Loewe Group
| Current Group Members |
Status |
Background |
Focus |
| Ehlert, Kurt |
undergraduate |
math, neuroscience |
implementing a stochastic simulation algorithm |
| Flores-Lorca, Iratxo | special student | applied math; biochemistry; bio |
making ordinary differential equation solvers accessible |
| Goldstein, Steve |
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| Keel, Seth |
solutions architect |
computer science; genetics |
computing infrastructure, evolution@home, genetics simulations |
| Loewe, Laurence |
principal investigator |
biology; mol sys bio; theor. population genetics; evolution |
evolutionary systems biology; multi-locus population genetics of mutations; see my research interests |
| Mau, Bob |
biostatistician |
statistics, genomics |
distributions of mutational effects |
| Scheuer, Katherine |
undergraduate | biology | circadian clocks in Drosophila |
| Payeng Yang | undergraduate | natural sciences, majoring in biology | parameters of the cholesterol pathway |
| Former Group Members |
|||
| Holmes, Peter |
undergraduate Summer 2011 |
math, chem. eng. |
implementing a stochastic simulation algorithm |
| Myers, Matt |
undergraduate | computer science |
implementations in C++ |
| Poon, Philip |
postdoc | physics, applied math, numerics |
ABC, numerics, simulations |
Laurence Loewe
Assistant Professor of GeneticsRoom 3164 WID
loewe@wisc.edu
Education
Dr. rer. nat., TU Munich, Germany, 2002
Postdoctoral Research: University of Edinburgh
Evolutionary processes are at the heart of many problems that we face in our world today, ranging from antibiotics resistance evolution to species extinction. Addressing such problems requires models of the underlying causes. I aim to improve the quality of these models by quantifying evolution with increasing precision.
To this end I estimate the strength of selection in various systems, using different approaches, including the analysis of DNA sequences by population genetics methods. I also develop a new approach that builds on existing quantitative models from current systems biology and links them to potential fitness correlates to help estimate distributions of mutational effects in silico. This is an important part of what I call evolutionary systems biology, which aims to combine the strengths of evolutionary genetics and systems biology.
