Technological Goal-Setting in The framework of the New Educational Area «Big Data» for the System of Professional Training of the Future Economist

A weighty argument in favor of including the new educational area «Big Data» in the practice of professional training of the future economist is the competence in the field of building adequate predictive models, which is in demand in the modern labor market. Indeed, any leader is interested in improving the quality of the decisions made. This interest increases in conditions of sanctions pressure and post-pandemic restrictions, in difficult socio-economic conditions, when most of the resources are limited, the previously identified cause-and-effect relationships lose their relevance and the responsibility for decisions is significantly increased. Features of the implementation of the technological approach to disclosing the content of the new educational area «Big Data» in the system of professional training of the future economist is presented in this article as follows: firstly, in the form of a system of micro-goals at the basic level, and secondly, in the form of a system of micro-goals at an advanced level. Thus, within the framework of the technological goal-setting of the content of the new educational field, the principle of variability of the professional training of the future economist is implemented. Substantively presented in the article micro-goals cover various issues of using quantitative methods, mathematical and computational modeling. In addition, the formulations of micro-goals include requirements for the development of new tools that support big data analysis. Note that the implementation of technological goal-setting is necessary to strengthen the applied orientation of the training of a future economist, allows us to make a methodological emphasis on applied problems of socio-economic topics, the methods of solving which are in demand in future professional activities. The material of the article can be useful to teachers of the higher school of economics, as well as to anyone interested in modern methodological approaches to structuring educational content and achievements in the field of big data.

LOICA: Logical Operators for Integrated Cell Algorithms

Mathematical and computational modeling is essential to genetic design automation and for the synthetic biology design-build-test-learn cycle. The construction and analysis of models is enabled by abstraction based on a hierarchy of components, devices, and systems that can be used to compose genetic circuits. These abstract elements must be parameterized from data derived from relevant experiments, and these experiments related to the part composition of the abstract components of the circuits measured. Here we present LOICA (Logical Operators for Integrated Cell Algorithms), a Python package for modeling and characterizing genetic circuits based on a simple object-oriented design abstraction. LOICA uses classes to represent different biological and experimental components, which generate models through their interactions. High-level designs are linked to their part composition via SynBioHub. Furthermore, LOICA communicates with Flapjack, a data management and analysis tool, to link to experimental data, enabling abstracted elements to characterize themselves.

What Bots Can (and Can’t) Do? (Model of Protest and Counter-Protest Political Mobilization)

Despite the increasing interest among scholars in the effect of Internet bots, or automated social media accounts, on the processes of political communication and mobilization in the online sphere, the extent of bots’ effectiveness and the specific mechanisms of their use remain largely understudied. The deficit of the overarching conceptual understanding and concrete results is arguably due to researchers’ aspiration to solve a problem in the empirical way, without attempting to combine data analysis with mathematical and computational modeling. Having analyzed the existing models on the topic, the authors offer their own model that is based on the spiral-of-silence theory. The key features of the model that set it apart from the existing ones are the following: a) taking into account differences in the types of motivation and costs associated with expressing protest and loyalist sentiments; b) including “partner effect” into the spiral-ofsilence mechanism; c) employing a neurological decision-ma king scheme according to which the same stimulus can prompt action and be a deterrent. On the basis of a series of computational experiments with the model, the authors demonstrate that bots are more effective in mobilizing opposition members when an individual motivated for political participation refrains from it because his local social community does not share his views. In this case, the emergence of a like-minded partner bot can destroy the spiral of silence created by this community and encourage this individual to openly express his position. On the contrary, when mobilizing loyalists, bots are most effective in relation to poorly motivated individuals. The model elaborated by the authors not only allows us to evaluate bots’ effects in a new way, but it also sheds light on how people make decisions in the framework of political communication and mobilization in social networks.

Mathematical and computational modeling of the role of homocysteine in the oxidative process and its influence in neuronal degenerative diseases

Degenerative neurological diseases, although common in the population, are difficult to diagnose. However, it is known that most of them are directly related to the so-called oxidative stress. Understanding and evaluating how this process takes place is of great interest and, in this sense, this work extends the existing mathematical and computational models for the oxidative stress process (REIS, 2005; REIS et al., 2006; VIANNA, 2005; VIANNA; REIS; CARVALHO, 2012), incorporating an aspect not previously evaluated, the influence of homocysteine indices on the oxidants present in this process. The results indicate that hyperhomocysteinemia can in fact cause oxidative stress and consequent neuronal death, leading to the appearance of neurodegenerative diseases.

Mathematical and computational modeling of biological systems: advances and perspectives

<abstract> <p>The recent developments in the fields of mathematics and computer sciences have allowed a more accurate description of the dynamics of some biological systems. On the one hand new mathematical frameworks have been proposed and employed in order to gain a complete description of a biological system thus requiring the definition of complicated mathematical structures; on the other hand computational models have been proposed in order to give both a numerical solution of a mathematical model and to derive computation models based on cellular automata and agents. Experimental methods are developed and employed for a quantitative validation of the modeling approaches. This editorial article introduces the topic of this special issue which is devoted to the recent advances and future perspectives of the mathematical and computational frameworks proposed in biosciences.</p> </abstract>