A computational approach for the discovery of protein–RNA networks

Protein–RNA interactions play important roles in a wide variety of cellular processes, ranging from transcriptional and posttranscriptional regulation of genes to host defense against pathogens. In this chapter we present the computational approach catRAPID to predict protein–RNA interactions and discuss how it could be used to find trends in ribonucleoprotein networks. We envisage that the combination of computational and experimental approaches will be crucial to unravel the role of coding and noncoding RNAs in protein networks

Machine Learning on Neutron and X-Ray Scattering

Neutron and X-ray scattering represent two state-of-the-art materials characterization techniques that measure materials' structural and dynamical properties with high precision. These techniques play critical roles in understanding a wide variety of materials systems, from catalysis to polymers, nanomaterials to macromolecules, and energy materials to quantum materials. In recent years, neutron and X-ray scattering have received a significant boost due to the development and increased application of machine learning to materials problems. This article reviews the recent progress in applying machine learning techniques to augment various neutron and X-ray scattering techniques. We highlight the integration of machine learning methods into the typical workflow of scattering experiments. We focus on scattering problems that faced challenge with traditional methods but addressable using machine learning, such as leveraging the knowledge of simple materials to model more complicated systems, learning with limited data or incomplete labels, identifying meaningful spectra and materials' representations for learning tasks, mitigating spectral noise, and many others. We present an outlook on a few emerging roles machine learning may play in broad types of scattering and spectroscopic problems in the foreseeable future.Comment: 56 pages, 12 figures. Feedback most welcom

Photonic Quantum Computing For Polymer Classification

We present a hybrid classical-quantum approach to the binary classification of polymer structures. Two polymer classes visual (VIS) and near-infrared (NIR) are defined based on the size of the polymer gaps. The hybrid approach combines one of the three methods, Gaussian Kernel Method, Quantum-Enhanced Random Kitchen Sinks or Variational Quantum Classifier, implemented by linear quantum photonic circuits (LQPCs), with a classical deep neural network (DNN) feature extractor. The latter extracts from the classical data information about samples chemical structure. It also reduces the data dimensions yielding compact 2-dimensional data vectors that are then fed to the LQPCs. We adopt the photonic-based data-embedding scheme, proposed by Gan et al. [EPJ Quantum Technol. 9, 16 (2022)] to embed the classical 2-dimensional data vectors into the higher-dimensional Fock space. This hybrid classical-quantum strategy permits to obtain accurate noisy intermediate-scale quantum-compatible classifiers by leveraging Fock states with only a few photons. The models obtained using either of the three hybrid methods successfully classified the VIS and NIR polymers. Their accuracy is comparable as measured by their scores ranging from 0.86 to 0.88. These findings demonstrate that our hybrid approach that uses photonic quantum computing captures chemistry and structure-property correlation patterns in real polymer data. They also open up perspectives of employing quantum computing to complex chemical structures when a larger number of logical qubits is available

Unifying metabolic networks, regulatory constraints, and resource allocation

Metabolic and gene regulatory networks are two classic models of systems biology. Biologically, gene regulatory networks are the control system of protein expression while metabolic networks, especially the genome-scale reconstructions consist of thousands of enzymatic reactions breaking down nutrients into precursors and energy to support the cellular survival. Metabolic-genetic networks, in addition, include the translational processes as an integrated model of classical metabolic networks and the gene expression machinery. Conversely, genetic regulation is also affected by the metabolic activities that provide feedbacks and precursors to the regulatory system. Thus, the two systems are highly interactive and depend on each other. Up to now, various efforts have been made to bridge the two network types. Yet, the dynamic integration of metabolic networks and genetic regulation remains a major challenge in computational systems biology. This PhD thesis is a contribution to mathematical modeling approaches for studying metabolic-regulatory systems. Inspired by regulatory flux balance analysis (rFBA), we first propose an analytic pipeline to explore the optimal solution space in rFBA. Then, our efforts focus on the dynamic combination of metabolic networks together with enzyme production costs and genetic regulation. For this purpose, we first explore the intuitive idea that incorporates Boolean regulatory rules while iterating resource balance analysis. However, with the iterative strategy, the gene expression states are only updated in discrete time steps. Furthermore, formalizing the metabolic-regulatory networks (MRNs) by hybrid automata provides a new mathematical framework that allows the quantitative integration of the metabolic-genetic network with the genetic regulation in a hybrid discrete-continuous system. For the application of this theoretical formalization, we develop a constraint-based approach regulatory dynamic enzyme-cost flux balance analysis (r-deFBA) as an optimal control strategy for the hybrid automata representing MRNs. This allows the prediction of optimal regulatory state transitions, dynamics of metabolism, and resource allocation capable of achieving a maximal biomass production over a time interval. Finally, this PhD project ends with a chapter on perspectives; we apply the theory of product automata to model the dynamics at population-level, integrating continuous metabolism and discrete regulatory states

Accelerating edit-distance sequence alignment on GPU using the wavefront algorithm

Sequence alignment remains a fundamental problem with practical applications ranging from pattern recognition to computational biology. Traditional algorithms based on dynamic programming are hard to parallelize, require significant amounts of memory, and fail to scale for large inputs. This work presents eWFA-GPU, a GPU (graphics processing unit)-accelerated tool to compute the exact edit-distance sequence alignment based on the wavefront alignment algorithm (WFA). This approach exploits the similarities between the input sequences to accelerate the alignment process while requiring less memory than other algorithms. Our implementation takes full advantage of the massive parallel capabilities of modern GPUs to accelerate the alignment process. In addition, we propose a succinct representation of the alignment data that successfully reduces the overall amount of memory required, allowing the exploitation of the fast shared memory of a GPU. Our results show that our GPU implementation outperforms by 3- 9× the baseline edit-distance WFA implementation running on a 20 core machine. As a result, eWFA-GPU is up to 265 times faster than state-of-the-art CPU implementation, and up to 56 times faster than state-of-the-art GPU implementations.This work was supported in part by the European Unions’s Horizon 2020 Framework Program through the DeepHealth Project under Grant 825111; in part by the European Union Regional Development Fund within the Framework of the European Regional Development Fund (ERDF) Operational Program of Catalonia 2014–2020 with a Grant of 50% of Total Cost Eligible through the Designing RISC-V-based Accelerators for next-generation Computers Project under Grant 001-P-001723; in part by the Ministerio de Ciencia e Innovacion (MCIN) Agencia Estatal de Investigación (AEI)/10.13039/501100011033 under Contract PID2020-113614RB-C21 and Contract TIN2015-65316-P; and in part by the Generalitat de Catalunya (GenCat)-Departament de Recerca i Universitats (DIUiE) (GRR) under Contract 2017-SGR-313, Contract 2017-SGR-1328, and Contract 2017-SGR-1414. The work of Miquel Moreto was supported in part by the Spanish Ministry of Economy, Industry and Competitiveness under Ramon y Cajal Fellowship under Grant RYC-2016-21104.Peer ReviewedPostprint (published version

Development of experimental and instrumental systems to study biological systems

Chapters 1-4 of this thesis describes the development of an experimental system to measure diffusion-limited reaction kinetics in a biological environment. About 100 years ago, the relationship between reaction rate and diffusion in homogenous solution, ie water or buffer, was described as a linear relationship by Smoluchowski. Applying this theory naively would suggest that since the diffusion coefficients drop by factors of 4-100 then the rates of reaction would drop by the same amount. However, recent theory and simulations suggest that this does not hold. Even though biological diffusion coefficients drop to 0.1-20% of that in buffer, these recent studies show that the reaction kinetics are much more weakly affected by the biological environment. Due to the lack of experimental evidence for biological diffusion, there is a great need for information in this area. Here, I describe a protein system, exogenous to E. coli¸ that will form a dimer in the presence of a small molecule. ^ I also describe the development of a new type of multivariate hyperspectral Raman instrument (MHI); the instrument is developed for use to study biological tissues and for high speed cell sorting applications. The new instrument design has a large speed advantage over traditional Raman instrumentation for rapid chemical imaging. While the MHI can reproduce the functionality of a traditional Raman spectrometer, its true speed advantage is realized after pre-training on known sample components. The MHI makes use of a spatial light modulator as a programmable optical filter that can be programmed with filters based on multivariate signal processing algorithms, such as PLS, in order to rapidly detect chemical components and create chemical maps. Chapters 5-8 of this thesis describe the development and construction of the MHI, as well as provide proof-of-concept experimental results demonstrating its functionality

White Paper 2: Origins, (Co)Evolution, Diversity & Synthesis Of Life

Publicado en Madrid, 185 p. ; 17 cm.How life appeared on Earth and how then it diversified into the different and currently existing forms of life are the unanswered questions that will be discussed this volume. These questions delve into the deep past of our planet, where biology intermingles with geology and chemistry, to explore the origin of life and understand its evolution, since “nothing makes sense in biology except in the light of evolution” (Dobzhansky, 1964). The eight challenges that compose this volume summarize our current knowledge and future research directions touching different aspects of the study of evolution, which can be considered a fundamental discipline of Life Science. The volume discusses recent theories on how the first molecules arouse, became organized and acquired their structure, enabling the first forms of life. It also attempts to explain how this life has changed over time, giving rise, from very similar molecular bases, to an immense biological diversity, and to understand what is the hylogenetic relationship among all the different life forms. The volume further analyzes human evolution, its relationship with the environment and its implications on human health and society. Closing the circle, the volume discusses the possibility of designing new biological machines, thus creating a cell prototype from its components and whether this knowledge can be applied to improve our ecosystem. With an effective coordination among its three main areas of knowledge, the CSIC can become an international benchmark for research in this field

Information-Directed Hybridization of Abiotic, Sequence-Defined Oligomers

The capacity for sequence-specific polymer strands to selectively assemble into intricate, folded structures and multimeric complexes relies upon the information borne by their residue sequences. Particularly suitable for the formation of multi-dimensional structures, nucleic acids have emerged as sophisticated nanoconstruction media where encoded sequences self-assemble in a designed manner through the gradual cooling of denatured and dissociated strands from raised temperatures. Unfortunately, the weakness of the hydrogen bonds holding the strands together affords nanoconstructs with thermal and mechanical instabilities. In contrast, molecular self-assembly employing dynamic covalent interactions has contributed to the improved mechanical and chemical stabilities of resultant structures. Nevertheless, compared with supramolecular chemistries, dynamic covalent interactions suffer from low dissociation rates, impeding rearrangement amongst the assembled components and often result in the kinetic trapping of non-equilibrium species. To overcome this limitation, molecular architectures are generally restricted to homo-functionalized constituents bearing few reactive sites or utilize harsh self-assembly conditions. This dissertation examines the deliberate equilibrium shifting of dynamic covalent interactions to fabricate sequence-selective molecular architectures with high degrees of functionalization. First, we explored the use of a Lewis acidic catalyst, scandium triflate, Sc(OTf)3, to affect the equilibrium of imine formation, a well-characterized dynamic covalent interaction. Here, high concentrations of scandium triflate, dissociated oligomeric-strands encoded with amine- and aldehyde-pendant group species. Upon removal of excess scandium triflate with a liquid-liquid extraction, the equilibrium was shifted as to promote imine-formation between complementary strands. Subsequent annealing of the self-assembly solutions at 70°C, enabling rearrangement and error-correction of out-of-registry or non-complementary sequences, afforded the simultaneous formation of three distinct information-bearing ladder species and a mechanism for information storage and retrieval of data by abiotic polymers. The information-directed self-assembly of encoded molecular ladders was further developed by incorporating an orthogonal reaction into the oligomeric strands to mimic the information dense, sequence-selective hybridization of DNA. Thus, the base-4 information-directed assembly of molecular ladders and grids bearing covalent bond-based rungs was demonstrated from encoded precursor strands using dual concurrent, orthogonal dynamic covalent interactions (i.e., amine/aldehyde and boronic acid/catechol condensation reactions). Additionally, the self-assembly of well-characterized ladder species employing the thermally-reversible Diels-Alder cycloaddition reaction was explored to establish a self-assembly mechanism requiring an external stimulus to alleviate or eliminate kinetic trapping. By utilizing furan-protected maleimide and furfurylamine residues, sequence-defined strands were synthesized simultaneously bearing both furan and maleimide species while precluding premature hybridization and self-assembled in an information-directed manner to form distinct ladder species using a temperature-mediated process. Finally, given the large-scale efforts underway to develop rapid SARS-CoV-2 (Severe Acute Respiratory Syndrome - coronavirus – 2) diagnostic tests, the fundamental principles of sequence-selective hybridization were applied to transform blood-typing tests into SARS-CoV-2 serology tests using robust gel card agglutination reactions in combination with easily prepared antibody-peptide bioconjugates.PHDChemical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162941/1/sleguiz_1.pd