 1 0 Total Credits 20 MAULANA ABUL KALAM AZAD UNIVERSITY OF TECHNOLOGY, WEST BENGAL NH-12 (Old NH-34), Simhat, Haringhata, Nadia -741249 Department of Bioinformatics B.Sc. in Bioinformatics Semester II Sl. No. CBCS Category Course Code Course Name L T P Credits Theory + Practical 1 CC-3 BSBINC 201 General Microbiology 4 0 4 6 BSBINC 291 General Microbiology Lab 2 CC-4 BSBINC 202 Chemistry 4 0 4 6 BSBINC 292 Chemistry Lab 3 CC-5 BSBINC 203 C Programming Language 4 0 4 6 BSBINC 293 C Programming Language Lab 4 AECC-2 BSBINA 201 Introduction to Environmental Science 2 0 0 2 5 GE-2 BSBING 201 Any One from the List of 4 0 4 6 Generic Elective / / / / Interdisciplinary Courses 5 1 0 Total Credits 26 Semester III Sl. No. CBCS Category Course Code Course Name L T P Credits Theory + Practical 1 CC-6 BSBINC 301 Biochemistry and Metabolism 4 0 4 6 BSBINC 391 Biochemistry and Metabolism Lab 2 CC-7 BSBINC 302 Basic Physics 4 0 4 6 BSBINC 392 Basic Physics Lab 3 CC-8 BSBINC 303 Data Structure 4 0 4 6 BSBINC 393 Data Structure Lab 4 SEC-1 BSBINS 301 Enzymology 2 0 0 2 BSBINS 302 Industrial Fermentation BSBINS 303 Molecular Biology 5 GE-3 BSBING 301 Any One from the List of 4 0 4 6 Generic Elective / / / / Interdisciplinary Courses 5 1 0 Total Credits 26 MAULANA ABUL KALAM AZAD UNIVERSITY OF TECHNOLOGY, WEST BENGAL NH-12 (Old NH-34), Simhat, Haringhata, Nadia -741249 Department of Bioinformatics B.Sc. in Bioinformatics Semester IV Sl. No. CBCS Category Course Code Course Name L T P Credits Theory + Practical 1 CC-9 BSBINC 401 Basic of Bioinformatics and Methods 4 0 4 6 BSBINC 491 Basic of Bioinformatics and Methods Lab 2 CC-10 BSBINC 402 Bioanalytical Tools 4 0 4 6 BSBINC 492 Bioanalytical Tools Lab 4 SEC-2 BSBINS 401 Molecular Diagnostics 2 0 0 2 BSBINS 402 Basic Forensic Science BSBINS 403 Research Methodology 5 GE-4 BSBING 401 Any One from the List of 4 0 4 6 Generic Elective / / / / Interdisciplinary Courses 5 1 0 Total Credits 20 Semester V Sl. No. CBCS Category Course Code Course Name L T P Credits Theory + Practical 1 CC-11 BSBINC 501 Structural Bioinformatics 4 0 4 6 BSBINC 591 Structural Bioinformatics Lab 2 CC-12 BSBINC 502 Programming in Python 4 0 4 6 BSBINC 592 Programming in Python Lab 4 DSE-1 BSBIND 501 Elective-I 4 0 4 6 A. Biostatistics / / / B. Plant Biotechnology 5 1 0 C. Medical Biotechnology 5 DSE-2 BSBIND 502 Elective-II 4 0 4 6 A. Linux & Shell Scripts / / / B. Genomes to Drug and Vaccine 5 1 0 Sessional 6 SEC-3 BSBINS 581 Seminar on Emerging Area of 2 0 0 2 Bioinformatics Total Credits 26 MAULANA ABUL KALAM AZAD UNIVERSITY OF TECHNOLOGY, WEST BENGAL NH-12 (Old NH-34), Simhat, Haringhata, Nadia -741249 Department of Bioinformatics B.Sc. in Bioinformatics Semester VI Sl. No. CBCS Category Course Code Course Name L T P Credits Theory + Practical 1 CC-13 BSBINC 601 Immunology 4 0 4 6 BSBINC 691 Immunology Lab 2 CC-14 BSBINC 602 Data Analysis with R 4 0 4 6 BSBINC 692 Data Analysis with R Lab 4 DSE-3 BSBIND 601 Elective-I 4 0 4 6 A. IPR, Biosafety and Ethical Issues / / / B. Environmental Biotechnology 5 1 0 5 DSE-4 BSBIND 682 Elective-II Sessional 4 0 4 6 Project/ Dissertation / / / 5 1 0 Sessional 6 SEC-4 BSBINS 681 Comprehensive viva 0 0 0 2 Total Credits 26 SEMESTER CREDITS I 20 II 26 III 26 IV 20 V 26 VI 26 TOTAL 144 MAULANA ABUL KALAM AZAD UNIVERSITY OF TECHNOLOGY, WEST BENGAL NH-12 (Old NH-34), Simhat, Haringhata, Nadia -741249 Department of Bioinformatics B.Sc. in Bioinformatics List of General Elective papers –Interdisciplinary: GE1 Basic Mathematics GE2 Introduction to Probability-Statistics GE3 Biomathematics GE4 Plant and Animal Tissue Culture GE5 Biotechnology and Human Welfare GE6 Numerical Methods GE7 Inheritance Biology Ecology and Environmental Management GE8 GE9 Entrepreneurship Development Program Outcome: GE10 Genetics Serial No Program Outcome Mapped Courses 1 Understanding of basic principles of Biology CC1 (BSBINC 101, BSBINC 191), GE4 2 Understanding of basic principles of CC4 (BSBINC 202, BSBIN 292), CC6 (BSBINC 301, BSBINC 391), Mathematics, Physics and Chemistry CC7 (BSBINC 302, BSBINC 392). AECC2 (BSBINA 201), GE1, GE2, GE3, GE6 3 Understanding of basic principles of Computer CC2 (BSBINC 102, BSBINC 192), CC8 (BSBINC 303, BSBINC Science 393), DSE2 (BSBIND 502), GE1 4 Understanding of basic principles of English and AECC1 (BSBINA 101), SEC3 (BSBINS 501) Communication 5 Understanding of basic principles of CC9 (BSBINC 401, BSBINC 491), GE BASKET 1, DSE2 (BSBIND Bioinformatics
Recommended publications
  • Bioinformatics 1
    Bioinformatics 1 Bioinformatics School School of Science, Engineering and Technology (http://www.stmarytx.edu/set/) School Dean Ian P. Martines, Ph.D. ([email protected]) Department Biological Science (https://www.stmarytx.edu/academics/set/undergraduate/biological-sciences/) Bioinformatics is an interdisciplinary and growing field in science for solving biological, biomedical and biochemical problems with the help of computer science, mathematics and information technology. Bioinformaticians are in high demand not only in research, but also in academia because few people have the education and skills to fill available positions. The Bioinformatics program at St. Mary’s University prepares students for graduate school, medical school or entry into the field. Bioinformatics is highly applicable to all branches of life sciences and also to fields like personalized medicine and pharmacogenomics — the study of how genes affect a person’s response to drugs. The Bachelor of Science in Bioinformatics offers three tracks that students can choose. • Bachelor of Science in Bioinformatics with a minor in Biology: 120 credit hours • Bachelor of Science in Bioinformatics with a minor in Computer Science: 120 credit hours • Bachelor of Science in Bioinformatics with a minor in Applied Mathematics: 120 credit hours Students will take 23 credit hours of core Bioinformatics classes, which included three credit hours of internship or research and three credit hours of a Bioinformatics Capstone course. BS Bioinformatics Tracks • Bachelor of Science
    [Show full text]
  • 13 Genomics and Bioinformatics
    Enderle / Introduction to Biomedical Engineering 2nd ed. Final Proof 5.2.2005 11:58am page 799 13 GENOMICS AND BIOINFORMATICS Spencer Muse, PhD Chapter Contents 13.1 Introduction 13.1.1 The Central Dogma: DNA to RNA to Protein 13.2 Core Laboratory Technologies 13.2.1 Gene Sequencing 13.2.2 Whole Genome Sequencing 13.2.3 Gene Expression 13.2.4 Polymorphisms 13.3 Core Bioinformatics Technologies 13.3.1 Genomics Databases 13.3.2 Sequence Alignment 13.3.3 Database Searching 13.3.4 Hidden Markov Models 13.3.5 Gene Prediction 13.3.6 Functional Annotation 13.3.7 Identifying Differentially Expressed Genes 13.3.8 Clustering Genes with Shared Expression Patterns 13.4 Conclusion Exercises Suggested Reading At the conclusion of this chapter, the reader will be able to: & Discuss the basic principles of molecular biology regarding genome science. & Describe the major types of data involved in genome projects, including technologies for collecting them. 799 Enderle / Introduction to Biomedical Engineering 2nd ed. Final Proof 5.2.2005 11:58am page 800 800 CHAPTER 13 GENOMICS AND BIOINFORMATICS & Describe practical applications and uses of genomic data. & Understand the major topics in the field of bioinformatics and DNA sequence analysis. & Use key bioinformatics databases and web resources. 13.1 INTRODUCTION In April 2003, sequencing of all three billion nucleotides in the human genome was declared complete. This landmark of modern science brought with it high hopes for the understanding and treatment of human genetic disorders. There is plenty of evidence to suggest that the hopes will become reality—1631 human genetic diseases are now associated with known DNA sequences, compared to the less than 100 that were known at the initiation of the Human Genome Project (HGP) in 1990.
    [Show full text]
  • Use of Bioinformatics Resources and Tools by Users of Bioinformatics Centers in India Meera Yadav University of Delhi, [email protected]
    University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln Library Philosophy and Practice (e-journal) Libraries at University of Nebraska-Lincoln 2015 Use of Bioinformatics Resources and Tools by Users of Bioinformatics Centers in India meera yadav University of Delhi, [email protected] Manlunching Tawmbing Saha Institute of Nuclear Physisc, [email protected] Follow this and additional works at: http://digitalcommons.unl.edu/libphilprac Part of the Library and Information Science Commons yadav, meera and Tawmbing, Manlunching, "Use of Bioinformatics Resources and Tools by Users of Bioinformatics Centers in India" (2015). Library Philosophy and Practice (e-journal). 1254. http://digitalcommons.unl.edu/libphilprac/1254 Use of Bioinformatics Resources and Tools by Users of Bioinformatics Centers in India Dr Meera, Manlunching Department of Library and Information Science, University of Delhi, India [email protected], [email protected] Abstract Information plays a vital role in Bioinformatics to achieve the existing Bioinformatics information technologies. Librarians have to identify the information needs, uses and problems faced to meet the needs and requirements of the Bioinformatics users. The paper analyses the response of 315 Bioinformatics users of 15 Bioinformatics centers in India. The papers analyze the data with respect to use of different Bioinformatics databases and tools used by scholars and scientists, areas of major research in Bioinformatics, Major research project, thrust areas of research and use of different resources by the user. The study identifies the various Bioinformatics services and resources used by the Bioinformatics researchers. Keywords: Informaion services, Users, Inforamtion needs, Bioinformatics resources 1. Introduction ‘Needs’ refer to lack of self-sufficiency and also represent gaps in the present knowledge of the users.
    [Show full text]
  • Challenges in Bioinformatics
    Yuri Quintana, PhD, delivered a webinar in AllerGen’s Webinars for Research Success series on February 27, 2018, discussing different approaches to biomedical informatics and innovations in big-data platforms for biomedical research. His main messages and a hyperlinked index to his presentation follow. WHAT IS BIOINFORMATICS? Bioinformatics is an interdisciplinary field that develops analytical methods and software tools for understanding clinical and biological data. It combines elements from many fields, including basic sciences, biology, computer science, mathematics and engineering, among others. WHY DO WE NEED BIOINFORMATICS? Chronic diseases are rapidly expanding all over the world, and associated healthcare costs are increasing at an astronomical rate. We need to develop personalized treatments tailored to the genetics of increasingly diverse patient populations, to clinical and family histories, and to environmental factors. This requires collecting vast amounts of data, integrating it, and making it accessible and usable. CHALLENGES IN BIOINFORMATICS Data collection, coordination and archiving: These technologies have evolved to the point Many sources of biomedical data—hospitals, that we can now analyze not only at the DNA research centres and universities—do not have level, but down to the level of proteins. DNA complete data for any particular disease, due in sequencers, DNA microarrays, and mass part to patient numbers, but also to the difficulties spectrometers are generating tremendous of data collection, inter-operability
    [Show full text]
  • Comparing Bioinformatics Software Development by Computer Scientists and Biologists: an Exploratory Study
    Comparing Bioinformatics Software Development by Computer Scientists and Biologists: An Exploratory Study Parmit K. Chilana Carole L. Palmer Amy J. Ko The Information School Graduate School of Library The Information School DUB Group and Information Science DUB Group University of Washington University of Illinois at University of Washington [email protected] Urbana-Champaign [email protected] [email protected] Abstract Although research in bioinformatics has soared in the last decade, much of the focus has been on high- We present the results of a study designed to better performance computing, such as optimizing algorithms understand information-seeking activities in and large-scale data storage techniques. Only recently bioinformatics software development by computer have studies on end-user programming [5, 8] and scientists and biologists. We collected data through semi- information activities in bioinformatics [1, 6] started to structured interviews with eight participants from four emerge. Despite these efforts, there still is a gap in our different bioinformatics labs in North America. The understanding of problems in bioinformatics software research focus within these labs ranged from development, how domain knowledge among MBB computational biology to applied molecular biology and and CS experts is exchanged, and how the software biochemistry. The findings indicate that colleagues play a development process in this domain can be improved. significant role in information seeking activities, but there In our exploratory study, we used an information is need for better methods of capturing and retaining use perspective to begin understanding issues in information from them during development. Also, in bioinformatics software development. We conducted terms of online information sources, there is need for in-depth interviews with 8 participants working in 4 more centralization, improved access and organization of bioinformatics labs in North America.
    [Show full text]
  • Database Technology for Bioinformatics from Information Retrieval to Knowledge Systems
    Database Technology for Bioinformatics From Information Retrieval to Knowledge Systems Luis M. Rocha Complex Systems Modeling CCS3 - Modeling, Algorithms, and Informatics Los Alamos National Laboratory, MS B256 Los Alamos, NM 87545 [email protected] or [email protected] 1 Molecular Biology Databases 3 Bibliographic databases On-line journals and bibliographic citations – MEDLINE (1971, www.nlm.nih.gov) 3 Factual databases Repositories of Experimental data associated with published articles and that can be used for computerized analysis – Nucleic acid sequences: GenBank (1982, www.ncbi.nlm.nih.gov), EMBL (1982, www.ebi.ac.uk), DDBJ (1984, www.ddbj.nig.ac.jp) – Amino acid sequences: PIR (1968, www-nbrf.georgetown.edu), PRF (1979, www.prf.op.jp), SWISS-PROT (1986, www.expasy.ch) – 3D molecular structure: PDB (1971, www.rcsb.org), CSD (1965, www.ccdc.cam.ac.uk) Lack standardization of data contents 3 Knowledge Bases Intended for automatic inference rather than simple retrieval – Motif libraries: PROSITE (1988, www.expasy.ch/sprot/prosite.html) – Molecular Classifications: SCOP (1994, www.mrc-lmb.cam.ac.uk) – Biochemical Pathways: KEGG (1995, www.genome.ad.jp/kegg) Difference between knowledge and data (semiosis and syntax)?? 2 Growth of sequence and 3D Structure databases Number of Entries 3 Database Technology and Bioinformatics 3 Databases Computerized collection of data for Information Retrieval Shared by many users Stored records are organized with a predefined set of data items (attributes) Managed by a computer program: the database
    [Show full text]
  • Bioinformatics Careers
    University of Pittsburgh | School of Arts and Sciences | Department of Biological Sciences BIOINFORMATICS CAREERS General Information • Bioinformatics is an interdisciplinary field that incorporates computer science and biology to research, develop, and apply computational tools and approaches to manage and process large sets of biological data. • The Bioinformatics career focuses on creating software tools to store, manage, interpret, and analyze data at the genome, proteome, transcriptome, and metabalome levels. Primary investigations consist of integrating information from DNA and protein sequences and protein structure and function. • Bioinformatics jobs exist in biomedical, molecular medicine, energy development, biotechnology, environmental restoration, homeland security, forensic investigations, agricultural, and animal science fields. • Most bioinformatics jobs require a Bachelor’s degree. Post-grad programs, University level teaching & research, and administrative positions require a graduate degree. The following list provides a brief sample of responsibilities, employers, jobs, and industries for individuals with a degree in this major. This is by no means an exhaustive listing, but is simply designed to give initial insight into a particular career field that would employ the skills and knowledge gained through this major. Areas Employers Environmental/Government • BLM • Private • Sequence genomes of bacteria useful in energy production, • DOE Contractors environmental cleanup, industrial processing, and toxic waste • EPA • National Parks reduction. • NOAA • State Wildlife • Examination of genomes dependent on carbon sources to address • USDA Agencies climate change concerns. • USFS • Army Corp of • Sequence genomes of plants and animals to produce stronger, • USFWS Engineers resistant crops and healthier livestock. • USGS • Transfer genes into plants to improve nutritional quality. Industrial • DOD • Study the genetic material of microbes and isolate the genes that give • DOE them their unique abilities to survive under extreme conditions.
    [Show full text]
  • Problems for Structure Learning: Aggregation and Computational Complexity
    (in press). In S. Das, D. Caragea, W. H. Hsu, & S. M. Welch (Eds.), Computational methodologies in gene regulatory networks. Hershey, PA: IGI Global Publishing. Problems for Structure Learning: Aggregation and Computational Complexity Frank Wimberly Carnegie Mellon University (retired), USA David Danks Carnegie Mellon University and Institute for Human & Machine Cognition, USA Clark Glymour Carnegie Mellon University and Institute for Human & Machine Cognition, USA Tianjiao Chu University of Pittsburgh, USA ABSTRACT Machine learning methods to find graphical models of genetic regulatory networks from cDNA microarray data have become increasingly popular in recent years. We provide three reasons to question the reliability of such methods: (1) a major theoretical challenge to any method using conditional independence relations; (2) a simulation study using realistic data that confirms the importance of the theoretical challenge; and (3) an analysis of the computational complexity of algorithms that avoid this theoretical challenge. We have no proof that one cannot possibly learn the structure of a genetic regulatory network from microarray data alone, nor do we think that such a proof is likely. However, the combination of (i) fundamental challenges from theory, (ii) practical evidence that those challenges arise in realistic data, and (iii) the difficulty of avoiding those challenges leads us to conclude that it is unlikely that current microarray technology will ever be successfully applied to this structure learning problem. INTRODUCTION An important goal of cell biology is to understand the network of dependencies through which genes in a tissue type regulate the synthesis and concentrations of protein species. A mediating step in such synthesis is the production of messenger RNA (mRNA).
    [Show full text]
  • Spontaneous Generation & Origin of Life Concepts from Antiquity to The
    SIMB News News magazine of the Society for Industrial Microbiology and Biotechnology April/May/June 2019 V.69 N.2 • www.simbhq.org Spontaneous Generation & Origin of Life Concepts from Antiquity to the Present :ŽƵƌŶĂůŽĨ/ŶĚƵƐƚƌŝĂůDŝĐƌŽďŝŽůŽŐLJΘŝŽƚĞĐŚŶŽůŽŐLJ Impact Factor 3.103 The Journal of Industrial Microbiology and Biotechnology is an international journal which publishes papers in metabolic engineering & synthetic biology; biocatalysis; fermentation & cell culture; natural products discovery & biosynthesis; bioenergy/biofuels/biochemicals; environmental microbiology; biotechnology methods; applied genomics & systems biotechnology; and food biotechnology & probiotics Editor-in-Chief Ramon Gonzalez, University of South Florida, Tampa FL, USA Editors Special Issue ^LJŶƚŚĞƚŝĐŝŽůŽŐLJ; July 2018 S. Bagley, Michigan Tech, Houghton, MI, USA R. H. Baltz, CognoGen Biotech. Consult., Sarasota, FL, USA Impact Factor 3.500 T. W. Jeffries, University of Wisconsin, Madison, WI, USA 3.000 T. D. Leathers, USDA ARS, Peoria, IL, USA 2.500 M. J. López López, University of Almeria, Almeria, Spain C. D. Maranas, Pennsylvania State Univ., Univ. Park, PA, USA 2.000 2.505 2.439 2.745 2.810 3.103 S. Park, UNIST, Ulsan, Korea 1.500 J. L. Revuelta, University of Salamanca, Salamanca, Spain 1.000 B. Shen, Scripps Research Institute, Jupiter, FL, USA 500 D. K. Solaiman, USDA ARS, Wyndmoor, PA, USA Y. Tang, University of California, Los Angeles, CA, USA E. J. Vandamme, Ghent University, Ghent, Belgium H. Zhao, University of Illinois, Urbana, IL, USA 10 Most Cited Articles Published in 2016 (Data from Web of Science: October 15, 2018) Senior Author(s) Title Citations L. Katz, R. Baltz Natural product discovery: past, present, and future 103 Genetic manipulation of secondary metabolite biosynthesis for improved production in Streptomyces and R.
    [Show full text]
  • Bioinformatics/Computational Biology/Data Science
    Bioinformatics/Computational biology/Data Science Potential Careers (includes, but not limited to): Academic Independent research: Running an independent research program at a University. Requires a PhD in Biology or related field, with significant training in Bioinformatics/Computational Biology. Lab may specialize in computational biology or use it as a tool to support wet-lab studies. Research Assistant: Working within a laboratory for independent researcher(s), using computational skills to help solve biological problems. MS would be helpful, but not required. Research support staff: Working in an institutional support facility, helping researchers to find computational solutions to their problems. MS or PhD would be helpful, but not required, depending on the position. Software developer – No formal education required. Healthcare informatics: Working for a hospital or healthcare system to gather data and analyze it, often supporting health care missions to promote best practices. E.g. a surgical team would like to present data that their procedure is superior to others. They ask you to gather data from surveys and medical records on the outcomes (mortality, infection rates, recovery time, patient satisfaction, ect), analyze it and summarize the outcome. Each of these careers may be regular salary/hourly employment or contract employment where you are hired to perform a specific task for a fee. Genetic Counseling – M.S. Required Examples of Grad programs (not a complete list) o IUPUI – 1 yr MS program in applied data science or Health informatics. Offer “Bootcamp” (extra semester of training in computational aspects). Also offer multiple online courses in computer coding. o NYU – MS is Biomedical Informatics Training for CMB students: B.S.
    [Show full text]
  • Algorithmic Complexity in Computational Biology: Basics, Challenges and Limitations
    Algorithmic complexity in computational biology: basics, challenges and limitations Davide Cirillo#,1,*, Miguel Ponce-de-Leon#,1, Alfonso Valencia1,2 1 Barcelona Supercomputing Center (BSC), C/ Jordi Girona 29, 08034, Barcelona, Spain 2 ICREA, Pg. Lluís Companys 23, 08010, Barcelona, Spain. # Contributed equally * corresponding author: [email protected] (Tel: +34 934137971) Key points: ● Computational biologists and bioinformaticians are challenged with a range of complex algorithmic problems. ● The significance and implications of the complexity of the algorithms commonly used in computational biology is not always well understood by users and developers. ● A better understanding of complexity in computational biology algorithms can help in the implementation of efficient solutions, as well as in the design of the concomitant high-performance computing and heuristic requirements. Keywords: Computational biology, Theory of computation, Complexity, High-performance computing, Heuristics Description of the author(s): Davide Cirillo is a postdoctoral researcher at the Computational biology Group within the Life Sciences Department at Barcelona Supercomputing Center (BSC). Davide Cirillo received the MSc degree in Pharmaceutical Biotechnology from University of Rome ‘La Sapienza’, Italy, in 2011, and the PhD degree in biomedicine from Universitat Pompeu Fabra (UPF) and Center for Genomic Regulation (CRG) in Barcelona, Spain, in 2016. His current research interests include Machine Learning, Artificial Intelligence, and Precision medicine. Miguel Ponce de León is a postdoctoral researcher at the Computational biology Group within the Life Sciences Department at Barcelona Supercomputing Center (BSC). Miguel Ponce de León received the MSc degree in bioinformatics from University UdelaR of Uruguay, in 2011, and the PhD degree in Biochemistry and biomedicine from University Complutense de, Madrid (UCM), Spain, in 2017.
    [Show full text]
  • Bioinformatics Syllabus
    Bioinformatics Syllabus Course Description: BINF 701/702 is the bioinformatics core course developed at the KU Center for Bioinformatics. The course is designed to introduce the most important and basic concepts, methods, and tools used in Bioinformatics. Topics include (but not limited to) bioinformatics databases, sequence and structure alignment, protein structure prediction, protein folding, protein-protein interaction, Monte Carlo simulation, and molecular dynamics. Emphasis will be put on the understanding and utilization of these concepts and algorithms. The objective is to help the students to reach rapidly the frontier of bioinformatics and be able to use the bioinformatics tools to solve the problems on their own research. Instructors: • Wonpil Im, Email: [email protected], Phone: 785-864-1993 • Ilya Vakser, Email: [email protected], Phone: 785-864-1057 • John Karanicolas, Email: [email protected], Phone: 785-864-4683 Schedule: 2:00-2:50pm, MWF Homework & Exams: There will be several homework assignments that will constitute 50% of your grade. The final examination constitutes another 50% of your grade. Student evaluation and grades: The grading scale will be: A = 90-100% B = 80-89 C = 70-79 D = 60-69 F = below 60 Textbook: A textbook is not required for this course. Assigned materials will either be handed out to the class or posted on the blackboard website. Course policies: Attendance and class participation are expected. Repeated absences without approval or valid justification will result in the reduction of the grade for the course. Late assignments will be docked 10% for being late, and an additional 10% for each additional day they are late.
    [Show full text]