Biological and Life Sciences

1. Biological and Life Sciences Science driving AI innovation Co-Leads: Ben Brown (LBNL), Jacob Hinkle (ORNL), Julie Mitchell (ORNL) 38 Participants

2. AI for health maintenance, beyond disease care Health monitoring using ubiquitous devices Integration of physiology and life history with molecular data Trustworthy, reproducible AI Secure, privacy preserving algorithms -- next-gen differential privacy Data availability and accessibility & resilience to unreliable data Fundamentally “small data” and disease focused Decision support for synthetic biology Self-driving labs Precision Ag AI for social and behavioral engineering & policy Emergency response & management Decision support: AI for actionable intelligence AI for Health Knowledge-constrained models Trustable models and bias avoidance -- ethical AI Dependability and UQ Lifelong learning Coping with high-impact rare events Massively multi-scale models Learning first-principles models: dynamics from data Hypothesis generation for combinatorially vast systems Learning multifactorial phenotypes from data, e.g., learning ontologies Predictive, Generative Models of Biological Systems Digital Twins / multi-scale simulations Virtual “biology” / counterfactuals Simulating synth. pops and communities / ecosystems Designing for new biological functions

3. Predictive, Generative Models of Biological Systems • Sufficient for counterfactual analysis and the instantiation and in silico study of “virtual life” • Multiscale simulations, connecting regimes using AI (parameter sweeps, controls, etc) → molecules, organelles, to cells, to organs and individuals; Multi-scale temporal models are needed as well ; ModSim needs to focus on non-equilibrium systems to provide utility in biology • Explainable AI methods for Combinatorially Vast Biological systems → enable the exploration of large polytopic spaces (environmental <> bio interactions) • Understanding the behavior of microbial communities from the genomes of constituent organisms ; using CRISPR for in situ editing of communities ; agent-based simulations to target perturbations to communities ; active learning to refine simulations and predictive models (reduce the number of experiments needed) • Digital twins of living systems • Synthetic individuals / communities -- generative models of living systems • Precision agriculture → many of the same concerns as precision health • Predicting protein function from primary sequence → beyond structure prediction (more broadly predict molecular function) • Designing proteins with new functions, e.g. synthetic polyketide synthases • Adding non-native metabolism, e.g. network engineering • Understanding the scopes of systems / mapping and inferring phenotypes and characteristics / automating the discovery of relevant scopes or emergent properties • AI for hypothesis generation

4. Predictive, Generative Models of Biological Systems What’s the gap? • Capacity to learn dynamics from data for systems that include scopes/regimes that currently lack first-principles models -- we need non-physics based framework for dynamical models of biological systems ; we want accurate models, we can’t expect the precision achievable in physical systems, and ultra-high resolution may not be desirable • Scalable massively multi-scale modeling frameworks -- how do we cope with the hideous “stiffness” of dynamics in biological systems • AI architectures capable of connecting very different data-types (e.g., genomics, physiology (initial state), environment and imaging) while preserving semantic meaning of the data layers • Design principles for AI systems that convey desirable properties What do we do today?

5. B) Decision support -- AI for actionable intelligence • Ethical AI • Epidemiology -- predictive epidemiology and real-time response management of outbreaks ; decision support • Precision Ag • Manufacturing • AI to inform social policy / government policy • AI to engineer individual behavior / social engineering (big ethical issues) • Decision Support for synthetic biology and bioengineering, including metabolic engineering -- optimizing processes in cells, organisms, and communities -- e.g. plant engineering for bioproducts in marginal lands. • AI for hypothesis generation • Feeding wisdom into AI -- and getting knowledge out -- interaction between domain knowledge and AI -- feedback, knowledge from AI • Trust and bias -- being aware of undesirable bias → explainability • AI to discover bias • Passive monitoring using ubiquitous technology for health care / support • AI for information extraction (as opposed to knowledge)-- e.g. to learn chemical signatures of microbial communities and ecologies ; Non-destructive surrogates for omics modalities ; automating data cleaning and wrangling

6. B) Decision support -- AI for actionable intelligence Where are the gaps? • Need systems to improve the availability of data / accessibility of data -- systems that span borders -- tracking provenance • Explainability for decision support systems to reveal / discover / reduce bias • Accuracy / reliability / UQ for systems that will support high-impact decision making, where the cost of being wrong is vast • Integration of security principles, dependability, resilience, fault tolerance -- frameworks for building dependability into decision engineering • UQ procedures that are agnostic to the underlying learning architecture • Data aggregation and communication frameworks for real-time decision support • Federated and Edge computing need to mature • Explainable and traceable decision recommendation -- causal analysis -- understanding the basis of decisions and asserted causal models -- knowing when we believe causal versus correlational relationships -- “what did you know, and when did you know it?” -- need to be able to answer this • AI learns from the data presented -- which may be inaccurate or biased -- how do we bridge the gap when we have only biased data available? • Methods for coping with rare events with high-impacts • Learn the data representations mapped during training. Methods that attempt to understand variance or uncertainty around input data, or relationships among observations in the training set and incoming unseen observations -- these assume an “embedding” of the the data in some intrinsic space, and in practice often default to metric embeddings. There is no reason to believe these -- we need to learn the natural embeddings learned by the machine at hand to understand its behavior.

7. C) AI for Health • Healthcare AI -- must be trustworthy, privacy preserving, and secure • Can we build AI that changes human behavior? Not always easy, e.g. anti-vaccers → need Decision Science ; • Who will use AI models? Administrators, point of care? AI is used now for patient risk assessment, medical image • AI to improve repeatability in health science • Can EHR data be used with AI to drive “health care”; EHRs designed for “disease care” • Ethical AI → how do we study and understand feedback between precision medicine and individual healthcare costs • Need good UQ for diagnoses → need methods for selecting tools for differential diagnosis and subsequently treatment regimes

8. C) AI for Health Where are the gaps? • Rare outcomes are a significant problem -- methods need to be able to learn from rare events • Need systems to improve the availability of data / accessibility of data -- systems that span borders -- tracking provenance • How to fuse Ethics and AI -- how to assert ethical behavior in AI with clinical responsibilities? • Ethical frameworks for how and when to apply AI -- also how to discuss and identify ethical concerns in AI applications • Transparency in the application of AI for social engineering -- work on preference theory address this • Next generation differential privacy -- generative models for human populations of sufficient fidelity to enable study (solves data distribution problem) • Frameworks for the ethical imputation of genetic and health-related data (e.g. from family information)

9. D) Cross Cutting • Good definitions for (multifactorial) phenotypes (including disease states) → how can big data and unsupervised AI refine phenotypes in humans and other systems. E.g., reinforcement learning to identify treatment regimes for complex pathologies (A,B,C) • Extraction of knowledge from data (or information), e.g. the generation of ontologies from data → data to semantics / semantic AI ; Process mining → how can we use AI to improve efficiency? (A,B,C) • Need AI for data wrangling, clean, validated data; Need tools to integrate data that take into account temporal data, phenotypes, genomics -- automating data cleaning == huge win (A,B,C) • Understanding how systems evolve in time, prediction of time evolutions • Complex, small data → as we learn individual states, we find our sample sizes shrink to single digits → methods to balance specificity with tractability; multi-scale models → need AI that doesn’t rely on averaging (A,B) • Need benchmarks to provide constant feedback → understand when we’re modeling at useful scales and levels of heterogeneity • AI systems need to be robust to changing data standards and formats • Transfer learning strategies