Life is perpetuated through a single-cell bottleneck between generations in many organisms. Here, I highlight that this cell holds information in two distinct stores: in the linear DNA sequence that is replicated during cell divisions, and in the three-dimensional arrangement of molecules that can change during development but is recreated at the start of each generation. These two interdependent stores of information – one replicating with each cell division and the other cycling with a period of one generation – coevolve (...) while perpetuating an organism. Unlike the genome sequence, the arrangement of molecules, including DNA, RNAs, proteins, sugars, lipids, etc., is not well understood. Because this arrangement and the genome sequence are transmitted together from one generation to the next, analysis of both is necessary to understand evolution and origins of inherited diseases. Recent developments suggest that tools are in place to examine how all the information to build an organism is encoded within a single cell, and how this cell code is reproduced in every generation. Life relies on the information for making organisms that is encoded in single cells and transmitted between generations. These cell codes include interdependent stores of information that replicate and that cycle. Understanding evolution and the origins of inherited diseases requires analysis of cell codes. (shrink)

The Neo‐Darwinian concept of natural selection is plausible when one assumes a straightforward causation of phenotype by genotype. However, such simple 1:1 mapping must now give place to the modern concepts of gene regulatory networks and gene expression noise. Both can, in the absence of genetic mutations, jointly generate a diversity of inheritable randomly occupied phenotypic states that could also serve as a substrate for natural selection. This form of epigenetic dynamics challenges Neo‐Darwinism. It needs to incorporate the non‐linear, stochastic (...) dynamics of gene networks. A first step is to consider the mathematical correspondence between gene regulatory networks and Waddington's metaphoric ‘epigenetic landscape’, which actually represents the quasi‐potential function of global network dynamics. It explains the coexistence of multiple stable phenotypes within one genotype. The landscape's topography with its attractors is shaped by evolution through mutational re‐wiring of regulatory interactions – offering a link between genetic mutation and sudden, broad evolutionary changes. (shrink)

The stunning possibility of “reprogramming” differentiated somatic cells to express a pluripotent stem cell phenotype (iPS, induced pluripotent stem cell) and the “ground state” character of pluripotency reveal fundamental features of cell fate regulation that lie beyond existing paradigms. The rarity of reprogramming events appears to contradict the robustness with which the unfathomably complex phenotype of stem cells can reliably be generated. This apparent paradox, however, is naturally explained by the rugged “epigenetic landscape” with valleys representing “preprogrammed” attractor states that (...) emerge from the dynamical constraints of the gene regulatory network. This article provides a pedagogical primer to the fundamental principles of gene regulatory networks as integrated dynamic systems and reviews recent insights in gene expression noise and fate determination, thereby offering a formal framework that may help us to understand why cell fate reprogramming events are inherently rare and yet so robust. (shrink)

The question of whether the modern evolutionary synthesis requires an extension has recently become a topic of discussion, and a source of controversy. We suggest that this debate is, for the most part, not about the modern synthesis at all. Rather, it is about the extent to which genetic mechanisms can be regarded as the primary determinants of phenotypic characters. The modern synthesis has been associated with the idea that phenotypes are the result of gene products, while supporters of the (...) extended synthesis have suggested that environmental factors, along with processes such as epigenetic inheritance, and niche construction play an important role in character formation. We argue that the methodology of the modern evolutionary synthesis has been enormously successful, but does not provide an accurate characterization of the origin of phenotypes. For its part, the extended synthesis has yet to be transformed into a testable theory, and accordingly, has yielded few results. We conclude by suggesting that the origin of phenotypes can only be understood by integrating findings from all levels of the organismal hierarchy. In most cases, parts and processes from a single level fail to accurately explain the presence of a given phenotypic trait. The debate between the proponents of the modern and extended syntheses is somewhat reminiscent of the duck-rabbit illusion. The two sides are probably talking about the same thing, but from different perspectives. If not, then we argue that the challenge is to do an experiment that rules out the alternative view. (shrink)

I hypothesize that re-occurring prior experience of complex systems mobilizes a fast response, whose attractor is encoded by their strongly connected network core. In contrast, responses to novel stimuli are often slow and require the weakly connected network periphery. Upon repeated stimulus, peripheral network nodes remodel the network core that encodes the attractor of the new response. This “core-periphery learning” theory reviews and generalizes the heretofore fragmented knowledge on attractor formation by neural networks, periphery-driven innovation, and a number of recent (...) reports on the adaptation of protein, neuronal, and social networks. The core-periphery learning theory may increase our understanding of signaling, memory formation, information encoding and decision-making processes. Moreover, the power of network periphery-related “wisdom of crowds” inventing creative, novel responses indicates that deliberative democracy is a slow yet efficient learning strategy developed as the success of a billion-year evolution. Also see the video abstract here: https://youtu.be/IIjP7zWGjVE. The network core triggers fast responses to previously experienced stimuli. Innovations require the network periphery that encodes a novel attractor by core-remodeling. This core-periphery learning theory is introduced as a general adaptation, learning, decision-making, and forgetting mechanism of all complex systems including protein, metabolic, signaling, neuronal, social networks, and ecosystems. (shrink)