Rainer Sachs, Professor

Open (1) Modeling cancer risk for astronauts on Mars missions or other extended voyages above low earth orbit.

Open. Apprentices needed for the spring semester. Please do NOT contact faculty before February 5th (the start of the 4th week of classes)! Enter your application on the web beginning January 9th. The deadline to apply is Tuesday, January 23rd at 8 AM.

I am interested in mathematical and computer modeling of the carcinogenesis process, particularly of cancers due to ionizing radiation. We are analyzing cancers produced when astronauts encounter galactic cosmic rays (GCR). GCR occur almost exclusively outside of what NASA terms low earth orbit. This high energy radiation realistically cannot be shielded against. We are using in silico modeling to estimate how dangerous GCR are. We are using modern mathematical synergy analysis to plan and interpret experiments at Brookhaven National Laboratory on the tumorigenic risk to mice of a mixed radiation field, and on the effect of such fields on cancer surrogate endpoints such as chromosome aberrations, using results of experiments on each individual radiation in the mixture.

I am planning to admit at most one new student this semester, and recently I have had more than 20 new applicants each semester. If you want to participate in URAP you may want to apply to other projects in addition to this one, due to my low anticipated acceptance rate.



Students will meet Prof. Sachs 1-one-1 or with one other student for an hour a week to discuss progress. At least six hrs/week of aditional work will be needed, mostly at your own time on your own computer. Students are required to sign up for URAP. However they may choose to take 0 hours, which avoids using up units needed for other courses. In many cases the primary result will be a broadening of the student's perspective. However, sometimes a student coauthorship on a published paper results. I believe the URAP program should be primarily for the education of the student, not for the convenience of the faculty members, but some routine tasks will be part of the assignments. There will be no specific benchmarks students have to meet, other than utilizing their strengths and filling in gaps in their knowledge and their mathematical/computational expertise.

Applied mathematicians typically find it much easier to do formal calculations than to gain a reasonable perspective on what calculations are useful. I hope the proposed project will help a hard-science major gain expertise in biomedical applications.

Qualifications: My best results with URAP have come when students try a project one semester and then choose to continue. Therefore I will prefer students whose anticipated graduation date is Jan 2019 or later though of course you don't need to commit to more than one semester now. This is an opportunity for students with backgrounds in applied math, statistics, computer science, pure math, physics, chemistry, or MCB to apply their knowledge to research. The student must be thoroughly familiar with computer programming in R and must give details of how much R they know in their application. Familiarity with other computer languages is not an adequate substitute. The student should also have one calculus-based course in probability or statistics. Very desirable but not essential: good understanding of lower division material on non-linear first order ordinary differential equations; upper division probability and statistics course(s). Grades in STEM courses should average A- or better, preferably with at least one A+. Lower division students should have mainly A and A+ grades in technical courses. Desirable: upper division courses in MCB. Because of the interdisciplinary nature of the projects, many students might need some extra background. In that case the first half of the semester would be devoted to rounding off the student's expertise and the second half would involve some calculations.

Weekly Hours: 6-9 hrs

Off-Campus Research Site: apart from weekly meetings most of the work will be done by the student at home or anywhere else the student finds convenient.

Related website: http://math.berkeley.edu/~sachs/index.html