Software



EVE: Evolution in Variable Environments
We have developed a abstract multiscale simulator of microbial evolution. The EVE simulator employs models of sub-cellular, cellular and evolutionary processes to simulate microbial functions over evolutionary time. The EVE code has been optimized for simulations in HPC environments and is currently extended to include process migrations, load balancing and other load mitigation strategies to counter-balance the undesired effects that evolutionary simulation models have. The first version of the simulator (available here) has been used successfully in the past to generate hypotheses related to regulatory network evolution in nutrient-limited microbial communities [J2]. EVE v2.1 was presented at the TeraGrid'11 conference [C9] and received two best paper awards (Best of Show and Best Paper in Science Track). The EVEVIS visualisation plugin was presented at IEEE Symposium on Biological Visualization (BioViz2011)[C10]. A parallel version of EVE was used to investigate the distribution of fitness effects in the presence of mobile elements and horizontal gene transfer [J3], and it led to the formulation of the guided evolution hypothesis, i.e. that the rate of evolution can be accelerated or decellerated based on specific similarity and complexity rules regarding the environmental context in which organisms evolve [J4].



A comprehensive tutorial, the source code and various builts are available below:



Please contact Prof. Ilias Tagkopoulos and Dr. Vadim Mozhayskiy if you have any suggestions, questions, or bugs to report.

References

[J2] I.Tagkopoulos, Y.Liu, S. Tavazoie, "Predictive Behavior Within Microbial Genetic Networks", Science, 320:1313-7, 2008

[J3] V.Mozhayskiy, I.Tagkopoulos, "Horizontal gene transfer dynamics and distribution of fitness effects during microbial In silico Evolution", 13:S13, BMC Bioinformatics, 2012

[J4] V.Mozhayskiy, I.Tagkopoulos, "Guided evolution of in silico microbial populations in complex environments accelerates evolutionary rates through a step-wise adaptation", 13:S10, BMC Bioinformatics, 2012

[C9] V.Mozhayskiy, R. Miller, KL. Ma, I.Tagkopoulos, "A Scalable Multi-scale Framework for Parallel Simulation and Visualization of Microbial Evolution", TeraGrid2011; Salt Lake City, Utah, 2011, DOI:10.1145/2016741.2016749

[C10] R. Miller, V.Mozhayskiy, I.Tagkopoulos, KL. Ma, "EVEVis: A Multi-Scale Visualization System for Dense Evolutionary Data", 1st IEEE Symposium on Biological Data Visualization, pp. 143-150, Providence, Rhode Island, 2011



SBROME: A scalable optimization and module matching framework for automated biosystems design
The Synthetic Biology Reusable Optimization Methodology (SBROME) is a computational platform for the modular, automatic design of synthetic circuits. It is supported by a module and parts database that contains characterized parts and experimentally validated synthetic designs. The SBROME framework first uses graph isomorphism algorithms to match modules to topology and then decompose the resulting topology to subcircuits that can be efficiently solved through exact or approximate optimization methods. Its methods include graph-theoretic approaches that allow both abstract and specific definitions of parts and modules. As a result, multiple underlying architectures can be considered as possible solutions to any functional or logical relationship, an important characteristic given the mechanistic diversity that underly biological systems.

Currently the SBROME manuscript is under revision for acceptance in ACS Synthetic Biology. The SBROME v0.8 source code can be downlaoded below. In addition, a beta version of an interactive GUI-based web platform is available. A comprehensive tutorial, sample files and builds for various systems and an online service will be available in the near future.





Please contact Prof. Ilias Tagkopoulos if you have any suggestions, questions, or bugs to report.

References

[J10] L. Huynh, A. Tsoukalas, M. Köppe, I.Tagkopoulos, "SBROME: A scalable optimization and module matching framework for automated biosystem design", under revision, ACS Synthetic Biology, 2012