Marcelino Campos

IgTM: An algorithm to predict transmembrane domains and topology in proteins

BackgroundDue to their role of receptors or transporters, membrane proteins play a key role in many important biological functions. In our work we used Grammatical Inference (GI) to localize transmembrane segments. Our GI process is based specifically on the inference of Even Linear Languages.ResultsWe obtained values close to 80% in both specificity and sensitivity. Six datasets have been used for the experiments, considering different encodings for the input sequences. An encoding that includes the topology changes in the sequence (from inside and outside the membrane to it and vice versa) allowed us to obtain the best results. This software is publicly available at: http://www.dsic.upv.es/users/tlcc/bio/bio.htmlConclusionWe compared our results with other well-known methods, that obtain a slightly better precision. However, this work shows that it is possible to apply Grammatical Inference techniques in an effective way to bioinformatics problems.

Protein motif prediction by grammatical inference

The rapid growth of protein sequence databases is exceeding the capacity of biochemically and structurally characterizing new proteins. Therefore, it is very important the development of tools to locate, within protein sequences, those subsequences with an associated function or specific feature. In our work, we propose a method to predict one of those functional motifs (coiled coil), related with protein interaction. Our approach uses even linear languages inference to obtain a transductor which will be used to label unknown sequences. The experiments carried out show that our method outperforms the results of previous approaches.

A membrane computing simulator of trans-hierarchical antibiotic resistance evolution dynamics in nested ecological compartments (ARES)

BackgroundAntibiotic resistance is a major biomedical problem upon which public health systems demand solutions to construe the dynamics and epidemiological risk of resistant bacteria in anthropogenically-altered environments. The implementation of computable models with reciprocity within and between levels of biological organization (i.e. essential nesting) is central for studying antibiotic resistances. Antibiotic resistance is not just the result of antibiotic-driven selection but more properly the consequence of a complex hierarchy of processes shaping the ecology and evolution of the distinct subcellular, cellular and supra-cellular vehicles involved in the dissemination of resistance genes. Such a complex background motivated us to explore the P-system standards of membrane computing an innovative natural computing formalism that abstracts the notion of movement across membranes to simulate antibiotic resistance evolution processes across nested levels of micro- and macro-environmental organization in a given ecosystem.ResultsIn this article, we introduce ARES (Antibiotic Resistance Evolution Simulator) a software device that simulates P-system model scenarios with five types of nested computing membranes oriented to emulate a hierarchy of eco-biological compartments, i.e. a) peripheral ecosystem; b) local environment; c) reservoir of supplies; d) animal host; and e) host’s associated bacterial organisms (microbiome). Computational objects emulating molecular entities such as plasmids, antibiotic resistance genes, antimicrobials, and/or other substances can be introduced into this framework and may interact and evolve together with the membranes, according to a set of pre-established rules and specifications. ARES has been implemented as an online server and offers additional tools for storage and model editing and downstream analysis.ConclusionsThe stochastic nature of the P-system model implemented in ARES explicitly links within and between host dynamics into a simulation, with feedback reciprocity among the different units of selection influenced by antibiotic exposure at various ecological levels. ARES offers the possibility of modeling predictive multilevel scenarios of antibiotic resistance evolution that can be interrogated, edited and re-simulated if necessary, with different parameters, until a correct model description of the process in the real world is convincingly approached. ARES can be accessed at http://gydb.org/ares.ReviewersThis article was reviewed by Eugene V. Koonin, and Eric Bapteste.

Accepting Networks of Genetic Processors are computationally complete

We propose a computational model that is inspired by genetic operations over strings such as mutation and crossover. The model, Accepting Network of Genetic Processors, is highly related to previously proposed ones such as Networks of Evolutionary Processors and Networks of Splicing Processors. These models are complete computational models inspired by DNA evolution and recombination. Here, we prove that the proposed model is computationally complete (it is equivalent to the Turing machine). Hence, it can accept any recursively enumerable language. In addition, we relate the proposed model with (parallel) Genetic Algorithms or Evolutionary Programs and we set these techniques as decision problem solvers.

Incremental multiple sequence alignment

This work proposes a new approach to the alignment of multiple sequences. We take profit from some results on Grammatical Inference that allow us to build iteratively an abstract machine that considers in each inference step an increasing amount of sequences. The obtained machine compile the common features of the sequences, and can be used to align these sequences. This method improves the time complexity of current approaches. The experimentation carried out compare the performance of our method and previous alignment methods.

Neural network approach to locate motifs in biosequences

In this work we tackle the task of detecting biological motifs, i.e. subsequences with an associated function. This task is important in bioinformatics because it is related to the prediction of the behaviour of the whole protein. Artificial neural networks are used to, somewhat, translate the sequence of amino acids of the protein into a code that shows the subsequences where the presence of the studied motif is expected. The experimentation performed prove the good performance of our approach.

Multi-Hierarchical Dynamics of Antimicrobial Resistance Simulated in a Nested Membrane Computing Model

The membrane-computing model in this work reproduces complex biological landscapes in the computer world. It uses nested “membrane-surrounded entities” able to divide, propagate and die, be transfer into other membranes, exchange informative material according to flexible rules, mutate and being selected by external agents. This allows the exploration of multi-hierarchical interactive dynamics resulting from the probabilistic interaction along time of genes (phenotypes), clones, species, hosts, environments, and antibiotic challenges in the real world. Our model facilitates the simultaneous analysis of several features of interest in the prediction of the rules governing the multi-level evolutionary biology of antibiotic resistance. These include the overall integrated dynamics of species, populations with resistance phenotypes, and mobile genetic elements in different environments (hospital and community) and experimental landscapes. In the examples included here, we predict the effects of different rates of patient’s flow from hospital to the community and viceversa, crosstransmission rates between patients with bacterial propagules of different sizes, the proportion of patients treated with antibiotics, antibiotics and dosing in opening spaces in the microbiota where resistant phenotypes multiply. We can also predict the selective strength of some drugs and the influence of the time-0 resistance composition of the species and bacterial lineages in the evolution of resistance phenotypes. However, many other analyses are possible to implement. In summary, we provide a bunch of examples about the multi-hierarchical dynamics of antibiotic resistance using a novel computable model with reciprocity within and between levels of biological organization, a type of approach that can be easily expanded to fulfil the needs of risk-analysis and evolutionary predictions in a multiplicity of complex ecological landscapes.Membrane Computing is a bio-inspired computing paradigm, whose devices are the so-called membrane systems or P systems. The P system designed in this work reproduces complex biological landscapes in the computer world. It uses nested membrane-surrounded entities able to divide, propagate and die, be transferred into other membranes, exchange informative material according to flexible rules, mutate and being selected by external agents. This allows the exploration of hierarchical interactive dynamics resulting from the probabilistic interaction of genes (phenotypes), clones, species, hosts, environments, and antibiotic challenges. Our model facilitates analysis of several aspects of the rules that govern the multi-level evolutionary biology of antibiotic resistance. We examine a number of selected landscapes where we predict the effects of different rates of patient flow from hospital to the community and viceversa, cross-transmission rates between patients with bacterial propagules of different sizes, the proportion of patients treated with antibiotics, antibiotics and dosing in opening spaces in the microbiota where resistant phenotypes multiply. We can also evaluate the selective strength of some drugs and the influence of the time-0 resistance composition of the species and bacterial clones in the evolution of resistance phenotypes. In summary, we provide case studies analyzing the hierarchical dynamics of antibiotic resistance using a novel computing model with reciprocity within and between levels of biological organization, a type of approach that may be expanded in the multi-level analysis of complex microbial landscapes.

A Model of Antibiotic Resistance Evolution Dynamics Through P Systems with Active Membranes and Communication Rules

In this work we describe a model of antibiotic resistance evolution dynamics based on a membrane computing approach. The model was implemented in a simulator tool first proposed in [3], with a naive set of rules and characteristics. In this paper, we describe the improvements over the first version of the model, we introduce new P system rules to manage all the elements of the system, and we explain a scenario in order to illustrate the experiments that can be carried out in the proposed framework.