V. Life's Corporeal Evolution Develops, Encodes and Organizes Itself: An Earthtwinian Genesis Synthesis

B. Systems Biology Unites: EvoDevo, Genomes, Cells, Networks, Symbiosis, Homology, Inherency

Perrimon, Norbert and Naama Barkai. The Era of Systems Developmental Biology. Current Opinion in Genetics & Development. 21/6, 2011. An introduction to a special issue of the Genetics of System Biology by the Harvard Medical School researchers. A typical article might be “Scaling of Morphogen Gradients” by Danny Ben-Zvi, et al. In this issue and journal “development” often pertains to embryological stages, as the “systems” vista once more revitalizes and reunites this archetypal phase.

Developmental biology, fueled by advances in genomics, proteomics, imaging, and applications of physics and mathematical modeling, is yet undergoing another renaissance – entering the era of ‘Systems Developmental Biology’. The goal of ‘Systems Developmental Biology’ is to go beyond our current understanding of what a single gene, or a few connected parts, do in a biological context. The challenge is to become more systematic, unbiased and quantitative in the analysis of developmental questions. Thus, we now want to identify all the parts and pathways involved and quantify some of the key parameters to build mathematical and computational models that describe and predict the behavior of the systems. (681)

Peter, Isabelle and Eric Davidson. Genomic Control Process: Development and Evolution. Cambridge, MA: Academic Press, 2015. A CalTech biology professor and the geneticist (1937-2015, search) who was the founding theorist of gene regulatory networks provide a consummate volume to date of this major expansion of active genetic phenomena.

Chapter 1 explains different levels of control affecting developmental gene expression in animal cells, and an overview of the physical nature of the regulatory genome. The book goes on to provide in depth understandings of GRNs, how they generate the regulatory conditions, cis-regulatory functions operating at the network nodes, and the dynamics of transcriptional activity in development. The next Chapters apply network theory to embryonic development of all major kinds; development of adult body parts and organs; and to cell fate specification. Chapter 6 examines the conceptual richness that has derived from various approaches to predictive, quantitative models of GRNs and GRN circuits. In The final section the notes applications to bilaterian evolution, including the underlying explanation of hierarchical animal phylogeny, and more. (Publisher excerpt)

Priami, Corrado. Algorthmic Systems Biology. Communications of the ACM. May, 2009. The University of Trento computer scientist and Director of its Microsoft funded Centre for Computational and Systems Biology extols the shift from a prior object emphasis to lately engage life’s dynamical phenomena. But a mechanistic metaphor prevails such that a philosophical disconnect remains.

Qureshi, Irfan and Mark Mehler. Emerging Roles of Non-Coding RNAs in Brain Evolution, Development, Plasticity and Disease. Nature Reviews Neuroscience. 13/8, 2012. Albert Einstein College of Medicine physicians and neuroscientists provide a good example of the welling 21st century AND phase of integrative genomics, as it opens to realizations of multifaceted nucleotide network interrelations.

Novel classes of small and long non-coding RNAs (ncRNAs) are being characterized at a rapid pace, driven by recent paradigm shifts in our understanding of genomic architecture, regulation and transcriptional output, as well as by innovations in sequencing technologies and computational and systems biology. These ncRNAs can interact with DNA, RNA and protein molecules; engage in diverse structural, functional and regulatory activities; and have roles in nuclear organization and transcriptional, post-transcriptional and epigenetic processes. This expanding inventory of ncRNAs is implicated in mediating a broad spectrum of processes including brain evolution, development, synaptic plasticity and disease pathogenesis. (Abstract)

Radde, Nicole and Marc-Thorsten Hutt. The Physics behind Systems Biology. EPJ Nonlinear Biomedical Physics. Online August, 2016. As an instance of the present cross-fertilization of life sciences into physical realms, which become animated by way of natural complexities, University of Stuttgart and Jacobs University, Bremen, theorists proceed to give this integrative turn a fundamental basis. A significant reason is the growing profusion of network studies everywhere since 2000. After the two infinities of atom and cosmos, this third phase and turn of realizing an actual organic genesis uniVerse is much underway.

Systems Biology is a young and rapidly evolving research field, which combines experimental techniques and mathematical modeling in order to achieve a mechanistic understanding of processes underlying the regulation and evolution of living systems. Systems Biology is often associated with an Engineering approach: The purpose is to formulate a data-rich, detailed simulation model that allows to perform numerical (‘in silico’) experiments and then draw conclusions about the biological system. While methods from Engineering may be an appropriate approach to extending the scope of biological investigations to experimentally inaccessible realms and to supporting data-rich experimental work, it may not be the best strategy in a search for design principles of biological systems and the fundamental laws underlying Biology.

Physics has a long tradition of characterizing and understanding emergent collective behaviors in systems of interacting units and searching for universal laws. Therefore, it is natural that many concepts used in Systems Biology have their roots in Physics. With an emphasis on Theoretical Physics, we will here review the ‘Physics core’ of Systems Biology, show how some success stories in Systems Biology can be traced back to concepts developed in Physics, and discuss how Systems Biology can further benefit from its Theoretical Physics foundation.

Rigoutsos, Isidore and Gregory Stephanopoulos, eds. Systems Biology. Oxford: Oxford University Press, 2007. A two volume set covering Genomics and Systems Biology (Vol. 1 Published) and Networks, Models, and Applications (Vol. 2 Forthcoming).

Systems biology is an integrated approach that brings together and leverages theoretical, experimental, and computational approaches in order to establish connections among important molecules or groups of molecules in order to aid the eventual mechanistic explanation of cellular processes and systems. (Vol. 1, xiii)

Written for a wide audience, each volume presents a timely compendium of essential information that is necessary for a comprehensive study of the subject. The chapters in the two volumes reflect the hierarchical nature of systems biology. Chapter authors-world-recognized experts in their fields-provide authoritative discussions on a wide range of topics along this hierarchy. Volume I explores issues pertaining to genomics that range from prebiotic chemistry to noncoding RNAs. Volume II covers an equally wide spectrum, from mass spectrometry to embryonic stem cells. (Oxford website)

Rulands, Steffen and Benjamin Simons. Emergence and Universality in the Regulation of Stem Cell Fate. Current Opinion in Systems Biology. 5/57, 2017. MPI Physics of Complex Systems and Cambridge University biophysicists profess a new level of integrity between biological dynamics and statistical physics, by which stem cell studies and their palliative avail can be advanced. A novel synthesis is laid out by way of phase transitions, renormalization theories, non-equilibrium thermodynamics, and more, so to define universal classes. This new journal contains similar examples such as Andrea Cavagna, et al (9/49, 2018) and Lea Geontoro (1/80, 2017), see each herein.

The mechanisms that control cell fate behaviour during development, and their dysregulation in disease, remain the subject of interest and debate. Advances in single-cell genomics have shifted emphasis towards the elucidation of molecular regulatory programmes and transcriptional cell states. However, quantitative statistical approaches based on cell lineage tracing data have provided fresh insight into stem and progenitor cell behaviour, questioning the role of cell fate stochasticity, transcriptional heterogeneity and state priming. These investigations, which draw upon conceptual insights from statistical physics and mathematics, provide a novel, generic and rigorous framework to resolve and classify stem cell self-renewal strategies. Here, using epithelial maintenance as an example, we consider the foundation, conceptual basis, utility and limitations of such quantitative approaches in cell biology. (Abstract)

Russo, Christopher, et al. Soft Modes as a Predictive Framework for Low-Dimensional Biological Systems Across Scales.. Annual Review of Biophysics. February, 2025. As the quote cites, University of Chicago biophysicists are now technically able to discern and finesse a new metabolic feature which further distinguishes life’s entire ascendant flourishall the way to our late learned notice and record.

All biological systems are subject to thermal fluctuations, environments, or mutations. Yet, while they consist of interacting components, recent experiments have shown that their response is low dimensional with a few stereotyped changes. In this review, we explore a unifying dynamical framework, dubbed soft modes, to explain and analyze low dimensionality from biomolecules to ecosystems. We argue that this condition generalizes classic ideas from developmental biology to disparate systems, namely phenocopying, dual buffering, and global epistasis. (Excerpt)

Saetzler, Kurt, et al. Systems Biology beyond Networks: Generating Order from Disorder through Self-Organization. Seminars in Cancer Biology. 21/3, 2012. In a special issue on Systems Biology and Cancer, with coauthors Carlos Sonnenschein and Ana Soto, (also issue coeditors), University of Ulster, N. Ireland, and Tufts University School of Medicine researchers draw upon an “organicism and emergentism” tradition going back to Ludwig von Bertalanffy and Paul Weiss to advance a 21st century systems turn as the necessary, vital way to perceive, and to heal and offset cancer cell malfeasance. See also in this issue “Outline of a Concept for Organismic Systems Biology” by Bernd Rosslenbroich, and “On the Intrinsic Inevitability of Cancer” by Sui Huang, who tells how to mollify this.

Erwin Schrödinger pointed out in his 1944 book "What is Life" that one defining attribute of biological systems seems to be their tendency to generate order from disorder defying the second law of thermodynamics. Almost parallel to his findings, the science of complex systems was founded based on observations on physical and chemical systems showing that inanimate matter can exhibit complex structures although their interacting parts follow simple rules. This is explained by a process known as self-organization and it is now widely accepted that multi-cellular biological organisms are themselves self-organizing complex systems in which the relations among their parts are dynamic, contextual and interdependent. In order to fully understand such systems, we are required to computationally and mathematically model their interactions as promulgated in systems biology. (Abstract)

Salazar-Ciudad, Isaac, and Hugo Cano-Fernandez. Evo-Devo Beyond Development: Generalizing Evo-Devo to All Levels of Phenotypic Evolution. BioEssays. March, 2023. Into this year, Universitat Autònoma de Barcelona system biologists propose an expanded unifed cross-synthesis between life’s embryonic, physiological and historic phases. In regard, their contribution is further suggestive of an embryonic gestation (all the way to our latest transition in pediawise individuality.

A foundational idea of evo-devo is that morphological variation is not isotropic in all directions. Instead, some directions are more likely than others from DNA variations which depend on development. We argue that this evo-devo perspective should apply not only to morphology but to evolution across its phenotypic phases. To do this, two types of arguments need be combined: generative about which phenotypic mode arises in each instance and selective issues about which passes to the offspring. (Excerpt)

Sauro, Herbert and Kyung Hyuk Kim. It’s an Analog World. Nature. 497/572, 2013. A news report on a technical article “Synthetic Analog Computation in Living Cells” in the same issue by Ramiz Daniel, et al, a MIT systems biology group, that is said to achieve an effective recognition and reciprocal utilization of both archetypal digital byte and relational integration analog modes.

The reality is that cells use a hybrid approach to information processing. In some cases they use digital yes-or-no decisions, but in many cases cellular signals are analog, with levels of gradation. (Sauro, 572) We propose that an efficient and accurate computational approach to synthetic biological networks will ultimately integrate both analog and digital processing. This mixed signal approach can utilize analog or collective analog computation for front-end processing in concert with decision-making digital circuits,or it may use graded feedback for simultaneous analog and digital computation, as in neuron networks in the brain. (Daniel, 623)

Schwenk, Kurt, et al. Grand Challenges in Organismal Biology. Integrative and Comparative Biology. 49/1, 2009. Mostly unnoticed, a grand revolution is underway in the biological and evolutionary sciences to perceive living systems as far more than passive, accidents. Rather personal organisms at every sequential, emergent stage are distinguished by a universal modular, self-organization.

A renaissance in organismal biology has been sparked by recent conceptual, theoretical, methodological, and computational advances in the life sciences, along with an unprecedented interdisciplinary integration with Mathematics, Engineering, and the physical sciences. Despite a decades-long trend toward reductionist approaches to biological problems, it is increasingly recognized that whole organisms play a central role in organizing and interpreting information from across the biological spectrum. (7)

Organisms operate across spatial scales from molecules to ecosystems and temporal scales from nanoseconds to eons. They are dynamic, multi-dimensional, hierarchical, and nonlinear networks characterized by positive and negative feedback, ill-defined boundaries, and high levels of stochasticity. Further, they operate within dynamic environments that display similar complexity. (10)

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