Dror Fried

The human gene connectome as a map of short cuts for morbid allele discovery

High-throughput genomic data reveal thousands of gene variants per patient, and it is often difficult to determine which of these variants underlies disease in a given individual. However, at the population level, there may be some degree of phenotypic homogeneity, with alterations of specific physiological pathways underlying the pathogenesis of a particular disease. We describe here the human gene connectome (HGC) as a unique approach for human Mendelian genetic research, facilitating the interpretation of abundant genetic data from patients with the same disease, and guiding subsequent experimental investigations. We first defined the set of the shortest plausible biological distances, routes, and degrees of separation between all pairs of human genes by applying a shortest distance algorithm to the full human gene network. We then designed a hypothesis-driven application of the HGC, in which we generated a Toll-like receptor 3-specific connectome useful for the genetic dissection of inborn errors of Toll-like receptor 3 immunity. In addition, we developed a functional genomic alignment approach from the HGC. In functional genomic alignment, the genes are clustered according to biological distance (rather than the traditional molecular evolutionary genetic distance), as estimated from the HGC. Finally, we compared the HGC with three state-of-the-art methods: String, FunCoup, and HumanNet. We demonstrated that the existing methods are more suitable for polygenic studies, whereas HGC approaches are more suitable for monogenic studies. The HGC and functional genomic alignment data and computer programs are freely available to noncommercial users from http://lab.rockefeller.edu/casanova/HGC and should facilitate the genome-wide selection of disease-causing candidate alleles for experimental validation.

Iterative Temporal Planning in Uncertain Environments With Partial Satisfaction Guarantees

This paper introduces a motion-planning framework for a hybrid system with general continuous dynamics to satisfy a temporal logic specification consisting of cosafety and safety components in a partially unknown environment. The framework employs a multilayered synergistic planner to generate trajectories that satisfy the specification and adopt an iterative replanning strategy to deal with unknown obstacles. When the discovery of an obstacle renders the specification unsatisfiable, a division between the constraints in the specification is considered. The cosafety component of the specification is treated as a soft constraint, whose partial satisfaction is allowed, while the safety component is viewed as a hard constraint, whose violation is forbidden. To partially satisfy the cosafety component, inspirations are taken from indoor-robotic scenarios, and three types of (unexpressed) restrictions on the ordering of subtasks in the specification are considered. For each type, a partial satisfaction method is introduced, which guarantees the generation of trajectories that do not violate the safety constraints while attending to partially satisfying the cosafety requirements with respect to the chosen restriction type. The efficacy of the framework is illustrated through case studies on a hybrid car-like robot in an office environment.

Complexity of Canadian traveler problem variants

The Canadian traveler problem (CTP) is the problem of traversing a given graph, where some of the edges may be blocked a state which is revealed only upon reaching an incident vertex. Originally st ...

BDD-Based Boolean Functional Synthesis

Boolean functional synthesis is the process of automatically obtaining a constructive formalization from a declarative relation that is given as a Boolean formula. Recently, a framework was proposed for Boolean functional synthesis that is based on Craig Interpolation and in which Boolean functions are represented as And-Inverter Graphs (AIGs). In this work we adapt this framework to the setting of Binary Decision Diagrams (BDDs), a standard data structure for representation of Boolean functions. Our motivation in studying BDDs is their common usage in temporal synthesis, a fundamental technique for constructing control software/hardware from temporal specifications, in which Boolean synthesis is a basic step. Rather than using Craig Interpolation, our method relies on a technique called Self-Substitution, which can be easily implemented by using existing BDD operations. We also show that this yields a novel way to perform quantifier elimination for BDDs. In addition, we look at certain BDD structures called input-first, and propose a technique called TrimSubstitute, tailored specifically for such structures. Experiments on scalable benchmarks show that both Self-Substitution and TrimSubstitute scale well for benchmarks with good variable orders and significantly outperform current Boolean-synthesis techniques.

Repeated-task Canadian Traveler Problem

In the Canadian Traveler Problem (CTP) a traveling agent is given a graph, where some of the edges may be blocked, with a known probability. A solution for CTP is a policy, that has the smallest expected traversal cost. CTP is intracable. Previous work has focused on the case of a single agent. We generalize CTP to a repeated task version where a number of agents need to travel to the same goal, minimizing their combined travel cost. We provide optimal algorithms for the special case of disjoint path graphs. Based on a previous UCT-based approach for the single agent case, a framework is developed for the multi-agent case and four variants are given - two of which are based on the results for disjoint-path graphs. Empirical results show the benefits of the suggested framework and the resulting heuristics. For small graphs where we could compare to optimal policies, our approach achieves near-optimal results at only a fraction of the computation cost.

Sequential Relational Decomposition

The concept of decomposition in computer science and engineering is considered a fundamental component of computational thinking and is prevalent in design of algorithms, software construction, hardware design, and more. We propose a simple and natural formalization of sequential decomposition, in which a task is decomposed into two sequential sub-tasks, with the first sub-task to be executed out before the second sub-task is executed. These tasks are specified by means of input/output relations. We define and study decomposition problems, which is to decide whether a given specification can be sequentially decomposed. Our main result is that decomposition itself is a difficult computational problem. More specifically, we study decomposition problems in three settings: where the input task is specified explicitly, by means of Boolean circuits, and by means of automatic relations. We show that in the first setting decomposition is NP-complete, in the second setting it is NEXPTIME-complete, and in the third setting there is evidence to suggest that it is undecidable. Our results indicate that the intuitive idea of decomposition as a system-design approach requires further investigation. In particular, we show that adding human to the loop by asking for a decomposition hint lowers the complexity of decomposition problems considerably.

On Modeling, Complexities, and Automatic Configuration of Wireless Industrial Control Networks

We propose a mathematical model for wireless industrial control systems and analyze the engineering challenge of co-scheduling networking and control in this context. We demonstrate the use of the model using a small case study and explain how our modelling approach allows for self-configuring and self-adapting systems. The main theoretical contribution of the paper is a mathematical formulation of the computational problem of synthesizing a co-design of scheduling and control for wireless industrial control systems and a mathematical proof that this problem is computationally hard. We also identify relevant special network topologies for which the problem can be efficiently solved.