ISCB Mentored Computational Biology Programming
The ISCB Mentored Computational Biology Programming Award was created to provide up to two students with a fellowship to learn scientific programming and software engineering.
The winning submission will receive a cash award of $2000 USD and will be announced at ISMB/ECCB 2021.
SUBMISSIONS
How to Submit:
The mentee should submit a work proposal via the link below to include brief proposal (similar to a grant proposal) that is no more than 6 pages. The proposal should clearly state the goals of the project, the timetable for development, the tools (language, libraries, etc..) that were used, a brief architectural overview and clear deliverables. Make sure to submit the project along with support letter from your mentors.
*Special consideration will be given to mentor-mentee collaborations across disparate geographic regions, and to mentees from low-income countries*
Mentee/Mentor Eligibility Requirements:
- Student mentee needs to be a current ISCB member.
- Mentors need sufficient software development experience.
- If the student’s existing supervisor is to be a mentor, an additional co-mentor is mandatory.
Submission Requirements:
- The projects can be part of the graduate project of the applicant.
- The project should include a timetable & be finite: no more than 3 months.
Submissions Closed. Check back for updates.
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2018 ISCB Accomplishment by a Senior Scientist Award Ruth Nussinov
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2018 ISCB Accomplishment by a Senior Scientist Award: Ruth Nussinov
Christiana N. Fogg1, Diane Kovats 2*, Ron Shamir3
1 Freelance Writer, Kensington, Maryland, United States of America,
2 International Society for Computational Biology,
3 Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel
* This email address is being protected from spambots. You need JavaScript enabled to view it.
Each year, the International Society for Computational Biology (ISCB) recognizes a leader in the fields computational biology and bioinformatics with the Accomplishments by a Senior Scientist Award. This is the highest award bestowed by ISCB in recognition of a scientist’s significant research, education, and service contributions. Ruth Nussinov, Senior Principal Scientist and Principal Investigator at the National Cancer Institute, National Institutes of Health and Professor Emeritus in the Department of Human Molecular Genetics & Biochemistry, School of Medicine at Tel Aviv University, Israel, was honored as the 2018 winner of the Accomplishment by a Senior Scientist Award. She received her award and presented a keynote address at ISCB’s premiere annual meeting, the 2018 Intelligent Systems for Molecular Biology (ISMB) conference in Chicago, Illinois, held on July 6–10, 2018.
RUTH NUSSINOV: IN SEARCH OF BIOLOGICAL SIGNIFICANCE
Ruth Nussinov is a computational biologist with research interests that have touched every aspect of the field, from her PhD research on RNA secondary structure prediction to her visionary work on DNA sequence analysis, to proposing that all protein (and other biomacromolecules) conformations preexist and that all dynamic proteins are allosteric, to her current studies focused on Ras signaling in cancer. Nussinov’s deep intellectual curiosity has guided her research interests throughout her career.
Nussinov was raised in Rehovot, Israel, and attributes her early interest in science to watching her father conduct pioneering agricultural research that focused on adapting crops to the Israeli climate [1,2]. Nussinov’s father, Shmuel Hurwitz, was born in Minsk, Russia, and studied chemistry at Moscow University but later immigrated to Palestine (present day Israel) after his arrest for Zionist activities. It was here he discovered the great need for agricultural research. He pursued these studies at Berlin University but left Nazi Germany after his graduation in 1933 to found the Agricultural Research Station in Rehovot. Hurwitz was a founding member of the Faculty of Agriculture at the Hebrew University and was recognized for his significant contributions to advancing Israel agriculture with the 1957 Israeli Prize. As a child, Nussinov often joined her father on trips to his field sites, and his devotion to research and intense work ethic influenced her deeply and shaped how she approaches her work.
Nussinov also attributes her success as a scientist to the unwavering support from her husband, Shmuel Nussinov. They married just after she completed her service in the Israeli Army, during which time he was pursuing his graduate studies in particle physics at the Weizmann Institute. Her husband’s research advisor moved to the University of Washington, so Nussinov continued her undergraduate studies there (in microbiology) and went on to pursue her master’s degree in biochemistry at Rutgers University while her husband pursued postdoctoral research at Princeton University. They returned to Israel when Shmuel Nussinov joined the faculty at Tel Aviv University. When they came back to the United States several years later for his sabbatical, Ruth Nussinov enrolled in a PhD program in biochemistry at Rutgers and was mentored by a newly arrived assistant professor named George Pieczenik who had just come from Cambridge (United Kingdom). Nussinov recalled, “He said, ‘You know Ruth, Fred Sanger has just developed a DNA sequencing method and consequently there will be RNA sequences, and we will need an algorithm for the prediction of the secondary structure of RNA.’” She ran with this idea and worked tirelessly to develop the foundational Nussinov dynamic programming algorithm that is still in use today [3]. Nussinov’s PhD research has driven her career long search for questions that tackle issues of biological significance. She worked relatively independently on her project and was able to graduate in two years, and this early autonomy was critical to shaping her career path as an independent researcher.
Nussinov and her family returned to Israel, and she pursued postdoctoral studies in the Structural Chemistry Department of the Weizmann Institute and made several seminal contributions to DNA sequence analysis. She also worked as a visiting scientist in the Chemistry Department at the University of California, Berkeley, and in the Biochemistry Department at Harvard University. In spite of her impressive body of work and concept driven approach to scientific inquiry, Nussinov faced difficulties in securing a position at Tel Aviv University in the mid 1980s due to her husband’s existing position at the university and her unconventional, independent career path [4]. In 1985, Nussinov was finally appointed as an associate professor at Tel Aviv University and also became affiliated with the National Cancer Institute (NCI)/National Institutes of Health (NIH). During these early years, she credits her husband for giving her valuable advice about handling criticism from manuscript reviewers. He urged her to trust in her work and to reflect on and revise her manuscripts and resubmit them, as publications matter to the progress of a junior and unknown scientist [2].
One of Nussinov’s most profound contributions to the field is the “conformational selection and population shift” model of molecular recognition [5 9]. She and her colleagues first proposed this model in 1999 as an alternative paradigm to the “induced fit” model of protein–protein interactions. The induced fit model hypothesizes that conformational changes to a protein occur in a stepwise fashion upon binding to a ligand. In contrast, the conformational selection model portends that unbound molecules exist in all possible structural conformations, but some unbound higher energy conformations preferentially associate with a binding partner and cause a shift in equilibrium that favors this conformation. This model can explain numerous interactions observed for protein–ligand, RNA–ligand, protein–protein, protein–DNA, and protein–RNA interactions and can explain mechanisms of biological regulation, including oncogenic signaling. Nussinov is currently focused on the Ras protein and its interactions with effectors, with a particular interest in KRAS driven adenocarcinomas. She observed that self as sociation of GTP dependent K Ras dimers at different interfaces regulates which effectors bind to the dimers, which can alter downstream activity [10]. Nussinov and her team have also described the critical role of calmod ulin selectively binding to the GTP bound K Ras4B onco genic isoform, which promotes the initiation and progres sion of adenocarcinomas due to full activation of PI3Kα/Akt signaling in addition to the mitogen activated protein kinase (MAPK) pathway. These mechanistic insights are critical to developing better cancer drugs, and this work was recognized in the “Best of the AACR Journals Collec tion 2015.” Nussinov is also starting to explore interac tions between the human proteome and pathogens, given the growing appreciation of the microbiome on human health. Nussinov’s impact to the fields of computational biology and bioinformatics is notable. She has published more than 500 articles and has been ranked as a Highly Cited Researcher (ranking among the top 3,000 researchers or 1% across all fields according to Thomson Reuters Essen tial Science Indicators, http://highlycited.com/ December 2015) with more than 43,000 citations to date. Nussinov has also given over 300 invited talks and continues to maintain an active speaker schedule.
Nussinov serves as the Editor in Chief of PLOS Computational Biology, and she has also served as an editor and reviewer for numerous leading journals. Her scientific contributions have been recognized through her election as a Fellow of the Biophysical Society (2011) and an ISCB Fellow (2013). Nussinov has been a devoted mentor and advisor to graduate students and trainees throughout her career, and she has mentored dozens of PhD students, including numerous women. She has tried to model her mentorship to how she was trained, and she said, “I very much encourage independence and like for students to suggest a problem to study.”
Nussinov has always felt a close connection with ISCB, and her recognition with the 2018 ISCB Accomplishments by a Senior Scientist Award is a fitting tribute to her contributions to ISCB and to computational biology in general. She said, “I feel that’s where I belong and that’s where I want to be. I care very much about the development and sustainability and contribution of computational biology to all biological, chemical, and physical sciences.”
References
http://en.hafakulta.agri.huji.ac.il/people/shmuel hurwitz
Nussinov, Ruth. “TrendsTalk An Interview with Ruth Nussinov.” (2017) 761 763.
Nussinov, Ruth, et al. “Algorithms for loop matchings.” SIAM Journal on Applied mathematics (1978) 35.1: 68 82.
Shehu, Amarda. “Computational biologist in profile: Ruth Nussinov.” ACM SIGBioinformatics Record 3.3 (2013) 12 14.
Tsai CJ, Kumar S, Ma B, Nussinov R. “Folding funnels, binding funnels, and protein function.” Protein Sci. (1999) 8: 1181 90.
Ma B, Kumar S, Tsai CJ, Nussinov R. “Folding funnels and binding mechanisms.” Protein Eng. (1999) 12: 713 20.
Tsai CJ, Ma B, Nussinov R. Folding and binding cascades: shifts in energy landscapes. Proc Natl Acad Sci U S A. (1999) 96: 9970 72.
Ma B, Shatsky M, Wolfson HJ, Nussinov R. Multiple diverse ligands binding at a single protein site: a matter of pre existing populations. Protein Sci. (2002) 11: 184 97.
Boehr, David D., Ruth Nussinov, and Peter E. Wright. “The role of dynamic conformational ensembles in biomolecular recognition.” Nature Chemical Biology (2009) 5.11: 789.
Nussinov R, Tsai CJ, Jang H. Oncogenic Ras Isoforms Signaling Specificity at the Membrane. Cancer Res. 2018 Feb 1;78(3):593 602.
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Full bibliographical articles profiling the award recipients please see the ISMB 2018 focus issue of the ISCB newsletter as well as the ISCB Society Pages in PLOS Computational Biology and OUP Bioinformatics. Later this year, articles will be publish in F1000 Research ISCB Community Journal.
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2020 Overton Prize Winner: Jian Peng, PhD
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2020 ISCB Overton Prize: Jian Peng, PhD
Jian Peng, PhD, Assistant Professor, Department of Computer Science, College of Medicine (by courtesy), Institute of Genomic Biology (affiliate)
Cancer Center at Illinois (affiliate), National Center of Supercomputing and Applications (affiliate), University of Illinois at Urbana-Champaign
A Lifelong Love of the Sequence- Structure- Function Relationship
Jian Peng grew up in Yichang, Hubei Province, China to parents who were both university professors. His earliest memories include taking pleasure in his t time spent reading from his parents’ home l library, even when he could not fully comprehend the content of some the books. He recalled, “My parents were college professors, who always gave me the freedom to choose what I Iiked to do.” Peng was 10 years old when his parents gave him his first personal computer. and he was quickly drawn to computer programming. He spent many hours teaching himself to program and read programming books on C/ C++, Windows, and data structures. In high school, Peng became interested in chemistry, but he returned to his early interest in computer programming while pursuing his bachelor’ s and master’ s degrees in computer science at Wuhan University. As an undergraduate, Peng became deeply interested in mathematical logic and i ts applications to programming languages and wanted to pursue this topic in graduate school. He said, “ I didn’t find many places to study this topic. I was fortunate to meet with Professor Jinbo Xu, who was giving a bioinformatics talk at Tsinghua University and kindly showed me several fascinating papers, including his seminal work on the protein side chain packing problem. He suggested that I spend time reading textbooks on machine learning (ML), as he believed that ML would become a very useful tool in computational biology when more data become available.”
Peng went on to complete his PhD in 2013 at the Toyota Technological Institute at Chicago under Xu, where his research focused on protein structure prediction and modeling using ML methods. These methods, which are known as Raptor X and are still widely used today, have excelled at alignments of hard targets. Peng then joined Bonnie Berger’ s lab as a postdoc and expanded his research scope to include systems biology and functional genomics.
He recalled, “We have had a great time working on a variety of problems, including structural bioinformatics, compressive genomics, systems biology, and disease genomics. I also really appreciated my time in the lab of (the late) Susan Lindquist, where I learned a lot from experimental and wet lab biologists and found ways to help address important problems in neurodegenerative diseases using my computational skills.” He is deeply appreciative of his mentorship under Xu, Berger, and Lindquist not just for the areas of research he worked on with them but also for the lessons he learned in conducting experiments correctly and with rigor.
In 2015 , Peng was appointed as an assistant professor in the Department of Computer Science, and affiliated with the College of Medicine, at the University of Illinois at Urbana-Champaign. Peng's perspective in identifying new research topics has evolved with his maturation as an academic. As a student, he was more drawn to problems that he thought were highly interesting, or he was swayed by the “coolness” of a method. Now Peng appreciates that his research interests must also address important scientific problems, and he feels i t is critical to convey this concept to his trainees as they apply their knowledge in computation and biology to solve problems that deeply interest them.
Through his research experiences, Peng has learned that scientists are often surprised by unexpected findings. He said, “ What I’ve learned in these years from successes and failures is how capable (and incapable) computational methods can be. Like many artificial intelligence/ ML researchers, I was initially focused on developing powerful ML models for problems with large datasets, which hopefully can provide us new biological insights. However, in many important problems, such as those related to protein function and design, disease mutations studies, and functional genomics, the effective sample sizes are much smaller than what we expect for ML.” Peng's research has always been driven by understanding the sequence- structure- function relationship. Currently, Peng’s research has shifted directions towards using biological insights for developing advanced machine learning models. Like Bayesian methods, he uses known biological insights to serve as the “structural” prior to constrain ML models and generate new hypotheses in line with existing knowledge. Two recent notable projects in this line are the Deep Contact algorithm for protein contact map prediction and the Mashup algorithm (with Berger and Cho) for heterogeneous biological network data integration.
He is interested in understanding the functional and structural consequences of protein mutations. Peng appreciates the importance of this area in terms of designing proteins with better and more biologically relevant functions, but also improving the annotation of missense mutations in human genomes for gaining insights in molecular mechanisms of human diseases.
Peng is greatly humbled and honored to receive the 2020 ISCB Overton Prize as it is a recognition from his peers within the ISCB community, and he shares his gratitude with the mentors, students and collaborators that have brought his work to fruition.
ISCB will present the Accomplishments by a Senior Scientist Award, Overton Prize, Innovator Award and Outstanding Contributions to ISCB Award, at ISMB 2020 (www.iscb.org/ismb2020), which will take place in Montreal, Quebec, Canada, July 12-16, 2020 where, in addition, Peng, Liu, and Salzberg will present keynote addresses during the conference.
Full bibliographical articles profiling the award recipients will be available in the ISMB 2020 focus issue of the ISCB newsletter later this year, as well as the ISCB Society Pages in OUP Bioinformatics, and F1000 Research ISCB Community Journal.
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