The Primacy of Openness in Ecological Complexity Theory

Five principles are at the foundation of complex systems theory: emergence, openness, contingency, historicity, and indeterminacy. Of those five, the principle of emergence is easily the most prevalent. Simply put, emergence refers to the idea that some wholes cannot be properly accounted for by appealing to individual explanations of the parts that compose it. In ecological complexity theory, the principle of emergence is strongly associated with the self-organizing feedbacks that often identify the structural framework of ecosystems. Within the last half century, the intense focus on the principle of emergence has engendered the development of many conceptual distinctions that have importantly contributed to explanations of ecological patterns and ideas about environmental management and restoration. I argue, however, that ecological complexity theory has become somewhat stagnant and myopic in its devout commitment to the principle of emergence. This dissertation highlights the issue of ecological complexity theory’s overreliance on the principle of emergence by investigating the role of the principle of openness. I argue the reverse of what is typically maintained in the literature – the principle of openness possesses metaphysical, epistemological, and ethical primacy. By beginning with the principle of openness and working towards the use of the principle of emergence in explanations of ecological phenomena, I urge greater appreciation for an ecosystem’s complete causal narrative and a reconsideration of the formulation and carrying out of future management and restoration practices and policies

FAST-Forward Protein Folding and Design: Development, Analysis, and Applications of the FAST Sampling Algorithm

Molecular dynamics simulations are a powerful tool to explore conformational landscapes, though limitations in computational hardware commonly thwart observation of biologically relevant events. Since highly specialized or massively parallelized distributed supercomputers are not available to most scientists, there is a strong need for methods that can access long timescale phenomena using commodity hardware. In this thesis, I present the goal-oriented sampling method, Fluctuation Amplification of Specific Traits (FAST), that takes advantage of Markov state models (MSMs) to adaptively explore conformational space using equilibrium-based simulations. This method follows gradients in conformational space to quickly explore relevant conformational transitions with orders of magnitude less aggregate simulation time than traditional simulations. Since each of the individual simulations are at equilibrium, all of the thermodynamics and kinetics in the final MSM are preserved. Here, I first describe the FAST method then demonstrate that it can be used for a variety of tasks, from folding proteins to finding cryptic pockets. Next, I validate that FAST discovers appropriate transition pathways between states. Lastly, I apply FAST in detailing the mechanism of stabilization for a clinically relevant mutation in TEM-1 β-lactamase. This mechanistic understanding is then used to design other stabilizing mutations, which are all supported experimentally

Synthesis of Biological and Mathematical Methods for Gene Network Control

abstract: Synthetic biology is an emerging field which melds genetics, molecular biology, network theory, and mathematical systems to understand, build, and predict gene network behavior. As an engineering discipline, developing a mathematical understanding of the genetic circuits being studied is of fundamental importance. In this dissertation, mathematical concepts for understanding, predicting, and controlling gene transcriptional networks are presented and applied to two synthetic gene network contexts. First, this engineering approach is used to improve the function of the guide ribonucleic acid (gRNA)-targeted, dCas9-regulated transcriptional cascades through analysis and targeted modification of the RNA transcript. In so doing, a fluorescent guide RNA (fgRNA) is developed to more clearly observe gRNA dynamics and aid design. It is shown that through careful optimization, RNA Polymerase II (Pol II) driven gRNA transcripts can be strong enough to exhibit measurable cascading behavior, previously only shown in RNA Polymerase III (Pol III) circuits. Second, inherent gene expression noise is used to achieve precise fractional differentiation of a population. Mathematical methods are employed to predict and understand the observed behavior, and metrics for analyzing and quantifying similar differentiation kinetics are presented. Through careful mathematical analysis and simulation, coupled with experimental data, two methods for achieving ratio control are presented, with the optimal schema for any application being dependent on the noisiness of the system under study. Together, these studies push the boundaries of gene network control, with potential applications in stem cell differentiation, therapeutics, and bio-production.Dissertation/ThesisDoctoral Dissertation Biomedical Engineering 201

Genetic Interactions and Gene-by-Environment Interactions in Evolution

The phenotypic effect of a mutation depends on both genetic interactions (G×G) and gene-by-environment interactions (G×E). G×G and G×E can distort the additive relationship between genotypes and phenotypes and complicate biological and biomedical studies. Understanding the patterns and mechanisms of these interactions is important for predicting evolutionary trajectories, designing plant and animal breeding strategies, detecting “missing heritability”, and guiding “personalized medicine”. In this thesis, I study how G×G and G×E affect mutational effects, including developing new methods and new models. Recent advancements in high-throughput DNA sequencing and high-throughput phenotyping provide powerful tools to study the relationships among genotypes, phenotypes, and the environment at unprecedented scales. Therefore, I take advantage of several published large datasets in my study, each containing hundreds to thousands of different genotypes of model organisms and their corresponding phenotypes in tens of environments. In Chapter 2, I report some general patterns of G×E and demonstrate the importance of considering potential environmental variations in mapping quantitative trait loci. In Chapter 3, I report how the environment affects diminishing returns epistasis and propose a modular life model to explain the patterns of diminishing returns. In Chapter 4, I propose and demonstrate that genetic dominance is a special case of diminishing returns epistasis. In Chapter 5, I report how and why the relationship between growth rate (r) and carrying capacity (K) in density-dependent population growth varies across environments. In Chapter 6, I demonstrate the existence of an intermediate optimal mating distance for hybrid performance in three model organisms. Overall, I find that large genomic and phenomic data are useful resources to address classical genetic questions, such as the origin of dominance (Chapter 4), the relationship between r and K (Chapter 5), and presence of an optimal mating distance (Chapter 6). The environment is a key player in the phenotypic effects of mutations, but it is also a high-dimension complex system that is hard to quantify. In this thesis, I define environment quality (Q) as the average fitness of many different genotypes measured in the environment. I demonstrate that Q is useful in studying how the environment affects additive (Chapter 3), interactive (Chapters 3 and 4), and pleiotropic mutational effects (Chapter 5). Many classical theories and models were developed based on observations made in a single environment, and they are often insufficient to explain across-environment observations. Studying across-environment effects provides valuable information for testing old models and for designing new models when old models fail. I conclude that studying G×G and G×E shed light on underlying biological mechanisms.PHDEcology and Evolutionary BiologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144160/1/xinzhuw_1.pd

Computational design and designability of gene regulatory networks

Nuestro conocimiento de las interacciones moleculares nos ha conducido hoy hacia una perspectiva ingenieril, donde diseños e implementaciones de sistemas artificiales de regulación intentan proporcionar instrucciones fundamentales para la reprogramación celular. Nosotros aquí abordamos el diseño de redes de genes como una forma de profundizar en la comprensión de las regulaciones naturales. También abordamos el problema de la diseñabilidad dada una genoteca de elementos compatibles. Con este fin, aplicamos métodos heuríticos de optimización que implementan rutinas para resolver problemas inversos, así como herramientas de análisis matemático para estudiar la dinámica de la expresión genética. Debido a que la ingeniería de redes de transcripción se ha basado principalmente en el ensamblaje de unos pocos elementos regulatorios usando principios de diseño racional, desarrollamos un marco de diseño computacional para explotar este enfoque. Modelos asociados a genotecas fueron examinados para descubrir el espacio genotípico asociado a un cierto fenotipo. Además, desarrollamos un procedimiento completamente automatizado para diseñar moleculas de ARN no codificante con capacidad regulatoria, basándonos en un modelo fisicoquímico y aprovechando la regulación alostérica. Los circuitos de ARN resultantes implementaban un mecanismo de control post-transcripcional para la expresión de proteínas que podía ser combinado con elementos transcripcionales. También aplicamos los métodos heurísticos para analizar la diseñabilidad de rutas metabólicas. Ciertamente, los métodos de diseño computacional pueden al mismo tiempo aprender de los mecanismos naturales con el fin de explotar sus principios fundamentales. Así, los estudios de estos sistemas nos permiten profundizar en la ingeniería genética. De relevancia, el control integral y las regulaciones incoherentes son estrategias generales que los organismos emplean y que aquí analizamos.Rodrigo Tarrega, G. (2011). Computational design and designability of gene regulatory networks [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/1417

Future exploration of Venus (post-Pioneer Venus 1978)

A comprehensive study was performed to determine the major scientific unknowns about the planet Venus to be expected in the post-Pioneer Venus 1978 time frame. Based on those results the desirability of future orbiters, atmospheric entry probes, balloons, and landers as vehicles to address the remaining scientific questions were studied. The recommended mission scenario includes a high resolution surface mapping radar orbiter mission for the 1981 launch opportunity, a multiple-lander mission for 1985 and either an atmospheric entry probe or balloon mission in 1988. All the proposed missions can be performed using proposed space shuttle upper stage boosters. Significant amounts of long-lead time supporting research and technology developments are required to be initiated in the near future to permit the recommended launch dates

Physics based supervised and unsupervised learning of graph structure

Graphs are central tools to aid our understanding of biological, physical, and social systems. Graphs also play a key role in representing and understanding the visual world around us, 3D-shapes and 2D-images alike. In this dissertation, I propose the use of physical or natural phenomenon to understand graph structure. I investigate four phenomenon or laws in nature: (1) Brownian motion, (2) Gauss\u27s law, (3) feedback loops, and (3) neural synapses, to discover patterns in graphs

The Ecological Economics of Resilience: Designing a Safe-Fail Civilization

There is mounting evidence that sustainable scale thresholds are now being exceeded worldwide and environmental resource shocks (e.g. climate change, water and oil shortages) may be inevitable in some regions of the world in the near future. These could result in severe economic breakdowns, welfare loss, and in the worst-case, the collapse of modern civilization. Therefore, a pre-eminent challenge of our times is to determine how to design a resilient (safe-fail) economy – one that can endure, adapt to and successfully recover from breakdowns when they occur. Surprisingly, while ecological economic theory relies heavily on natural science concepts such as thermodynamics, insufficient attention has been paid to the important ecological concept of resilience, particularly as it applies to economic design. The three major policy goals of current ecological economic theory (sustainable scale, just distribution and efficient allocation) focus instead on preventing environmental resource shocks and breakdowns, but given their unpredictability prevention may not always be possible. How resilience can inform the blossoming field of ecological economics is thus explored in this theoretical, transdisciplinary paper. Drawing on literature as diverse as archaeology and disaster planning, it develops six key principles of economic resilience and applies them to analyze the resilience of key societal systems including our money, electricity, water, transportation, information/communication and emergency response systems. Overall, economic resilience appears to be a unique concern that is not readily subsumed under any of the three existing ecological economic policy pillars. In fact, efforts to build in resilience have the potential to both complement and at times contradict the other three goals, especially efficiency. The need to further study these possible tradeoffs provides strong justification for adding a fourth distinct policy pillar, namely “Resilient Design”, to core ecological economic theory. Indeed, ecological economist’s longstanding criticism of economic growth meshes readily with the Resilience Alliance’s own figure-8 adaptive cycle theory critiquing the resilience costs of growth, providing significant opportunities for the future collaboration of these two fields in broadening global system theory

Climate Change and Environmental Sustainability-Volume 1

Climate change has been widely recognised as a major challenge to the world, with significant environmental, economic and social consequences. Given this, addressing climate change is an urgent and profound task of society, a complex and difficult mission of several generations. To address the challenge of climate change, there is a need to develop a holistic climate change mitigation and adaptation framework that can cover as many climate-related topics as possible and connect as many stakeholders as possible across the globe. This book is an important one, bringing together key climate-related topics, including climate-induced impact assessment, environmental vulnerability and resilience assessment, greenhouse gas emission dynamics and sequestration, climate change mitigation and adaptation strategies in addition to climate-related governance. Results reported in this book are conducive to a better understanding of the climate emergency, climate-related impacts and the solutions. We expect the book to benefit decision makers, practitioners and researchers in different fields such as climate modelling and prediction, forest ecosystems, land management, urban planning and design, urban governance in addition to institutional operation. Prof. Bao-Jie He acknowledges Project NO. 2021CDJQY-004, supported by the Fundamental Research Funds for the Central Universities. We appreciate the assistance from Mr. Lifeng Xiong, Mr. Wei Wang, Ms. Xueke Chen and Ms. Anxian Chen at the School of Architecture and Urban Planning, Chongqing University, China