Assembly theory explains and quantifies selection and evolution.

Scientists have grappled with reconciling biological evolution1,2 with the immutable laws of the Universe defined by physics. These laws underpin life's origin, evolution and the development of human culture and technology, yet they do not predict the emergence of these phenomena. Evolutionary theory explains why some things exist and others do not through the lens of selection. To comprehend how diverse, open-ended forms can emerge from physics without an inherent design blueprint, a new approach to understanding and quantifying selection is necessary3-5. We present assembly theory (AT) as a framework that does not alter the laws of physics, but redefines the concept of an 'object' on which these laws act. AT conceptualizes objects not as point particles, but as entities defined by their possible formation histories. This allows objects to show evidence of selection, within well-defined boundaries of individuals or selected units. We introduce a measure called assembly (A), capturing the degree of causation required to produce a given ensemble of objects. This approach enables us to incorporate novelty generation and selection into the physics of complex objects. It explains how these objects can be characterized through a forward dynamical process considering their assembly. By reimagining the concept of matter within assembly spaces, AT provides a powerful interface between physics and biology. It discloses a new aspect of physics emerging at the chemical scale, whereby history and causal contingency influence what exists.

Scaling laws in enzyme function reveal a new kind of biochemical universality.

All life on Earth is unified by its use of a shared set of component chemical compounds and reactions, providing a detailed model for universal biochemistry. However, this notion of universality is specific to known biochemistry and does not allow quantitative predictions about examples not yet observed. Here, we introduce a more generalizable concept of biochemical universality that is more akin to the kind of universality found in physics. Using annotated genomic datasets including an ensemble of 11,955 metagenomes, 1,282 archaea, 11,759 bacteria, and 200 eukaryotic taxa, we show how enzyme functions form universality classes with common scaling behavior in their relative abundances across the datasets. We verify that these scaling laws are not explained by the presence of compounds, reactions, and enzyme functions shared across known examples of life. We demonstrate how these scaling laws can be used as a tool for inferring properties of ancient life by comparing their predictions with a consensus model for the last universal common ancestor (LUCA). We also illustrate how network analyses shed light on the functional principles underlying the observed scaling behaviors. Together, our results establish the existence of a new kind of biochemical universality, independent of the details of life on Earth's component chemistry, with implications for guiding our search for missing biochemical diversity on Earth or for biochemistries that might deviate from the exact chemical makeup of life as we know it, such as at the origins of life, in alien environments, or in the design of synthetic life.

A Model for Providing Psychological Support to Healthcare Leaders and Teams in Times of Crisis.

OBJECTIVE: Pre-pandemic, the healthcare workforce was already at risk for higher burnout than the general population and, in some roles (e.g., physicians, nurses), at higher risk for clinical distress and suicide. Studies of healthcare workforce well-being during and after past pandemics reflect that distress can persist after a pandemic subsides, if adequate support within the workplace is not forthcoming and accessible. The current report presents the rationale for and development of a wellness consult service to provide support to leaders and teams in an academic medical center during the COVID-19 pandemic and now as teams work to recover and rebuild after years of significant pandemic and other stressors. METHODS: Healthcare workers who participated in supportive Listening Sessions or Interactive Workshops facilitated by the wellness consult service were invited to complete an anonymous post-session survey. RESULTS: Between March 2020 and November 2022, 185 leaders and teams participated in 342 supportive Listening Sessions and Interactive Workshops. Of the respondents to the post-session survey (N = 701), 89% rated the intervention as "good to excellent" and 84% were likely or very likely to recommend this service. Leaders rated the experience more highly than non-leaders (F (1,307) = 13.99, p < 0.001) and were more likely to report feeling emotionally supported (F (1,304) = 19.836, p < 0.001). CONCLUSIONS: Supporting leader and team well-being may be critical to post-pandemic recovery of the healthcare workforce. The current report presents a feasible, theory-driven model for doing so, which was rated as highly satisfactory by participants.

Environmental control programs the emergence of distinct functional ensembles from unconstrained chemical reactions.

Many approaches to the origin of life focus on how the molecules found in biology might be made in the absence of biological processes, from the simplest plausible starting materials. Another approach could be to view the emergence of the chemistry of biology as process whereby the environment effectively directs "primordial soups" toward structure, function, and genetic systems over time. This does not require the molecules found in biology today to be made initially, and leads to the hypothesis that environment can direct chemical soups toward order, and eventually living systems. Herein, we show how unconstrained condensation reactions can be steered by changes in the reaction environment, such as order of reactant addition, and addition of salts or minerals. Using omics techniques to survey the resulting chemical ensembles we demonstrate there are distinct, significant, and reproducible differences between the product mixtures. Furthermore, we observe that these differences in composition have consequences, manifested in clearly different structural and functional properties. We demonstrate that simple variations in environmental parameters lead to differentiation of distinct chemical ensembles from both amino acid mixtures and a primordial soup model. We show that the synthetic complexity emerging from such unconstrained reactions is not as intractable as often suggested, when viewed through a chemically agnostic lens. An open approach to complexity can generate compositional, structural, and functional diversity from fixed sets of simple starting materials, suggesting that differentiation of chemical ensembles can occur in the wider environment without the need for biological machinery.

Information Theory as an Experimental Tool for Integrating Disparate Biophysical Signaling Modules.

There is a growing appreciation in the fields of cell biology and developmental biology that cells collectively process information in time and space. While many powerful molecular tools exist to observe biophysical dynamics, biologists must find ways to quantitatively understand these phenomena at the systems level. Here, we present a guide for the application of well-established information theory metrics to biological datasets and explain these metrics using examples from cell, developmental and regenerative biology. We introduce a novel computational tool named after its intended purpose, calcium imaging, (CAIM) for simple, rigorous application of these metrics to time series datasets. Finally, we use CAIM to study calcium and cytoskeletal actin information flow patterns between Xenopus laevis embryonic animal cap stem cells. The tools that we present here should enable biologists to apply information theory to develop a systems-level understanding of information processing across a diverse array of experimental systems.

Formalising the Pathways to Life Using Assembly Spaces.

Assembly theory (referred to in prior works as pathway assembly) has been developed to explore the extrinsic information required to distinguish a given object from a random ensemble. In prior work, we explored the key concepts relating to deconstructing an object into its irreducible parts and then evaluating the minimum number of steps required to rebuild it, allowing for the reuse of constructed sub-objects. We have also explored the application of this approach to molecules, as molecular assembly, and how molecular assembly can be inferred experimentally and used for life detection. In this article, we formalise the core assembly concepts mathematically in terms of assembly spaces and related concepts and determine bounds on the assembly index. We explore examples of constructing assembly spaces for mathematical and physical objects and propose that objects with a high assembly index can be uniquely identified as those that must have been produced using directed biological or technological processes rather than purely random processes, thereby defining a new scale of aliveness. We think this approach is needed to help identify the new physical and chemical laws needed to understand what life is, by quantifying what life does.

Division of labour promotes the spread of information in colony emigrations by the ant Temnothorax rugatulus.

The fitness of group-living animals often depends on how well members share information needed for collective decision-making. Theoretical studies have shown that collective choices can emerge in a homogeneous group of individuals following identical rules, but real animals show much evidence for heterogeneity in the degree and nature of their contribution to group decisions. In social insects, for example, the transmission and processing of information is influenced by a well-organized division of labour. Studies that accurately quantify how this behavioural heterogeneity affects the spread of information among group members are still lacking. In this paper, we look at nest choices during colony emigrations of the ant Temnothorax rugatulus and quantify the degree of behavioural heterogeneity of workers. Using clustering methods and network analysis, we identify and characterize four behavioural castes of workers-primary, secondary, passive and wandering-covering distinct roles in the spread of information during an emigration. This detailed characterization of the contribution of each worker can improve models of collective decision-making in this species and promises a deeper understanding of behavioural variation at the colony level.

Cell phenotypes as macrostates of the GRN dynamics.

The two most fundamental processes describing change in biology, development, and evolution, occur over drastically different timescales. Development involves temporal sequences of cell states controlled by hierarchies of regulatory structures. It occurs over the lifetime of a single individual and is associated with gene expression level change of a given genotype. Evolution, by contrast, entails genotypic change through mutation, the acquisition/loss of genes and changes in the network topology of interactions among genes. It involves the emergence of new, environmentally selected phenotypes over the lifetimes of many individuals. We start by reviewing the most limiting aspects of the theoretical modeling of gene regulatory networks (GRNs) which prevent the study of both timescales in a common, mathematical language. We then consider the simple framework of Boolean network models of GRNs and point out its inadequacy to include evolutionary processes. As opposed to one-to-one maps to specific attractors, we adopt a many-to-one map which makes each phenotype correspond to multiple attractors. This definition no longer requires a fixed size for the genotype and opens the possibility for modeling the phenotypic change of a genotype, which is itself changing over evolutionary timescales. At the same time, we show that this generalized framework does not interfere with established numerical techniques for the identification of the kernel of controlling nodes responsible for cell differentiation through external signals.

Criticality Distinguishes the Ensemble of Biological Regulatory Networks.

The hypothesis that many living systems should exhibit near-critical behavior is well motivated theoretically, and an increasing number of cases have been demonstrated empirically. However, a systematic analysis across biological networks, which would enable identification of the network properties that drive criticality, has not yet been realized. Here, we provide a first comprehensive survey of criticality across a diverse sample of biological networks, leveraging a publicly available database of 67 Boolean models of regulatory circuits. We find all 67 networks to be near critical. By comparing to ensembles of random networks with similar topological and logical properties, we show that criticality in biological networks is not predictable solely from macroscale properties such as mean degree ⟨K⟩ and mean bias in the logic functions ⟨p⟩, as previously emphasized in theories of random Boolean networks. Instead, the ensemble of real biological circuits is jointly constrained by the local causal structure and logic of each node. In this way, biological regulatory networks are more distinguished from random networks by their criticality than by other macroscale network properties such as degree distribution, edge density, or fraction of activating conditions.

Origins of life: a problem for physics, a key issues review.

The origins of life stands among the great open scientific questions of our time. While a number of proposals exist for possible starting points in the pathway from non-living to living matter, these have so far not achieved states of complexity that are anywhere near that of even the simplest living systems. A key challenge is identifying the properties of living matter that might distinguish living and non-living physical systems such that we might build new life in the lab. This review is geared towards covering major viewpoints on the origin of life for those new to the origin of life field, with a forward look towards considering what it might take for a physical theory that universally explains the phenomenon of life to arise from the seemingly disconnected array of ideas proposed thus far. The hope is that a theory akin to our other theories in fundamental physics might one day emerge to explain the phenomenon of life, and in turn finally permit solving its origins.

Re-conceptualizing the origins of life.

Over the last several hundred years of scientific progress, we have arrived at a deep understanding of the non-living world. We have not yet achieved an analogous, deep understanding of the living world. The origins of life is our best chance at discovering scientific laws governing life, because it marks the point of departure from the predictable physical and chemical world to the novel, history-dependent living world. This theme issue aims to explore ways to build a deeper understanding of the nature of biology, by modelling the origins of life on a sufficiently abstract level, starting from prebiotic conditions on Earth and possibly on other planets and bridging quantitative frameworks approaching universal aspects of life. The aim of the editors is to stimulate new directions for solving the origins of life. The present introduction represents the point of view of the editors on some of the most promising future directions.This article is part of the themed issue 'Reconceptualizing the origins of life'.

How causal analysis can reveal autonomy in models of biological systems.

Standard techniques for studying biological systems largely focus on their dynamical or, more recently, their informational properties, usually taking either a reductionist or holistic perspective. Yet, studying only individual system elements or the dynamics of the system as a whole disregards the organizational structure of the system-whether there are subsets of elements with joint causes or effects, and whether the system is strongly integrated or composed of several loosely interacting components. Integrated information theory offers a theoretical framework to (1) investigate the compositional cause-effect structure of a system and to (2) identify causal borders of highly integrated elements comprising local maxima of intrinsic cause-effect power. Here we apply this comprehensive causal analysis to a Boolean network model of the fission yeast (Schizosaccharomyces pombe) cell cycle. We demonstrate that this biological model features a non-trivial causal architecture, whose discovery may provide insights about the real cell cycle that could not be gained from holistic or reductionist approaches. We also show how some specific properties of this underlying causal architecture relate to the biological notion of autonomy. Ultimately, we suggest that analysing the causal organization of a system, including key features like intrinsic control and stable causal borders, should prove relevant for distinguishing life from non-life, and thus could also illuminate the origin of life problem.This article is part of the themed issue 'Reconceptualizing the origins of life'.

Relative contributions of specific frequency bands to the loudness of broadband sounds.

Listeners with normal hearing (NH) and sensorineural hearing loss (SNHL) were asked to compare pairs of noise stimuli and choose the louder noise in each pair. Each noise was made up of 15, two-ERBN (equivalent rectangular bandwidth) wide frequency bands that varied independently over a 12-dB range from one presentation to the next. Mean levels of the bands followed the long-term average speech spectrum (LTASS) or were set to 43, 51, or 59 dB sound pressure level (SPL). The relative contribution of each band to the total loudness of the noise was determined by computing the correlation between the difference in levels for a given band on every trial and the listener's decision on that trial. Weights for SNHL listeners were governed by audibility and the spectrum of the noise stimuli, with bands near the spectral peak of the LTASS noise receiving greatest weight. NH listeners assigned greater weight to the lowest and highest bands, an effect that increased with overall level, but did not assign greater weight to bands near the LTASS peak. Additional loudness-matching and paired-comparison studies using stimuli missing one of the 15 bands showed a significant contribution by the highest band, but properties other than loudness may have contributed to the decisions.

Use of white matter reference regions for detection of change in florbetapir positron emission tomography from completed phase 3 solanezumab trials.

INTRODUCTION: We compared subject-specific white matter (SSWM) and whole cerebellum (CBL) reference regions for power to detect longitudinal change in amyloid positron emission tomography signal. METHODS: Positive florbetapir positron emission tomography scans were analyzed from participants (66 placebo treated and 63 solanezumab treated) with mild dementia caused by Alzheimer's disease from the EXPEDITION and EXPEDITION2 studies. For comparison to CBL, a second normalization was performed on longitudinal data using an SSWM correction factor (SSWM normalization ratio [SSWMnr]). Analysis of covariance assessed baseline to 18-month change between treatment with solanezumab and placebo. Sample and effect size estimations provided magnitude of observed treatment changes. RESULTS: Longitudinal percent change between placebo and solanezumab using CBL was not significant (P = .536) but was significant for SSWMnr (P = .042). Compared with CBL, SSWMnr technique increased the power to detect a treatment difference, more than tripling the effect size and reducing the sample size requirements by 85% to 90%. DISCUSSION: Adjusting longitudinal standardized uptake value ratios with an SSWM reference region in these antiamyloid treatment trials increased mean change detection and decreased variance resulting in the substantial improvement in statistical power to detect change.

The Relationships Between Spiritual Well-Being, Quality of Life, and Psychological Factors Before Radiotherapy for Prostate Cancer.

Given shifting trends of religious identities in the USA, better understanding the impact of patients' religious identities on health-related quality of life (QOL) may help tailor the use of psychological interventions. Men with prostate cancer (N = 43) completed measures of quality of life (QOL), spiritual well-being in two domains (i.e., Faith and Meaning/Peace), psychological state, and psychological trait before undergoing radiotherapy. We hypothesized that (1) higher existential Meaning/Peace would correlate with higher QOL and psychological trait protective factors (e.g., Agreeableness) and that (2) higher existential Meaning/Peace would correlate with lower depression, anxiety, and Neuroticism (i.e., a psychological trait risk factor). We did not anticipate similar relationships between religious Faith and QOL, depression, anxiety, or psychological traits and consider related analyses to be exploratory in nature. Meaning/Peace was indeed negatively associated with depression, anxiety, and Neuroticism. Meaning/Peace was positively correlated with Physical, Social, Functional, and Emotional well-being, as well as Extraversion. Religious Faith was positively associated with Functional well-being, but not the other state, trait, or QOL domains. In sum, prostate cancer patients' sense of existential Meaning/Peace prior to radiotherapy was associated with well-being in many domains, whereas religious Faith appeared less so.

Template-based protein structure prediction in CASP11 and retrospect of I-TASSER in the last decade.

We report the structure prediction results of a new composite pipeline for template-based modeling (TBM) in the 11th CASP experiment. Starting from multiple structure templates identified by LOMETS based meta-threading programs, the QUARK ab initio folding program is extended to generate initial full-length models under strong constraints from template alignments. The final atomic models are then constructed by I-TASSER based fragment reassembly simulations, followed by the fragment-guided molecular dynamic simulation and the MQAP-based model selection. It was found that the inclusion of QUARK-TBM simulations as an intermediate modeling step could help improve the quality of the I-TASSER models for both Easy and Hard TBM targets. Overall, the average TM-score of the first I-TASSER model is 12% higher than that of the best LOMETS templates, with the RMSD in the same threading-aligned regions reduced from 5.8 to 4.7 Å. Nevertheless, there are nearly 18% of TBM domains with the templates deteriorated by the structure assembly pipeline, which may be attributed to the errors of secondary structure and domain orientation predictions that propagate through and degrade the procedures of template identification and final model selections. To examine the record of progress, we made a retrospective report of the I-TASSER pipeline in the last five CASP experiments (CASP7-11). The data show no clear progress of the LOMETS threading programs over PSI-BLAST; but obvious progress on structural improvement relative to threading templates was witnessed in recent CASP experiments, which is probably attributed to the integration of the extended ab initio folding simulation with the threading assembly pipeline and the introduction of atomic-level structure refinements following the reduced modeling simulations. Proteins 2016; 84(Suppl 1):233-246. © 2015 Wiley Periodicals, Inc.

Integration of QUARK and I-TASSER for Ab Initio Protein Structure Prediction in CASP11.

We tested two pipelines developed for template-free protein structure prediction in the CASP11 experiment. First, the QUARK pipeline constructs structure models by reassembling fragments of continuously distributed lengths excised from unrelated proteins. Five free-modeling (FM) targets have the model successfully constructed by QUARK with a TM-score above 0.4, including the first model of T0837-D1, which has a TM-score = 0.736 and RMSD = 2.9 Å to the native. Detailed analysis showed that the success is partly attributed to the high-resolution contact map prediction derived from fragment-based distance-profiles, which are mainly located between regular secondary structure elements and loops/turns and help guide the orientation of secondary structure assembly. In the Zhang-Server pipeline, weakly scoring threading templates are re-ordered by the structural similarity to the ab initio folding models, which are then reassembled by I-TASSER based structure assembly simulations; 60% more domains with length up to 204 residues, compared to the QUARK pipeline, were successfully modeled by the I-TASSER pipeline with a TM-score above 0.4. The robustness of the I-TASSER pipeline can stem from the composite fragment-assembly simulations that combine structures from both ab initio folding and threading template refinements. Despite the promising cases, challenges still exist in long-range beta-strand folding, domain parsing, and the uncertainty of secondary structure prediction; the latter of which was found to affect nearly all aspects of FM structure predictions, from fragment identification, target classification, structure assembly, to final model selection. Significant efforts are needed to solve these problems before real progress on FM could be made. Proteins 2016; 84(Suppl 1):76-86. © 2015 Wiley Periodicals, Inc.

The hidden simplicity of biology.

Life is so remarkable, and so unlike any other physical system, that it is tempting to attribute special factors to it. Physics is founded on the assumption that universal laws and principles underlie all natural phenomena, but is it far from clear that there are 'laws of life' with serious descriptive or predictive power analogous to the laws of physics. Nor is there (yet) a 'theoretical biology' in the same sense as theoretical physics. Part of the obstacle in developing a universal theory of biological organization concerns the daunting complexity of living organisms. However, many attempts have been made to glimpse simplicity lurking within this complexity, and to capture this simplicity mathematically. In this paper we review a promising new line of inquiry to bring coherence and order to the realm of biology by focusing on 'information' as a unifying concept.

Decreased Fronto-Limbic Activation and Disrupted Semantic-Cued List Learning in Major Depressive Disorder.

OBJECTIVES: Individuals with major depressive disorder (MDD) demonstrate poorer learning and memory skills relative to never-depressed comparisons (NDC). Previous studies report decreased volume and disrupted function of frontal lobes and hippocampi in MDD during memory challenge. However, it has been difficult to dissociate contributions of short-term memory and executive functioning to memory difficulties from those that might be attributable to long-term memory deficits. METHODS: Adult males (MDD, n=19; NDC, n=22) and females (MDD, n=23; NDC, n=19) performed the Semantic List Learning Task (SLLT) during functional magnetic resonance imaging. The SLLT Encoding condition consists of 15 lists, each containing 14 words. After each list, a Distractor condition occurs, followed by cued Silent Rehearsal instructions. Post-scan recall and recognition were collected. Groups were compared using block (Encoding-Silent Rehearsal) and event-related (Words Recalled) models. RESULTS: MDD displayed lower recall relative to NDC. NDC displayed greater activation in several temporal, frontal, and parietal regions, for both Encoding-Silent Rehearsal and the Words Recalled analyses. Groups also differed in activation patterns in regions of the Papez circuit in planned analyses. The majority of activation differences were not related to performance, presence of medications, presence of comorbid anxiety disorder, or decreased gray matter volume in MDD. CONCLUSIONS: Adults with MDD exhibit memory difficulties during a task designed to reduce the contribution of individual variability from short-term memory and executive functioning processes, parallel with decreased activation in memory and executive functioning circuits. Ecologically valid long-term memory tasks are imperative for uncovering neural correlates of memory performance deficits in adults with MDD.

Comorbid anxiety increases cognitive control activation in Major Depressive Disorder.

BACKGROUND: Major Depressive Disorder (MDD) and anxiety disorders often co-occur, with poorer treatment response and long-term outcomes. However, little is known about the shared and distinct neural mechanisms of comorbid MDD and anxiety (MDD+Anx). This study examined how MDD and MDD+Anx differentially impact cognitive control. METHODS: Eighteen MDD, 29 MDD+Anx, and 54 healthy controls (HC) completed the Parametric Go/No-Go (PGNG) during fMRI, including Target, Commission, and Rejection trials. RESULTS: MDD+Anx had more activation in the anterior dorsolateral prefrontal cortex, hippocampus, and caudate during Rejections, and inferior parietal lobule during correct Targets than MDD and HC. During Rejections HC had greater activation in a number of cognitive control regions compared to MDD; in the posterior cingulate compared to MDD+Anx; and in the fusiform gyrus compared to all MDD. During Commissions HC had greater activation in the right inferior frontal gyrus than all MDD. MDD had more activation in the mid-cingulate, inferior parietal lobule, and superior temporal gyrus than MDD+Anx during Commissions. CONCLUSIONS: Despite similar performance, MDD and MDD+Anx showed distinct differences in neural mechanisms of cognitive control in relation to each other, as well as some shared differences in relation to HC. The results were consistent with our hypothesis of hypervigilance in MDD+Anx within the cognitive control network, but inconsistent with our hypothesis that there would be greater engagement of salience and emotion network regions. Comorbidity of depression and anxiety may cause increased heterogeneity in study samples, requiring further specificity in detection and measurement of intermediate phenotypes and treatment Targets.