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Short Abstract: The Stochastic Simulation Algorithm (SSA) is a method to model the dynamical behavior of complex systems. Despite its increasing relevance, most implementations cannot deal properly with the combinatorial diversity and spatial heterogeneity of biological systems. This work describes the development of PISKa, a multiscale modeling tool to perform agent-based simulation of spatially explicit biological systems. PISKa expands SSA to include explicit compartments that are parallely executed where agents representing the interacting biological entities, produce the dynamics of the system. Links accounting for different transport processes may be used to interconnect compartments. Three models demonstrate PISKa’s features: a cell signaling network of the mammal circadian clock; a protozoan predator-prey system where the habitat undergoes spatial rearrangements; and a model to study the influence of information on the spread of Ebola fever. PISKa allows massive simulations composed up to millions of agents distributed in different compartments that can be controlled by up to thousands of reactions or rules. By using the Kappa language, PISKa model files are simpler and easier to write and understand than those employed by other implementations. The divide and conquer approach used to solve the parallel compartments allows higher speed-up because rules or reactions will also be split between compartments. We show how speed-up and correlation with sequential simulations behave when we use different parameter configurations, including synchronization step and number of computing nodes and cores. Considering our results, we propose PISKa as a fast and versatile simulation tool to study the dynamic behavior of complex systems.
Short Abstract: Gene order changes, under rearrangements, insertions, deletions and duplications, have attracted increasing attention as a new type of data source for phylogenetic analysis. Since these changes are considered as rare evolutionary events (compared to sequence mutations), they allow the reconstruction of evolutionary history far back in time. Many software tools have been developed for the inference of gene-order phylogenies, including widely used maximum parsimony methods and maximum likelihood methods. However, both methods face challenges in dealing with large genomes with many duplicated genes, especially in the presence of whole genome duplication.
In this study, we propose three simple yet powerful maximum-likelihood (ML) based methods which take into account both gene adjacency and gene content information for phylogenetic reconstruction, using Variable Length Binary Encoding schemes (VLBE) to take into account the variations of copy number of genes. We conducted extensive experiments on simulated data sets and our new method derives the most accurate phylogenies compared to existing approaches. We also tested our method on real whole-genome data from eleven mammals and six plants.
Our new encoding schemes successfully make use of the multiplicity information of gene adjacencies and gene content in different genomes, and apply maximum-likelihood method designed for sequence data to reconstruct phylogenies for whole-genome data. Our experiments show that this new approach is particularly useful in handling haploid or polyploid species, in the presence of whole-genome duplication.
Short Abstract:
Background:
Tracing the evolution of complex traits from the molecular to the anatomical levels is an ongoing challenge. In bacteria, operons and gene blocks provide an interesting example of evolutionary complexity at the molecular level. Gene blocks are syntenic gene structures, and operons are gene blocks which are co-transcribed on a single mRNA molecule. The genes in operons and gene blocks typically work together in the same system and/or molecular complex.
Problem:
We recently developed an event-based method to study gene block evolution in bacteria. Our method shows that the evolution of gene blocks in bacteria can be described by a small set of events. These events include the insertion of genes into, or the splitting of genes out of a gene block, gene loss, and gene duplication. The question at hand is how to reconstruct ancestral gene blocks using the information in known, extant blocks.
Approach:
Since our model is indifferent to gene order, we treat gene blocks as sets. Under our assumptions of a three-event model and gene order indifference we use a fast maximum parsimony approach to reconstruct the ancestral nodes. For any given phylogenetic tree, we show that traversing it bottom-up in two iterations is enough to optimize our results.
Result:
We have developed a two-step method to reconstruct ancestral gene blocks. This method can be used effectively over a large number of bacterial taxa to hypothesize intermediate stated in gene block and operon evolution.
Short Abstract: Without a scale-change in agriculture, the needs of humanity cannot be met sustainably. Different novel methods are needed to enable progress in agriculture, many of which would benefit from advancements in data analysis and access. D3AI is a broad initiative to develop data-driven approaches to solving crop breeding problems at three scales: (1) model organisms and environments, (2) individual plants in fields, and (3) field aggregate analyses. Initial projects that involve analyzing and making accessible images of well-described genetic and developmental phenomena, assessing individual plants within fields to detect and predict economically important diseases and stresses, and field aggregate analysis that aims to improve crop yield prediction and farm resource planning. Cross-cutting themes include machine-learning and image analysis methodologies, as well as efforts to address ethical, legal, societal, and environmental implications (ELSEI) of data-driven research in agriculture. To learn more about D3AI, visit http://d3ai.iastate.edu.
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