Computing with viruses

In recent years, different computing models have emerged within the area of Unconven-tional Computation, and more specifically within Natural Computing, getting inspiration from mechanisms present in Nature. In this work, we incorporate concepts in virology and theoretical computer science to propose a novel computational model, called Virus Ma-chine. Inspired by the manner in which viruses transmit from one host to another, a virus machine is a computational paradigm represented as a heterogeneous network that con-sists of three subnetworks: virus transmission, instruction transfer, and instruction-channel control networks. Virus machines provide non-deterministic sequential devices. As num-ber computing devices, virus machines are proved to be computationally complete, that is, equivalent in power to Turing machines. Nevertheless, when some limitations are imposed with respect to the number of viruses present in the system, then a characterization for semi-linear sets is obtained

Solving Common Algorithmic Problem by Recognizer Tissue P Systems

Common Algorithmic Problem is an optimization problem, which has the nice property that several other NP-complete problems can be reduced to it in linear time. In this work, we deal with its decision version in the framework of tissue P systems. A tissue P system with cell division is a computing model which has two types of rules: communication and division rules. The ability of cell division allows us to obtain an exponential amount of cells in linear time and to design cellular solutions to computationally hard problems in polynomial time. We here present an effective solution to Common Algorithmic Decision Problem by using a family of recognizer tissue P systems with cell division. Furthermore, a formal verification of this solution is given.Ministerio de Ciencia e Innovación TIN2009–13192Junta de Andalucía P08-TIC-0420

Beneficial Plant-Microbe Interactions to Improve Nutrient Uptake and Biotic Stress Response in Crops

Mutualism is a very common phenomenon among living organisms on earth. Legumes because of their high protein content, serve as a great nutrient resource for animals. This group of plants can form a mutualistic symbiosis with beneficial microbes. For example, Alfalfa (Medicago) and soybean (Glycine max) can get colonized with arbuscular mycorrhizal fungi (AMF) and rhizobia bacteria simultaneously forming a complex tripartite interaction for nutrient benefits. Most of the previous research evaluated individual symbionts, either rhizobia bacteria or AMF, but not both. There are only a few reports which discuss the nutrient exchange mechanisms in a tripartite interaction. Thus, there is a lack of fundamental understanding of how the resources are exchanged in tripartite interactions. Nitrogen (N) and phosphorus (P) are essential nutrients for plant growth; AMF can supply both P and N, while rhizobia bacteria can only supply N to their host plant. Both root symbionts can provide other benefits like abiotic and biotic stress tolerance. In return, the host plant distributes a substantial amount of its photosynthetic carbon (C) produced in the leaves to its root symbionts. However, the regulation mechanisms on C resources allocation by the host plant to its root symbionts is not well understood. In my first experiment, I hypothesized that the N-fixing capability of the rhizobia bacteria affects the C allocation pattern in a tripartite system with AMF. I evaluated C allocation to the symbionts under in a tripartite interaction with various nutrient access scenarios including the use of a rhizobial strain that lacks biological nitrogen fixation (BNF) capability and AMF having access to a labeled N source. The dual inoculation of N fixing rhizobia (Fix+) and AMF results in a synergistic increase in shoot biomass, enhanced N and P uptake in the sink (roots) but low delivery toward the source (leaves). On the other hand, tripartite interactions of Fix- rhizobia that lack biological N fixation activity and AMF lead to a significant increase in N uptake and delivery towards the source but a significant drop in carbon allocation towards Fix- rhizobia root. Consistent with these findings, we found changes in SUCROSE UPTAKE TRANSPORTER (SUT) and SUGAR WILL EVENTUALLY BE EXPORTED TRANSPORTER (SWEET) genes. These results provide substantial new information about how host plants control their carbon allocations under the different status of N demand in presence of rhizobia and AMF inoculation. During tripartite interactions, rhizobia bacteria are restricted to the host roots but extraradical mycelia (ERM) of AMF can go beyond, colonizing another host root. This leads to the development of common networks among two or more plants which are known as the common mycelial Network (CMN), creating a biological market for nutrient transport. The nitrogen-fixing capability of rhizobia bacteria can affect the transport of nitrogen (N) by AMF to host plants connected by CMNs. In the second experiment, I hypothesized that access of exogenous 15N to AMF would allocate more N to host plants colonized by Fix- rhizobia that lack BNF capability than those colonized by Fix+ rhizobia. We found that co-inoculation with Fix- rhizobia with AMF or non-mycorrhizal control plants resulted in elevated 15N enrichment in the shoot of the host plant. This suggests that AMF allocates most of the N they uptake from the soil to the host plant with a greater N demand due to the lack of access to fixed nitrogen. As expected, we found that AMF does not transfer as much N with host plants colonized by Fix+ rhizobia because their N demand can be fulfilled by the rhizobia bacteria. Plant diseases can be managed in various ways, including the use of disease-resistant and/or tolerant crop varieties, chemical controls, and biological controls. A diseaseresistant variety can lose its resistance due to the development of a new variant of the pathogen. Chemicals used in agriculture and other systems can have a very adverse effect on the environment. The use of Microbes for controlling plant diseases is safer and offers environmental sustainability compared to chemical pesticides. In my third experiment, I evaluated if AMF could mitigate the destructive effect of Soybean cyst nematode (SCN: Heterodera glycines), one of the most dreadful pests in soybean. Soybean plants infested with SCN do not show any aboveground symptoms in most of the cases, so the field gets unrecognized for a long time. Through the AMF symbiosis, plant hosts receive protection from pathogens as well among other benefits. In this experiment, we evaluated the effects of a commercially available AMF soil additive called MycoApply® (consists of an equal ratio of Glomus mossaea, Rhizophagus irregulare, G. etunicatum, G. aggregatum) under greenhouse and field conditions on the reproduction of SCN and the soybean growth and yield increase. We observed increased shoot weight for AMF-treated SCN susceptible variety (Williams-82) infested with SCN but no effect on the resistant variety, Jack (PI88788) in a greenhouse but no differences were found in SCN egg number. However, soybean seed yield was increased up to 40 % in mycorrhizal treated plots than nonmycorrhizal plots (they do have a natural community of AMF). Our results show that commercially available AMF inoculum can be used to increase soybean production even in the field infested with SCN. However, further investigation should be conducted to know the actual mechanism of how these fungi are able to increase soybean production without any change in AM colonization rate and reduction in SCN egg population in the soil. In summary, tripartite interactions of legumes with AM fungi and rhizobia bacteria led synergistically increase in plant growth independent of N fixing capability of rhizobia. However, delivery of N by AMF towards shoot increased when plants only have AMF for N source. Consistent with the biological market model, the host plant allocates a significant amount of C to benefit root symbionts. Similar trends were found when plants were interconnected via CMNs. On the other hand, AMF does not provide nutritional benefits but also can provide biotic stress tolerance such as enhanced SCN tolerance. All these indicated a bigger potential role for beneficial microbes in sustainable agriculture

Combining Support Vector Machines to Predict Novel Angiogenesis Genes

Vähk on tänapäeval üks levinumaid ja ohtlikumaid haigusi põhjustades igal aastal 13% kõigist surmajuhtumitest üle maailma. Hoolimata aastatepikkustest jõupingutustest ei ole seni ikka veel efektiivset ravi selle haiguse vastu leitud. Küll on aga teada, et vähi arengus on olulisel kohal angiogenees, mille käigus vähk paneb enda ümber asuvad veresooned hargnema ja kasvama. Parem arusaamine sellest protsessist võimaldaks potentsiaalselt luua uusi ja efektiivsemaid ravimeetodeid. Aastate jooksul tehtud eksperimentide käigus on mõõdetud enamiku inimese geenide ekpressiooni rohkem kui 5000 tingimuses. Lisaks on meie koostööpartnerid koostanud nimekirja 341-st veresoonte loomega seotud geenist. Käesoleva töö eesmärgiks ongi uurida, kuidas geeniekspressiooni andmete ja väikese hulga tuntud angiogeneesi geenide põhjal on võimalik ennustada uusi angiogeneesiga seotud geene. Selleks võrreldakse kõigepealt mitmeid olemasolevaid masinõppe meetodeid ja avalikult kättesaadavaid bioinformaatika tööriistu, mida saaks kasutada kandidaatgeenide ennustamiseks. Kõigi nende meetodite puhul kasutatakse sisendiks võimalikult sarnaseid andmeid ning mõõdetakse siis 10-kordse ristvalideerimise abil, kui edukad need on juba tuntud angiogeneesi geenide ülesleidmisel. Töö teises osas pakutakse välja uudne Comb-SVM meetod kandidaatgeenide ennustamiseks. Selle põhiidee baseerub kolmel sammul. Kõigepealt kasutatakse juba tuntud angiogeneesi geene ning juhuslikult valitud negatiivseid geene, et treenida paralleelselt mitu tugivektormasinal (ingl k Support Vector Machine) põhinevat klassifitseerijat. Järgnevalt kasutakse neid klassifitseerijaid uute angiogeneesi geenide ennustamiseks. Viimaks agregeeritakse kõigi klassifitseerijate tulemused kokku üheks ennustuseks. Töö lõpus näidatakse, et 10-kordse ristvalideerimise põhjal on Comb-SVM täpsem kui enamik olemasolevaid meetodeid. Lisaks näidatakse, et Comb-SVM ennustused on oluliselt stabiilsemad väikeste muudatuste suhtes treeningandmetes kui paremuselt teise algoritmi tulemused. Kõige lõpuks kasu- tatakse teaduskirjandust ning Gene Ontology andmebaasi veendumaks, et uued ennustatud geenid on tõpoolest seotud angiogeneesiga.Angiogenesis is the process of growing new blood vessels. It is part of normal bodily functions like wound healing, but it also plays an important role in cancer development. Without angiogenesis, tumors would not be able to grow larger than 1-2 millimeters in diameter due to the lack of oxygen and nutrients. However, only a part of the genes involved in angiogenesis are known. In this work, we proposed a new Comb-SVM machine learning method to predict new members to the positive class, that does not require a clearly defined negative examples. The idea is to train multiple Support Vector Machines (SVMs) using known genes as positive samples and various randomly selected sets of genes as negative examples. The multiple SVMs are then used to separately classify all remaining human genes and the results are finally aggregated using a rank aggregation algorithm. The outcome is a list of genes ranked according to their similarity to known input genes. We applied this method to 341 known angiogenesis genes. Experiments were conducted on a large Affymetrix microarray gene expression matrix consisting of 5732 experiments and 22283 probe sets obtained from ArrayExpress. We compared Comb-SVM to many other state-of-the-art approaches. According to cross-validation experiments, our method outperformed most of the existing methods when looking at areas under Receiver Operator Characteristic and Precision-Recall curves. We also determined that our method gave significantly more stable results than the second best approach. Finally, we verified the biological relevance of the predicted genes by searching the literature and Gene Ontology

Targeting Somatostatin Receptors By Functionalized Mesoporous Silica Nanoparticles - Are We Striking Home?

The concept of delivering nanoformulations to desired tissues by means of targeting membrane receptors of high local abundance by ligands anchored to the nanocarrier has gained a lot of attention over the last decade. Currently, there is no unanimous opinion on whether surface functionalization of nanocarriers by targeting ligands translates into any real benefit in terms of pharmacokinetics or treatment outcomes. Having examined the published nanocarriers designed to engage with somatostatin receptors, we realized that in the majority of cases targetability claims were not supported by solid evidence of targeting ligand-targeted receptor coupling, which is the very crux of a targetability concept. Here, we present an approach to characterize targetability of mesoporous silica-based nanocarriers functionalized with ligands of somatostatin receptors. The targetability proof in our case comes from a functional assay based on a genetically-encoded cAMP probe, which allows for real-time capture of receptor activation in living cells, triggered by targeting ligands on nanoparticles. We elaborate on the development and validation of the assay, highlighting the power of proper functional tests in the characterization pipeline of targeted nanoformulations

A Computational Framework for Learning from Complex Data: Formulations, Algorithms, and Applications

Many real-world processes are dynamically changing over time. As a consequence, the observed complex data generated by these processes also evolve smoothly. For example, in computational biology, the expression data matrices are evolving, since gene expression controls are deployed sequentially during development in many biological processes. Investigations into the spatial and temporal gene expression dynamics are essential for understanding the regulatory biology governing development. In this dissertation, I mainly focus on two types of complex data: genome-wide spatial gene expression patterns in the model organism fruit fly and Allen Brain Atlas mouse brain data. I provide a framework to explore spatiotemporal regulation of gene expression during development. I develop evolutionary co-clustering formulation to identify co-expressed domains and the associated genes simultaneously over different temporal stages using a mesh-generation pipeline. I also propose to employ the deep convolutional neural networks as a multi-layer feature extractor to generate generic representations for gene expression pattern in situ hybridization (ISH) images. Furthermore, I employ the multi-task learning method to fine-tune the pre-trained models with labeled ISH images. My proposed computational methods are evaluated using synthetic data sets and real biological data sets including the gene expression data from the fruit fly BDGP data sets and Allen Developing Mouse Brain Atlas in comparison with baseline existing methods. Experimental results indicate that the proposed representations, formulations, and methods are efficient and effective in annotating and analyzing the large-scale biological data sets

Aeration and Mode of Nutrient Delivery Affects Growth Of Peas in a Controlled Environment

The development of a plant growth chamber capable of sustaining plant growth over multiple generations is a necessary step towards the attainment of a Controlled Ecological Life Support System (CELSS). The studies herein examine the effects of aeration abilities and rates on plants grown in three model nutrient delivery systems during germination and over the life-cycle of the plant. These studies are the first time a porous tube nutrient delivery system was compared to another active nutrient mist delivery system. During germination an indicator of hypoxic stress, alcohol dehydrogenase (ADH) activity, was measured and was more affected by aeration rate than mode of nutrient delivery. Over the life-cycle of the plant, however, plants grown in the porous tube system had the least ADH activity and the highest levels of shoot (leaf + stem), root and leaf biomass. None of the plants in any system, however, produced viable seed. This study highlights the need to optimize aeration capabilities in the root zone of enclosed chambers

Emergence of Spatio-Temporal Pattern Formation and Information Processing in the Brain.

The spatio-temporal patterns of neuronal activity are thought to underlie cognitive functions, such as our thoughts, perceptions, and emotions. Neurons and glial cells, specifically astrocytes, are interconnected in complex networks, where large-scale dynamical patterns emerge from local chemical and electrical signaling between individual network components. How these emergent patterns form and encode for information is the focus of this dissertation. I investigate how various mechanisms that can coordinate collections of neurons in their patterns of activity can potentially cause the interactions across spatial and temporal scales, which are necessary for emergent macroscopic phenomena to arise. My work explores the coordination of network dynamics through pattern formation and synchrony in both experiments and simulations. I concentrate on two potential mechanisms: astrocyte signaling and neuronal resonance properties. Due to their ability to modulate neurons, we investigate the role of astrocytic networks as a potential source for coordinating neuronal assemblies. In cultured networks, I image patterns of calcium signaling between astrocytes, and reproduce observed properties of the network calcium patterning and perturbations with a simple model that incorporates the mechanisms of astrocyte communication. Understanding the modes of communication in astrocyte networks and how they form spatial temporal patterns of their calcium dynamics is important to understanding their interaction with neuronal networks. We investigate this interaction between networks and how glial cells modulate neuronal dynamics through microelectrode array measurements of neuronal network dynamics. We quantify the spontaneous electrical activity patterns of neurons and show the effect of glia on the neuronal dynamics and synchrony. Through a computational approach I investigate an entirely different theoretical mechanism for coordinating ensembles of neurons. I show in a computational model how biophysical resonance shifts in individual neurons can interact with the network topology to influence pattern formation and separation. I show that sub-threshold neuronal depolarization, potentially from astrocytic modulation among other sources, can shift neurons into and out of resonance with specific bands of existing extracellular oscillations. This can act as a dynamic readout mechanism during information storage and retrieval. Exploring these mechanisms that facilitate emergence are necessary for understanding information processing in the brain.PHDApplied PhysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111493/1/lshtrah_1.pd