The Biophysics of Regenerative Repair Suggests New Perspectives on Biological Causation

Evolution exploits the physics of non-neural bioelectricity to implement anatomical homeostasis: a process in which embryonic patterning, remodeling, and regeneration achieve invariant anatomical outcomes despite external interventions. Linear “developmental pathways” are often inadequate explanations for dynamic large-scale pattern regulation, even when they accurately capture relationships between molecular components. Biophysical and computational aspects of collective cell activity toward a target morphology reveal interesting aspects of causation in biology. This is critical not only for unraveling evolutionary and developmental events, but also for the design of effective strategies for biomedical intervention. Bioelectrical controls of growth and form, including stochastic behavior in such circuits, highlight the need for the formulation of nuanced views of pathways, drivers of system-level outcomes, and modularity, borrowing from concepts in related disciplines such as cybernetics, control theory, computational neuroscience, and information theory. This approach has numerous practical implications for basic research and for applications in regenerative medicine and synthetic bioengineering.     

The promise of synthetic biology

DNA synthesis has become one of the technological bases of a new concept in biology: synthetic biology. The vision of synthetic biology is a systematic, hierarchical design of artificial, biology-inspired systems using robust, standardized, and well-characterized building blocks. The design concept and examples from four fields of application (genetic circuits, protein design, platform technologies, and pathway engineering) are discussed, which demonstrate the usefulness and the promises of synthetic biology. The vision of synthetic biology is to develop complex systems by simplified solutions using available material and knowledge. Synthetic biology also opens a door toward new biomaterials that do not occur in nature.     

The emerging age of cell-free synthetic biology

The engineering of and mastery over biological parts has catalyzed the emergence of synthetic biology. This field has grown exponentially in the past decade. As increasingly more applications of synthetic biology are pursued, more challenges are encountered, such as delivering genetic material into cells and optimizing genetic circuits in vivo. An in vitro or cell-free approach to synthetic biology simplifies and avoids many of the pitfalls of in vivo synthetic biology. In this review, we describe some of the innate features that make cell-free systems compelling platforms for synthetic biology and discuss emerging improvements of cell-free technologies. We also select and highlight recent and emerging applications of cell-free synthetic biology.     

Bottom-up approaches in synthetic biology and biomaterials for tissue engineering applications

Synthetic biologists use engineering principles to design and construct genetic circuits for programming cells with novel functions. A bottom-up approach is commonly used to design and construct genetic circuits by piecing together functional modules that are capable of reprogramming cells with novel behavior. While genetic circuits control cell operations through the tight regulation of gene expression, a diverse array of environmental factors within the extracellular space also has a significant impact on cell behavior. This extracellular space offers an addition route for synthetic biologists to apply their engineering principles to program cell-responsive modules within the extracellular space using biomaterials. In this review, we discuss how taking a bottom-up approach to build genetic circuits using DNA modules can be applied to biomaterials for controlling cell behavior from the extracellular milieu. We suggest that, by collectively controlling intrinsic and extrinsic signals in synthetic biology and biomaterials, tissue engineering outcomes can be improved.     

Estimating counterfactuals for evaluation of ecological and conservation impact: an introduction to matching methods

Matching methods encompass non-parametric approaches to estimating counterfactual states through a rigorous selection of control units with similar characteristics to units submitted to an intervention. These methods enable comparisons between treated and control units in a way that facilitates understanding of causal relationships between interventions and outcomes. Matching methods have been used only recently in ecology and conservation biology, where such applications changed the way the field investigates causal questions, for example, in impact-evaluation studies. However, the strengths and limitations of matching methods are not well understood by most ecologists and environmental scientists. Herein, we review state-of-the-art matching methods aiming to help fill this gap in understanding. First, we present relevant theoretical concepts related to matching methods and related subjects such as counterfactual states and causation. Next, we propose guidelines and strategies for the application of matching methods in ecology and conservation biology. Finally, we discuss the possibilities for future applications of matching methods in the environmental sciences.     

On physicalism and Downward Causation in Developmental and Cancer Biology

The dominant position in Philosophy of Science contends that downward causation is an illusion. Instead, we argue that downward causation doesn't introduce vicious circles either in physics or in biology. We also question the metaphysical claim that "physical facts fix all the facts." Downward causation does not imply any contradiction if we reject the assumption of the completeness and the causal closure of the physical world that this assertion contains. We provide an argument for rejecting this assumption. Furthermore, this allows us to reconsider the concept of diachronic emergence.     

The Science and Philosophy of a Method for Assessing Environmental Causes

When an environmental impairment has been identified, it becomes necessary to identify the cause so that an appropriate action can be planned. However, causation is difficult to establish—both conceptually and in practice. To ensure that the U.S. Environmental Protection Agency's (USEPA's) method for causal assessment is appropriate and defensible, we reviewed concepts of causation from philosophers, statisticians, epidemiologists, and others. This article summarizes the results of that review and explains how it relates to the USEPA's method. We include a five-step process: (1) identify alternative candidate causes; (2) logically eliminate when possible; (3) diagnose when possible; (4) analyze the strength of evidence for remaining candidate causes; and (5) identify the most likely cause. We also encourage three practices: (1) use a consistent process; (2) do not claim proof of causation; and (3) document the evidence and inferences. This approach allows assessors to identify the most likely cause or, failing that, to reduce the set of possible causes and identify information needs for another iteration of causal assessment.     

Use of genetic markers and gene-diet interactions for interrogating population-level causal influences of diet on health

Differences in diet appear to contribute substantially to the burden of disease in populations, and therefore changes in diet could lead to major improvements in public health. This is predicated on the reliable identification of causal effects of nutrition on health, and unfortunately nutritional epidemiology has deficiencies in terms of identifying these. This is reflected in the many cases where observational studies have suggested that a nutritional factor is protective against disease, and randomized controlled trials have failed to verify this. The use of genetic variants as proxy measures of nutritional exposure-an application of the Mendelian randomization principle-can contribute to strengthening causal inference in this field. Genetic variants are not subject to bias due to reverse causation (disease processes influencing exposure, rather than vice versa) or recall bias, and if obvious precautions are applied are not influenced by confounding or attenuation by errors. This is illustrated in the case of epidemiological studies of alcohol intake and various health outcomes, through the use of genetic variants related to alcohol metabolism (in ALDH2 and ADH1B). Examples from other areas of nutritional epidemiology and of the informative nature of gene-environment interactions interpreted within the Mendelian randomization framework are presented, and the potential limitations of the approach addressed.     

Synthetic biology evolves

Synthetic biology is advancing rapidly as biologists, physicists and engineers are combining their efforts to understand and program cell function. By characterizing isolated genetic components or modules, experimentalists have paved the way for more quantitative analyses of genetic networks. A recent paper presents a method of computational, or in silico, evolution in which a set of components can evolve into networks that display desired behaviors. An integrated approach that includes a strategy of in silico design by evolution, together with efforts exploiting directed evolution in vivo, is likely to be the next step in the evolution of synthetic biology.     

The causal structure of mechanisms

Recently, a number of philosophers of science have claimed that much explanation in the sciences, especially in the biomedical and social sciences, is mechanistic explanation. I argue the account of mechanistic explanation provided in this tradition has not been entirely satisfactory, as it has neglected to describe in complete detail the crucial causal structure of mechanistic explanation. I show how the interventionist approach to causation, especially within a structural equations framework, provides a simple and elegant account of the causal structure of mechanisms. This account explains the many useful insights of traditional accounts of mechanism, such as Carl Craver’s account in his book Explaining the Brain (2007), but also helps to correct the omissions of such accounts. One of these omissions is the failure to provide an explicit formulation of a modularity constraint that plays a significant role in mechanistic explanation. One virtue of the interventionist/structural equations framework is that it allows for a simple formulation of a modularity constraint on mechanistic explanation. I illustrate the role of this constraint in the last section of the paper, which describes the form that mechanistic explanation takes in the computational, information-processing paradigm of cognitive psychology.     

Knowledge-making distinctions in synthetic biology

Synthetic biology is an increasingly high-profile area of research that can be understood as encompassing three broad approaches towards the synthesis of living systems: DNA-based device construction, genome-driven cell engineering and protocell creation. Each approach is characterized by different aims, methods and constructs, in addition to a range of positions on intellectual property and regulatory regimes. We identify subtle but important differences between the schools in relation to their treatments of genetic determinism, cellular context and complexity. These distinctions tie into two broader issues that define synthetic biology: the relationships between biology and engineering, and between synthesis and analysis. These themes also illuminate synthetic biology's connections to genetic and other forms of biological engineering, as well as to systems biology. We suggest that all these knowledge-making distinctions in synthetic biology raise fundamental questions about the nature of biological investigation and its relationship to the construction of biological components and systems.     

How Distinctive is Genetic Information?

There is extensive discussion of the ethical, social, economic and political issues associated with the use of technologies based on DNA techniques. Many of these debates are premised on the assumption that DNA, and the genetic information that may be derived from it, have unique features which raise new social and ethical issues. In this paper it is argued that several of the features associated with DNA which are sometimes regarded as unique are shared with other biological materials. Others owe more to the cultural image of DNA and some of the metaphors used to discuss it in biology and in wider debates than to the biological properties of DNA. The paper discusses the concepts of genetic material and genetic information and the social construction of DNA in relation to forensic DNA databases, paternity testing and genetic testing for disease. The paper concludes by suggesting that there are seven areas where issues related to DNA and genetic information are at least relatively distinct.     

Noise-Driven Causal Inference in Biomolecular Networks

Single-cell RNA and protein concentrations dynamically fluctuate because of stochastic ("noisy") regulation. Consequently, biological signaling and genetic networks not only translate stimuli with functional response but also random fluctuations. Intuitively, this feature manifests as the accumulation of fluctuations from the network source to the target. Taking advantage of the fact that noise propagates directionally, we developed a method for causation prediction that does not require time-lagged observations and therefore can be applied to data generated by destructive assays such as immunohistochemistry. Our method for causation prediction, "Inference of Network Directionality Using Covariance Elements (INDUCE)," exploits the theoretical relationship between a change in the strength of a causal interaction and the associated changes in the single cell measured entries of the covariance matrix of protein concentrations. We validated our method for causation prediction in two experimental systems where causation is well established: in an E. coli synthetic gene network, and in MEK to ERK signaling in mammalian cells. We report the first analysis of covariance elements documenting noise propagation from a kinase to a phosphorylated substrate in an endogenous mammalian signaling network.     

Synthetic biology advancing clinical applications

The 'omics' era, with its identification of genetic and protein components, has combined with systems biology, which provided insights into network structures, to set the stage for synthetic biology, an emerging interdisciplinary life science that uses engineering principles. By capitalizing on an iterative design cycle that involves molecular and computational biology tools to assemble functional designer devices from a comprehensive catalogue of standardized biological components with predictable functions, synthetic biology has significantly advanced our understanding of complex control dynamics that program living systems. Such insights, collected over the past decade, are priming a variety of synthetic biology-inspired biomedical applications that have the potential to revolutionize drug discovery and production technologies, as well as treatment strategies for infectious diseases and metabolic disorders.     

Counterfactual overdetermination vs. the causal exclusion problem

This paper aims to show that a counterfactual approach to causation is not sufficient to provide a solution to the causal exclusion problem in the form of systematic overdetermination. Taking into account the truthmakers of causal counterfactuals provides a strong argument in favour of the identity of causes in situations of translevel, causation.     

Evolution of complex modular biological networks

Biological networks have evolved to be highly functional within uncertain environments while remaining extremely adaptable. One of the main contributors to the robustness and evolvability of biological networks is believed to be their modularity of function, with modules defined as sets of genes that are strongly interconnected but whose function is separable from those of other modules. Here, we investigate the in silico evolution of modularity and robustness in complex artificial metabolic networks that encode an increasing amount of information about their environment while acquiring ubiquitous features of biological, social, and engineering networks, such as scale-free edge distribution, small-world property, and fault-tolerance. These networks evolve in environments that differ in their predictability, and allow us to study modularity from topological, information-theoretic, and gene-epistatic points of view using new tools that do not depend on any preconceived notion of modularity. We find that for our evolved complex networks as well as for the yeast protein–protein interaction network, synthetic lethal gene pairs consist mostly of redundant genes that lie close to each other and therefore within modules, while knockdown suppressor gene pairs are farther apart and often straddle modules, suggesting that knockdown rescue is mediated by alternative pathways or modules. The combination of network modularity tools together with genetic interaction data constitutes a powerful approach to study and dissect the role of modularity in the evolution and function of biological networks.     

A systems approach to animal communication

Why animal communication displays are so complex and how they have evolved are active foci of research with a long and rich history. Progress towards an evolutionary analysis of signal complexity, however, has been constrained by a lack of hypotheses to explain similarities and/or differences in signalling systems across taxa. To address this, we advocate incorporating a systems approach into studies of animal communication—an approach that includes comprehensive experimental designs and data collection in combination with the implementation of systems concepts and tools. A systems approach evaluates overall display architecture, including how components interact to alter function, and how function varies in different states of the system. We provide a brief overview of the current state of the field, including a focus on select studies that highlight the dynamic nature of animal signalling. We then introduce core concepts from systems biology (redundancy, degeneracy, pluripotentiality, and modularity) and discuss their relationships with system properties (e.g. robustness, flexibility, evolvability). We translate systems concepts into an animal communication framework and accentuate their utility through a case study. Finally, we demonstrate how consideration of the system-level organization of animal communication poses new practical research questions that will aid our understanding of how and why animal displays are so complex.