Hot-spot analysis for drug discovery targeting protein-protein interactions

Introduction: Protein-protein interactions are important for biological processes and pathological situations, and are attractive targets for drug discovery. However, rational drug design targeting protein-protein interactions is still highly challenging. Hot-spot residues are seen as the best option to target such interactions, but their identification requires detailed structural and energetic characterization, which is only available for a tiny fraction of protein interactions. Areas covered: In this review, the authors cover a variety of computational methods that have been reported for the energetic analysis of protein-protein interfaces in search of hot-spots, and the structural modeling of protein-protein complexes by docking. This can help to rationalize the discovery of small-molecule inhibitors of protein-protein interfaces of therapeutic interest. Computational analysis and docking can help to locate the interface, molecular dynamics can be used to find suitable cavities, and hot-spot predictions can focus the search for inhibitors of protein-protein interactions. Expert opinion: A major difficulty for applying rational drug design methods to protein-protein interactions is that in the majority of cases the complex structure is not available. Fortunately, computational docking can complement experimental data. An interesting aspect to explore in the future is the integration of these strategies for targeting PPIs with large-scale mutational analysis.This work has been funded by grants BIO2016-79930-R and SEV-2015-0493 from the Spanish Ministry of Economy, Industry and Competitiveness, and grant EFA086/15 from EU Interreg V POCTEFA. M Rosell is supported by an FPI fellowship from the Severo Ochoa program. The authors are grateful for the support of the the Joint BSC-CRG-IRB Programme in Computational Biology.Peer ReviewedPostprint (author's final draft

Contemporary Innovation Policy and Instruments: Challenges and Implications

In this paper we review major theoretical (neoclassical economics, evolutionary, systemic and knowledge-based) insights about innovation and we analyse their implications for the characteristics of contemporary innovation policy and instruments. We show that the perspectives complement each other but altogether reveal the need to redefine the current general philosophy as well as the modes of operationalisation of contemporary innovation policy. We argue that systemic instruments ensuring proper organisation of innovation systems give a promise of increased rates and desired (more sustainable) direction of innovation.systemic instruments, innovation policy, innovation theory, policy mix, innovation system, sustainability

SoS in Disasters: Why following the manual can be a mistake

According to both the US Geological Survey and the World Bank, $280 billion dollars could have been saved if$40 billion dollars had been invested in disaster prevention. Natural and human-made disasters that have occurred over the last few years show that there is a gap in disaster prevention caused by the interconnected nature of risks, which cannot be foreseen with current risk management methods. In this paper we point out how disaster management could benefit from a SoS approach in emergency response and preparedness strategies. Using recent disasters as case studies, we identify some keys to success in managing a SoS in preparation, during and in the aftermath of a disaster. In particular, we discuss the idea of the interconnectedness of risks in independent and interdependent systems and the application of Boardman and Sauser’s concept of “creative disobedience”, which are fundamental for goal achievement of systems belonging to a SoS

A role-based software architecture to support mobile service computing in IoT scenarios

The interaction among components of an IoT-based system usually requires using low latency or real time for message delivery, depending on the application needs and the quality of the communication links among the components. Moreover, in some cases, this interaction should consider the use of communication links with poor or uncertain Quality of Service (QoS). Research efforts in communication support for IoT scenarios have overlooked the challenge of providing real-time interaction support in unstable links, making these systems use dedicated networks that are expensive and usually limited in terms of physical coverage and robustness. This paper presents an alternative to address such a communication challenge, through the use of a model that allows soft real-time interaction among components of an IoT-based system. The behavior of the proposed model was validated using state machine theory, opening an opportunity to explore a whole new branch of smart distributed solutions and to extend the state-of-the-art and the-state-of-the-practice in this particular IoT study scenario.Peer ReviewedPostprint (published version

Sustaining Economic Exploitation of Complex Ecosystems in Computational Models of Coupled Human-Natural Networks

Understanding ecological complexity has stymied scientists for decades. Recent elucidation of the famously coined "devious strategies for stability in enduring natural systems" has opened up a new field of computational analyses of complex ecological networks where the nonlinear dynamics of many interacting species can be more realistically mod-eled and understood. Here, we describe the first extension of this field to include coupled human-natural systems. This extension elucidates new strategies for sustaining extraction of biomass (e.g., fish, forests, fiber) from ecosystems that account for ecological complexity and can pursue multiple goals such as maximizing economic profit, employment and carbon sequestration by ecosystems. Our more realistic modeling of ecosystems helps explain why simpler "maxi-mum sustainable yield" bioeconomic models underpinning much natural resource extraction policy leads to less profit, biomass, and biodiversity than predicted by those simple models. Current research directions of this integrated natu-ral and social science include applying artificial intelligence, cloud computing, and multiplayer online games

Revealing networks from dynamics: an introduction

What can we learn from the collective dynamics of a complex network about its interaction topology? Taking the perspective from nonlinear dynamics, we briefly review recent progress on how to infer structural connectivity (direct interactions) from accessing the dynamics of the units. Potential applications range from interaction networks in physics, to chemical and metabolic reactions, protein and gene regulatory networks as well as neural circuits in biology and electric power grids or wireless sensor networks in engineering. Moreover, we briefly mention some standard ways of inferring effective or functional connectivity.Comment: Topical review, 48 pages, 7 figure

Learning Particle Dynamics for Manipulating Rigid Bodies, Deformable Objects, and Fluids

Real-life control tasks involve matters of various substances---rigid or soft bodies, liquid, gas---each with distinct physical behaviors. This poses challenges to traditional rigid-body physics engines. Particle-based simulators have been developed to model the dynamics of these complex scenes; however, relying on approximation techniques, their simulation often deviates from real-world physics, especially in the long term. In this paper, we propose to learn a particle-based simulator for complex control tasks. Combining learning with particle-based systems brings in two major benefits: first, the learned simulator, just like other particle-based systems, acts widely on objects of different materials; second, the particle-based representation poses strong inductive bias for learning: particles of the same type have the same dynamics within. This enables the model to quickly adapt to new environments of unknown dynamics within a few observations. We demonstrate robots achieving complex manipulation tasks using the learned simulator, such as manipulating fluids and deformable foam, with experiments both in simulation and in the real world. Our study helps lay the foundation for robot learning of dynamic scenes with particle-based representations.Comment: Accepted to ICLR 2019. Project Page: http://dpi.csail.mit.edu Video: https://www.youtube.com/watch?v=FrPpP7aW3L