The paper discusses how systems biology is working toward complex accounts that integrate explanation in terms of mechanisms and explanation by mathematical models—which some philosophers have viewed as rival models of explanation. Systems biology is an integrative approach, and it strongly relies on mathematical modeling. Philosophical accounts of mechanisms capture integrative in the sense of multilevel and multifield explanations, yet accounts of mechanistic explanation have failed to address how a mathematical model could contribute to such explanations. (...) I discuss how mathematical equations can be explanatorily relevant. Several cases from systems biology are discussed to illustrate the interplay between mechanistic research and mathematical modeling, and I point to questions about qualitative phenomena, where quantitative models are still indispensable to the explanation. Systems biology shows that a broader philosophical conception of mechanisms is needed, which takes into account functional-dynamical aspects, interaction in complex networks with feedback loops, system-wide functional properties such as distributed functionality and robustness, and a mechanism’s ability to respond to perturbations. I offer general conclusions for philosophical accounts of explanation. (shrink)

Systems biology is a vigorous and expanding discipline, in many ways a successor to genomics and perhaps unprecendented in its combination of biology with a ...

The emergence of systems biology raises many fascinating questions: What does it mean to take a systems approach to problems in biology? To what extent is the use of mathematical and computational modelling changing the life sciences? How does the availability of big data influence research practices? What are the major challenges for biomedical research in the years to come? This book addresses such questions of relevance not only to philosophers and biologists but also to readers (...) interested in the broader implications of systems biology for science and society. The book features reflections and original work by experts from across the disciplines including systems biologists, philosophers, and interdisciplinary scholars investigating the social and educational aspects of systems biology. In response to the same set of questions, the experts develop and defend their personal perspectives on the distinctive character of systems biology and the challenges that lie ahead. Readers are invited to engage with different views on the questions addressed, and may explore numerous themes relating to the philosophy of systems biology. This edited work will appeal to scholars and all levels, from undergraduates to researchers, and to those interested in a variety of scholarly approaches such as systems biology, mathematical and computational modelling, cell and molecular biology, genomics, systems theory, and of course, philosophy of biology. (shrink)

We address the question of whether and to what extent explanatory and modelling strategies in systems biology are mechanistic. After showing how dynamic mathematical models are actually required for mechanistic explanations of complex systems, we caution readers against expecting all systems biology to be about mechanistic explanations. Instead, the aim may be to generate topological explanations that are not standardly mechanistic, or to arrive at design principles that explain system organization and behaviour in general, but (...) not specific mechanisms. These abstraction strategies serve various aims, including prediction and control, that are central to understanding the epistemic diversity of systems biology. (shrink)

This article explores the overlap between systems biology and predictive neuroscience, placing them in their larger context, the contemporary trend of bioinformatic convergence across the sciences. These two domains overlap with respect to their interest in data accumulation and data integration; their reliance on computational statistical correlation; and their translational goals, that is, producing practical fruits and applications from the interscientific cross-pollination that contemporary data-integrative approaches make possible. The interventions that such translational conversations generate are medical and social (...) in nature, and are aimed at both prevention and treatment. It will be argued that such approaches, socially and medically applied, contain potential for conveying both agency-enhancing and agency-diminishing social messages. The article concludes with a call to balance the overwhelmingly quantitative focus characteristic of predictive neuroscience with more qualitative empirical methodologies. This would represent a double helical approach. (shrink)

Evolutionary systems biology (ESB) is a rapidly growing integrative approach that has the core aim of generating mechanistic and evolutionary understanding of genotype‐phenotype relationships at multiple levels. ESB's more specific objectives include extending knowledge gained from model organisms to non‐model organisms, predicting the effects of mutations, and defining the core network structures and dynamics that have evolved to cause particular intracellular and intercellular responses. By combining mathematical, molecular, and cellular approaches to evolution, ESB adds new insights and methods (...) to the modern evolutionary synthesis, and offers ways in which to enhance its explanatory and predictive capacities. This combination of prediction and explanation marks ESB out as a research manifesto that goes further than its two contributing fields. Here, we summarize ESB via an analysis of characteristic research examples and exploratory questions, while also making a case for why these integrative efforts are worth pursuing. (shrink)

Systems biology is today such a widespread discipline that it becomes difficult to propose a clear definition of what it really is. For some, it remains restricted to the genomic field. For many, it designates the integrated approach or the corpus of computational methods employed to handle the vast amount of biological or medical data and investigate the complexity of the living. Although defining systems biology might be difficult, on the other hand its purpose is clear: (...) systems biology, with its emerging subfields systems medicine and systems pharmacology, clearly aims at making sense of complex observations/experimental and clinical datasets to improve our understanding of diseases and their treatments without putting aside the context in which they appear and develop. In this short review, we aim to specifically focus on these new subfields with the new theoretical tools and approaches that were developed in the context of cancer. Systems pharmacology and medicine now give hope for major improvements in cancer therapy, making personalized medicine closer to reality. As we will see, the current challenge is to be able to improve the clinical practice according to the paradigm shift of systems sciences. (shrink)

- A recent debate has touched upon the question of whether diseases can be understood as dysfunctional mechanisms or whether there are "pathological" mechanisms that deserve to be investigated and explained independently (Nervi 2010; Moghaddam Taaheri 2011). Here I suggest that both views tell us something important about disease but that in many instances only a systemic view can shed light on the relationship between physiology and pathology. I provide examples from the literature in systems biology in support (...) of my position. As a result of my analysis, I conclude that a perspective narrowly focusing on mechanisms is insufficient if the goal is to get a comprehensive picture of disease. (shrink)

Due to the variation, contingency and complexity of living systems, biology is often taken to be a science without fundamental theories, laws or general principles. I revisit this question in light of the quest for design principles in systems biology and show that different views can be reconciled if we distinguish between different types of generality. The philosophical literature has primarily focused on generality of specific models or explanations, or on the heuristic role of abstraction. This (...) paper takes a different approach in emphasizing a theory-constituting role of general principles. Design principles signify general dependency-relations between structures and functions, given a set of formally defined constraints. I contend that design principles increase our understanding of living systems by relating specific models to general types. The categorization of types is based on a delineation of the scope of biological possibilities, which serves to identify and define the generic features of classes of systems. To characterize the basis for general principles through generic abstraction and reasoning about possibility spaces, I coin the term constraint-based generality. I show that constraint-based generality is distinct from other types of generality in biology, and argue that general principles play a unifying role that does not entail theory reduction. (shrink)

The progress of the molecular biosciences has been so enormous that a discipline studying how cellular functioning emerges out of the behaviors of their molecular constituents has become reality. Systems biology studies cells as spatiotemporal networks of interacting molecules using an integrative approach of theory , experimental biology , and quantitative network-wide analytical measurement . Its aim is to understand how molecules jointly bring about life. Systems biology is rapidly discovering principles governing the functioning of (...) molecular networks and methods to model and measure them. The application of the conceptual strength of systems sciences such as physics and engineering are opening up new ways of experimentally studying the design and functioning of molecular networks. Typically, in such studies models are used to highlight principles, design experiments, or to act in a predictive mode to specifically modify cellular behavior. In this trend article, theoretical approaches being applied in systems biology will be discussed. A number of books have recently appeared that deal with theoretical aspects of systems biology. In this article they are highlighted to facilitate the communication of systems biology to a broader audience. (shrink)

To understand how complex dynamic systems, living or non-living, linguistic or non-linguistic, come to be organized as systems, to understand how their inherent dynamic nature gives rise to organisations and forms that have found a balance between potentiality for change and evolution on the one hand, and requisite stability in a given environment on the other, is the main ambition of the study of evolutionary systems. The aim of the present volume is to elucidate the scientific and (...) philosophical backgrounds that play a role in one of the major debates taking place in that field, namely that on the relation between selection and self-organization. The book represents a genuine interdisciplinary forum in which the major representatives of evolutionary systems take part. Audience: This volume will be interest to biologists, philosophers of science, systems scientists, mathematicians, physicists, sociologists of science. It is highly recommended to those interested in an interdisciplinary and complex approach to evolution, as well as to those interested in developing a genuinely historical viewpoint in the sciences. (shrink)

In this chapter we explore basic mathematical and other constraints which limit the often novel uses of computation employed in modern computational system biology. These constraints generate substantial obstacles for one goal prominent in the field; namely, the goal of producing models valid for predictive uses in clinical and other contexts. However on closer examination many applications of computation and simulation in the field have more pragmatic or investigative goals in mind, suggesting an important role for rationalizing uses of (...) computation in systems biology and elsewhere as investigative tools. We discuss the concept of an “investigative tool”, and what insights it might offer our understanding of modern computational strategies and the bases for them. (shrink)

In the context of scientists' reflections on genomics, we examine some fundamental issues in the emerging postgenomic discipline of systems biology. Systems biology is best understood as consisting of two streams. One, which we shall call ‘pragmatic systems biology’, emphasises large‐scale molecular interactions; the other, which we shall refer to as ‘systems‐theoretic biology’, emphasises system principles. Both are committed to mathematical modelling, and both lack a clear account of what biological systems (...) are. We discuss the underlying issues in identifying systems and how causality operates at different levels of organisation. We suggest that resolving such basic problems is a key task for successful systems biology, and that philosophers could contribute to its realisation. We conclude with an argument for more sociologically informed collaboration between scientists and philosophers. BioEssays 27:1270–1276, 2005. © 2005 Wiley Periodicals, Inc. (shrink)

This paper adds to the philosophical literature on mechanistic explanation by elaborating two related explanatory functions of idealisation in mechanistic models. The first function involves explaining the presence of structural/organizational features of mechanisms by reference to their role as difference-makers for performance requirements. The second involves tracking counterfactual dependency relations between features of mechanisms and features of mechanistic explanandum phenomena. To make these functions salient, we relate our discussion to an exemplar from systems biological research on the mechanism for (...) countering heat shock—the heat shock response system—in Escherichia coli bacteria. This research also reinforces a more general lesson: ontic constraint accounts in the literature on mechanistic explanation provide insufficiently informative normative appraisals of mechanistic models. We close by outlining an alternative view on the explanatory norms governing mechanistic representation. (shrink)

When a group of processes achieves such closure that a set of states of affairs recurs continually, then the effect of that coherence on the world differs from what would occur in the absence of that closure. Such altered effectiveness is an attribute of the system as a whole, and would have consequences. This indicates that the network of processes, as a unit, has ontological significance. Whenever a network of processes generates continual return to a limited set of states of (...) affairs, the system may function as a “whole”— with respect to appropriate interaction partners. The balance achieved by the processes provides the form of definiteness of a unified agent. The causal powers of such coherent aggregates are indeed just the powers of the “constituents acting in concert”. However, the components act in concert in the specific way they do only because of their inclusion in the closed set of interactions that defines the coherence. This renders the causal powers of the coherence defined by that closure non-redundant, and hence the coherence, as a unit, is ontologically significant. The form of definiteness that provides internal coherence also grounds external efficacy of the societal aggregation. The closure is a structural feature of the coherence — possibly, but not necessarily, apparent in spatial structuring. This approach can provide a unified account that includes quantum microphysics, systems biology, and the philosophy of organism ─ without reducing any of these to another. (shrink)

It is argued that once biological systems reach a certain level of complexity, mechanistic explanations provide an inadequate account of many relevant phenomena. In this article, I evaluate such claims with respect to a representative programme in systems biological research: the study of regulatory networks within single-celled organisms. I argue that these networks are amenable to mechanistic philosophy without need to appeal to some alternate form of explanation. In particular, I claim that we can understand the mathematical modelling (...) techniques of systems biologists as part of a broader practice of constructing and evaluating mechanism schemas. This argument is elaborated by considering the case of bacterial chemotactic networks, where some research has been interpreted as explaining phenomena by means of abstract design principles. (shrink)

The study of the mammalian immune system offers many advantages to systems biologists. The cellular components of the mammalian immune system are experimentally tractable; they can be isolated or differentiated from in vivo and ex vivo sources and have an essential role in health and disease. For these reasons, the major effectors cells of the innate immune system, macrophages, have been a particular focus in international genome and transcriptome consortia. Genomescale analysis of the transcriptome, and transcription initiation has enabled (...) the construction of predictive models of transcription control in macrophages that identify the points of control (the major nodes of networks) and the ways in which they interact. (shrink)

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. (shrink)

The sense of touch informs us of the physical properties of our surroundings and is a critical aspect of communication. Before touches are perceived, mechanical signals are transmitted quickly and reliably from the skin's surface to mechano‐electrical transduction channels embedded within specialized sensory neurons. We are just beginning to understand how soft tissues participate in force transmission and how they are deformed. Here, we review empirical and theoretical studies of single molecules and molecular ensembles thought to be involved in mechanotransmission (...) and apply the concepts emerging from this work to the sense of touch. We focus on the nematode Caenorhabditis elegans as a well‐studied model for touch sensation in which mechanics can be studied on the molecular, cellular, and systems level. Finally, we conclude that force transmission is an emergent property of macromolecular cellular structures that mutually stabilize one another. (shrink)

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 (...) class='Hi'>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. (shrink)

Integrative systems biology is an emerging field that attempts to integrate computation, applied mathematics, engineering concepts and methods, and biological experimentation in order to model large-scale complex biochemical networks. The field is thus an important contemporary instance of an interdisciplinary approach to solving complex problems. Interdisciplinary science is a recent topic in the philosophy of science. Determining what is philosophically important and distinct about interdisciplinary practices requires detailed accounts of problem-solving practices that attempt to understand how specific practices (...) address the challenges and constraints of interdisciplinary research in different contexts. In this paper we draw from our 5-year empirical ethnographic study of two systems biology labs and their collaborations with experimental biologists to analyze a significant problem-solving approach in ISB, which we call adaptive problem solving. ISB lacks much of the methodological and theoretical resources usually found in disciplines in the natural sciences, such as methodological frameworks that prescribe reliable model-building processes. Researchers in our labs compensate for the lack of these and for the complexity of their problems by using a range of heuristics and experimenting with multiple methods and concepts from the background fields available to them. Using these resources researchers search out good techniques and practices for transforming intractable problems into potentially solvable ones. The relative freedom lab directors grant their researchers to explore methodological options and find good practices that suit their problems is not only a response to the complex interdisciplinary nature of the specific problem, but also provides the field itself with an opportunity to discover more general methodological approaches and develop theories of biological systems. Such developments in turn can help to establish the field as an identifiably distinct and successful approach to understanding biological systems. (shrink)

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 (...) class='Hi'>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. (shrink)

Evolutionary systems biology (ESB) aims to integrate methods from systems biology and evolutionary biology to go beyond the current limitations in both fields. This article clarifies some conceptual difficulties of this integration project, and shows how they can be overcome. The main challenge we consider involves the integration of evolutionary biology with developmental dynamics, illustrated with two examples. First, we examine historical tensions between efforts to define general evolutionary principles and articulation of detailed mechanistic (...) explanations of specific traits. Next, these tensions are further clarified by considering a recent case from another field focused on developmental dynamics: stem cell biology. In the stem cell case, incompatible explanatory aims block integration. Experimental approaches aim at mechanistic explanation while dynamical system models offer explanation in terms of general principles. We then discuss an ESB case in which integration succeeds: search for general attractors using a dynamical systems framework synergizes with the experimental search for detailed mechanisms. Contrasts between the positive and negative cases suggest general lessons for achieving an integrated understanding of developmental and evolutionary dynamics. The key integrative move is to acknowledge two complementary aims, both relevant to explanation: identifying the space of possible dynamic states and trajectories, and mechanistic understanding of causal interactions underlying a specific phenomenon of interest. These two aims can support one another in a joint project characterizing dynamic aspects of evolving lineages. This more inclusive project can lead to insights that cannot be reached by either approach in isolation. (shrink)

Bioactive peptides are a group of diverse intercellular signalling molecules. Almost half a century of research on this topic has resulted in an enormous amount of data. In this essay, a general perspective to interpret all these data will be given. In classical endocrinology, neuropeptides were thought of as simple signalling molecules that each elicit one response. However, the fact that the total bioactive peptide signal is far from simple puts this view under pressure. Cells and tissues express many different (...) bioactive peptides and they are also able to respond to many different bioactive peptides, indicating that multiple receptors and signal transduction pathways are present in a single cell. Therefore, the authors suggest that the bioactive peptide signalling system should be regarded in the context of network and systems biology. Bioactive peptides can best be viewed as an extension of the protein interaction network that allows regulating and fine‐tuning the metabolism of the different cells and tissues in the body. The cell thus responds to the ‘peptidome’ instead of to a single peptide. The intracellular part of this signalling network consists of the various signalling transduction cascades. Recently, new systems biology approaches have emerged for the modelling of cell signalling. The network and systems biology approach is also able to shed new light on the evolution of intercellular signalling. (shrink)

Thomas Polger and Lawrence Shapiro claim that unlike human-made artifacts cases of multiple realization in naturally occurring systems are uncommon. Drawing on cases from systems biology, I argue t...

Report of the symposium 'Towards a Philosophy of Systems Biology' held at the Vrije Universiteit of Amsterdam (VUA), the Netherlands, from 2 to 3 June 2005.

Polger and Shapiro (2016) claim that unlike human-made artifacts cases of multiple realization in naturally occurring systems are uncommon. Drawing on cases from systems biology, I argue that multiple realization in naturally occurring systems is not as uncommon as Polger and Shapiro initially thought. The relevant cases, which I draw from systems biology, involve generalizable design principles called network motifs which recur in different organisms and species and perform specific functions. I show that network (...) motifs with entirely different underlying causal structures can perform the same function of interest. The article also considers the scope problem of multiple realization. (shrink)

Systems biology is sometimes presented as providing a superior approach to the problem of biological complexity. Its use of ‘unbiased’ methods and formal quantitative tools might lead to the impression that the human factor is effectively eliminated. However, a closer look reveals that this impression is misguided. Systems biologists cannot simply assemble molecular information and compute biological behavior. Instead, systems biology’s main contribution is to accelerate the discovery of mechanisms by applying models as heuristic tools. (...) These models rely on a variety of idealizing and simplifying assumptions in order to be efficient for this purpose. The strategies of systems biologists are similar to those of experimentalists in that they attempt to reduce the complexity of the discovery process. Analyzing and comparing these strategies, or ‘heuristics’, reveals the importance of the human factor in computational approaches and helps to situate systems biology within the epistemic landscape of the life sciences. (shrink)

In my field, animal developmental biology, and in what could be regarded as its “deep time derivative,” the evolutionary biology of the animal body plan, there exist two kinds of experimentally supported causal explanation. These can be described as “rooted” and “unrooted.” Rooted causal explanation provides logical links to and from the genomic regulatory code, extending right into the genomic sequences that control regulatory gene expression. The genomic regulatory code ultimately determines the developmental process in a direct way, (...) since subsequent developmental events all depend directly and specifically on what regulatory genes are being expressed and where in the developing organism they are being expressed.. (shrink)

In the last decade, Systems Biology has emerged as a conceptual and explanatory alternative to reductionist-based approaches in molecular biology. However, the foundations of this new discipline need to be fleshed out more carefully. In this paper, we claim that a relational ontology is a necessary tool to ground both the conceptual and explanatory aspects of Systems Biology. A relational ontology holds that relations are prior—both conceptually and explanatory—to entities, and that in the biological realm (...) entities are defined primarily by the context they are embedded within—and hence by the web of relations they are part of. (shrink)

Understanding how scientific activities use naming stories to achieve disciplinary status is important not only for insight into the past, but for evaluating current claims that new disciplines are emerging. In order to gain a historical understanding of how new disciplines develop in relation to these baptismal narratives, we compare two recently formed disciplines, systems biology and genomics, with two earlier related life sciences, genetics and molecular biology. These four disciplines span the twentieth century, a period in (...) which the processes of disciplinary demarcation fundamentally changed from those characteristic of the nineteenth century. We outline how the establishment of each discipline relies upon an interplay of factors that include paradigmatic achievements, technological innovation, and social formations. Our focus, however, is the baptism stories that give the new discipline a founding narrative and articulate core problems, general approaches and constitutive methods. The highly plastic process of achieving disciplinary identity is further marked by the openness of disciplinary definition, tension between technological possibilities and the ways in which scientific issues are conceived and approached, synthesis of reductive and integrative strategies, and complex social interactions. The importance – albeit highly variable – of naming stories in these four cases indicates the scope for future studies that focus on failed disciplines or competing names. Further attention to disciplinary histories could, we suggest, give us richer insight into scientific development. (shrink)

Prediction and control sufficient for reliable medical and other interventions are prominent aims of modeling in systems biology. The short-term attainment of these goals has played a strong role in projecting the importance and value of the field. In this paper I identify the standard models must meet to achieve these objectives as predictive robustness—predictive reliability over large domains. Drawing on the results of an ethnographic investigation and various studies in the systems biology literature, I explore (...) four current obstacles to achieving predictive robustness; data constraints, parameter uncertainty, collaborative constraints and system-scale requirements. I use a case study and the commentary of systems biologists themselves to show that current practices in the field, rather than pursuing these goals, frequently use models heuristically to investigate and build understanding of biological systems that do not meet standards of predictive robustness but are nonetheless effective uses of computation. A more heuristic conception of modeling allows us to interpret current practices as ways that manage these obstacles more effectively, particularly collaborative constraints, such that modelers can in the long-run at least work towards prediction and control. (shrink)

The gap between Science and the Humanities becomes tangible when they both attempt to address the same problem. One such case is relationship between Life and biological molecules. Traditionally, molecular biology has attempted to explain biological processes in terms of physicochemical characteristics of individual macromolecules. The new science of systems biology largely ignores the molecular characteristics of specific molecules and endeavors to analyze biological processes through the relationship between thousands of molecules. On the face of it, the (...) difference between the molecular and the systems approach to biological research corresponds to the tension between the reductionist and the emergentist view in the philosophy of biology. However, a closer analysis reveals that experimental science and philosophy are concerned with different aspects of the problem and therefore search for different solutions. A main reason for this is the difference in their respective concepts of “question” and “answer”, which is essentially unbridgeable. (shrink)

Complex Systems Biology approaches are here considered from the viewpoint of Robert Rosen’s (M,R)-systems, Relational Biology and Quantum theory, as well as from the standpoint of computer modeling. Realizability and Entailment of (M,R)-systems are two key aspects that relate the abstract, mathematical world of organizational structure introduced by Rosen to the various physicochemical structures of complex biological systems. Their importance for understanding biological function and life itself, as well as for designing new strategies for (...) treating diseases such as cancers, is pointed out. The roles played by multiple metastable states in the “continuous uphill flow of Life” supported through internal bioenergetic processes that are coupled to essential inflows are also discussed in relation to dynamic realizations of (M,R)-systems. Furthermore, the roles played by the underlying, many-valued, quantum logics and symbolic computations for ultra-complex biological systems are also briefly discussed. (shrink)

The Hooks et al. review of microbiota-gut-brain literature provides a constructive criticism of the general approaches encompassing MGB research. This commentary extends their review by: highlighting capabilities of advanced systems-biology “-omics” techniques for microbiome research and recommending that combining these high-resolution techniques with intervention-based experimental design may be the path forward for future MGB research.

The ontogeny of the vertebrate retina has been a topic of interest to developmental biologists and human geneticists for many decades. Understanding the unfolding of the genetic program that transforms a field of progenitors cells into a functionally complex and multi‐layered sensory organ is a formidable challenge. Although classical genetic studies succeeded in identifying the key regulators of retina specification, understanding the architecture of their gene network and predicting their behavior are still a distant hope. The emergence of next‐generation sequencing (...) platforms revolutionized the field unlocking the access to genome‐wide datasets. Emerging techniques such as RNA‐seq, ChIP‐seq, ATAC‐seq, or single cell RNA‐seq are used to characterize eye developmental programs. These studies provide valuable information on the transcriptional and cis‐regulatory profiles of precursors and differentiated cells, outlining the trajectories that connect each intermediate state. Here, recent systems biology efforts are reviewed to understand the genetic programs shaping the vertebrate retina. (shrink)

Summary: Based on the book, the overall impression is that systems biology struggles with the limits of first-order cybernetics and tries to overcome it by mixing bottom up and top down methods from classical approaches such as genetics, molecular biology and enzymology. However, the contributors avoid the step from first-order to second-order cybernetics.

Systems biologists often distance themselves from reductionist approaches and formulate their aim as understanding living systems “as a whole.” Yet, it is often unclear what kind of reductionism they have in mind, and in what sense their methodologies would offer a superior approach. To address these questions, we distinguish between two types of reductionism which we call “modular reductionism” and “bottom-up reductionism.” Much knowledge in molecular biology has been gained by decomposing living systems into functional modules (...) or through detailed studies of molecular processes. We ask whether systems biology provides novel ways to recompose these findings in the context of the system as a whole via computational simulations. As an example of computational integration of modules, we analyze the first whole-cell model of the bacterium M. genitalium. Secondly, we examine the attempt to recompose processes across different spatial scales via multi-scale cardiac models. Although these models rely on a number of idealizations and simplifying assumptions as well, we argue that they provide insight into the limitations of reductionist approaches. Whole-cell models can be used to discover properties arising at the interfaces of dynamically coupled processes within a biological system, thereby making more apparent what is lost through decomposition. Similarly, multi-scale modeling highlights the relevance of macroscale parameters and models and challenges the view that living systems can be understood “bottom-up.” Specifically, we point out that system-level properties constrain lower-scale processes. Thus, large-scale modeling reveals how living systems at the same time are more and less than the sum of the parts. (shrink)

As a result of the time‐ and context‐dependency of gene expression, gene regulatory and signaling pathways undergo dynamic changes during development. Creating a model of the dynamics of molecular interaction networks offers enormous potential for understanding how a genome orchestrates the developmental processes of an organism. The dynamic nature of pathway topology calls for new modeling strategies that can capture transient molecular links at the runtime. The aim of this paper is to present a brief and informative, but not all‐inclusive, (...) viewpoint on the computational aspects of modeling and simulation of a non‐static molecular network. BioEssays 26:68–72, 2004. © 2003 Wiley Periodicals, Inc. (shrink)

Mechanistic models in molecular systems biology are generally mathematical models of the action of networks of biochemical reactions, involving metabolism, signal transduction, and/or gene expression. They can be either simulated numerically or analyzed analytically. Systems biology integrates quantitative molecular data acquisition with mathematical models to design new experiments, discriminate between alternative mechanisms and explain the molecular basis of cellular properties. At the heart of this approach are mechanistic models of molecular networks. We focus on the articulation (...) and development of mechanistic models, identifying five constraints which guide the articulation of models in molecular systems biology. These constraints are not independent of one another, with the result that modeling becomes an iterative process. We illustrate the use of these constraints in the modeling of the mechanism for bistability in the lac operon. (shrink)