Publications of 2018

Mathematical modeling is a very powerful tool for understanding natural phenomena. Such a tool carries its own assumptions and should always be used critically. In this chapter, we highlight the key ingredients and steps of modeling and focus on their biological interpretation. In particular, we discuss the role of theoretical principles in writing models. We also highlight the meaning and interpretation of equations. The main aim of this chapter is to facilitate the interaction between biologists and mathematical modelers. We focus on the case of cell proliferation and motility in the context of multicellular organisms.

Keywords: Equations, Mathematical modeling, Parameters, Proliferation, Theory

La répétitivité et la réversibilité ont longtemps été considérées comme des traits caractéristiques de la connaissance scientifique. Dans la génétique des populations, la répétitivité est illustrée par un certain nombre d’équilibres réalisés dans des conditions spécifiques. Étant donné que ces équilibres sont maintenus en dépit du renouvellement des générations (réarrangement de gènes, reproduction ...), on peut légitimement dire que la génétique des populations révèle d’importantes propriétés d’invariance par transformation. La réversibilité est un sujet plus controversé. Ici, le parallèle avec la mécanique classique est beaucoup plus faible. La réversibilité est incontestable dans certains modèles stochastiques, mais au prix d’un concept probabiliste particulier de réversibilité. Par contre, elle ne semble pas être une propriété de la plupart des modèles déterministes classiques décrivant la dynamique des changements évolutifs au niveau des populations. Nous distinguons plusieurs sens de la « réversibilité ». En particulier, la symétrie par inversion du temps ne doit pas être confondue avec la rétrodiction.

Keywords: génétique des populations, répétition, rétrodiction, réversibilité

Open peer commentary on the article “What Is a Cognizing Subject? Construction, Autonomy and Original Causation” by Niall Palfreyman & Janice Miller-Young. http://constructivist.info/13/3/362.palfreyman Upshot: I discuss convergences between the approach of the authors and my work aiming for a theory of organisms. I also discuss some pitfalls and challenges pertaining to biological randomness, which, I argue, require original developments.

Summary This chapter briefly presents and describes the three main principles that the group proposes for a theory of organisms, namely: the default state, proliferation with variation and motility, the principle of variation and the principle of organization. It is crucial to critique the philosophical and theoretical position on which the biological research feeding into the program is based and which has dominated biomedical research for the last 70 years. Physical theories are founded on stable mathematical structures, based onregularities and especially on theoretical symmetries. At the time of cell theory formulation and still today, cell theory plays a federating role between evolution biology and organism biology. Finally, analysis of the differences between the physics of inanimate and living matter leads to the proposal of three principles that provide aviable perspective for the construction of a necessary theory of organisms.

Keywords: cell theory, evolution biology, mathematical structures, organism biology, philosophical position, physical theories, theoretical symmetries

Computer simulations brought remarkable novelties to knowledge construction. In this chapter, we first distinguish between mathematical modeling, computer implementations of these models and purely computational approaches. In all three cases, different answers are provided to the questions the observer may have concerning the processes under investigation. These differences will be highlighted by looking at the different theoretical symmetries of each frame. In the latter case, the peculiarities of agent-based or object oriented languages allow to discuss the role of phase spaces in mathematical analyses of physical versus biological dynamics. Symmetry breaking and randomness are finally correlated in the various contexts where they may be observed.

Keywords: Phase Space, Symmetry Breaking, Chaotic Dynamic, Object Oriented Programming, Genetically Modify Organism

Some authors assert that the analysis of huge databases could replace the scientific method. On the contrary, we argue that the best way to make these new technologies bear fruits is to frame them with theories concerning the phenomena of interest. Such theories hint to the observable that should be taken into account and the mathematical structures that may link them. In biology, we argue that the community urgently needs an overarching theory of organisms that would provide a precise framework to understand lifecycles. Among other benefits, such a theory should make explicit what we can and cannot predict in principle.

Keywords: Big Data, biological variation, cancer biology, knowledge, theory