Complexity, Emergent Systems and Complex Biological Systems:\ud Complex Systems Theory and Biodynamics. [Edited book by I.C. Baianu, with listed contributors (2011)]

An overview is presented of System dynamics, the study of the behaviour of complex systems, Dynamical system in mathematics Dynamic programming in computer science and control theory, Complex systems biology, Neurodynamics and Psychodynamics.\u

Comparative Analysis of Conformational Dynamics and Systematic Characterization of Cryptic Pockets in the SARS-CoV-2 Omicron BA.2, BA.2.75 and XBB.1 Spike Complexes with the ACE2 Host Receptor: Confluence of Binding and Structural Plasticity in Mediating Networks of Conserved Allosteric Sites

In the current study, we explore coarse-grained simulations and atomistic molecular dynamics together with binding energetics scanning and cryptic pocket detection in a comparative examination of conformational landscapes and systematic characterization of allosteric binding sites in the SARS-CoV-2 Omicron BA.2, BA.2.75 and XBB.1 spike full-length trimer complexes with the host receptor ACE2. Microsecond simulations, Markov state models and mutational scanning of binding energies of the SARS-CoV-2 BA.2 and BA.2.75 receptor binding domain complexes revealed the increased thermodynamic stabilization of the BA.2.75 variant and significant dynamic differences between these Omicron variants. Molecular simulations of the SARS-CoV-2 Omicron spike full-length trimer complexes with the ACE2 receptor complemented atomistic studies and enabled an in-depth analysis of mutational and binding effects on conformational dynamic and functional adaptability of the Omicron variants. Despite considerable structural similarities, Omicron variants BA.2, BA.2.75 and XBB.1 can induce unique conformational dynamic signatures and specific distributions of the conformational states. Using conformational ensembles of the SARS-CoV-2 Omicron spike trimer complexes with ACE2, we conducted a comprehensive cryptic pocket screening to examine the role of Omicron mutations and ACE2 binding on the distribution and functional mechanisms of the emerging allosteric binding sites. This analysis captured all experimentally known allosteric sites and discovered networks of inter-connected and functionally relevant allosteric sites that are governed by variant-sensitive conformational adaptability of the SARS-CoV-2 spike structures. The results detailed how ACE2 binding and Omicron mutations in the BA.2, BA.2.75 and XBB.1 spike complexes modulate the distribution of conserved and druggable allosteric pockets harboring functionally important regions. The results are significant for understanding the functional roles of druggable cryptic pockets that can be used for allostery-mediated therapeutic intervention targeting conformational states of the Omicron variants

Electron cryo-microscopy studies of bacteriophage phi8 and archaeal virus SH1

Symmetry is a key principle in viral structures, especially the protein capsid shells. However, symmetry mismatches are very common, and often correlate with dynamic functionality of biological significance. The three-dimensional structures of two isometric viruses, bacteriophage phi8 and the archaeal virus SH1 were reconstructed using electron cryo-microscopy. Two image reconstruction methods were used: the classical icosahedral method yielded high resolution models for the symmetrical parts of the structures, and a novel asymmetric in-situ reconstruction method allowed us to resolve the symmetry mismatches at the vertices of the viruses. Evidence was found that the hexameric packaging enzyme at the vertices of phi8 does not rotate relative to the capsid. The large two-fold symmetric spikes of SH1 were found not to be responsible for infectivity. Both virus structures provided insight into the evolution of viruses. Comparison of the phi8 polymerase complex capsid with those of phi6 and other dsRNA viruses suggests that the quaternary structure in dsRNA bacteriophages differs from other dsRNA viruses. SH1 is unusual because there are two major types of capsomers building up the capsid, both of which seem to be composed mainly of single beta-barrels perpendicular to the capsid surface. This indicates that the beta-barrel may be ancestral to the double beta-barrel fold.Virukset koostuvat yksinkertaisimmillaan perimäaineksesta (DNA tai RNA) ja sitä suojaavasta proteiinikuoresta. Proteiinikuoren rakenne on usein symmetrinen: monta kopiota samaa proteiinia nivoutuu yhteen säännölliseen muodostelmaan. Symmetria on yleensä joko helikaalinen (kierreportaat), jolloin virus on sauvamainen, tai ikosahedraalinen (5- ja 6-kulmioista ommeltu jalkapallo), jolloin syntyy pallomaisia viruksia. On kuitenkin tavallista, että jotkin viruksen toiminnan kannalta tärkeät rakenteet eivät noudata symmetriaa. Jalkapallossa esimerkiksi on vain yksi venttiilin paikka, eli yksi nahkapalasista poikkeaa muista. Viruksen tapauksessa taas vastaavalla tavalla muista poikkeavassa paikassa saattaa olla perimäaineksen pakkaamiseen tarvittava koneisto. Tässä työssä on tutkittu kahden pallomaisen viruksen, phi8:n ja SH1 kolmiulotteisia (3D) rakenteita. phi8 sairastuttaa erästä bakteeria, joka puolestaan sairastuttaa tiettyjä palkokasveja. SH1 sairastuttaa arkkieliöitä (bakteerien tapaisia yksisoluisia eliöitä), joita löytyy vaaleanpunaisista suolajärvistä Australiasta. Rakenteet määritettiin elektronimikroskooppikuvista laskennallisin keinoin. Perusajatus on, että kun kaksiulotteisissa mikroskooppikuvissa virus näkyy monesta eri suunnasta, nämä kuvat yhdistämällä saadaan selville viruksen 3D rakenne. Virukset erottuvat heikosti mikroskooppikuvissa, joten myös laskettu 3D rakenne on epäselvä. Sitä voidaan kuitenkin selkeyttää käyttäen hyväksi symmetriaa. Tällöin oletetaan, että virus on täysin symmetrinen, mistä seuraa, että laskettu 3D rakenne näyttää virheellisesti ne osat, jotka eivät seuraa symmetriaa. Esimerkiksi jalkapallon venttiilin paikka saattaisi ilmaantua 12 eri paikkaan tai kadota kokonaan näkyvistä. Tässä työssä jatkokehitettiin menetelmää, jonka avulla voidaan saada oikea kuva ventiilinpaikoista. Menetelmää sovellettiin molempiin tutkittuihin viruksiin. Virusten rakenteet kertovat niiden sukulaisuussuhteista, joten rakennetutkimus on myös sukututkimusta. Vain yhden phi8:n lähisukulaisen rakenne tunnettiin aiemmin, joten määritetty rakenne mahdollisti perheen sisäisen vertailun. SH1:n perheestä puolestaan ei ollut mitään tietoa, eikä sen rakennekaan nyt paljastanut varmuudella sen olevan sukua tunnetuille viruksille. On tosin mahdollista, että eräs yleinen viruskuoriproteiinityyppi on kehittynyt SH1:n tapaisen viruksen kuoresta

High-Throughput Screening for Drug Discovery

The book focuses on various aspects and properties of high-throughput screening (HTS), which is of great importance in the development of novel drugs to treat communicable and non-communicable diseases. Chapters in this volume discuss HTS methodologies, resources, and technologies and highlight the significance of HTS in personalized and precision medicine

Simulation Intelligence: Towards a New Generation of Scientific Methods

The original "Seven Motifs" set forth a roadmap of essential methods for the field of scientific computing, where a motif is an algorithmic method that captures a pattern of computation and data movement. We present the "Nine Motifs of Simulation Intelligence", a roadmap for the development and integration of the essential algorithms necessary for a merger of scientific computing, scientific simulation, and artificial intelligence. We call this merger simulation intelligence (SI), for short. We argue the motifs of simulation intelligence are interconnected and interdependent, much like the components within the layers of an operating system. Using this metaphor, we explore the nature of each layer of the simulation intelligence operating system stack (SI-stack) and the motifs therein: (1) Multi-physics and multi-scale modeling; (2) Surrogate modeling and emulation; (3) Simulation-based inference; (4) Causal modeling and inference; (5) Agent-based modeling; (6) Probabilistic programming; (7) Differentiable programming; (8) Open-ended optimization; (9) Machine programming. We believe coordinated efforts between motifs offers immense opportunity to accelerate scientific discovery, from solving inverse problems in synthetic biology and climate science, to directing nuclear energy experiments and predicting emergent behavior in socioeconomic settings. We elaborate on each layer of the SI-stack, detailing the state-of-art methods, presenting examples to highlight challenges and opportunities, and advocating for specific ways to advance the motifs and the synergies from their combinations. Advancing and integrating these technologies can enable a robust and efficient hypothesis-simulation-analysis type of scientific method, which we introduce with several use-cases for human-machine teaming and automated science

3D Architectural Analysis of Neurons, Astrocytes, Vasculature & Nuclei in the Motor and Somatosensory Murine Cortical Columns

Characterization of the complex cortical structure of the brain at a cellular level is a fundamental goal of neuroscience which can provide a better understanding of both normal function as well as disease state progression. Many challenges exist however when carrying out this form of analysis. Immunofluorescent staining is a key technique for revealing 3-dimensional structure, but subsequent fluorescence microscopy is limited by the quantity of simultaneous targets that can be labeled and intrinsic lateral and isotropic axial point-spread function (PSF) blurring during the imaging process in a spectral and depth-dependent manner. Even after successful staining, imaging and optical deconvolution, the sheer density of filamentous processes in the neuropil significantly complicates analysis due to the difficulty of separating individual cells in a highly interconnected network of tightly woven cellular arbors. In order to solve these problems, a variety of methodologies were developed and validated for improved analysis of cortical anatomy. An enhanced immunofluorescent staining and imaging protocol was utilized to precisely locate specific functional regions within brain slices at high magnification and collect four-channel, complete cortical columns. A powerful deconvolution routine was established which collected depth variant PSFs using an optical phantom for image restoration. Fractional volume analysis (FVA) was used to provide preliminary data of the proportions of each stained component in order to statistically characterize the variability within and between the functional regions in a depth-dependent and depth-independent manner. Finally, using machine learning techniques, a supervised learning model was developed that could automatically classify neuronal and astrocytic nuclei within the large cortical column datasets based on perinuclear fluorescence. These annotated nuclei were then used as seed points within their corresponding fluorescent channel for cell individualization in a highly interconnected network. For astrocytes, this technique provides the first method for characterization of complex morphology in an automated fashion over large areas without laborious dye filling or manual tracing

Predicting multibody assembly of proteins

textThis thesis addresses the multi-body assembly (MBA) problem in the context of protein assemblies. [...] In this thesis, we chose the protein assembly domain because accurate and reliable computational modeling, simulation and prediction of such assemblies would clearly accelerate discoveries in understanding of the complexities of metabolic pathways, identifying the molecular basis for normal health and diseases, and in the designing of new drugs and other therapeutics. [...] [We developed] F²Dock (Fast Fourier Docking) which includes a multi-term function which includes both a statistical thermodynamic approximation of molecular free energy as well as several of knowledge-based terms. Parameters of the scoring model were learned based on a large set of positive/negative examples, and when tested on 176 protein complexes of various types, showed excellent accuracy in ranking correct configurations higher (F² Dock ranks the correcti solution as the top ranked one in 22/176 cases, which is better than other unsupervised prediction software on the same benchmark). Most of the protein-protein interaction scoring terms can be expressed as integrals over the occupied volume, boundary, or a set of discrete points (atom locations), of distance dependent decaying kernels. We developed a dynamic adaptive grid (DAG) data structure which computes smooth surface and volumetric representations of a protein complex in O(m log m) time, where m is the number of atoms assuming that the smallest feature size h is [theta](r[subscript max]) where r[subscript max] is the radius of the largest atom; updates in O(log m) time; and uses O(m)memory. We also developed the dynamic packing grids (DPG) data structure which supports quasi-constant time updates (O(log w)) and spherical neighborhood queries (O(log log w)), where w is the word-size in the RAM. DPG and DAG together results in O(k) time approximation of scoring terms where k << m is the size of the contact region between proteins. [...] [W]e consider the symmetric spherical shell assembly case, where multiple copies of identical proteins tile the surface of a sphere. Though this is a restricted subclass of MBA, it is an important one since it would accelerate development of drugs and antibodies to prevent viruses from forming capsids, which have such spherical symmetry in nature. We proved that it is possible to characterize the space of possible symmetric spherical layouts using a small number of representative local arrangements (called tiles), and their global configurations (tiling). We further show that the tilings, and the mapping of proteins to tilings on arbitrary sized shells is parameterized by 3 discrete parameters and 6 continuous degrees of freedom; and the 3 discrete DOF can be restricted to a constant number of cases if the size of the shell is known (in terms of the number of protein n). We also consider the case where a coarse model of the whole complex of proteins are available. We show that even when such coarse models do not show atomic positions, they can be sufficient to identify a general location for each protein and its neighbors, and thereby restricts the configurational space. We developed an iterative refinement search protocol that leverages such multi-resolution structural data to predict accurate high resolution model of protein complexes, and successfully applied the protocol to model gp120, a protein on the spike of HIV and currently the most feasible target for anti-HIV drug design.Computer Science

Optimization of neural networks for deep learning and applications to CT image segmentation

[eng] During the last few years, AI development in deep learning has been going so fast that even important researchers, politicians, and entrepreneurs are signing petitions to try to slow it down. The newest methods for natural language processing and image generation are achieving results so unbelievable that people are seriously starting to think they can be dangerous for society. In reality, they are not dangerous (at the moment) even if we have to admit we reached a point where we have no more control over the flux of data inside the deep networks. It is impossible to open a modern deep neural network and interpret how it processes the information and, in many cases, explain how or why it gives back that particular result. One of the goals of this doctoral work has been to study the behavior of weights in convolutional neural networks and in transformers. We hereby present a work that demonstrates how to invert 3x3 convolutions after training a neural network able to learn how to classify images, with the future aim of having precisely invertible convolutional neural networks. We demonstrate that a simple network can learn to classify images on an open-source dataset without loss in accuracy, with respect to a non-invertible one. All that with the ability to reconstruct the original image without detectable error (on 8-bit images) in up to 20 convolutions stacked in a row. We present a thorough comparison between our method and the standard. We tested the performances of the five most used transformers for image classification on an open- source dataset. Studying the embedded matrices, we have been able to provide two criteria that can help transformers learn with a training time reduction of up to 30% and with no impact on classification accuracy. The evolution of deep learning techniques is also touching the field of digital health. With tens of thousands of new start-ups and more than 1B $ of investments only in the last year, this field is growing rapidly and promising to revolutionize healthcare. In this thesis, we present several neural networks for the segmentation of lungs, lung nodules, and areas affected by pneumonia induced by COVID-19, in chest CT scans. The architecturesm we used are all residual convolutional neural networks inspired by UNet and Inception. We customized them with novel loss functions and layers studied to achieve high performances on these particular applications. The errors on the surface of nodule segmentation masks are not over 1mm in more than 99% of the cases. Our algorithm for COVID-19 lesion detection has a specificity of 100% and overall accuracy of 97.1%. In general, it surpasses the state-of-the-art in all the considered statistics, using UNet as a benchmark. Combining these with other algorithms able to detect and predict lung cancer, the whole work was presented in a European innovation program and judged of high interest by worldwide experts. With this work, we set the basis for the future development of better AI tools in healthcare and scientific investigation into the fundamentals of deep learning.[spa] Durante los últimos años, el desarrollo de la IA en el aprendizaje profundo ha ido tan rápido que Incluso importantes investigadores, políticos y empresarios están firmando peticiones para intentar para ralentizarlo. Los métodos más nuevos para el procesamiento y la generación de imágenes y lenguaje natural, están logrando resultados tan increíbles que la gente está empezando a preocuparse seriamente. Pienso que pueden ser peligrosos para la sociedad. En realidad, no son peligrosos (al menos de momento) incluso si tenemos que admitir que llegamos a un punto en el que ya no tenemos control sobre el flujo de datos dentro de las redes profundas. Es imposible abrir una moderna red neuronal profunda e interpretar cómo procesa la información y, en muchos casos, explique cómo o por qué devuelve ese resultado en particular, uno de los objetivos de este doctorado. El trabajo ha consistido en estudiar el comportamiento de los pesos en redes neuronales convolucionales y en transformadores. Por la presente presentamos un trabajo que demuestra cómo invertir 3x3 convoluciones después de entrenar una red neuronal capaz de aprender a clasificar imágenes, con el objetivo futuro de tener redes neuronales convolucionales precisamente invertibles. Nosotros queremos demostrar que una red simple puede aprender a clasificar imágenes en un código abierto conjunto de datos sin pérdida de precisión, con respecto a uno no invertible. Todo eso con la capacidad de reconstruir la imagen original sin errores detectables (en imágenes de 8 bits) en hasta 20 convoluciones apiladas en fila. Presentamos una exhaustiva comparación entre nuestro método y el estándar. Probamos las prestaciones de los cinco transformadores más utilizados para la clasificación de imágenes en abierto. conjunto de datos de origen. Al estudiar las matrices incrustadas, hemos sido capaz de proporcionar dos criterios que pueden ayudar a los transformadores a aprender con un tiempo de capacitación reducción de hasta el 30% y sin impacto en la precisión de la clasificación. La evolución de las técnicas de aprendizaje profundo también está afectando al campo de la salud digital. Con decenas de miles de nuevas empresas y más de mil millones de dólares en inversiones sólo en el año pasado, este campo está creciendo rápidamente y promete revolucionar la atención médica. En esta tesis, presentamos varias redes neuronales para la segmentación de pulmones, nódulos pulmonares, y zonas afectadas por neumonía inducida por COVID-19, en tomografías computarizadas de tórax. La arquitectura que utilizamos son todas redes neuronales convolucionales residuales inspiradas en UNet. Las personalizamos con nuevas funciones y capas de pérdida, estudiado para lograr altos rendimientos en estas aplicaciones particulares. Los errores en la superficie de las máscaras de segmentación de los nódulos no supera 1 mm en más del 99% de los casos. Nuestro algoritmo para la detección de lesiones de COVID-19 tiene una especificidad del 100% y en general precisión del 97,1%. En general supera el estado del arte en todos los aspectos considerados, estadísticas, utilizando UNet como punto de referencia. Combinando estos con otros algoritmos capaces de detectar y predecir el cáncer de pulmón, todo el trabajo se presentó en una innovación europea programa y considerado de gran interés por expertos de todo el mundo. Con este trabajo, sentamos las bases para el futuro desarrollo de mejores herramientas de IA en Investigación sanitaria y científica sobre los fundamentos del aprendizaje profundo