The aim of this proposal is to continue, through the renewal of the System and Integrative Biology Training Grant (SIBTG), the unique yearly pre-doctoral training of six students in mathematical and computational biology. The rigorous mathematical and biological course requirements of the SIBTG have given it a track record of producing highly knowledgeable and versatile biomedical researchers, including many trainees who have gone on to high-profile academic and industrial positions. In the modern era of data-driven biomedical research, the mission of the SIBTG is even more urgent. For example, expanded acquisition and resolution of data in space and time will likely lead to higher false positive rates for detection of tumors, brain and cardiac signals, genetic testing, etc. Despite a seemingly unlimited amount of data, the resources available for further investigating the biological mechanisms implied by these data remain limited. Therefore, careful quantitative analysis, refined physical understanding, and enlightened biological insight will be ever critical in biomedical research where one is often concept-limited rather than data-limited. The SIBTG provides training orthogonal to that of typical grants for quantitative biology by stressing the acquisition of expertise in theoretical/computational mathematics and its judicial application to biology. In this proposal, a number of adjustments and improvements to the SIBTG are being proposed to complement the increased use of data in medicine, the recent establishment of the UCLA Institute of Quantitative and Computational Biology, the initiation of the Grand Challenge in Depression at UCLA, and the evolution of other training grants on campus. Specifically, (1) a new director, Prof. Tom Chou, has been named, (2) the administrative structure has been streamlined and diversified, (3) the roster of participating faculty has been significantly updated, (4) mentoring capacity in neurobiology has been increased, (5) institutional support from UCLA has been obtained to support an additional trainee selected under the same core criteria, (6) the core seminar requirement will be expanded into a full 4-unit course that incorporates training in scientific writing and presentations, and (7) collaborative outreach and recruitment activities with Cal State-Northridge, a Hispanic-serving institution will be developed and formalized. The leadership and member faculty will be selected to ensure independence and nominal overlap with other training grants at UCLA. These changes and the funding of this grant will enable a new generation of scientists to be rigorously trained in quantitative methods, allowing them to truly harvest the fruits of big data.
The goal of the Systems and Integrative Biology Training Program at UCLA is to train predoctoral students in both advanced mathematical and biological/medical sciences, in order to provide them the skills needed for addressing the quantitative and increasingly complex aspects of biomedical research.
| Lee, Calvin K; de Anda, Jaime; Baker, Amy E et al. (2018) Multigenerational memory and adaptive adhesion in early bacterial biofilm communities. Proc Natl Acad Sci U S A 115:4471-4476 |
| Takahashi, Toshiya; Kulkarni, Nikhil Nitin; Lee, Ernest Y et al. (2018) Cathelicidin promotes inflammation by enabling binding of self-RNA to cell surface scavenger receptors. Sci Rep 8:4032 |
| Crawford, Forrest W; Ho, Lam Si Tung; Suchard, Marc A (2018) Computational methods for birth-death processes. Wiley Interdiscip Rev Comput Stat 10: |
| Lee, Michelle W; Lee, Ernest Y; Wong, Gerard C L (2018) What Can Pleiotropic Proteins in Innate Immunity Teach Us about Bioconjugation and Molecular Design? Bioconjug Chem 29:2127-2139 |
| Lee, Ernest Y; Wong, Gerard C L; Ferguson, Andrew L (2018) Machine learning-enabled discovery and design of membrane-active peptides. Bioorg Med Chem 26:2708-2718 |
| Lee, Ernest Y; Lee, Michelle W; Wong, Gerard C L (2018) Modulation of toll-like receptor signaling by antimicrobial peptides. Semin Cell Dev Biol : |
| Beichman, Annabel C; Phung, Tanya N; Lohmueller, Kirk E (2017) Comparison of Single Genome and Allele Frequency Data Reveals Discordant Demographic Histories. G3 (Bethesda) 7:3605-3620 |
| Lee, Ernest Y; Lee, Michelle W; Fulan, Benjamin M et al. (2017) What can machine learning do for antimicrobial peptides, and what can antimicrobial peptides do for machine learning? Interface Focus 7:20160153 |
| Stolzenberg, Ethan; Berry, Deborah; Yang, De et al. (2017) A Role for Neuronal Alpha-Synuclein in Gastrointestinal Immunity. J Innate Immun 9:456-463 |
| de Anda, Jaime; Lee, Ernest Y; Lee, Calvin K et al. (2017) High-Speed ""4D"" Computational Microscopy of Bacterial Surface Motility. ACS Nano 11:9340-9351 |
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