Aerospace Medicine and Biology: A continuing bibliography with indexes (supplement 258)

This bibliography lists 308 reports, articles and other documents introduced into the NASA scientific and technical information system in April 1984

Advancing multiple model-based control of complex biological systems: Applications in T cell biology

Activated CD4+ T cells are important regulators of the adaptive immune response against invading pathogens and cancerous host cells. The process of activation is mediated by the T cell receptor and a vast network of intracellular signal transduction pathways, which recognize and interpret antigenic signals to determine the cell\u27s response. The critical role of these early signaling events in normal cell function and the pathogenesis of disease ultimately make them attractive therapeutic targets for numerous autoimmune diseases and cancers. Scientists increasingly rely on predictive mathematical models and control-theoretic tools to design effective strategies to manipulate cellular processes for the advancement of knowledge or therapeutic gain. However, the application of modern control theory to intracellular signal transduction is complicated by a unique set of intrinsic properties and technical limitations. These include complexities in the signaling network such as crosstalk, feedback and nonlinearity, and a dearth of rapid quantitative measurement techniques and specific and orthogonal modulators, the major consequences of which are uncertainty in the model representation and the prevention of real-time measurement feedback. Integrating such uncertainties and limitations into a control-theoretic approach under practical constraints represents an open challenge in controller design. The work presented in this dissertation addresses these challenges through the development of a computational methodology to aid in the design of experimental strategies to predictably manipulate intracellular signaling during the process of CD4+ T cell activation. This work achieves two main objectives: (1) the development of a generalized control-theoretic tool to effectively control uncertain nonlinear systems in the absence of real-time measurement feedback, and (2) the development and calibration of a predictive mathematical model (or collection of models) of CD4+ T cell activation to help derive experimental inputs to robustly force the system dynamics along prescribed trajectories. The crux of this strategy is the use of multiple data-supported models to inform the controller design. These models may represent alternative hypotheses for signaling mechanisms and give rise to distinct network topologies or kinetic rate scenarios and yet remain consistent with available data. Here, a novel adaptive weighting algorithm predicts variations in the models\u27 predictive accuracy over the admissible input space to produce a more reliable compromise solution from multiple competing objectives, a result corroborated by several experimental studies. This dissertation provides a practical means to effectively utilize the collective predictive capacity of multiple prediction models to predictably and robustly direct CD4 + T cells to exhibit regulatory, helper and anergic T cell-like signaling profiles through pharmacological manipulations in the absence of measurement feedback. The framework and procedures developed herein are expected to widely applicable to a more general class of continuous dynamical systems for which real-time feedback is not readily available. Furthermore, the ability to predictably and precisely control biological systems could greatly advance how we study and interrogate such systems and aid in the development of novel therapeutic designs for the treatment of disease

Expanding the Toolbox for Computational Analysis in Rational Drug Discovery: Using Biomolecular Solvation to Predict Thermodynamic, Kinetic and Structural Properties of Protein-Ligand Complexes

Most biomolecular interactions occur in aqueous environment. Therefore, one must consider the interactions between proteins and water molecules when developing a drug molecule against a target protein. The study of these interactions is challenging using experimental techniques alone, therefore computer simulations are commonly used to study the molecular details of protein-water or ligand-water interactions. In the first study presented in this doctoral dissertation (Chapter 2), the development, parameterization and testing of an approach is presented that can be used to calculate the solvation contribution in protein-ligand binding thermodynamics. The approach uses an extensive amount of molecular dynamics trajectories in conjunction with GIST calculations in order to obtain models that can predict relative protein-ligand solvation thermodynamics. In order to validate the approach, the model system thrombin is investigated using a set of 53 ligands with experimentally characterized protein-ligand structures and ITC profiles. We found that the binding thermodynamics of 186 congeneric pairs of ligands can be accurately described using our solvation-based models. The relative free energy of binding for these 186 pairs can be calculated from the desolvation free energy of the ligand molecules alone. Furthermore, complete thermodynamic profiles for protein-ligand binding reactions (i.e. free energy, enthalpy and entropy of binding) are accurately predicted by incorporating GIST solvent data from the unbound ligand as well as the protein-ligand complex. In Chapter 3, the aforementioned approach is applied to develop a strategy that enables to equip drug molecules with a desired set of solvation thermodynamics properties. For this purpose, the thrombin ligands (same ligand series as in previous Chapter 2) and the corresponding GIST integrals are decomposed into smaller building block molecules. In the next step, the solvation thermodynamics for the building blocks in the ligand molecule as well as the solvation thermodynamics for the isolated building block in aqueous solution are calculated. We found greatly varying solvation thermodynamics for the different building blocks, demonstrating their potential to design ligands with a wide range of solvation characteristics. Also, we found that the building block decomposition of ligand molecules and the corresponding GIST integrals can be readily used to understand remote solvent structuring effects. These effects occur in the unbound ligand molecule and describe the enhanced solvent structuring on a building block in the ligand molecule due to the presence of another building block at a distal site of the ligand. Furthermore, we demonstrated that the fluorination of building blocks leads to an increased unfavorable desolvation free energy and thus disfavors binding for the presented dataset. The research presented in Chapter 2 and Chapter 3 was accomplished with the computer program Gips that was developed as part of this doctoral dissertation. In the following Chapter 4, the mechanism and time scale of desolvation is being analyzed for the protein-ligand dissociation reaction of trypsin and thrombin in complex with benzamidine and N amidinopiperidine. The analysis is carried out using umbrella sampling free energy calculations and LoCorA calculations. The LoCorA approach is a method for the analysis of residence times of water molecules on the surface of amino acids. It was found that water molecules reside approximately 1.3 ns in the binding pocket of thrombin, whereas in trypsin they are residing one order of magnitude shorter (0.3 ns). This difference is explained with special solvent channels that connect the interior of the binding pocket to bulk solvent environment. The solvent channels are present in thrombin but not in trypsin. Furthermore, the selectivity profiles of benzamidine and N amidinopiperidine are related to a solvent-mediated free energy barrier that is present in thrombin but not trypsin. Also due to the presence of the solvent channels, the water molecules show similar residence time for both complexes in the case of thrombin but differing residence times in the case of the two trypsin complexes. The LoCorA approach is implemented in the computer program LoCorA (same name as the approach itself), which was developed as part of this doctoral dissertation. In the course of this doctoral dissertation, further computational studies were carried out in combination with experimental ones. These can be found in chapter 5 of this dissertation. Each of these studies is preceded by a separate abstract and a statement concerning the author contribution

Towards a unifying, systems biology understanding of large-scale cellular death and destruction caused by poorly liganded iron: Parkinson’s, Huntington’s, Alzheimer’s, prions, bactericides, chemical toxicology and others as examples

Exposure to a variety of toxins and/or infectious agents leads to disease, degeneration and death, often characterised by circumstances in which cells or tissues do not merely die and cease to function but may be more or less entirely obliterated. It is then legitimate to ask the question as to whether, despite the many kinds of agent involved, there may be at least some unifying mechanisms of such cell death and destruction. I summarise the evidence that in a great many cases, one underlying mechanism, providing major stresses of this type, entails continuing and autocatalytic production (based on positive feedback mechanisms) of hydroxyl radicals via Fenton chemistry involving poorly liganded iron, leading to cell death via apoptosis (probably including via pathways induced by changes in the NF-κB system). While every pathway is in some sense connected to every other one, I highlight the literature evidence suggesting that the degenerative effects of many diseases and toxicological insults converge on iron dysregulation. This highlights specifically the role of iron metabolism, and the detailed speciation of iron, in chemical and other toxicology, and has significant implications for the use of iron chelating substances (probably in partnership with appropriate anti-oxidants) as nutritional or therapeutic agents in inhibiting both the progression of these mainly degenerative diseases and the sequelae of both chronic and acute toxin exposure. The complexity of biochemical networks, especially those involving autocatalytic behaviour and positive feedbacks, means that multiple interventions (e.g. of iron chelators plus antioxidants) are likely to prove most effective. A variety of systems biology approaches, that I summarise, can predict both the mechanisms involved in these cell death pathways and the optimal sites of action for nutritional or pharmacological interventions

The Spatial Distribution and Dynamics of CXCL13 in Lymphoid Tissues

Morphogens are soluble signalling molecules that regulate a broad spectrum of biological processes. However, the distances and scales over which this regulation occurs are unclear. To date, many studies have highlighted source-sink mechanisms for morphogen gradient formation but fail to take the role of the tissue microenvironment into account. Using a systems-based approach we show that the chemokine CXCL13 is regulated by the B-cell microenvironment on distinct but interconnected levels of biological organization. CXCL13 is a key determinant of humoral immune responses, regulating the localisation of lymphocytes within lymphoid tissues. Due to a complex and dynamic interaction network occurring over broad spatiotemporal scales, mapping the spatial distribution of CXCL13 in situ is challenging. To address this we have mapped the 3-dimensional organisation of CXCL13+ stromal cells in situ using a fluorescent reporter system, identifying three distinct but interconnected stromal subsets that are unique in their network properties. We quantify CXCL13 dynamics using high-speed narrowfield microscopy in collagen matrix and lymph node tissue sections with results suggesting that diffusion is highly constrained by local tissue microanatomy. However, this data alone is insufficient to describe CXCL13 gradient formation. To consolidate this data we employ a quantitative modelling approach hybridising different techniques into a high fidelity in silico representation of the B-follicle, where immune cells can interact with stroma capable of creating and shaping complex physiological gradients. Simulation analyses and immunohistochemistry suggest that chemokine fields within the follicle are dynamic and non-uniform, with multiobjective optimization analysis suggesting that this spatial configuration is designed to promote scanning rates. Taken in concert, our data suggests that CXCL13 acts over short distances creating a complex landscape of expression. Importantly, this study provides a basis for understanding the spatial distribution of morphogens with complex binding behaviours

Computational approaches to Explainable Artificial Intelligence:Advances in theory, applications and trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9th International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications.</p

Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications