The impact of molecular biology on neuroscience

How our brains work is one of the major unsolved problems of biology. This paper describes some of the techniques of molecular biology that are already being used to study the brains of animals. Mainly as a result of the human genome project many new techniques will soon become available which could decisively influence the progress of neuroscience. I suggest that neuroscientists should tell molecular biologists what their difficulties are, in the hope that this will stimulate the production of useful new biological tools.     

Why Are Computational Neuroscience and Systems Biology So Separate?

Despite similar computational approaches, there is surprisingly little interaction between the computational neuroscience and the systems biology research communities. In this review I reconstruct the history of the two disciplines and show that this may explain why they grew up apart. The separation is a pity, as both fields can learn quite a bit from each other. Several examples are given, covering sociological, software technical, and methodological aspects. Systems biology is a better organized community which is very effective at sharing resources, while computational neuroscience has more experience in multiscale modeling and the analysis of information processing by biological systems. Finally, I speculate about how the relationship between the two fields may evolve in the near future.     

Sociogenomics: social life in molecular terms

Spectacular progress in molecular biology, genome-sequencing projects and genomics makes this an appropriate time to attempt a comprehensive understanding of the molecular basis of social life. Promising results have already been obtained in identifying genes that influence animal social behaviour and genes that are implicated in social evolution. These findings - derived from an eclectic mix of species that show varying levels of sociality - provide the foundation for the integration of molecular biology, genomics, neuroscience, behavioural biology and evolutionary biology that is necessary for this endeavour.     

Combining microscience and neurobiology

There is a wide range of literature on soft lithography, organic surface science (especially self-assembled monolayers of organic thiols adsorbed on gold) and microfluidics. These areas have developed in the fields of physical and surface chemistry, materials science and condensed matter physics, but they offer broad new capabilities in the development of relevant micro- and nanosystems to users in biology in general, and in cell biology in particular. The ability to integrate these techniques for fabricating materials and for controlling the chemistry of surfaces with electrical and electrochemical measurements should be especially relevant in neurobiology. The major impediment to the development of a field of 'microfabrication and measurement' in neuroscience is the absence of effective collaborative interactions between the communities of fabricators and neurobiologists.     

The economic approach to 'theory of mind'

Theory of mind (ToM) is a great evolutionary achievement. It is a special intelligence that can assess not only one's own desires and beliefs, but also those of others. Whether it is uniquely human or not is controversial, but it is clear that humans are, at least, significantly better at ToM than any other animal. Economists and game theorists have developed sophisticated and powerful models of ToM and we provide a detailed summary of this here. This economic ToM entails a hierarchy of beliefs. I know my preferences, and I have beliefs (a probabilistic distribution) about your preferences, beliefs about your beliefs about my preferences, and so on. We then contrast this economic ToM with the theoretical approaches of neuroscience and with empirical data in general. Although this economic view provides a benchmark and makes useful suggestions about empirical tendencies, it does not always generate a close fit with the data. This provides an opportunity for a synergistic interdisciplinary production of a falsifiable theory of bounded rationality. In particular, a ToM that is founded on evolutionary biology might well be sufficiently structured to have predictive power, while remaining quite general. We sketch two papers that represent preliminary steps in this direction.     

The problem of prediction in invasion biology

Invasion biology is a relatively young discipline which is important, interesting and currently in turmoil. Biological invaders can threaten native ecosystems and global biodiversity; they can incur massive economic costs and even introduce diseases. Invasion biologists generally agree that being able to predict when and where an invasion will occur is essential for progress in their field. However, successful predictions of this type remain elusive. This has caused a rift, as some researchers are pessimistic and believe that invasion biology has no future, whereas others are more optimistic and believe that the key to successful prediction is the creation of a general, unified theoretical framework which encompasses all invasion events. Although I agree that there is a future for invasion biology, extensive synthesis is not the way to better predictions. I argue that the causes of invasion phenomena are exceedingly complex and heterogeneous, hence it is impossible to make generalizations over particular events without sacrificing causal detail. However, this causal detail is just what is needed for the specific predictions which the scientists wish to produce. Instead, I show that a limited type of synthesis (integration of data and methods) is a more useful tool for generating successful predictions. An important implication of my view is that it points to a more pluralistic approach to invasion biology, where generalization and prediction are treated as important yet distinct research goals.     

From 'understanding the brain by creating the brain' towards manipulative neuroscience

Ten years have passed since the Japanese 'Century of the Brain' was promoted, and its most notable objective, the unique 'creating the brain' approach, has led us to apply a humanoid robot as a neuroscience tool. Here, we aim to understand the brain to the extent that we can make humanoid robots solve tasks typically solved by the human brain by essentially the same principles. I postulate that this 'Understanding the Brain by Creating the Brain' approach is the only way to fully understand neural mechanisms in a rigorous sense. Several humanoid robots and their demonstrations are introduced. A theory of cerebellar internal models and a systems biology model of cerebellar synaptic plasticity is discussed. Both models are experimentally supported, but the latter is more easily verifiable while the former is still controversial. I argue that the major reason for this difference is that essential information can be experimentally manipulated in molecular and cellular neuroscience while it cannot be manipulated at the system level. I propose a new experimental paradigm, manipulative neuroscience, to overcome this difficulty and allow us to prove cause-and-effect relationships even at the system level.     

Parts and Theories in Compositional Biology

I analyze the importance of parts in the style of biological theorizing that I call compositional biology. I do this by investigating various aspects, including partitioning frames and explanatory accounts, of the theoretical perspectives that fall under and are guided by compositional biology. I ground this general examination in a comparative analysis of three different disciplines with their associated compositional theoretical perspectives: comparative morphology, functional morphology, and developmental biology. I glean data for this analysis from canonical textbooks and defend the use of such texts for the philosophy of science. I end with a discussion of the importance of recognizing formal and compositional biology as two genuinely different ways of doing biology – the differences arising more from their distinct methodologies than from scientific discipline included or natural domain studied. Ultimately, developing a translation manual between the two styles would be desirable as they currently are, at times, in conflict.     

Distinguished words in data sequences: Analysis and applications to neural coding and other fields

This paper concerns sequences of letters in which certain “distinguished” words are of interest. Such sequences arise as data in numerous fields including genetics and neuroscience. A probability distribution is given for the number of occurrences of a chosen word in a randomized sequence of letters. Such words are considered “favored” if they occur more than expected at random. Favored words have been discovered in nerve impulse trains and may reflect a neural coding scheme. This article is dedicated to my mother, Margaret Oakley Dayhoff, whose enthusiasm encouraged me to pursue research in mathematical biology.     

Simple fasting methods to assess insulin sensitivity in childhood

The 'gold standard' techniques used to measure insulin sensitivity in children are the hyperinsulinaemic-euglycaemic clamp and Bergman's minimal model. Although precise, these techniques are complex, invasive and time consuming. Alternative indirect measures of insulin sensitivity have been developed that utilize fasting glucose and insulin data in algorithms or computer programs. These methods include homeostatic model assessment (HOMA), the quantitative insulin sensitivity check index (QUICKI) and the glucose to insulin ratio (G:I). Each of these three fasting techniques has been developed and validated in adults, with little or no validation in children. Increasingly, HOMA and QUICKI are being used in childhood studies to assess insulin sensitivity. In a group of 79 pre-pubertal children, we found that the correlation between the minimal model and RHOMA (r = -0.4) was no better than that between the minimal model and fasting insulin (r = 0.4), with an even weaker correlation between the minimal model and QUICKI (r = 0.2). In addition, neither HOMA nor QUICKI were able to detect a reduction in insulin sensitivity with obesity or during growth hormone therapy, unlike the minimal model. In children with normal glucose levels, neither HOMA nor QUICKI was superior to fasting insulin. Validation of the derivation formulae for these methods in children is needed before they are more widely used. The potential benefits of these simple fasting techniques is that they are useful in large field studies. However, if the study groups are small or longitudinal changes in insulin sensitivity are sought, more precise techniques such as the clamp or minimal model should be used.     

Grist and mills: on the cultural origins of cultural learning

Cumulative cultural evolution is what 'makes us odd'; our capacity to learn facts and techniques from others, and to refine them over generations, plays a major role in making human minds and lives radically different from those of other animals. In this article, I discuss cognitive processes that are known collectively as 'cultural learning' because they enable cumulative cultural evolution. These cognitive processes include reading, social learning, imitation, teaching, social motivation and theory of mind. Taking the first of these three types of cultural learning as examples, I ask whether and to what extent these cognitive processes have been adapted genetically or culturally to enable cumulative cultural evolution. I find that recent empirical work in comparative psychology, developmental psychology and cognitive neuroscience provides surprisingly little evidence of genetic adaptation, and ample evidence of cultural adaptation. This raises the possibility that it is not only 'grist' but also 'mills' that are culturally inherited; through social interaction in the course of development, we not only acquire facts about the world and how to deal with it (grist), we also build the cognitive processes that make 'fact inheritance' possible (mills).     

Challenges in quantitative single molecule localization microscopy

Single molecule localization microscopy (SMLM), which can provide up to an order of magnitude improvement in spatial resolution over conventional fluorescence microscopy, has the potential to be a highly useful tool for quantitative biological experiments. It has already been used for this purpose in varied fields in biology, ranging from molecular biology to neuroscience. In this review article, we briefly review the applications of SMLM in quantitative biology, and also the challenges involved and some of the solutions that have been proposed. Due to its advantages in labeling specificity and the relatively low overcounting caused by photoblinking when photo-activable fluorescent proteins (PA-FPs) are used as labels, we focus specifically on Photo-Activated Localization Microscopy (PALM), even though the ideas presented might be applicable to SMLM in general. Also, we focus on the following three quantitative measurements: single molecule counting, analysis of protein spatial distribution heterogeneity and co-localization analysis.     

The economic approach to ‘theory of mind’

Theory of mind (ToM) is a great evolutionary achievement. It is a special intelligence that can assess not only one''s own desires and beliefs, but also those of others. Whether it is uniquely human or not is controversial, but it is clear that humans are, at least, significantly better at ToM than any other animal. Economists and game theorists have developed sophisticated and powerful models of ToM and we provide a detailed summary of this here. This economic ToM entails a hierarchy of beliefs. I know my preferences, and I have beliefs (a probabilistic distribution) about your preferences, beliefs about your beliefs about my preferences, and so on. We then contrast this economic ToM with the theoretical approaches of neuroscience and with empirical data in general. Although this economic view provides a benchmark and makes useful suggestions about empirical tendencies, it does not always generate a close fit with the data. This provides an opportunity for a synergistic interdisciplinary production of a falsifiable theory of bounded rationality. In particular, a ToM that is founded on evolutionary biology might well be sufficiently structured to have predictive power, while remaining quite general. We sketch two papers that represent preliminary steps in this direction.     

Toll-like receptors in the brain and their potential roles in neuropathology

The explosion of the toll-like receptors (TLRs) over the past decade has touched almost every field of mammalian biology and neuroscience is not an exception. The current advent of research papers examining the TLRs in the central nervous system (CNS) signifies that these receptors are not only involved in peripheral innate immunity but may also play a role in the development and regulation of CNS inflammation, neurodegeneration and brain trauma. This review addresses the potential role of TLRs in the brain and how they may be involved in various neuropathologies.     

The long journey to a Systems Biology of neuronal function

Computational neurobiology was born over half a century ago, and has since been consistently at the forefront of modelling in biology. The recent progress of computing power and distributed computing allows the building of models spanning several scales, from the synapse to the brain. Initially focused on electrical processes, the simulation of neuronal function now encompasses signalling pathways and ion diffusion. The flow of quantitative data generated by the "omics" approaches, alongside the progress of live imaging, allows the development of models that will also include gene regulatory networks, protein movements and cellular remodelling. A systems biology of brain functions and disorders can now be envisioned. As it did for the last half century, neuroscience can drive forward the field of systems biology.     

A Discussion of Molecular Biology Methods for Protein Engineering

A number of molecular biology techniques are available to generate variants from a particular start gene for eventual protein expression. We discuss the basic principles of these methods in a repertoire that may be used to achieve the elemental steps in protein engineering. These include site-directed, deletion and insertion mutagenesis. We provide detailed case studies, drawn from our own experiences, packaged together with conceptual discussions and include an analysis of the techniques presented with regards to their uses in protein engineering.     

Computer Simulation of Macromolecules

Computer simulation techniques are now an essential part of modern structural molecular biology. They are used in many different ways in order to study the conformation, dynamics and interactions of proteins and nucleic acids. In this paper, I shall review several of these applications and then focus on three specific areas, namely the conformation and dynamics of proteins including the use of free energy perturbation methods to study mutant proteins, the conformation and dynamics of DNA and DNA-drug complexes, and the use of computers with parallel architectures. Although simulation of molecules as large and complex as proteins and nucleic acids may be considered a grand challenge in itself, there are even greater challenges for the future.     

Saltational evolution: hopeful monsters are here to stay

Since 150 years it is hypothesized now that evolution always proceeds in a countless number of very small steps (Darwin in On the origin of species by means of natural selection or the preservation of favoured races in the struggle of life, Murray, London, 1859), a view termed “gradualism”. Few contemporary biologists will doubt that gradualism reflects the most frequent mode of evolution, but whether it is the only one remains controversial. It has been suggested that in some cases profound (“saltational”) changes may have occurred within one or a few generations of organisms. Organisms with a profound mutant phenotype that have the potential to establish a new evolutionary lineage have been termed “hopeful monsters”. Recently I have reviewed the concept of hopeful monsters in this journal mainly from a historical perspective, and provided some evidence for their past and present existence. Here I provide a brief update on data and discussions supporting the view that hopeful monsters and saltational evolution are valuable biological concepts. I suggest that far from being mutually exclusive scenarios, both gradual and saltational evolution are required to explain the complexity and diversity of life on earth. In my view, gradual changes represent the usual mode of evolution, but are unlikely to be able to explain all key innovations and changes in body plans. Saltational changes involving hopeful monsters are probably very exceptional events, but since they have the potential to establish profound novelties sometimes facilitating adaptive radiations, they are of quite some importance, even if they would occur in any evolutionary lineage less than once in a million years. From that point of view saltational changes are not more bizarre scenarios of evolutionary change than whole genome duplications, endosymbiosis or impacts of meteorites. In conclusion I argue that the complete dismissal of saltational evolution is a major historical error of evolutionary biology tracing back to Darwin that needs to be rectified.     

Functional screening revisited in the postgenomic era

Functional screening can reveal a hidden function of a gene. cDNA library-based functional screening has flourished in various fields of biology so far, such as cancer biology, developmental biology and neuroscience. In the postgenomic era, however, various sequence database and public full-length cDNA resources are available, which now allow us to perform more straightforward, gene-oriented screening. Furthermore, the advent of RNA interference techniques has made it possible to perform effective loss-of-function screening. Gene-based functional screening is able to bridge the gap between genes and biological phenomena and raise important biological questions which should be tackled by integration of 'omic' datasets. These possible roles of functional screening will become more and more important in modern molecular biology moving toward the system level understanding of living organisms.