Laurence Loewe

Laurence Loewe
Assistant Professor
330 North Orchard Street, Room 3164
Madison WI 53715
(608) 316-4324, (608) 316-4326
Joined WID: 2010

Developing a stable, user-friendly programming language compiler for reproducible biodata analyses and general modeling in biology while building in-depth models for bioresearch


Naming Fun
Naming is hard. In fact it is one of the hardest problems in biology, computer science, math, etc., and also the most costly activity in the Loewe Lab so far. Hence the lab's research on naming. For example, we defined the data type 'BioBinary'. In a funny twist of fate, the name of the PI ('Loewe') is rarely pronounced correctly. The PI doesn't care, as long as the semantics is clear: 'Loewe' is German for 'Lion'.


  • Jugend Forscht (similar to US Science Talent Search), extensive, self-directed research on Miller-Urey chemical evolution experiments, supported by several departments from different disciplines at the University of Erlangen, Germany; 5 competitions, 1987-1991.
  • Biology MSc-equiv., focus on molecular aspects, experiments, University of Konstanz, Germany, 1995.
  • Dr. rer. nat., Microbiology, Muller's ratchet, evolution@home, Technical University of Munich, Germany, 2002.
  • Postdoc in theoretical population genetics, University of Edinburgh, UK, 2003-2006.
  • Lecturer in Evolutionary Genetics (sabbatical replacement), University of Edinburgh, UK
  • Postdoc in process algebra modelling and quantitative analysis, focus on molecular systems biology, CSBE, Laboratory for Foundations of Computer Science, University of Edinburgh, UK, 2007-2010.


Research Description

Nothing in biology makes sense except when properly quantified in the light of evolution.

From antibiotics resistance evolution and cancer to species extinction, evolution governs many challenges we face today; finding ways forward often requires causal insights from mechanistic models.

The Loewe Lab aims to improve the quality of diverse biological models by quantifying evolution with increasing precision. This inspired the PI to estimate the strength of selection in population genetics models from DNA sequence analyses and mutation accumulation experiments, approaches that struggle to quantify many types of mutational effects. Thus, Loewe investigates how to analyze molecular systems biology models from a population genetics view. He defined a framework for mechanistic evolutionary systems biology, which facilitates exploring mechanistic fitness landscapes by the versatile composition and analysis of complex biological simulation models.

To meet these challenging modeling demands, Loewe initiated the development of Evolvix, a modeling language he is designing to simplify mathematically accurate descriptions of concurrent systems that change over time. Such systems are often studied in biology, from biochemical reaction networks to evolving ecological webs. He develops a programming language architecture that integrates his 20+ years of experience in computational biology, in order to make Evolvix as user-friendly and as stable as possible. This work has inspired searching across disciplines for the best abstractions available for assisting biologists in navigating the complexity and diversity of biological systems. The Loewe Lab implemented a prototype of Evolvix that is currently used in the evolutionary systems biology modeling introduction course, which he developed at UW-Madison (Genetics 546).

Work with this prototype and in-depth usability reviews convinced Loewe that biology needs a dedicated general-purpose programming language designed by biologists for biologists. He develops formalisms and concepts for extending Evolvix accordingly without compromising its user-friendliness, which required a new approach to designing programming language architecture. As simulating evolution can quickly challenge any big computer, he is integrating Evolvix with evolution@home, the first globally distributed computing system for evolutionary biology that he started in 2001.

To keep Evolvix development relevant for working biologists, the in silico Loewe Lab also engages with a broad range of in-depth biological research to provide opportunities for testing on real-world problems. Such work includes building relevant Evolvix simulation models and the biodata science required for curating realistic model parameters. Research in his lab has investigated circadian clocks in flies, cholesterol biosynthesis, how selection shapes genomes, growing cancer cells, stochasticity in the evolution of cooperative behavior in game theory, potential genetic causes for species extinction, the evolution of antibiotics resistance, and more.

In-depth research with these models has also led to rich interactions with related theories for modeling, inspiring abstract aspects of compiler construction and contributing to the modeling framework developed for Evolvix. It also resulted in new perspectives on reproducibility, biosystems curation, biodata science, statistical logic, and the formal computing tools that are required for enabling biologists of the future to more efficiently learn from past results, positive or negative, in order to better build on today’s biological data and results (see FlyClockbase, 2017).

The ability to build efficiently and reproducibly on existing computational results is key to solving many biological problems with a practical impact.

More details can be found on the

Annual EvoSysBio Meetings

Loewe organized EvoSysBio meetings in various forms and maintains the most complete curated list of past meetings in evolutionary systems biology. Entries are added as new meetings become known.

If you know about an EvoSysBio meeting that should be listed or would like to contribute to an upcoming meeting, get into contact.

Interested in the Loewe Lab?

Interested in working on modeling, biosystems curation, or reproducibility in the Loewe Lab? Ready for in silico biology, but can’t program? Not scared about improving names for concepts or exploring semantics in-depth? Interactive writing support sounds cool? If you have the tenacity to work and learn in the heat where concepts for tomorrow are being forged, specify what you look for, what you bring to the lab, and why the lab is a great fit. Argue well and you might earn an interview; but be warned, the Lab builds biodata science tools for the professional biosystems curators of tomorrow. To speed them up, some steps today need slow-motion analysis or very advanced programming skills. Breaking new ground is not fast. So, most computational biology is more fun elsewhere, your industry experience in C++, CMake, node.js etc., deserves what the Lab can’t pay, and you need to get the MOs in the Lab’s (2017) BESTnames paper to program here.

Yet, this unusual Lab thrives on synergies between its members’ special skills for its own ‘Mission Impossible’. Do your hidden talents fit now? Impossible to predict. Apply with all details you want to share, so the Lab can reply as opportunities appear. Some may require that you are self-funded, or can volunteer time, or enroll in a special course, or find a training grant (see below), or that the Lab has funds. An easy way to learn more about the type of modeling work done in the Lab is to attend the evolutionary systems biology modeling introduction course, (Genetics 546).

Teaching Innovation


The Loewe Lab regularly participates at these events:

If you are interested in organizing a special outreach event that involves modeling some aspects in biology or other aspects of our work, do not hesitate to contact the LoeweLab.

For extending outreach activities, the Loewe Lab is looking for a middle-school or high-school teacher interested in the (paid) summer work of developing the materials for more efficiently teaching high-school students about modeling by using the Evolvix platform developed in the lab. 


The Loewe Lab at the University of Wisconsin-Madison is affiliated with:

PhD students in the following training programs can join the lab more easily than others:


NSF Career Award (2012) Modeling made easy: Extending systems biology modeling approaches to genetics and ecology