The dawn of mathematical biology

In this paper I describe the early development of the so-called mathematical biophysics, as conceived by Nicolas Rashevsky back in the 1920's, as well as his latter idealization of a "relational biology". I also underline that the creation of the journal "The Bulletin of Mathematical Biophysics" was instrumental in legitimating the efforts of Rashevsky and his students, and I finally argue that his pioneering efforts, while still largely unacknowledged, were vital for the development of important scientific contributions, most notably the McCulloch-Pitts model of neural networks.Comment: 9 pages, without figure

A New Biology: A Modern Perspective on the Challenge of Closing the Gap between the Islands of Knowledge

This paper discusses the rebirth of the old quest for the principles of biology along the discourse line of machine-organism disanalogy and within the context of biocomputation from a modern perspective. It reviews some new attempts to revise the existing body of research and enhance it with new developments in some promising fields of mathematics and computation. The major challenge is that the latter are expected to also answer the need for a new framework, a new language and a new methodology capable of closing the existing gap between the different levels of complex system organization

The Future of Biomath: Growing Beyond Collaboration

Biomathematics as a field has grown substantially over the last 50 years. It has found success in modeling biological phenomena in a variety of areas ranging from ecology to molecular biology [Mackey and Maini, 2015]. Furthermore, the continued development of biomath may be invaluable in understanding current challenges in biology, such as predicting the effects of climate change on different ecosystems. All successful interdisciplinary research depends different types of scientists having the ability to understand and collaborate well with each other. Traditionally, mathematicians are exclusively trained in theoretical systems, while biologists usually work in experimentally driven laboratory settings. As a result, collaboration can lead to miscommunications and fundamental misunderstandings about both the system being studied and the mathematical tools being used. The author argues that until biomath becomes fully integrated into biology such miscommunications cannot be avoided and the field will not reach its full potential

The dawn of mathematical biology

In this paper I describe the early development of the so-called mathematical biophysics, as conceived by Nicolas Rashevsky back in the 1920´s, as well as his latter idealization of a “relational biology”. I also underline that the creation of the journal The Bulletin of Mathematical Biophysics was instrumental in legitimating the efforts of Rashevsky and his students, and I finally argue that his pioneering efforts, while still largely unacknowledged, were vital for the development of important scientific contributions, most notably the McCulloch-Pitts model of neural networks

Using spreadsheets as learning tools for neural network simulation

The article supports the need for training techniques for neural network computer simulations in a spreadsheet context. Their use in simulating artificial neural networks is systematically reviewed. The authors distinguish between fundamental methods for addressing the issue of network computer simulation training in the spreadsheet environment, joint application of spreadsheets and tools for neural network simulation, application of third-party add-ins to spreadsheets, development of macros using embedded languages of spreadsheets, use of standard spreadsheet add-ins for non-linear optimization, creation of neural networks in the spreadsheet environment without add-ins, and On the article, methods for creating neural network models in Google Sheets, a cloud-based spreadsheet, are discussed. The classification of multidimensional data presented in R. A. Fisher's "The Use of Multiple Measurements in Taxonomic Problems" served as the model's primary inspiration. Discussed are various idiosyncrasies of data selection as well as Edgar Anderson's participation in the 1920s and 1930s data preparation and collection. The approach of multi-dimensional data display in the form of an ideograph, created by Anderson and regarded as one of the first effective methods of data visualization, is discussed here.The article supports the need for training techniques for neural network computer simulations in a spreadsheet context. Their use in simulating artificial neural networks is systematically reviewed. The authors distinguish between fundamental methods for addressing the issue of network computer simulation training in the spreadsheet environment, joint application of spreadsheets and tools for neural network simulation, application of third-party add-ins to spreadsheets, development of macros using embedded languages of spreadsheets, use of standard spreadsheet add-ins for non-linear optimization, creation of neural networks in the spreadsheet environment without add-ins, and On the article, methods for creating neural network models in Google Sheets, a cloud-based spreadsheet, are discussed. The classification of multidimensional data presented in R. A. Fisher's "The Use of Multiple Measurements in Taxonomic Problems" served as the model's primary inspiration. Discussed are various idiosyncrasies of data selection as well as Edgar Anderson's participation in the 1920s and 1930s data preparation and collection. The approach of multi-dimensional data display in the form of an ideograph, created by Anderson and regarded as one of the first effective methods of data visualization, is discussed here

Overcoming the Newtonian Paradigm: The Unfinished Project of Theoretical Biology from a Schellingian Perspective

Defending Robert Rosen’s claim that in every confrontation between physics and biology it is physics that has always had to give ground, it is shown that many of the most important advances in mathematics and physics over the last two centuries have followed from Schelling’s demand for a new physics that could make the emergence of life intelligible. Consequently, while reductionism prevails in biology, many biophysicists are resolutely anti-reductionist. This history is used to identify and defend a fragmented but progressive tradition of anti-reductionist biomathematics. It is shown that the mathematicoephysico echemical morphology research program, the biosemiotics movement, and the relational biology of Rosen, although they have developed independently of each other, are built on and advance this antireductionist tradition of thought. It is suggested that understanding this history and its relationship to the broader history of post-Newtonian science could provide guidance for and justify both the integration of these strands and radically new work in post-reductionist biomathematics

Tit for Tat and Beyond: The Legendary Work of Anatol Rapoport

This article pays tribute to Anatol Rapoport. Rapoport’s contributions spanned scientific disciplines and included the application of mathematical models to biology and the social sciences, alongside metatheoretical work bridging semantics, ethics, and philosophy. Known for formulating the “Tit for Tat” strategy, his approach to game theory reflected a nuanced understanding of knowledge, wisdom, and ethics; the differences, for example, between modeling behavior—what works as an algorithm in a structured game—and solving complex human social interactions. While developing a science of human conflict, Rapoport focused on simple ideas to promote cooperation; his ultimate goal was to foster world peace. A career overview alongside testimonials by scholars and family provide a glimpse of Anatol Rapoport, the scientist and the person. The legacy and thinking of Anatol Rapoport continue to resonate and reverberate today whenever we conduct rigorous scholarship toward transforming conflict into peaceful harmony, whether among people or nations.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/153763/1/ncmr12172.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153763/2/ncmr12172_am.pd

Використання електронних таблиць як засобів навчання комп'ютерного моделювання нейронних мереж

The article substantiates the necessity to develop training methods of computer simulation of neural networks in the spreadsheet environment. The systematic review of their application to simulating artificial neural networks is performed. The authors distinguish basic approaches to solving the problem of network computer simulation training in the spreadsheet environment, joint application of spreadsheets and tools of neural network simulation, application of third-party add-ins to spreadsheets, development of macros using the embedded languages of spreadsheets; use of standard spreadsheet add-ins for non-linear optimization, creation of neural networks in the spreadsheet environment with-out add-ins and macros. The article considers ways of building neural network models in cloud-based spreadsheets, Google Sheets. The model is based on the problem of classifying multi-dimensional data provided in “The Use of Multiple Measurements in Taxonomic Problems” by R. A. Fisher. Edgar Anderson’s role in collecting and preparing the data in the 1920s-1930s is discussed as well as some peculiarities of data selection. There are presented data on the method of multi-dimensional data presentation in the form of an ideograph developed by Anderson and considered one of the first efficient ways of data visualization.У статті обґрунтовано необхідність розробки методів навчання комп'ютерного моделювання нейронних мереж у середовищі електронних таблиць. Проводиться систематичний огляд їх застосування для моделювання штучних нейронних мереж. Автори виділяють основні підходи до вирішення проблеми навчання нейромережевого комп'ютерного моделювання в середовищі електронних таблиць: спільне застосування електронних таблиць та засобів нейромережевого моделювання, застосування сторонніх надбудов до електронних таблиць, розробка макросів з використанням убудованих мов електронних таблиць, використання стандартних надбудов електронних таблиць для нелінійної оптимізації, створення нейронних мереж у середовищі електронних таблиць без надбудов і макросів. У статті розглядаються способи побудови моделей нейронних мереж у хмарних електронних таблицях Google Sheets. Модель ґрунтується на проблемі класифікації багатовимірних даних, представленій у роботі Р. А. Фішера «Використання множинних вимірювань у таксономічних задачах». Обговорюється роль Едгара Андерсона у зборі та підготовці даних у 1920-1930-х роках, а також деякі особливості відбору даних. Представлені дані про метод багатовимірного подання даних у вигляді ідеографу, розробленого Андерсоном, який вважається одним з перших ефективних способів візуалізації даних