Scientists are using AI to better predict embryo health in real time. Embryologists are the scientists behind the scenes of in vitro fertilization who oversee the development and selection of embryos, prepare them for transfer, and maintain the lab environment. They've been a critical part of IVF for decades, but their job has gotten a whole lot busier in recent years as demand for the fertility treatment skyrockets and clinics struggle to keep up. The United States is in fact facing a critical shortage of both embryologists and genetic counselors. Klaus Wiemer, a veteran embryologist and IVF lab director, believes artificial intelligence might help by predicting embryo health in real time and unlocking new avenues for productivity in the lab. Wiemer is the chief scientific officer and head of clinical affairs at Fairtility, a company that uses artificial intelligence to shed light on the viability of eggs and embryos before proceeding with IVF.

The positioning of new cellular walls during cell division plays a key role in shaping plant tissue organization. The influence of cell geometry on the positioning of division planes has been previously captured into various geometrical rules. Accordingly, linking cell shape to division orientation has relied on the comparison between observed division patterns and predictions under specific rules. The need to define a priori the tested rules is a fundamental limitation of this hypothesis-driven approach. As an alternative, we introduce a data-based approach to investigate the relation between cell geometry and division plane positioning, exploiting the ability of deep neural network to learn complex relationships across multidimensional spaces. Adopting an image-based cell representation, we show how division patterns can be learned and predicted from mother cell geometry using a UNet architecture modified to operate on cell masks. Using synthetic data and A. thaliana embryo cells, we evaluate the model performances on a wide range of diverse cell shapes and division patterns. We find that the trained model accounted for embryo division patterns that were previously irreconcilable under existing geometrical rules. Our work shows the potential of deep networks to understand cell division patterns and to generate new hypotheses on the control of cell division positioning.

Inferring protein production kinetics for dividing cells is complicated due to protein inheritance from the mother cell. For instance, fluorescence measurements -- commonly used to assess gene activation -- may reflect not only newly produced proteins but also those inherited through successive cell divisions. In such cases, observed protein levels in any given cell are shaped by its division history. As a case study, we examine activation of the glc3 gene in yeast involved in glycogen synthesis and expressed under nutrient-limiting conditions. We monitor this activity using snapshot fluorescence measurements via flow cytometry, where GFP expression reflects glc3 promoter activity. A naïve analysis of flow cytometry data ignoring cell division suggests many cells are active with low expression. Explicitly accounting for the (non-Markovian) effects of cell division and protein inheritance makes it impossible to write down a tractable likelihood -- a key ingredient in physics-inspired inference, defining the probability of observing data given a model. The dependence on a cell's division history breaks the assumptions of standard (Markovian) master equations, rendering traditional likelihood-based approaches inapplicable. Instead, we adapt conditional normalizing flows (a class of neural network models designed to learn probability distributions) to approximate otherwise intractable likelihoods from simulated data. In doing so, we find that glc3 is mostly inactive under stress, showing that while cells occasionally activate the gene, expression is brief and transient.

Understanding the rules underlying organismal development is a major unsolved problem in biology. Each cell in a developing organism responds to signals in its local environment by dividing, excreting, consuming, or reorganizing, yet how these individual actions coordinate over a macroscopic number of cells to grow complex structures with exquisite functionality is unknown. Here we use recent advances in automatic differentiation to discover local interaction rules and genetic networks that yield emergent, systems-level characteristics in a model of development. We consider a growing tissue with cellular interactions are mediated by morphogen diffusion, differential cell adhesion and mechanical stress. Each cell has an internal genetic network that it uses to make decisions based on its local environment. We show that one can simultaneously learn parameters governing the cell interactions and the genetic network for complex developmental scenarios, including the symmetry breaking of an embryo from an initial cell, the creation of emergent chemical gradients,homogenization of growth via mechanical stress, programmed growth into a prespecified shape, and the ability to repair from damage. When combined with recent experimental advances measuring spatio-temporal dynamics and gene expression of cells in a growing tissue, the methodology outlined here offers a promising path to unravelling the cellular basis of development.

Large Language Models (LLMs) are widely used in critical fields such as healthcare, education, and finance due to their remarkable proficiency in various language-related tasks. However, LLMs are prone to generating factually incorrect responses or "hallucinations," which can lead to a loss of credibility and trust among users. To address this issue, we propose a multi-stage framework that generates the rationale first, verifies and refines incorrect ones, and uses them as supporting references to generate the answer. The generated rationale enhances the transparency of the answer and our framework provides insights into how the model arrived at this answer, by using this rationale and the references to the context. In this paper, we demonstrate its effectiveness in improving the quality of responses to drug-related inquiries in the life sciences industry. Our framework improves traditional Retrieval Augmented Generation (RAG) by enabling OpenAI GPT-3.5-turbo to be 14-25% more faithful and 16-22% more accurate on two datasets. Furthermore, fine-tuning samples based on our framework improves the accuracy of smaller open-access LLMs by 33-42% and competes with RAG on commercial models.

ChatGPT is a large language model developed by OpenAI. Despite its impressive performance across various tasks, no prior work has investigated its capability in the biomedical domain yet. To this end, this paper aims to evaluate the performance of ChatGPT on various benchmark biomedical tasks, such as relation extraction, document classification, question answering, and summarization. To the best of our knowledge, this is the first work that conducts an extensive evaluation of ChatGPT in the biomedical domain. Interestingly, we find based on our evaluation that in biomedical datasets that have smaller training sets, zero-shot ChatGPT even outperforms the state-of-the-art fine-tuned generative transformer models, such as BioGPT and BioBART. This suggests that ChatGPT's pre-training on large text corpora makes it quite specialized even in the biomedical domain. Our findings demonstrate that ChatGPT has the potential to be a valuable tool for various tasks in the biomedical domain that lack large annotated data.

SYNTHETIC cells made by combining components of Mycoplasma bacteria with a chemically synthesised genome can grow and divide into cells of uniform shape and size, just like most natural bacterial cells. In 2016, researchers led by Craig Venter at the J. Craig Venter Institute in San Diego, California, announced that they had created synthetic "minimal" cells. The genome in each cell contained just 473 key genes thought to be essential for life. The cells were named JCVI-syn3.0 But on closer inspection of the dividing cells, Elizabeth Strychalski at the US National Institute of Standards and Technology and her colleagues noticed that they weren't splitting uniformly and evenly to produce identical daughter cells as most natural bacteria do.

Advances in experimental approaches for single-cell analysis allow in situ sequencing, genomic barcoding, and mapping of cell lineages within tissues and organisms. Large amounts of data have thus accumulated and present an analytical challenge. Stadler et al. recognized the need for conceptual and computational approaches to fully exploit these technological advances for the understanding of normal and disease states. The authors review ideas taken from phylodynamics of infectious disease and show how similar tree-building techniques can be applied to monitoring changes in somatic cell lineages for applications ranging from development and differentiation to cancer biology. Science , this issue p. [eaah6266][1] ### BACKGROUND The birth, death, and diversification of individuals are events that drive biological processes across all scales. This is true whether the individuals in question represent nucleic acids, cells, whole organisms, populations, or species. The ancestral relationships of individuals can be visualized as branching trees or phylogenies, which are long-established representations in the fields of evolution, ecology, and epidemiology. Molecular phylogenetics is the discipline concerned with the reconstruction of such trees from gene or genome sequence data. The shape and size of such phylogenies depend on the past birth and death processes that generated them, and in phylodynamics, mathematical models are used to infer and quantify the dynamical behavior of biological populations from ancestral relationships. New technological advances in genetics and cell biology have led to a growing body of data about the molecular state and ancestry of individual cells in multicellular organisms. Ideas from phylogenetics and phylodynamics are being applied to these data to investigate many questions in tissue formation and tumorigenesis. ### ADVANCES Trees offer a valuable framework for tracing cell division and change through time, beginning with individual ancestral stem cells or fertilized eggs and resulting in complex tissues, tumors, or whole organisms (see the figure). They also provide the basis for computational and statistical methods with which to analyze data from cell biology. Our Review explains how “tree-thinking” and phylodynamics can be beneficial to the interpretation of empirical data pertaining to the individual cells of multicellular organisms. We summarize some recent research questions in developmental and cancer biology and briefly introduce the new technologies that allow us to observe the spatiotemporal histories of cell division and change. We provide an overview of the various and sometimes confusing ways in which graphical models, based on or represented by trees, have been applied in cell biology. To provide conceptual clarity, we outline four distinct graphical representations of the history of cell division and differentiation in multicellular organisms. We highlight that cells from an organism cannot be always treated as statistically independent observations but instead are often correlated because of phylogenetic history, and we explain how this can cause difficulties when attempting to infer dynamical behavior from experimental single-cell data. We introduce simple ecological null models for cell populations and illustrate some potential pitfalls in hypothesis testing and the need for quantitative phylodynamic models that explicitly incorporate the dependencies caused by shared ancestry. ### OUTLOOK We expect the rapid growth in the number of cell-level phylogenies to continue, a trend enhanced by ongoing technological advances in cell lineage tracing, genomic barcoding, and in situ sequencing. In particular, we anticipate the generation of exciting datasets that combine phenotypic measurements for individual cells (such as through transcriptome sequencing) with high-resolution reconstructions of the ancestry of the sampled cells. These developments will offer new ways to study developmental, oncogenic, and immunological processes but will require new and appropriate conceptual and computational tools. We discuss how models from phylogenetics and phylodynamics will benefit the interpretation of the data sets generated in the foreseeable future and will aid the development of statistical tests that exploit, and are robust to, cell shared ancestry. We hope that our discussion will initiate the integration of cell-level phylodynamic approaches into experimental and theoretical studies of development, cancer, and immunology. We sketch out some of the theoretical advances that will be required to analyze complex spatiotemporal cell dynamics and encourage explorations of these new directions. Powerful new statistical and computational tools are essential if we are to exploit fully the wealth of new experimental data being generated in cell biology. ![Figure][2] Multicellular organisms develop from a single fertilized egg. The division, apoptosis, and differentiation of cells can be displayed in a development tree, with the fertilized egg being the root of the tree. The development of any particular tissue within an organism can be traced as a subtree of the full developmental tree. Subtrees that represent cancer tumors or B cell clones may exhibit rapid growth and genetic change. Here, we illustrate the developmental tree of a human and expand the subtree representing haematopoiesis (blood formation) in the bone marrow. Stem cells in the bone marrow differentiate, giving rise to the numerous blood cell types in humans. The structure of the tree that underlies haematopoiesis and the formation of all tissues is unclear. Phylogenetic and phylodynamic tools can help to describe and statistically explore questions about this cell differentiation process. Multicellular organisms are composed of cells connected by ancestry and descent from progenitor cells. The dynamics of cell birth, death, and inheritance within an organism give rise to the fundamental processes of development, differentiation, and cancer. Technical advances in molecular biology now allow us to study cellular composition, ancestry, and evolution at the resolution of individual cells within an organism or tissue. Here, we take a phylogenetic and phylodynamic approach to single-cell biology. We explain how “tree thinking” is important to the interpretation of the growing body of cell-level data and how ecological null models can benefit statistical hypothesis testing. Experimental progress in cell biology should be accompanied by theoretical developments if we are to exploit fully the dynamical information in single-cell data. [1]: /lookup/doi/10.1126/science.aah6266 [2]: pending:yes

This paper presents the formalization of a biological process, cell division, in modular action language ALM. We show how the features of ALM — modularity, separation between an uninterpreted theory and its interpretation — lead to a simple and elegant solution that can be used in answering questions from biology textbooks.