Hierarchy, determinism, and specificity in theories of development and evolution

Contrasting philosophical and scientific views in the long history of studying the generation of form in development

Focusing on the opposing ways of thinking of philosophers and scientists to explain the generation of form in biological development, I show that today's controversies over explanations of early development bear fundamental similarities to the dichotomy of preformation theory versus epigenesis in Greek antiquity. They are related to the acceptance or rejection of the idea of a physical form of what today would be called information for the generating of the embryo as a necessary pre-requisite for specific development and heredity. As a recent example, I scrutinize the dichotomy of genomic causality versus self-organization in 20th and 21st century theories of the generation of form. On the one hand, the generation of patterns and form, as well as the constant outcome in development, are proposed to be causally related to something that is "preformed" in the germ cells, the nucleus of germ cells, or the genome. On the other hand, it is proposed that there is no pre-existing form or information, and development is seen as a process where genuinely new characters emerge from formless matter, either by immaterial "forces of life," or by physical-chemical processes of self-organization. I also argue that these different ways of thinking and the research practices associated with them are not equivalent, and maintain that it is impossible to explain the generation of form and constant outcome of development without the assumption of the transmission of pre-existing information in the form of DNA sequences in the genome. Only in this framework of "preformed" information can "epigenesis" in the form of physical and chemical processes of self-organization play an important role.

Self-Organization and Genomic Causality in Models of Morphogenesis

The debate about what causes the generation of form and structure in embryological development goes back to antiquity. Most recently, it has focused on the divergent views as to whether the generation of patterns and form in development is a largely self-organized process or is mainly determined by the genome, in particular, complex developmental gene regulatory processes. This paper presents and analyzes pertinent models of pattern formation and form generation in a developing organism in the past and the present, with a special emphasis on Alan Turing's 1952 reaction-diffusion model. I first draw attention to the fact that Turing's paper remained, at first, without a noticeable impact on the community of biologists because purely physical-chemical models were unable to explain embryological development and often also simple repetitive patterns. I then show that from the year 2000 and onwards, Turing's 1952 paper was increasingly cited also by biologists. The model was updated to include gene products and now seemed able to account for the generation of biological patterns, though discrepancies between models and biological reality remained. I then point out Eric Davidson's successful theory of early embryogenesis based on gene-regulatory network analysis and its mathematical modeling that not only was able to provide a mechanistic and causal explanation for gene regulatory events controlling developmental cell fate specification but, unlike reaction-diffusion models, also addressed the effects of evolution and organisms' longstanding developmental and species stability. The paper concludes with an outlook on further developments of the gene regulatory network model.

Quantifying collective intelligence and behaviours of SARS-CoV-2 via environmental resources from virus' perspectives

Collective intelligence of viruses is witnessed in many research articles. Most of the researches focus on the qualitative properties or observations. In this research, we model the behaviours and collective intelligence of SARS-CoV-2 by minimal spanning trees (MSTs), which specify the underlying mechanisms of resource allocation in the viral colony. The vertices of the trees are 50 states, DC and NYC in the USA. The weights of the edges are assigned by the reciprocal of the sum of cases or deaths of COVID-19. The types of trees are decided by the chosen 18 factors. We sample 304 time-series data and compute their MST-based auto-correlations for stability analysis. Then we perform correlated analysis and comparative analysis on these stable factors. Our results show MST approach fits the collective intelligence modelling very well; the total cases and total deaths over areas are highly correlated in terms of MSTs; and these stable factors have little to do with the geographical distance. The results also indicate the colonisation of SARS-CoV-2 is pretty mature and organised. Based on the results, for environmental or health policies, we should also turn our attention to the transmission routes that are independent of or far away from human population or densities. The viruses' colonies might already exist in the wild in a large scale, not only in the populated or polluted cities. We shall build or conduct a monitoring system of their colonisation and survival techniques, in order to terminate, contain or live with their communities.

From Gregor Mendel to Eric Davidson: Mathematical Models and Basic Principles in Biology

Mathematical models have been widespread in biology since its emergence as a modern experimental science in the 19th century. Focusing on models in developmental biology and heredity, this article (1) presents the properties and epistemological basis of pertinent mathematical models in biology from Mendel's model of heredity in the 19th century to Eric Davidson's model of developmental gene regulatory networks in the 21st; (2) shows that the models differ not only in their epistemologies but also in regard to explicitly or implicitly taking into account basic biological principles, in particular those of biological specificity (that became, in part, replaced by genetic information) and genetic causality. The article claims that models disregarding these principles did not impact the direction of biological research in a lasting way, although some of them, such as D'Arcy Thompson's models of biological form, were widely read and admired and others, such as Turing's models of development, stimulated research in other fields. Moreover, it suggests that successful models were not purely mathematical descriptions or simulations of biological phenomena but were based on inductive, as well as hypothetico-deductive, methodology. The recent availability of large amounts of sequencing data and new computational methodology tremendously facilitates system approaches and pattern recognition in many fields of research. Although these new technologies have given rise to claims that correlation is replacing experimentation and causal analysis, the article argues that the inductive and hypothetico-deductive experimental methodologies have remained fundamentally important as long as causal-mechanistic explanations of complex systems are pursued.

Mechanistic philosophies of development: Theodor Boveri and Eric H. Davidson

Theodor Boveri's (1862-1915) and Eric Davidson's (1937-2015) achievements represent thoroughly two quite distant time frames in the history of the mechanistic approaches to development, that Jane Maienschein (2014) has characterized respectively as the era of the "experimental embryo" and of the "computed embryo". Nonetheless, Davidson's special bond to Boveri is meant to emphasize the genealogical continuity of an embryological tradition of mechanistic philosophy that, differently from molecular biology, is committed to an explanation of the hereditary transmission of organization. Davidson's genealogical claim is reconsidered through a contextualized analysis of the function of machine-like models and of the role of experiment in the making of their respective mechanistic philosophies. This analysis may help to shed light on resilience and change in the understanding of a mechanistic approach to development.