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planning problems. These methods have been evaluated only in simulated environments and many applications (in robotics, automation and other domains) remain to be investigated. Keywords-Optimal path planning, cost space path planning, anytime path planning, sampling-based path planning.

The Transition-based RRT (T-RRT) algorithm enables to solve motion planning problems involving configuration spaces over which cost functions are defined, or cost spaces for short. T-RRT has been successfully applied to diverse problems in robotics and structural biology. In this paper, we aim at enhancing T-RRT to solve ever more difficult problems involving larger and more complex cost spaces. We compare several variants of T-RRT by evaluating them on various motion planning problems involving different types of cost functions and different levels of geometrical complexity. First, we explain why applying as such classical extensions of RRT to T-RRT is not helpful, both in a mono-directional and in a bidirectional context. Then, we propose an efficient Bidirectional T-RRT, based on a bidirectional scheme tailored to cost spaces. Finally, we illustrate the new possibilities offered by the Bidirectional T-RRT on an industrial inspection problem.

Understanding the mechanisms involved in the activation of an immune response is essential to many fields in human health, including vaccine development and personalized cancer immunotherapy. A central step in the activation of the adaptive immune response is the recognition, by T-cell lymphocytes, of peptides displayed by a special type of receptor known as Major Histocompatibility Complex (MHC). Considering the key role of MHC receptors in T-cell activation, the computational prediction of peptide binding to MHC has been an important goal for many immunological applications. Sequence-based methods have become the gold standard for peptide-MHC binding affinity prediction, but structure-based methods are expected to provide more general predictions (i.e., predictions applicable to all types of MHC receptors). In addition, structural modeling of peptide-MHC complexes has the potential to uncover yet unknown drivers of T-cell activation, thus allowing for the development of better and safer therapies. In this review, we discuss the use of computational methods for the structural modeling of peptide-MHC complexes (i.e., binding mode prediction) and for the structure-based prediction of binding affinity.

Molecular docking is a standard computational approach to predict binding modes of protein-ligand complexes, by exploring alternative orientations and conformations of the ligand (i.e., by exploring ligand flexibility). Docking tools are largely used for virtual screening of small drug-like molecules, but their accuracy and efficiency greatly decays for ligands with more than 10 flexible bonds. This prevents a broader use of these tools to dock larger ligands such as peptides, which are molecules of growing interest in cancer research. To overcome this limitation, our group has previously proposed a meta-docking strategy, called DINC, to predict binding modes of large ligands. By incrementally docking overlapping fragments of a ligand, DINC allowed predicting binding modes of peptide-based inhibitors of transcription factors involved in cancer. Here we describe DINC 2.0, a revamped version of the DINC webserver with enhanced capabilities and a more user-friendly interface. DINC 2.0 allows docking ligands that were previously too challenging for DINC, such as peptides with more than 25 flexible bonds. The webserver is freely accessible at http://dinc.kavrakilab.org, together with additional documentation and video tutorials. Our team will provide continuous support for this tool and is working on extending its applicability to other challenging fields, such as personalized immunotherapy against cancer.

Abstract-Performing aerial 6-dimensional manipulation using flying robots is a challenging problem, to which only little work has been devoted. This paper proposes a motion planning approach for the reliable 6-dimensional quasi-static manipulation with an aerial towed-cable system. The novelty of this approach lies in the use of a cost-based motion-planning algorithm together with some results deriving from the static analysis of cabledriven manipulators. Based on the so-called wrench-feasibility constraints applied to the cable tensions, as well as thrust constraints applied to the flying robots, we formally characterize the set of feasible configurations of the system. Besides, the expression of these constraints leads to a criterion to evaluate the quality of a configuration. This allows us to define a cost function over the configuration space, which we exploit to compute good-quality paths using the T-RRT algorithm. As part of our approach, we also propose an aerial towed-cable system that we name the FlyCrane. It consists of a platform attached to three flying robots using six fixed-length cables. We validate the proposed approach on two simulated 6-D quasi-static manipulation problems involving such a system, and show the benefit of taking the cost function into account for such motion planning tasks.

This paper presents an individual-based predator-prey model with, for the first time, each agent behavior being modeled by a Fuzzy Cognitive Map (FCM), allowing the evolution of the agent behavior through the epochs of the simulation. The FCM enables the agent to evaluate its environment (e.g., distance to predator/prey, distance to potential breeding partner, distance to food, energy level), its internal state (e.g., fear, hunger, curiosity) with memory and choosing several possible actions such as evasion, eating or breeding. The FCM of each individual is unique and is the outcome of the evolution process throughout the simulation. The notion of species is also implemented in a way that species emerge from the evolving population of agents. To our knowledge, our system is the only one that allows modeling the links between behavior patterns and speciation. The simulation produces a lot of data including: number of individuals, level of energy by individual, choice of action, age of the individuals, average FCM associated to each species, number of species. This study investigates patterns of macroevolutionary processes such as the emergence of species in a simulated ecosystem and proposes a general framework for the study of specific ecological problems such as invasive species and species diversity patterns. We present promising results showing coherent behaviors of the whole simulation with the emergence of strong correlation patterns also observed in existing ecosystems.

Abstract-This paper addresses the problem of improving the performance of the Rapidly-exploring Random Tree (RRT) algorithm by parallelizing it. For scalability reasons we do so on a distributed-memory architecture, using the message-passing paradigm. We present three parallel versions of RRT along with the technicalities involved in their implementation. We also evaluate the algorithms and study how they behave on different motion planning problems.