A spectrum of modularity in multi‐functional gene circuits

A major challenge in systems biology is to understand the relationship between a circuit's structure and its function, but how is this relationship affected if the circuit must perform multiple distinct functions within the same organism? In particular, to what extent do multi‐functional circuits contain modules which reflect the different functions? Here, we computationally survey a range of bi‐functional circuits which show no simple structural modularity: They can switch between two qualitatively distinct functions, while both functions depend on all genes of the circuit. Our analysis reveals two distinct classes: hybrid circuits which overlay two simpler mono‐functional sub‐circuits within their circuitry, and emergent circuits, which do not. In this second class, the bi‐functionality emerges from more complex designs which are not fully decomposable into distinct modules and are consequently less intuitive to predict or understand. These non‐intuitive emergent circuits are just as robust as their hybrid counterparts, and we therefore suggest that the common bias toward studying modular systems may hinder our understanding of real biological circuits.     

Emergence,therefore antireductionism? A critique of emergent antireductionism

Emergent antireductionism in biological sciences states that even though all living cells and organisms are composed of molecules, molecular wholes are characterized by emergent properties that can only be understood from the perspective of cellular and organismal levels of composition. Thus, an emergence claim (molecular wholes are characterized by emergent properties) is thought to support a form of antireductionism (properties of higher-level molecular wholes can only be understood by taking into account concepts, theories and explanations dealing with higher-level entities). I argue that this argument is flawed: even if molecular wholes are characterized by emergent properties and even if many successful explanations in biology are not molecular, there is no entailment between the two claims.     

Biological complexity and drug discovery: a practical systems biology approach

Drugs fail in clinical studies most often from lack of efficacy or unexpected toxicities. These failures result from an inadequate understanding of drug action and follow, in part, from our dependence on drug discovery technologies that do not take into account the complexity of human disease biology. Biological systems exhibit many features of complex engineering systems, including modularity, redundancy, robustness, and emergent properties. Addressing these features has contributed to the successful design of an improved biological assay technology for inflammation drug discovery. This approach, termed Biologically Multiplexed Activity Profiling (BioMAP), involves the statistical analysis of protein datasets generated from novel complex primary human cell-based assay systems. Compound profiling in these systems has revealed that a surprisingly large number of biological mechanisms can be detected and distinguished. Features of these assays relevant to the behaviour of complex systems are described.     

Systems biology,emergence and antireductionism

This study explores the conceptual history of systems biology and its impact on philosophical and scientific conceptions of reductionism, antireductionism and emergence. Development of systems biology at the beginning of 21st century transformed biological science. Systems biology is a new holistic approach or strategy how to research biological organisms, developed through three phases. The first phase was completed when molecular biology transformed into systems molecular biology. Prior to the second phase, convergence between applied general systems theory and nonlinear dynamics took place, hence allowing the formation of systems mathematical biology. The second phase happened when systems molecular biology and systems mathematical biology, together, were applied for analysis of biological data. Finally, after successful application in science, medicine and biotechnology, the process of the formation of modern systems biology was completed.Systems and molecular reductionist views on organisms were completely opposed to each other. Implications of systems and molecular biology on reductionist–antireductionist debate were quite different. The analysis of reductionism, antireductionism and emergence issues, in the era of systems biology, revealed the hierarchy between methodological, epistemological and ontological antireductionism. Primarily, methodological antireductionism followed from the systems biology. Only after, epistemological and ontological antireductionism could be supported.     

Lipid mimetics: A versatile toolbox for lipid biology and beyond

Being the principal component of biological membranes lipids are essential building blocks of life. Given their huge biological importance, the investigation of lipids, their properties, interactions and metabolic pathways is of prime importance for the fundamental understanding of living cells and organisms as well as the emergence of diseases. Different strategies have been applied to investigate lipid-mediated biological processes, one of them being the use of lipid mimetics. They structurally resemble their natural counterparts but are equipped with functionality that can be used to probe or manipulate lipid-mediated biological processes and biomembranes. Lipid mimetics therefore constitute an indispensable toolbox for lipid biology and membrane research but also beyond for potential applications in medicine or synthetic biology. Herein, we highlight recent advances in the development and application of lipid-mimicking compounds.     

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.     

From protein-protein interactions to protein co-expression networks: a new perspective to evaluate large-scale proteomic data

The reductionist approach of dissecting biological systems into their constituents has been successful in the first stage of the molecular biology to elucidate the chemical basis of several biological processes. This knowledge helped biologists to understand the complexity of the biological systems evidencing that most biological functions do not arise from individual molecules; thus, realizing that the emergent properties of the biological systems cannot be explained or be predicted by investigating individual molecules without taking into consideration their relations. Thanks to the improvement of the current -omics technologies and the increasing understanding of the molecular relationships, even more studies are evaluating the biological systems through approaches based on graph theory. Genomic and proteomic data are often combined with protein-protein interaction (PPI) networks whose structure is routinely analyzed by algorithms and tools to characterize hubs/bottlenecks and topological, functional, and disease modules. On the other hand, co-expression networks represent a complementary procedure that give the opportunity to evaluate at system level including organisms that lack information on PPIs. Based on these premises, we introduce the reader to the PPI and to the co-expression networks, including aspects of reconstruction and analysis. In particular, the new idea to evaluate large-scale proteomic data by means of co-expression networks will be discussed presenting some examples of application. Their use to infer biological knowledge will be shown, and a special attention will be devoted to the topological and module analysis.     

The road to modularity

A network of interactions is called modular if it is subdivided into relatively autonomous, internally highly connected components. Modularity has emerged as a rallying point for research in developmental and evolutionary biology (and specifically evo-devo), as well as in molecular systems biology. Here we review the evidence for modularity and models about its origin. Although there is an emerging agreement that organisms have a modular organization, the main open problem is the question of whether modules arise through the action of natural selection or because of biased mutational mechanisms.     

Phylogenetic analysis of modularity in protein interaction networks

Background  

In systems biology, comparative analyses of molecular interactions across diverse species indicate that conservation and divergence of networks can be used to understand functional evolution from a systems perspective. A key characteristic of these networks is their modularity, which contributes significantly to their robustness, as well as adaptability. Consequently, analysis of modular network structures from a phylogenetic perspective may be useful in understanding the emergence, conservation, and diversification of functional modularity.     

Synthetic biology of minimal systems

“Synthetic biology” is a concept that has developed together with, or slightly after, “systems biology”. But while systems biology aims at the full understanding of large systems by integrating more and more details into their models, synthetic biology phrases different questions, namely: what particular biological function could be obtained with a certain known subsystem of reduced complexity; can this function be manipulated or engineered in artificial environments or genetically modified organisms; and if so, how? The most prominent representation of synthetic biology has so far been microbial engineering by recombinant DNA technology, employing modular concepts known from information technology. However, there are an increasing number of biophysical groups who follow similar strategies of dissecting cellular processes and networks, trying to identify functional minimal modules that could then be combined in a bottom-up approach towards biology. These modules are so far not as particularly defined by their impact on DNA processing, but rather influenced by core fields of biophysics, such as cell mechanics and membrane dynamics. This review will give an overview of some classical and some quite new biophysical strategies for constructing minimal systems of certain cellular modules. We will show that with recent advances in understanding of cytoskeletal and membrane elements, the time might have come to experimentally challenge the concept of a minimal cell.     

The Evolutionary Origins of Hierarchy

Hierarchical organization—the recursive composition of sub-modules—is ubiquitous in biological networks, including neural, metabolic, ecological, and genetic regulatory networks, and in human-made systems, such as large organizations and the Internet. To date, most research on hierarchy in networks has been limited to quantifying this property. However, an open, important question in evolutionary biology is why hierarchical organization evolves in the first place. It has recently been shown that modularity evolves because of the presence of a cost for network connections. Here we investigate whether such connection costs also tend to cause a hierarchical organization of such modules. In computational simulations, we find that networks without a connection cost do not evolve to be hierarchical, even when the task has a hierarchical structure. However, with a connection cost, networks evolve to be both modular and hierarchical, and these networks exhibit higher overall performance and evolvability (i.e. faster adaptation to new environments). Additional analyses confirm that hierarchy independently improves adaptability after controlling for modularity. Overall, our results suggest that the same force–the cost of connections–promotes the evolution of both hierarchy and modularity, and that these properties are important drivers of network performance and adaptability. In addition to shedding light on the emergence of hierarchy across the many domains in which it appears, these findings will also accelerate future research into evolving more complex, intelligent computational brains in the fields of artificial intelligence and robotics.     

Emergence and maintenance of functional modules in signaling pathways

Background  

While detection and analysis of functional modules in biological systems have received great attention in recent years, we still lack a complete understanding of how such modules emerge. One theory is that systems must encounter a varying selection (i.e. environment) in order for modularity to emerge. Here, we provide an alternative and simpler explanation using a realistic model of biological signaling pathways and simulating their evolution.     

Modularity: a new perspective in biology

Several decades of research in biochemistry and molecular biology have been devoted for studies on isolated enzymes and proteins. Recent high throughput technologies in genomics and proteomics have resulted in avalanche of information about several genes, proteins and enzymes in variety of living systems. Though these efforts have greatly contributed to the detailed understanding of a large number of individual genes and proteins, this explosion of information has simultaneously brought out the limitations of reductionism in understanding complex biological processes. The genes or gene products do not function in isolation in vivo. A delicate and dynamic molecular architecture is required for precision of the chemical reactions associated with "life". In future, a paradigm shift is, therefore, envisaged, in biology leading to exploration of molecular organizations in physical and genomic context, a subtle transition from conventional molecular biology to modular biology. A module can be defined as an organization of macromolecules performing a synchronous function in a given metabolic pathway. In modular biology, the biological processes of interest are explored as complex systems of functionally interacting macromolecules. The present article describes the perceptions of the concept of modularity, in terms of associations among genes and proteins, presenting a link between reductionist approach and system biology.     

From autopoiesis to neurophenomenology: Francisco Varela's exploration of the biophysics of being

This paper reviews in detail Francisco Varela's work on subjectivity and consciousness in the biological sciences. His original approach to this "hard problem" presents a subjectivity that is radically intertwined with its biological and physical roots. It must be understood within the framework of his theory of a concrete, embodied dynamics, grounded in his general theory of autonomous systems. Through concepts and paradigms such as biological autonomy, embodiment and neurophenomenology, the article explores the multiple levels of circular causality assumed by Varela to play a fundamental role in the emergence of human experience. The concept of biological autonomy provides the necessary and sufficient conditions for characterizing biological life and identity as an emergent and circular self-producing process. Embodiment provides a systemic and dynamical framework for understanding how a cognitive self--a mind--can arise in an organism in the midst of its operational cycles of internal regulation and ongoing sensorimotor coupling. Global subjective properties can emerge at different levels from the interactions of components and can reciprocally constrain local processes through an ongoing, recursive morphodynamics. Neurophenomenology is a supplementary step in the study of consciousness. Through a rigorous method, it advocates the careful examination of experience with first-person methodologies. It attempts to create heuristic mutual constraints between biophysical data and data produced by accounts of subjective experience. The aim is to explicitly ground the active and disciplined insight the subject has about his/her experience in a biophysical emergent process. Finally, we discuss Varela's essential contribution to our understanding of the generation of consciousness in the framework of what we call his "biophysics of being."     

An exploratory data analysis method to reveal modular latent structures in high-throughput data

Background  

Modular structures are ubiquitous across various types of biological networks. The study of network modularity can help reveal regulatory mechanisms in systems biology, evolutionary biology and developmental biology. Identifying putative modular latent structures from high-throughput data using exploratory analysis can help better interpret the data and generate new hypotheses. Unsupervised learning methods designed for global dimension reduction or clustering fall short of identifying modules with factors acting in linear combinations.     

EXTINCTIONS IN HETEROGENEOUS ENVIRONMENTS AND THE EVOLUTION OF MODULARITY

Extinctions of local subpopulations are common events in nature. Here, we ask whether such extinctions can affect the design of biological networks within organisms over evolutionary timescales. We study the impact of extinction events on modularity of biological systems, a common architectural principle found on multiple scales in biology. As a model system, we use networks that evolve toward goals specified as desired input–output relationships. We use an extinction–recolonization model, in which metapopulations occupy and migrate between different localities. Each locality displays a different environmental condition (goal), but shares the same set of subgoals with other localities. We find that in the absence of extinction events, the evolved computational networks are typically highly optimal for their localities with a nonmodular structure. In contrast, when local populations go extinct from time to time, we find that the evolved networks are modular in structure. Modular circuitry is selected because of its ability to adapt rapidly to the conditions of the free niche following an extinction event. This rapid adaptation is mainly achieved through genetic recombination of modules between immigrants from neighboring local populations. This study suggests, therefore, that extinctions in heterogeneous environments promote the evolution of modular biological network structure, allowing local populations to effectively recombine their modules to recolonize niches.     

The end of "naive reductionism": rise of systems biology or renaissance of physiology?

Systems biology is an emerging discipline focused on tackling the enormous intellectual and technical challenges associated with translating genome sequence into a comprehensive understanding of how organisms are built and run. Physiology and systems biology share the goal of understanding the integrated function of complex, multicomponent biological systems ranging from interacting proteins that carry out specific tasks to whole organisms. Despite this common ground, physiology as an academic discipline runs the real risk of fading into the background and being superseded organizationally and administratively by systems biology. My goal in this article is to discuss briefly the cornerstones of modern systems biology, specifically functional genomics, nonmammalian model organisms and computational biology, and to emphasize the need to embrace them as essential components of 21st-century physiology departments and research and teaching programs.