To better understand the potential of drug repurposing in COVID-19, we analyzed control strategies over essential host factors for SARS-CoV-2 infection. We constructed comprehensive directed protein-protein interaction (PPI) networks integrating the top-ranked host factors, the drug target proteins and directed PPI data. We analyzed the networks to identify drug targets and combinations thereof that offer efficient control over the host factors. We validated our findings against clinical studies data and bioinformatics studies. Our method offers a new insight into the molecular details of the disease and into potentially new therapy targets for it. Our approach for drug repurposing is significant beyond COVID-19 and may be applied also to other diseases.

We define a variant of accepting splicing system that can be used as a problem solver. A condition for halting the computation on a given input as well as a condition for making a decision as soon as the computation has stopped is considered. An algorithm based on this accepting splicing system that solves a well-known NP-complete problem, namely the 3-colorability problem is presented. We discuss an efficient solution in terms of running time and additional resources (axioms, supplementary symbols, number of splicing rules. More precisely, for a given graph with n vertices and m edges, our solution runs in O(nm) time, and needs O(mn2) other resources. Two variants of this algorithm of a reduced time complexity at an exponential increase of the other resources are finally discussed.

Spiking neural P systems are a class of third generation neural networks belonging to the framework of membrane computing. Spiking neural P systems with communication on request (SNQ P systems) are a type of spiking neural P system where the spikes are requested from neighboring neurons. SNQ P systems have previously been proved to be universal (computationally equivalent to Turing machines) when two types of spikes are considered. This paper studies a simplified version of SNQ P systems, i.e. SNQ P systems with one type of spike. It is proved that one type of spike is enough to guarantee the Turing universality of SNQ P systems. Theoretical results are shown in the cases of the SNQ P system used in both generating and accepting modes. Furthermore, the influence of the number of unbounded neurons (the number of spikes in a neuron is not bounded) on the computation power of SNQ P systems with one type of spike is investigated. It is found that SNQ P systems functioning as number generating devices with one type of spike and four unbounded neurons are Turing universal.

Spiking neural P (SN P) systems are a class of parallel computation models inspired by neurons, where the firing condition of a neuron is described by a regular expression associated with spiking rules. However, it is NP-complete to decide whether the number of spikes is in the length set of the language associated with the regular expression. In this paper, in order to avoid using regular expressions, two major and rather natural modifications in their form and functioning are proposed: the spiking rules no longer check the number of spikes in a neuron, but, in exchange, a polarization is associated with neurons and rules, one of the three electrical charges -, 0,+. Surprisingly enough, the computing devices obtained are still computationally complete, which are able to compute all Turing computable sets of natural numbers. On this basis, the number of neurons in a universal SN P system with polarizations is estimated. Several research directions are mentioned at the end of this paper.

We refine the modeling in the P systems area of the way bacteria transmit genetic information in bacterial colonies, specifically the conjugation process. We study this new model from the computational power perspective using methods and ideas in the area; we are able to prove the universality of t hese systems. We show that systems working in a homogeneous manner and using only 75 species of objects in the regions and 13 species of “on-membrane” objects are enough for reaching universality. The system starts in a initial state with only few (nine) bacteria needed and the “bacteria” from this system are homogeneous, all have the same rules.

Spiking neural P systems with rules on synapses (RSSN P systems, for short) are a class of distributed and parallel computation models inspired by the way in which neurons process and communicate information with each other by means of spikes, where neurons only contain spikes and the evolution rules are on synapses. RSSN P systems have been proved to be Turing universal, using the strategy that restricts all the applied rules to consume the same number of spikes from the given neuron, termed as equal spikes consumption strategy. In this work, in order to avoid imposing the equal spikes consumption restriction on the application of rules, a new strategy for rule application, termed as sum spikes consumption strategy, is considered in RSSN P systems, where a maximal set of enabled rules from synapses starting from the same neuron is nondeterministically chosen to be applied, in the sense that no further synapse can use any of its rules, and the sum of these numbers of spikes that all the applied rules consume is removed from the neuron. In this way, the proposed strategy avoids checking whether all the applied rules consume the same number of spikes from the given neuron. The computation power of RSSN P systems working in the proposed strategy is investigated, and it is proved that such systems characterize the semilinear sets of natural numbers, i.e., such systems are not universal. Furthermore, RSSN P systems with weighted synapses working in the proposed strategy are proved to be Turing universal. These results show that the weight on synapses is a powerful ingredient of RSSN P systems in terms of the computation power, which makes RSSN P systems working in sum spikes consumption strategy become universal from non-universality.

Axon P systems are computing models with a linear structure in the sense that all nodes (i.e., computing units) are arranged one by one along the axon. Such models have a good biological motivation: an axon in a nervous system is a complex information processor of impulse signals. Because the structure of axon P systems is linear, the computational power of such systems has been proved to be greatly restricted; in particular, axon P systems are not universal as language generators. It remains open whether axon P systems are universal as number generators. In this paper, we prove that axon P systems are universal as both number generators and function computing devices, and investigate the number of nodes needed to construct a universal axon P system. It is proved that four nodes (respectively, nine nodes) are enough for axon P systems to achieve universality as number generators (respectively, function computing devices). These results illustrate that the simple linear structure is enough for axon P systems to achieve a desired computational power.

We improve and extend a recent result showing that spiking neural P systems with the same rules in all neurons of the system (homogenous) and working in the max sequential manner are universal. The previous work in this area reported by the group led

Several characterizations of recursively enumerable languages are given, using H systems with permitting contexts having splicing rules of small radius. Representations of context-free languages are also obtained in certain particular cases. These results improve previous related results which were recently published. Some open problems are also pointed out.