V. Life's Evolutionary Development Organizes Itself: A 2020s Genesis Synthesis
B. Systems Biology Integrates: Genomes, Networks, Symbiosis, Deep Homology
Di Ventura, Barbara and Victor Sourjik. Self-Organized Partitioning of Dynamically Localized Proteins in Bacterial Cell Division. Molecular Systems Biology. 7/457, 2011. In an exemplary article for this online journal, University of Heidelberg biologists propose that such formative dynamics now being discovered vitally at work everywhere ought to be then appreciated and availed upon as nature’s independent, intrinsic propensity. We offer two quotes, and a note about the journal.
How cells manage to get equal distribution of their structures and molecules at cell division is a crucial issue in biology. In principle, a feedback mechanism could always ensure equality by measuring and correcting the distribution in the progeny. However, an elegant alternative could be a mechanism relying on self-organization, with the interplay between system properties and cell geometry leading to the emergence of equal partitioning. Our findings reveal a novel and effective mechanism of protein partitioning in dividing cells and emphasize the importance of self-organization in basic cellular processes. (457)
DiFrisco, James and Johannes Jaeger. Genetic Causation in Complex Regulatory Systems: An Integrative Dynamic Perspective. BioEssays. 42/6, 2020. A biological studies advance, KU Leuven philosopher and a Complexity Science Hub, Vienna systems biologist seek to add a relational network vista which can inform the historic turn from discrete nucleotides to whole entities, be it genomes or organisms.
The logic of genetic discovery remains in place, but the focus of biology is shifting from genotype–phenotype relationships to complex metabolic, physiological, developmental, and behavioral traits. In light of this, the reductionist view of genes as privileged causes is re‐examined. The scope of genetic effects in complex regulatory systems, in which dynamics are driven by feedback and hierarchical interactions across levels, are considered. This review argues that genes can be treated as specific difference‐makers for the molecular regulation of their expression. However, they are not stable, proportional or specific as causes of the behavior of regulatory networks. Proper dynamical models can illuminate cause‐and‐effect in complex biological systems so to gain an integrative understanding of underlying complex phenotypes. (Abstract edit)
Faragalla, Kyrillos, et al. From Gene List to Gene Network: Recognizing Functional Connections that Regulate Behavioral Traits. Journal of Experimental Zoology B. Online November, 2018. Western University, Ontario biologists in coauthor Graham Thompson’s group post a decisive review of the need to shift from a particulate nucleotide phase, which winds up with long tabulations, to equally real multiplex interrelations. The paper uniquely goes on to extend a “network ladder” of node first, interactions next onto protein, neuronal, social and ecosystem stages, which appear as emergent radiations of the same dynamic topology.
The study of social breeding systems is often gene focused, and the field of insect sociobiology has been successful at assimilating tools and techniques from molecular biology. One common output from sociogenomic studies is a gene list, which is readily generated from microarray, RNA sequencing, or other molecular screens. Gene lists, however, are limited because the tabular information does not explain how genes interact with each other, or how they change in real time circumstances. Here, we promote a view from molecular systems biology, where gene lists are converted into gene networks that better describe these functional connections that regulate behavioral traits. We argue that because network analyses are not restricted to “genes” as nodes, their implementation can connect multiple levels of biological organization into a single, progressively complex study system. (Abstract excerpt)
Fu, Pengcheng and Sven Panke, eds. Systems Biology and Synthetic Biology. Hoboken, NJ: Wiley, 2009. A significant tome which covers not only theory and practice, but also the philosophical implications of being able to begin a new biological creation via this informational revolution. In such regard, Cliff Hooker, the University of Newcastle, Australia director of its Complex Adaptive Systems Research Group, (Google) along with Fu, contributes papers to the extent that a novel dynamical paradigm, essentially a newly appreciated conducive, animate cosmos, will be required going forward.
Systems biology…aims at system-level understanding of biological processes and biochemical networks as a whole. This “system-oriented” new biology is shifting our focus from examining particular molecular details to studying the information flows at all biological levels: Genomic DNA, mRNA, proteins, informational pathways, and regulatory networks. (Fu, 2) These examples illustrate that we are able to not only “read” the genetic code to understand living systems but also “write” the message for the creation of new life forms. (Fu, 4)
Garcia-Ojalvo, Jordi. Physical Approaches to the Dynamics of Genetic Circuits. Contemporary Physics. 52/5, 2011. As another instance of both the discovery in genomes of pervasive regulatory networks, and their association with and rooting in statistical mechanics, a Universitat Politècnica de Catalunya, Terrassa, Spain physicist provides a lengthy tutorial introduction. A growing number of such projects and papers are on their way to a whole scale reconception of the nature of genotype and phenotype, and by an affinity with a nonlinear materiality, to imply a conducive genesis cosmos.
Cellular behaviour is governed by gene regulatory processes that are intrinsically dynamic and nonlinear, and are subject to non-negligible amounts of random fluctuations. Such conditions are ubiquitous in physical systems, where they have been studied for decades using the tools of statistical and nonlinear physics. The goal of this introductory tutorial is to show how approaches traditionally used in physics can help in reaching a systems-level understanding of living cells. (Abstract, 439)
Garcia-Sancho, Miguel. Biology, Computing, and the History of Molecular Sequencing. New York: Palgrave Macmillan, 2012. A Spanish National Research Council historian of genetics provides a thorough course from Frederick Sanger’s 1950s rudimentary techniques to 1960s basic instrumentation, later 1980-1990s automation onset and onto 21st century rapid, large-scale machine computations. But we include because this worldwide collaborative, cumulative project can quite appear as our human phenomenal way that a genesis uniVerse tries to read its own genetic code.
Gazestani, Vahid and Nathan Lewis. From Genotype to Phenotype: Augmenting Deep Learning with Networks and Systems Biology. Current Opinion in Systems Biology. 15/68, 2019. UC San Diego bioscientists consider a timely synthesis of these genetic, systems, network, and AI aspects and methods, which appear to have a natural, innate affinity with each other. A subsection is Generalizability, Transferability, and Interpretability as our worldwise learning phase proceeds to reconstruct how we came to be, so as to better go forward.
Cells, as complex systems, consist of diverse interacting biomolecules arranged in dynamic hierarchical modules. Recent advances in deep methods now allow one to encode this existing knowledge in the architecture of the learning procedure. By encoding biological networks this way, one can develop flexible techniques that propagate information through the molecular networks to successfully classify cell states. Moreover, this flexibility can be harnessed to model the hierarchical structure of real biological systems, efficiently converting gene-level data to pathway-level information with an impact on the cell phenotype. (Abstract excerpt)
Geontoro, Lea. Cross-Hierarchy Systems Principles. Current Opinion in Systems Biology. 1/80, 2017. In an inaugural Future of Systems Biology issue, a CalTech biologist alludes to nascent perceptions of an innate geometry and mathematics at work that serves to guide cells, organisms, and species. A common cycle of initial, diverse explorations, later formation of relational networks, and an emergence of integral wholes is described. Akin to Martin Davis in cosmic computations, and Jan Boeyens for periodic elements, recurrent similarities are seen to grace life’s ascent unto our retrospect witness.
One driving motivation of systems biology is the search for general principles that govern the design of biological systems. But questions often arise as to what kind of general principles biology could have. Concepts from engineering such as robustness and modularity are indeed becoming a regular way of describing biological systems. Another source of potential general principles is the emerging similarities found in processes across biological hierarchies. In this piece, I describe several emerging cross-hierarchy similarities. Identification of more cross-hierarchy principles, and understanding the implications these convergence have on the construction of biological systems, I believe, present exciting challenges for systems biology in the decades to come. (Abstract)
Gilpin, William, et al. Learning Dynamics from Large Biological Data Sets: Machine Learning Meets Systems Biology. Current Opinion in Systems Biology. July 30, 2020. As is the current case for many scientific fields, Harvard and Dartmouth researchers scope out ways by which a suitable apply of AI deep neural net techniques can effectively interface with life studies so to enhance research methods and results.
In the past few decades, mathematical models based on dynamical systems theory have provided new insight into diverse biological systems. In this review, we ask whether the recent success of machine learning techniques for large-scale biological data analysis can provide a complementary, beneficial approach to traditional modeling. Recent applications of machine learning have been used to study biological dynamics in diverse systems from neuroscience to animal behavior. We propose several avenues for bridging dynamical systems theory with large-scale analysis enabled by machine learning. (Abstract excerpt)
Green, Sara, et al. Explanatory Integration Challenges in Evolutionary Systems Biology. Biological Theory. Online July, 2014. Philosophers of science Sara Green, Melinda Fagan, and Johannes Jaeger, from the USA, Spain and Denmark, are engaged with colleagues in a project to discern the presence general biological principles. Since an reductionist phase has run its course, there is much need, for example in stem cell research, to define unifying properties and propensities which seem to be truly there. In regard, Jaeger has studied holistic biology with the late Brian Goodwin, so a neo-rational, structural, generative vista guides the effort. This school and view is then contrasted with the narrow, inadequate neo-Darwinian focus. It is then said that evolutionary theory can be integrated by virtue of common code-like agencies evident in their independence and as instantiated in organisms.
Green, Sara, et al. Network Analyses in Systems Biology: New Strategies for Dealing with Biological Complexity. Synthese. Online January, 2017. As many fields such as systems chemistry (Peter Stadler) are lately integrating network features, so Green and Maria Serban, University of Copenhagen, Raphael Scholl, University of Geneva, Nicholaos Jones, University of Alabama, along with Ingo Brigandt and William Bechtel, UC San Diego proceed with an expansion of this title endeavor. A better report of interactive linkages, computational strategies, epigenetic stabilities, and more, can be achieved. One benefit, it is noted, might be perceptions of a “cancer attractor.”
The increasing application of network models to interpret biological systems raises a number of important methodological and epistemological questions. What novel insights can network analysis provide in biology? Are network approaches an extension of or in conflict with mechanistic research strategies? When and how can network and mechanistic approaches interact in productive ways? In this paper we address these questions by focusing on how biological networks are represented and analyzed in a diverse class of case studies. Our examples span from the investigation of organizational properties of biological networks using tools from graph theory to the application of dynamical systems theory to understand the behavior of complex biological systems. We show how network approaches support and extend traditional mechanistic strategies but also offer novel strategies for dealing with biological complexity. (Abstract)
Gross, Fridolin and Sara Green. The Sum of the Parts: Large-Scale Modeling in Systems Biology. Philosophy, Theory, and Practice in Biology. Volume 9, 2017. University of Kassel, Germany and University of Copenhagen philosophers provide an update review as this 21st century integral turn and method proceeds to put life back together again.