Can biological complexity be reverse engineered?

Clinical Phenotypes of Cardiovascular and Heart Failure Diseases Can Be Reversed? The Holistic Principle of Systems Biology in Multifaceted Heart Diseases

Recent advances in cardiology and biological sciences have improved quality of life in patients with complex cardiovascular diseases (CVDs) or heart failure (HF). Regardless of medical progress, complex cardiac diseases continue to have a prolonged clinical course with high morbidity and mortality. Interventional coronary techniques together with drug therapy improve quality and future prospects of life, but do not reverse the course of the atherosclerotic process that remains relentlessly progressive. The probability of CVDs and HF phenotypes to reverse can be supported by the advances made on the medical holistic principle of systems biology (SB) and on artificial intelligence (AI). Studies on clinical phenotypes reversal should be based on the research performed in large populations of patients following gathering and analyzing large amounts of relative data that embrace the concept of complexity. To decipher the complexity conundrum, a multiomics approach is needed with network analysis of the biological data. Only by understanding the complexity of chronic heart diseases and explaining the interrelationship between different interconnected biological networks can the probability for clinical phenotypes reversal be increased.

Systems Medicine Disease: Disease Classification and Scalability Beyond Networks and Boundary Conditions

In order to accommodate the forthcoming wealth of health and disease related information, from genome to body sensors to population and the environment, the approach to disease description and definition demands re-examination. Traditional classification methods remain trapped by history; to provide the descriptive features that are required for a comprehensive description of disease, systems science, which realizes dynamic processes, adaptive response, and asynchronous communication channels, must be applied (Wolkenhauer et al., 2013). When Disease is viewed beyond the thresholds of lines and threshold boundaries, disease definition is not only the result of reductionist, mechanistic categories which reluctantly face re-composition. Disease is process and synergy as the characteristics of Systems Biology and Systems Medicine are included. To capture the wealth of information and contribute meaningfully to medical practice and biology research, Disease classification goes beyond a single spatial biologic level or static time assignment to include the interface of Disease process and organism response (Bechtel, 2017a; Green et al., 2017).

Viral species, viral genomes and HIV vaccine design: is the rational design of biological complexity a utopia?

A common logical confusion is prevalent in the whole of biology, namely that biological species are viewed both as an abstract category in an hierarchical classification and as a concrete kind of organism. This is partly due to the fact that the vast majority of living organisms do not have common names that differ from the Latin name of the species to which the organism belongs. However, it is somewhat astonishing that the same confusion exists in virology since every virus has a common name, different from the species name to which the virus belongs, which could be used to refer to the infectious viral entity as a concrete material object. The original 1991 ICTV definition of virus species stated that a virus species is a polythetic class of viruses and thus that a species is a class, namely a conceptual construction of the mind and not a physical, real object located in space and time. In 2013, the ICTV redefined a virus species no longer as a class but as a material object consisting of a monophyletic group of viruses that were all physically part of the species. This new definition is reminiscent of an earlier school of thought known as bionominalism which considered species to be concrete individuals rather than classes. Both bionominalism and the new ICTV definition are based on the logical fallacy of reification which treats abstractions such as classes as if they were concrete physical entities. The implications of this new ontology of virus species for virus taxonomy and for the possibility of incorporating nucleotide metagenomic sequences in the current ICTV classification is discussed.

Biology meets physics: Reductionism and multi-scale modeling of morphogenesis

A common reductionist assumption is that macro-scale behaviors can be described "bottom-up" if only sufficient details about lower-scale processes are available. The view that an "ideal" or "fundamental" physics would be sufficient to explain all macro-scale phenomena has been met with criticism from philosophers of biology. Specifically, scholars have pointed to the impossibility of deducing biological explanations from physical ones, and to the irreducible nature of distinctively biological processes such as gene regulation and evolution. This paper takes a step back in asking whether bottom-up modeling is feasible even when modeling simple physical systems across scales. By comparing examples of multi-scale modeling in physics and biology, we argue that the "tyranny of scales" problem presents a challenge to reductive explanations in both physics and biology. The problem refers to the scale-dependency of physical and biological behaviors that forces researchers to combine different models relying on different scale-specific mathematical strategies and boundary conditions. Analyzing the ways in which different models are combined in multi-scale modeling also has implications for the relation between physics and biology. Contrary to the assumption that physical science approaches provide reductive explanations in biology, we exemplify how inputs from physics often reveal the importance of macro-scale models and explanations. We illustrate this through an examination of the role of biomechanical modeling in developmental biology. In such contexts, the relation between models at different scales and from different disciplines is neither reductive nor completely autonomous, but interdependent.

Can functionality in evolving networks be explained reductively?

Philosophers of biology disagree about an adequate explication of the concept of function. Instead of perpetuating the debate on the level of in principle-arguments, this paper aims first at reconstructing functional talk in the biological research papers of Marom and Braun, which focus on two different kinds of evolving networks, and in discussing the ontological consequences which the authors draw from their results. Marom investigates evolving neural networks controlling Braitenberg vehicles. Braun observes the evolutionary rearrangement or "rewiring" of the genetic network of genetically modified yeast on a short time scale. In both cases, the parameters under investigation are defined in functional terms. However, both authors report striking differences in the structures that realize one and the same function, as well as striking differences in the function of identical structures. From this, they construct an argument against reductionism. The second aim of my paper is an inquiry into the epistemic legitimacy of this conclusion. This requires addressing critically several concepts on which Marom and Braun's argument is built.