Robustness and evolution: concepts, insights and challenges from a developmental model system

Robustness, the persistence of an organismal trait under perturbations, is a ubiquitous property of complex living systems. We here discuss key concepts related to robustness with examples from vulva development in the nematode Caenorhabditis elegans. We emphasize the need to be clear about the perturbations a trait is (or is not) robust to. We discuss two prominent mechanistic causes of robustness, namely redundancy and distributed robustness. We also discuss possible evolutionary causes of robustness, one of which does not involve natural selection. To better understand robustness is of paramount importance for understanding organismal evolution. Part of the reason is that highly robust systems can accumulate cryptic variation that can serve as a source of new adaptations and evolutionary innovations. We point to some key challenges in improving our understanding of robustness.     

Accounting for robustness in modeling signal transduction responses

Abstract

A classical question in systems biology is to find a Boolean model which is able to predict the observed responses of a signaling network. It has been previously shown that such models can be tailored based on experimental data. While fitting a minimum-size network to the experimentally observed data is a natural assumption, it can potentially result in a network which is not so robust against the noises in the training dataset. Indeed, it is widely accepted now that biological systems are generally evolved to be very robust. Therefore, in the present work, we extended the classical formulation of Boolean network construction in order to put weight on the robustness of the created network. We show that our method results generally in more relevant networks. Consequently, considering robustness as a design principle of biological networks can result in more realistic models.     

Evolving Robust Gene Regulatory Networks

Design and implementation of robust network modules is essential for construction of complex biological systems through hierarchical assembly of ‘parts’ and ‘devices’. The robustness of gene regulatory networks (GRNs) is ascribed chiefly to the underlying topology. The automatic designing capability of GRN topology that can exhibit robust behavior can dramatically change the current practice in synthetic biology. A recent study shows that Darwinian evolution can gradually develop higher topological robustness. Subsequently, this work presents an evolutionary algorithm that simulates natural evolution in silico, for identifying network topologies that are robust to perturbations. We present a Monte Carlo based method for quantifying topological robustness and designed a fitness approximation approach for efficient calculation of topological robustness which is computationally very intensive. The proposed framework was verified using two classic GRN behaviors: oscillation and bistability, although the framework is generalized for evolving other types of responses. The algorithm identified robust GRN architectures which were verified using different analysis and comparison. Analysis of the results also shed light on the relationship among robustness, cooperativity and complexity. This study also shows that nature has already evolved very robust architectures for its crucial systems; hence simulation of this natural process can be very valuable for designing robust biological systems.     

On the Emergence of Living Systems

If the problem of the origin of life is conceptualized as a process of emergence of biochemistry from proto-biochemistry, which in turn emerged from the organic chemistry and geochemistry of primitive earth, then the resources of the new sciences of complex systems dynamics can provide a more robust conceptual framework within which to explore the possible pathways of chemical complexification leading to living systems and biosemiosis. In such a view the emergence of life, and concomitantly of natural selection and biosemiosis, is the result of deep natural laws (the outlines of which we are only beginning to perceive) and reflects a degree of holism in those systems that led to life. Further, such an approach may lead to the development of a more general theory of biology and of natural organization, one informed by semiotic concepts.     

Emergence of robust regulatory motifs from in silico evolution of sustained oscillation

The relationship between robustness and evolvability (easiness to evolve), and the evolutionary emergence of robust genetic circuits in biology have attracted much attention in systems biology. This paper investigates in silico the influence of the cis-regulation logic and the coupling of feedback loops on the evolvability and robustness of gene regulatory motifs that can generate sustained oscillation. Our simulation results indicate that both evolvability and robustness of the considered regulatory motifs depend on the cis-regulation logic and the way in which positive and negative feedback loops are coupled. Most interestingly, our findings suggest that robust regulatory motifs can emerge from evolution without an explicit selection pressure on robustness and adding noise in the parameters during the evolution is likely to promote the evolution of sustained oscillation.     

Nonequilibrium thermodynamics and different axioms of evolution

Proponents of two axioms of biological evolutionary theory have attempted to find justification by reference to nonequilibrium thermodynamics. One states that biological systems and their evolutionary diversification are physically improbable states and transitions, resulting from a selective process; the other asserts that there is an historically constrained inherent directionality in evolutionary dynamics, independent of natural selection, which exerts a self-organizing influence. The first, the Axiom of Improbability, is shown to be nonhistorical and thus, for a theory of change through time, acausal. Its perception of the improbability of living states is at least partially an artifact of closed system thinking. The second, the Axiom of Historically Determined Inherent Directionality, is supported evidentially and has an explicit historical component. Historically constrained dynamic populations are inherently nonequilibrium systems. It is argued that living, evolving systems, when considered to be historically constrained nonequilibrium systems, do not appear improbable at all. Thus, the two axioms are not compatible. Instead, the Axiom of Improbability is considered to result from an unjustified attempt to extend the contingent proximal actions of natural selection into the area of historical, causal explanations. It is thus denied axiomatic status, and the effects of natural selection are subsumed as an additional level of constraint in an evolutionary theory derived from the Axiom of Historically Determined Inherent Directionality.     

Theses on Darwin.

The received view that teleology has been successfully eliminated from the modern scientific worldview is challenged. It is argued that both the theory of natural selection and molecular biology presuppose the existence of natural teleology, and so cannot explain it. A number of other issues in the foundations of biology are briefly examined, while stress is laid throughout on empirical evidence of the rational agency inherent in life. It is urged that teleology be rehabilitated and that the reigning functionalist philosophy be replaced by a realistic view of biological functions as emergent properties of living matter within a broad, selforganization framework.     

In silico genetic robustness analysis of microRNA secondary structures: potential evidence of congruent evolution in microRNA

Background  

Robustness is a fundamental property of biological systems and is defined as the ability to maintain stable functioning in the face of various perturbations. Understanding how robustness has evolved has become one of the most attractive areas of research for evolutionary biologists, as it is still unclear whether genetic robustness evolved as a direct consequence of natural selection, as an intrinsic property of adaptations, or as congruent correlate of environment robustness. Recent studies have demonstrated that the stem-loop structures of microRNA (miRNA) are tolerant to some structural changes and show thermodynamic stability. We therefore hypothesize that genetic robustness may evolve as a correlated side effect of the evolution for environmental robustness.     

Selection Shapes the Robustness of Ligand-Binding Amino Acids

The phenotypes of biological systems are to some extent robust to genotypic changes. Such robustness exists on multiple levels of biological organization. We analyzed this robustness for two categories of amino acids in proteins. Specifically, we studied the codons of amino acids that bind or do not bind small molecular ligands. We asked to what extent codon changes caused by mutation or mistranslation may affect physicochemical amino acid properties or protein folding. We found that the codons of ligand-binding amino acids are on average more robust than those of non-binding amino acids. Because mistranslation is usually more frequent than mutation, we speculate that selection for error mitigation at the translational level stands behind this phenomenon. Our observations suggest that natural selection can affect the robustness of very small units of biological organization.     

Buyer beware: robustness analyses in economics and biology

Theoretical biology and economics are remarkably similar in their reliance on mathematical models, which attempt to represent real world systems using many idealized assumptions. They are also similar in placing a great emphasis on derivational robustness of modeling results. Recently philosophers of biology and economics have argued that robustness analysis can be a method for confirmation of claims about causal mechanisms, despite the significant reliance of these models on patently false assumptions. We argue that the power of robustness analysis has been greatly exaggerated. It is best regarded as a method of discovery rather than confirmation.     

Evolution of plant breeding systems

Breeding systems are important, and often neglected, aspects of the natural biology of organisms, affecting homozygosity and thus many aspects of their biology, including levels and patterns of genetic diversity and genome evolution. Among the different plant mating systems, it is useful to distinguish two types of systems: 'sex systems', hermaphroditic versus male/female and other situations; and the 'mating systems' of hermaphroditic populations, inbreeding, outcrossing or intermediate. Evolutionary changes in breeding systems occur between closely related species, and some changes occur more often than others. Understanding why such changes occur requires combined genetical and ecological approaches. I review the ideas of some of the most important theoretical models, showing how these are based on individual selection using genetic principles to ask whether alleles affecting plants' outcrossing rates or sex morphs will spread in populations. After discussing how the conclusions are affected by some of the many relevant ecological factors, I relate these theoretical ideas to empirical data from some of the many recent breeding system studies in plant populations.     

Ins and Outs of Systems Biology vis-à-vis Molecular Biology: Continuation or Clear Cut?

The comprehension of living organisms in all their complexity poses a major challenge to the biological sciences. Recently, systems biology has been proposed as a new candidate in the development of such a comprehension. The main objective of this paper is to address what systems biology is and how it is practised. To this end, the basic tools of a systems biological approach are explored and illustrated. In addition, it is questioned whether systems biology ‘revolutionizes’ molecular biology and ‘transcends’ its assumed reductionism. The strength of this claim appears to depend on how molecular and systems biology are characterised and on how reductionism is interpreted. Doing credit to molecular biology and to methodological reductionism, it is argued that the distinction between molecular and systems biology is gradual rather than sharp. As such, the classical challenge in biology to manage, interpret and integrate biological data into functional wholes is further intensified by systems biology’s use of modelling and bioinformatics, and by its scale enlargement.     

Two Explanations of Evolutionary Progress

Natural selection explains how living forms are fitted to theirconditions of life. Darwin argued that selection also explains what hecalled the gradual advancement of the organisation, i.e.evolutionary progress. Present-day selectionists disagree. In theirview, it is happenstance that sustains conditions favorable to progress,and therefore happenstance, not selection, that explains progress. Iargue that the disagreement here turns not on whether there exists aselection-based condition bias – a belief now attributed to Darwin – but on whether there needs to be such a bias for selection to count as explaining progress. In Darwin's own view, selection explained progressso far as more complex organisms have the selective advantage whenselection operates unimpeded. I show that these two explanations ofevolutionary progress, selection and happenstance, answer for theirobjectivity to different standards, and for their truth or falsehood todifferent features of the world.     

Engineering the robustness of industrial microbes through synthetic biology

Microbial fermentations and bioconversions play a central role in the production of pharmaceuticals, enzymes and chemicals. To meet the demands of industrial production, it is desirable that microbes maintain a maximized carbon flux towards target metabolites regardless of fluctuations in intracellular or extracellular environments. This requires cellular systems that maintain functional stability and dynamic homeostasis in a given physiological state, or manipulate transitions between different physiological states. Stable maintenance or smooth transition can be achieved through engineering of dynamic controllability, modular and hierarchical organization, or functional redundancy, three key features of biological robustness in a cellular system. This review summarizes how synthetic biology can be used to improve the robustness of industrial microbes.     

Robustness and aging—A systems-level perspective

The theory of robustness describes a system level property of evolutionary systems, which predicts tradeoffs of great interest for the systems biology of aging, such as accumulation of non-heritable damage, occurrence of fragilities and limitations in performance, optimized allocation of restricted resources and confined redundancies. According to the robustness paradigm cells and organisms evolved into a state of highly optimized tolerance (HOT), which provides robustness to common perturbations, but causes tradeoffs generally characterized as “robust yet fragile”. This raises the question whether the ultimate cause of aging is more than a lack of adaptation, but an inherent fragility of complex evolutionary systems. Since robustness connects to evolutionary designs, consideration of this theory provides a deeper connection between evolutionary aspects of aging, mathematical models and experimental data. In this review several mechanisms influential for aging are re-evaluated in support of robustness tradeoffs. This includes asymmetric cell division improving performance and specialization with limited capacities to prevent and repair age-related damage, as well as feedback control mechanisms optimized to respond to acute stressors, but unable to halt nor revert aging. Improvement in robustness by increasing efficiencies through cellular redundancies in larger organisms alleviates some of the damaging effects of cellular specialization, which can be expressed in allometric relationships. The introduction of the robustness paradigm offers unique insights for aging research and provides novel opportunities for systems biology endeavors.     

Evolution of mutational robustness in an RNA virus

Mutational (genetic) robustness is phenotypic constancy in the face of mutational changes to the genome. Robustness is critical to the understanding of evolution because phenotypically expressed genetic variation is the fuel of natural selection. Nonetheless, the evidence for adaptive evolution of mutational robustness in biological populations is controversial. Robustness should be selectively favored when mutation rates are high, a common feature of RNA viruses. However, selection for robustness may be relaxed under virus co-infection because complementation between virus genotypes can buffer mutational effects. We therefore hypothesized that selection for genetic robustness in viruses will be weakened with increasing frequency of co-infection. To test this idea, we used populations of RNA phage φ6 that were experimentally evolved at low and high levels of co-infection and subjected lineages of these viruses to mutation accumulation through population bottlenecking. The data demonstrate that viruses evolved under high co-infection show relatively greater mean magnitude and variance in the fitness changes generated by addition of random mutations, confirming our hypothesis that they experience weakened selection for robustness. Our study further suggests that co-infection of host cells may be advantageous to RNA viruses only in the short term. In addition, we observed higher mutation frequencies in the more robust viruses, indicating that evolution of robustness might foster less-accurate genome replication in RNA viruses.     

Selection for distinct gene expression properties favours the evolution of mutational robustness in gene regulatory networks

Mutational robustness is a genotype's tendency to keep a phenotypic trait with little and few changes in the face of mutations. Mutational robustness is both ubiquitous and evolutionarily important as it affects in different ways the probability that new phenotypic variation arises. Understanding the origins of robustness is specially relevant for systems of development that are phylogenetically widespread and that construct phenotypic traits with a strong impact on fitness. Gene regulatory networks are examples of this class of systems. They comprise sets of genes that, through cross‐regulation, build the gene activity patterns that define cellular responses, different tissues or distinct cell types. Several empirical observations, such as a greater robustness of wild‐type phenotypes, suggest that stabilizing selection underlies the evolution of mutational robustness. However, the role of selection in the evolution of robustness is still under debate. Computer simulations of the dynamics and evolution of gene regulatory networks have shown that selection for any gene activity pattern that is steady and self‐sustaining is sufficient to promote the evolution of mutational robustness. Here, I generalize this scenario using a computational model to show that selection for different aspects of a gene activity phenotype increases mutational robustness. Mutational robustness evolves even when selection favours properties that conflict with the stationarity of a gene activity pattern. The results that I present support an important role for stabilizing selection in the evolution of robustness in gene regulatory networks.     

Guiding the self-organization of random Boolean networks

Random Boolean networks (RBNs) are models of genetic regulatory networks. It is useful to describe RBNs as self-organizing systems to study how changes in the nodes and connections affect the global network dynamics. This article reviews eight different methods for guiding the self-organization of RBNs. In particular, the article is focused on guiding RBNs toward the critical dynamical regime, which is near the phase transition between the ordered and dynamical phases. The properties and advantages of the critical regime for life, computation, adaptability, evolvability, and robustness are reviewed. The guidance methods of RBNs can be used for engineering systems with the features of the critical regime, as well as for studying how natural selection evolved living systems, which are also critical.