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Research Overview

Loewe Lab: Envisioning new ways to help biologists capture their ideas as models in the larger context of Evolutionary Systems Biology

Evolutionary processes are at the heart of many problems that we face in our world today, ranging from antibiotics resistance evolution to species extinction. To address such problems, we need to understand how change happens in our world.

 

Since such lofty insights are hidden, we are developing approaches to poke in the dark, a process known as 'modeling'. Everybody builds models, even if just intuitively, but math and empirical observations can help us weed out those models that contradict themselves, reality, or both. We call the many models that remain 'reasonable'. They help us quantify the uncertainty associated with our knowledge.

 

Our lab aims to improve the quality of these models by quantifying evolution with increasing precision. To this end, we have been working on several strands:

  • We are developing a rigorous definition of Evolutionary Systems Biology (EvoSysBio) to help bring more precision to this exciting new field of research, as there is more to EvoSysBio than its generalized goal of combining the strengths of evolutionary genetics and current systems biology.
  • To enable EvoSysBio, we need to build many models. To speed this up and simplify it for bench-biologists, we have been developing Evolvix, which is on its way to become the first general programming language designed by biologists for biologist (as far as we know). The purpose of Evolvix is to make accurate modeling easy by describing models in a way that is easy for humans to understand and for computers to automatically analyze. Because these computational tasks can be very challenging, we will be integrating Evolvix as soon as feasible with Evolution@home, the first globally distributed computing system for evolutionary biology.
  • Using Evolvix, we are in the process of integrating what is known about the molecular circadian clock in the fruitfly Drosophila melanogaster into a thoroughly documented, fully mechanistic simulation model inspired by EvoSysBio questions.
  • To predict evolution, we need estimates of the strength of selection and distributions of mutational effects (DMEs). We have been inferring these from DNA sequences using population genetics methods.
  • We are developing an approach to investigate DMEs from a new angle by using models from molecular systems biology (like the circadian clock) to compute Incomplete Fitness Traits (IFTs) that help quantify Landscapes of Incomplete Fitness Traits (LIFTs), which are at the core of EvoSysBio.

 

The work in our lab is ‘in silico’ only, but addresses a broad range of systems in biology, such as circadian clocks, cholesterol biosynthesis, antibiotics resistance evolution, genome evolution, population genetics of harmful mutations, species extinction, and more. We use a broad range of approaches from diverse disciplines beyond biology, such as math, stats, computer science, chemistry, physics, etc., all aiming to better quantify evolutionary processes, since this is key to developing practical strategies for tackling many applied problems.

 

 

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