The automatic parameter-exploration with a machine-learning-like approach: Powering the evolutionary modeling on the origin of life

The origin of life involved complicated evolutionary processes. Computer modeling is a promising way to reveal relevant mechanisms. However, due to the limitation of our knowledge on prebiotic chemistry, it is usually difficult to justify parameter-setting for the modeling. Thus, typically, the studies were conducted in a reverse way: the parameter-space was explored to find those parameter values “supporting” a hypothetical scene (that is, leaving the parameter-justification a later job when sufficient knowledge is available). Exploring the parameter-space manually is an arduous job (especially when the modeling becomes complicated) and additionally, difficult to characterize as regular “Methods” in a paper. Here we show that a machine-learning-like approach may be adopted, automatically optimizing the parameters. With this efficient parameter-exploring approach, the evolutionary modeling on the origin of life would become much more powerful. In particular, based on this, it is expected that more near-reality (complex) models could be introduced, and thereby theoretical research would be more tightly associated with experimental investigation in this field–hopefully leading to significant steps forward in respect to our understanding on the origin of life.

Model of monitoring of oil soil pollution and its termination

The assessment of impact of oil production economic activities on land pollution in Russia contributes to evolutionary management decision making. Oil industrial pollution affects negatively flora and fauna. Thus, its important to identify the level of its exposure and danger, the site of contamination. A system approach is needed. When studying the environment, its necessary to consider the presence of risk situations and stochastic irreversible changes. Its essential to identify the nature and type of soil contamination with petroleum products using high-tech tools, intellectual procedures. The work considers modeling of such situation, forecasting and identification of oil contaminants. The submodel of optimal termination of monitoring is also considered. Ending monitoring of environmental optimization will result in lower monitoring costs, since monitoring oilcontaminated environments is an expensive and complex technological mechanism, often requiring satellite data. The proposed algorithm for modeling and system analysis is based on situational modeling. Evolutionary modeling allows to adapt the procedure (methodology) of forecasting and assessment to environmental risk factors. It increases the accuracy (formalization and evidence) and completeness of conclusions, the efficiency of situation analysis, which affects manageability of risk both for the oil complex and for individual enterprise in the industry. The results of the research may be used for development of software tools, in particular expert and predictive systems. Situational models are needed when oil companies are solving multi-criteria and multifactor problems.

Controlling long-term SARS-CoV-2 infections can slow viral evolution and reduce the risk of treatment failure

The rapid emergence and expansion of novel SARS-CoV-2 variants threatens our ability to achieve herd immunity for COVID-19. These novel SARS-CoV-2 variants often harbor multiple point mutations, conferring one or more evolutionarily advantageous traits, such as increased transmissibility, immune evasion and longer infection duration. In a number of cases, variant emergence has been linked to long-term infections in individuals who were either immunocompromised or treated with convalescent plasma. In this paper, we used a stochastic evolutionary modeling framework to explore the emergence of fitter variants of SARS-CoV-2 during long-term infections. We found that increased viral load and infection duration favor emergence of such variants. While the overall probability of emergence and subsequent transmission from any given infection is low, on a population level these events occur fairly frequently. Targeting these low-probability stochastic events that lead to the establishment of novel advantageous viral variants might allow us to slow the rate at which they emerge in the patient population, and prevent them from spreading deterministically due to natural selection. Our work thus suggests practical ways to achieve control of long-term SARS-CoV-2 infections, which will be critical for slowing the rate of viral evolution.

Data-Driven Modelling of Water Table Oscillations for a Porous Aquifer Occasionally Flowing under Pressure

Modelling of shallow porous aquifers in scenarios where boundary conditions change over time can be a difficult task. In particular, this is true when data modelling is pursued, i.e., models are directly constructed by measured data. In fact, data contain not only the information related to the physical phenomenon under investigation, but also the effects of time-varying boundary conditions, which work as a disturbance. This undesired component conditions the training of data-driven models, as they are fitted by models, which can produce predictions diverging from measured data. Here, a very shallow porous aquifer is modelled in terms of its response to water table to precipitation. The aquifer is characterized by the presence of a low permeability silty top layer covering the lower sandy strata, where the aquifer normally flows. Therefore, when the piezometric level increases up to the low permeability layer, the aquifer changes its behavior from phreatic to confined. This determines the changing boundary condition, which makes the response of the aquifer to rain precipitations complex, as it is related to a two-fold condition: confined or phreatic. The aquifer here is investigated by two machine learning approaches, the earlier based on an evolutionary modeling, and the latter based on artificial neural networks. Evolutionary modeling returned explicit equations with a fitness efficiency up to 0.8 for 1 month for predictions and 0.48 for simulations, while neural networks arrived at 0.85 and 0.28, respectively. The aim of this study is to get an explicit model of the response of the piezometric heights of the aquifer to the precipitations, which is useful for planning the use of groundwater resources.

Risk of rapid evolutionary escape from biomedical interventions targeting SARS-CoV-2 spike protein

The spike protein receptor-binding domain (RBD) of SARS-CoV-2 is the molecular target for many vaccines and antibody-based prophylactics aimed at bringing COVID-19 under control. Such a narrow molecular focus raises the specter of viral immune evasion as a potential failure mode for these biomedical interventions. With the emergence of new strains of SARS-CoV-2 with altered transmissibility and immune evasion potential, a critical question is this: how easily can the virus escape neutralizing antibodies (nAbs) targeting the spike RBD? To answer this question, we combined an analysis of the RBD structure-function with an evolutionary modeling framework. Our structure-function analysis revealed that epitopes for RBD-targeting nAbs overlap one another substantially and can be evaded by escape mutants with ACE2 affinities comparable to the wild type, that are observed in sequence surveillance data and infect cells in vitro. This suggests that the fitness cost of nAb-evading mutations is low. We then used evolutionary modeling to predict the frequency of immune escape before and after the widespread presence of nAbs due to vaccines, passive immunization or natural immunity. Our modeling suggests that SARS-CoV-2 mutants with one or two mildly deleterious mutations are expected to exist in high numbers due to neutral genetic variation, and consequently resistance to vaccines or other prophylactics that rely on one or two antibodies for protection can develop quickly -and repeatedly- under positive selection. Predicted resistance timelines are comparable to those of the decay kinetics of nAbs raised against vaccinal or natural antigens, raising a second potential mechanism for loss of immunity in the population. Strategies for viral elimination should therefore be diversified across molecular targets and therapeutic modalities.

Railroad track technological construction process modeling and optimization using a genetic algorithm

The article discusses theoretical and practical research in the field of evolutionary modeling application for solving construction works planning problems in the formation of railroad track technological construction process. The study is a part of the developed engineering and technical support subsystem for railway construction: engineering and intellectual support of railroad track technological construction process. This subsystem is based on the effective use of automated systems with artificial intelligence elements. Development and implementation of these automated systems are focused on achieving a single end result, such as a finished railroad track facility with suitable functionality within the established deadlines, planned working cost and labor costs, as well as meeting modern safety requirements throughout the entire operation period. Taking into account railway construction dynamic, the use of new materials and technologies, new trends were identified within the study scope in the field of technological processes operational development, and optimal work sequence determination in the railway facilities construction. In particular, the occurrence of deviations from the planned requirements in the construction work course requires a quick reconsideration of the decisions already made, due to the railway construction stochastic behavior. This is a forced measure to ensure the fulfillment of the planned targets. The existing methods allow to correct the construction work organization, however, the technology, and in particular, the technological process remains unchanged. The static nature of the technological process is largely dictated by the project documentation requirements and work safety. But modern advances in the information and intelligent technologies field, with the account of technological process structure variability, can give a technological process dynamic properties. The technological process's ability to adapt to changing work production conditions will provide flexibility in the railway facilities construction. To solve this problem, an automated evolutionary modeling mode and optimization of the railway facility construction technological process were used. The peculiarities of existing optimization methods, in terms of the dimension of the active task and taking into account several criteria, force us to turn to intelligent methods. The article describes a technological processes optimization method for the railway facilities construction using a genetic directed search algorithm in the space of solutions. In this case, several design constraints are taken into account: resource, technological, organizational, informational. For this, a computational and logical model was developed, which made it possible to assess the target function (fitness function), with the account of the dynamic distribution nature of the contractor's available resources for construction work. Based on the theoretical research results, the article presents the practical aspects of evolutionary modeling and optimization of the railway facilities construction technological process using a genetic algorithm on the example of the flooded railway roadbed embankment construction. The results presented in this article were obtained during the dissertation research made by the author.

Evolutionary Optimization of Case-Based Forecasting Algorithms in Chaotic Environments

The problem of dynamic adaptation of prediction algorithms in chaotic environments based on identification of the situations-analogs in the database of retrospective observations is considered. Under conditions of symmetrical and unsymmetrical chaotic dynamics, traditional computational schemes of precedent prediction turn out to be ineffective. In this regard, a dynamic adaptation of precedent analysis algorithms based on the method of evolutionary modeling is proposed. Implementation of the computational precedent prediction scheme for chaotic processes as well as the evolutionary modeling method are described.