Small business in Ukraine as the engine of national economic development

У статті розглянуто стан малого підприємництва в Україні, охарактеризовано слабкі сторони його діяльності та чинники, що впливають на даний сектор економіки. Для переконливішого пояснення зроблених висновків, наведено статистичну інформацію щодо частки малих підприємств України в загальній кількості підприємств і їх розподіл за регіонами.This article examines the state of small business in Ukraine, gives a description of the weaknesses of its activities; describes factors that affect this sector of the economy. For a more convincing explanation of the findings, statistics on the share of small business in Ukraine, the total number of companies and their distribution by region are presented

Measuring Biochemical Possibility Spaces in Evolutionary Engineering

At the molecular level, artificial selection—controlling the forces of evolution to improve or design new biochemical functions— makes up one of our strongest tools for finding better biocatalysts, pharmaceuticals, and biosensors, as well as for studying the history and process of evolution itself. But fully harnessing evolution requires knowledge of the shape and dynamics of complete evolutionary spaces. Prior to this work, very little research existed comparing the real dynamics of artificial selection to any of the theoretical work that has been written to support it. By updating the classical theory of simple selections towards an engineering focus, and combining this with direct observations of direct evolving populations, my work has shown the first mathematical descriptions of how whole populations evolve during the selection of novel biocatalysts.This work seeks to address the analysis of evolutionary fitness and chemical activity spaces at several levels. First, we offer a broad-ranging theoretical approach to mapping the distribution of fitness effects in any system under driven selection. Through both simulations and recent experimental data, we show that it is possible to estimate the initial distribution of fitness for nearly any selected population. In addition to potential applications in automated gene engineering, this theoretical solution also makes it possible to approximate the overall distribution of any selectable chemical function across random molecular space, a necessary condition for theoretical optimization of nearly any in vitro selection.Zooming in, we next develop tools to view an entire population of active catalysts and how it dynamically changes over the course of an entire selection. Working with a model selection for de novo RNA triphosphorylation catalysts, we develop a new high-throughput method to measure many active catalysts in parallel, building the first portrait of how tens of thousands of different functional molecules enrich or disappear over the course of an entire artificial selection. New heuristics for assessing the effectiveness of various activity- estimation methods allowed us to efficiently identify highly active ribozymes, as well as estimating catalytic activity without performing any additional experiments. We also present the first picture of non-ideality during a real selection, demonstrating that stochastic effects can be a powerful and quantifiable confounding factor on predicted selection dynamics. Finally, this analysis allows us to build the highest-resolution extant picture of a biocatalyst activity distribution, showing a catalytic activity that is log-normal, consistent with a mechanism for the emergence of activity as the product of many independent contributions.Finally, we design our own model selection to investigate the evolution of a theoretical aminoacylase RNA whose existence may have been crucial to the origin of the genetic code. Using this system, we have developed techniques for Sequencing to determine Catalytic Activity Paired with Evolution (SCAPE), a comprehensive workflow that allows complete mapping of large, dynamic landscape of chemical activity. By measuring catalytic activity of millions of evolved biomolecules simultaneously, we pair kinetic variations with genetic sequence at single nucleotide resolution, building the first complete map of all evolutionary pathways to an engineered function from anywhere in genetic space. The resulting map contains approximately six orders of magnitude more data than any previously- measured landscape of catalytic data, and suggests features of genetic epistasis and evolutionary ruggedness may be remarkably consistent across many unrelated biocatalysts with similar function. Our methods and results suggest general applicability to more complicated systems, as a viable alternative to the heuristic methods typically used to evaluate molecular selections, as well as validating a suite of capable tools for quantifying and optimizing the emergence of a wide range of evolvable biocatalytic functions

Measuring Biochemical Possibility Spaces in Evolutionary Engineering

At the molecular level, artificial selection—controlling the forces of evolution to improve or design new biochemical functions— makes up one of our strongest tools for finding better biocatalysts, pharmaceuticals, and biosensors, as well as for studying the history and process of evolution itself. But fully harnessing evolution requires knowledge of the shape and dynamics of complete evolutionary spaces. Prior to this work, very little research existed comparing the real dynamics of artificial selection to any of the theoretical work that has been written to support it. By updating the classical theory of simple selections towards an engineering focus, and combining this with direct observations of direct evolving populations, my work has shown the first mathematical descriptions of how whole populations evolve during the selection of novel biocatalysts.This work seeks to address the analysis of evolutionary fitness and chemical activity spaces at several levels. First, we offer a broad-ranging theoretical approach to mapping the distribution of fitness effects in any system under driven selection. Through both simulations and recent experimental data, we show that it is possible to estimate the initial distribution of fitness for nearly any selected population. In addition to potential applications in automated gene engineering, this theoretical solution also makes it possible to approximate the overall distribution of any selectable chemical function across random molecular space, a necessary condition for theoretical optimization of nearly any in vitro selection.Zooming in, we next develop tools to view an entire population of active catalysts and how it dynamically changes over the course of an entire selection. Working with a model selection for de novo RNA triphosphorylation catalysts, we develop a new high-throughput method to measure many active catalysts in parallel, building the first portrait of how tens of thousands of different functional molecules enrich or disappear over the course of an entire artificial selection. New heuristics for assessing the effectiveness of various activity- estimation methods allowed us to efficiently identify highly active ribozymes, as well as estimating catalytic activity without performing any additional experiments. We also present the first picture of non-ideality during a real selection, demonstrating that stochastic effects can be a powerful and quantifiable confounding factor on predicted selection dynamics. Finally, this analysis allows us to build the highest-resolution extant picture of a biocatalyst activity distribution, showing a catalytic activity that is log-normal, consistent with a mechanism for the emergence of activity as the product of many independent contributions.Finally, we design our own model selection to investigate the evolution of a theoretical aminoacylase RNA whose existence may have been crucial to the origin of the genetic code. Using this system, we have developed techniques for Sequencing to determine Catalytic Activity Paired with Evolution (SCAPE), a comprehensive workflow that allows complete mapping of large, dynamic landscape of chemical activity. By measuring catalytic activity of millions of evolved biomolecules simultaneously, we pair kinetic variations with genetic sequence at single nucleotide resolution, building the first complete map of all evolutionary pathways to an engineered function from anywhere in genetic space. The resulting map contains approximately six orders of magnitude more data than any previously- measured landscape of catalytic data, and suggests features of genetic epistasis and evolutionary ruggedness may be remarkably consistent across many unrelated biocatalysts with similar function. Our methods and results suggest general applicability to more complicated systems, as a viable alternative to the heuristic methods typically used to evaluate molecular selections, as well as validating a suite of capable tools for quantifying and optimizing the emergence of a wide range of evolvable biocatalytic functions

Kinetic sequencing (k-Seq) as a massively parallel assay for ribozyme kinetics: utility and critical parameters.

Characterizing genotype-phenotype relationships of biomolecules (e.g. ribozymes) requires accurate ways to measure activity for a large set of molecules. Kinetic measurement using high-throughput sequencing (e.g. k-Seq) is an emerging assay applicable in various domains that potentially scales up measurement throughput to over 106 unique nucleic acid sequences. However, maximizing the return of such assays requires understanding the technical challenges introduced by sequence heterogeneity and DNA sequencing. We characterized the k-Seq method in terms of model identifiability, effects of sequencing error, accuracy and precision using simulated datasets and experimental data from a variant pool constructed from previously identified ribozymes. Relative abundance, kinetic coefficients, and measurement noise were found to affect the measurement of each sequence. We introduced bootstrapping to robustly quantify the uncertainty in estimating model parameters and proposed interpretable metrics to quantify model identifiability. These efforts enabled the rigorous reporting of data quality for individual sequences in k-Seq experiments. Here we present detailed protocols, define critical experimental factors, and identify general guidelines to maximize the number of sequences and their measurement accuracy from k-Seq data. Analogous practices could be applied to improve the rigor of other sequencing-based assays

Molecular Fitness Landscapes from High-Coverage Sequence Profiling.

The function of fitness (or molecular activity) in the space of all possible sequences is known as the fitness landscape. Evolution is a random walk on the fitness landscape, with a bias toward climbing hills. Mapping the topography of real fitness landscapes is fundamental to understanding evolution, but previous efforts were hampered by the difficulty of obtaining large, quantitative data sets. The accessibility of high-throughput sequencing (HTS) has transformed this study, enabling large-scale enumeration of fitness for many mutants and even complete sequence spaces in some cases. We review the progress of high-throughput studies in mapping molecular fitness landscapes, both in vitro and in vivo, as well as opportunities for future research. Such studies are rapidly growing in number. HTS is expected to have a profound effect on the understanding of real molecular fitness landscapes

Molecular Fitness Landscapes from High-Coverage Sequence Profiling.

The function of fitness (or molecular activity) in the space of all possible sequences is known as the fitness landscape. Evolution is a random walk on the fitness landscape, with a bias toward climbing hills. Mapping the topography of real fitness landscapes is fundamental to understanding evolution, but previous efforts were hampered by the difficulty of obtaining large, quantitative data sets. The accessibility of high-throughput sequencing (HTS) has transformed this study, enabling large-scale enumeration of fitness for many mutants and even complete sequence spaces in some cases. We review the progress of high-throughput studies in mapping molecular fitness landscapes, both in vitro and in vivo, as well as opportunities for future research. Such studies are rapidly growing in number. HTS is expected to have a profound effect on the understanding of real molecular fitness landscapes

Analysis of in vitro evolution reveals the underlying distribution of catalytic activity among random sequences.

The emergence of catalytic RNA is believed to have been a key event during the origin of life. Understanding how catalytic activity is distributed across random sequences is fundamental to estimating the probability that catalytic sequences would emerge. Here, we analyze the in vitro evolution of triphosphorylating ribozymes and translate their fitnesses into absolute estimates of catalytic activity for hundreds of ribozyme families. The analysis efficiently identified highly active ribozymes and estimated catalytic activity with good accuracy. The evolutionary dynamics follow Fisher's Fundamental Theorem of Natural Selection and a corollary, permitting retrospective inference of the distribution of fitness and activity in the random sequence pool for the first time. The frequency distribution of rate constants appears to be log-normal, with a surprisingly steep dropoff at higher activity, consistent with a mechanism for the emergence of activity as the product of many independent contributions