The giant mimiviruses challenged the well-established concept of viruses, blurring the roots of the tree of life, mainly due to their genetic content. Along with other nucleo-cytoplasmic large DNA viruses, they compose a new proposed order—named Megavirales—whose origin and evolution generate heated debate in the scientific community. The presence of an arsenal of genes not widespread in the virosphere related to important steps of the translational process, including transfer RNAs, aminoacyl-tRNA synthetases, and translation factors for peptide synthesis, constitutes an important element of this debate. In this review, we highlight the main findings to date about the translational machinery of the mimiviruses and compare their distribution along the distinct members of the family Mimiviridae. Furthermore, we discuss how the presence and/or absence of the translation-related genes among mimiviruses raises important insights to boost the debate on their origin and evolutionary history.

This paper presents a novel, complete, and flexible optimization algorithm, which relies on recursive executions that re-constrains a model-checking procedure based on Satisfiability Modulo Theories (SMT). This SMT-based optimization technique is able to optimize a wide range of functions, including non-linear and non-convex problems using fixed-point arithmetic. Although SMT-based optimization is not a new technique, this work is the pioneer in solving non-linear and non-convex problems based on SMT; previous applications are only able to solve integer and rational linear problems. The proposed SMT-based optimization algorithm is compared to other traditional optimization techniques. Experimental results show the efficiency and effectiveness of the proposed algorithm, which finds the optimal solution in all evaluated benchmarks, while traditional techniques are usually trapped by local minima.

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This paper describes three variants of a counterexample guided inductive optimization (CEGIO) approach based on Satisfiability Modulo Theories (SMT) solvers. In particular, CEGIO relies on iterative executions to constrain a verification procedure, in order to perform inductive generalization, based on counterexamples extracted from SMT solvers. CEGIO is able to successfully optimize a wide range of functions, including non-linear and non-convex optimization problems based on SMT solvers, in which data provided by counterexamples are employed to guide the verification engine, thus reducing the optimization domain. The present algorithms are evaluated using a large set of benchmarks typically employed for evaluating optimization techniques. Experimental results show the efficiency and effectiveness of the proposed algorithms, which find the optimal solution in all evaluated benchmarks, while traditional techniques are usually trapped by local minima.