Cross-disciplinary information for understanding macroevolution

Many different macroevolutionary models can produce the same observations. Despite efforts in building more complex and realistic models, it may still be difficult to distinguish the processes that have generated the biodiversity we observe. In this opinion we argue that we can make new progress by reaching out across disciplines, relying on independent data and theory to constrain macroevolutionary inference. Using mainly paleontological insights and data, we illustrate how we can eliminate less plausible or implausible models, and/or parts of parameter space, while applying comparative phylogenetic approaches. We emphasize that such cross-disciplinary insights and data can be drawn between many other disciplines relevant to macroevolution. We urge cross-disciplinary training, and collaboration using common-use databases as a platform for increasing our understanding.     

Data mining for traffic information

Modern cell biology is now rich with data acquired at the whole genome and proteome level. We can add value to this data through integration and application of specialist knowledge. To illustrate, we will focus on the SNARE and RAB proteins; key regulators of intracellular fusion specificity and organelle identity. We examine published mass spectrometry data to gain an estimate of protein copy number and organelle distribution in HeLa cells for each family member. We also survey recent global CRISPR/Cas9 screens for essential genes from these families. We highlight instances of co‐essentiality with other genes across a large panel of cell lines that allows for the identification of functionally coherent clusters. Examples of such correlations include RAB10 with the SNARE protein Syntaxin4 (STX4) and RAB7/RAB21 with the WASH and the CCC (C OMMD/C CDC22/C CDC93) complexes, both of which are linked to endosomal recycling pathways.     

Exciting information for inhibitory neurons

One unsolved issue in brain development is how interneurons migrating tangentially into the cortex acquire their regional addresses and laminar positions. The study by Lodato et al. in this issue shows that projection neurons regulate the laminar fates of cortical interneurons.     

Pathway information for systems biology

Pathway information is vital for successful quantitative modeling of biological systems. The almost 170 online pathway databases vary widely in coverage and representation of biological processes, making their use extremely difficult. Future pathway information systems for querying, visualization and analysis must support standard exchange formats to successfully integrate data on a large scale. Such integrated systems will greatly facilitate the constructive cycle of computational model building and experimental verification that lies at the heart of systems biology.     

A structure for Associative information processing

The model described explains how associations of complex information can be achieved at speeds which have previously only been known for the juman central nervous system. Explicit utilization of the connexion information-time-information enables information to be combined associatively. Time acts as the connecting link between items of information at the same time point in the individual memory units. Moreover, whole chains of events can be combined simultaneously with its help. The condition for the practical application of this connexion is a storage and search algorithm which I have termed hololistic. The hololistic principle is based on the associative coordination of information proceeding on a trial and error method. The simulation of various memory performances such as remembering, forgetting and multiple associations can occur with the aid of the five part system. So-called intelligent systems can be synthetically produced with this structure (e.g., language translators, chess players etc.).     

Strategies for integrating disparate social information

Social information use is widespread in the animal kingdom, helping individuals rapidly acquire useful knowledge and adjust to novel circumstances. In humans, the highly interconnected world provides ample opportunities to benefit from social information but also requires navigating complex social environments with people holding disparate or conflicting views. It is, however, still largely unclear how people integrate information from multiple social sources that (dis)agree with them, and among each other. We address this issue in three steps. First, we present a judgement task in which participants could adjust their judgements after observing the judgements of three peers. We experimentally varied the distribution of this social information, systematically manipulating its variance (extent of agreement among peers) and its skewness (peer judgements clustering either near or far from the participant''s judgement). As expected, higher variance among peers reduced their impact on behaviour. Importantly, observing a single peer confirming a participant''s own judgement markedly decreased the influence of other—more distant—peers. Second, we develop a framework for modelling the cognitive processes underlying the integration of disparate social information, combining Bayesian updating with simple heuristics. Our model accurately accounts for observed adjustment strategies and reveals that people particularly heed social information that confirms personal judgements. Moreover, the model exposes strong inter-individual differences in strategy use. Third, using simulations, we explore the possible implications of the observed strategies for belief updating. These simulations show how confirmation-based weighting can hamper the influence of disparate social information, exacerbate filter bubble effects and deepen group polarization. Overall, our results clarify what aspects of the social environment are, and are not, conducive to changing people''s minds.