Theory formation in postulating enzyme kinetic mechanisms: reasoning with constraints

MASSpy: Building, simulating, and visualizing dynamic biological models in Python using mass action kinetics

Mathematical models of metabolic networks utilize simulation to study system-level mechanisms and functions. Various approaches have been used to model the steady state behavior of metabolic networks using genome-scale reconstructions, but formulating dynamic models from such reconstructions continues to be a key challenge. Here, we present the Mass Action Stoichiometric Simulation Python (MASSpy) package, an open-source computational framework for dynamic modeling of metabolism. MASSpy utilizes mass action kinetics and detailed chemical mechanisms to build dynamic models of complex biological processes. MASSpy adds dynamic modeling tools to the COnstraint-Based Reconstruction and Analysis Python (COBRApy) package to provide an unified framework for constraint-based and kinetic modeling of metabolic networks. MASSpy supports high-performance dynamic simulation through its implementation of libRoadRunner: the Systems Biology Markup Language (SBML) simulation engine. Three examples are provided to demonstrate how to use MASSpy: (1) a validation of the MASSpy modeling tool through dynamic simulation of detailed mechanisms of enzyme regulation; (2) a feature demonstration using a workflow for generating ensemble of kinetic models using Monte Carlo sampling to approximate missing numerical values of parameters and to quantify biological uncertainty, and (3) a case study in which MASSpy is utilized to overcome issues that arise when integrating experimental data with the computation of functional states of detailed biological mechanisms. MASSpy represents a powerful tool to address challenges that arise in dynamic modeling of metabolic networks, both at small and large scales.

Why conservation biology can benefit from sensory ecology

Global expansion of human activities is associated with the introduction of novel stimuli, such as anthropogenic noise, artificial lights and chemical agents. Progress in documenting the ecological effects of sensory pollutants is weakened by sparse knowledge of the mechanisms underlying these effects. This severely limits our capacity to devise mitigation measures. Here, we integrate knowledge of animal sensory ecology, physiology and life history to articulate three perceptual mechanisms-masking, distracting and misleading-that clearly explain how and why anthropogenic sensory pollutants impact organisms. We then link these three mechanisms to ecological consequences and discuss their implications for conservation. We argue that this framework can reveal the presence of 'sensory danger zones', hotspots of conservation concern where sensory pollutants overlap in space and time with an organism's activity, and foster development of strategic interventions to mitigate the impact of sensory pollutants. Future research that applies this framework will provide critical insight to preserve the natural sensory world.

Beginnings of Artificial Intelligence in Medicine (AIM): Computational Artifice Assisting Scientific Inquiry and Clinical Art - with Reflections on Present AIM Challenges

BACKGROUND

The rise of biomedical expert heuristic knowledge-based approaches for computational modeling and problem solving, for scientific inquiry and medical decision-making, and for consultation in the 1970's led to a major change in the paradigm that affected all of artificial intelligence (AI) research. Since then, AI has evolved, surviving several "winters", as it has oscillated between relying on expensive and hard-to-validate knowledge-based approaches, and the alternative of using machine learning methods for inferring classification rules from labelled datasets. In the past couple of decades, we are seeing a gradual but progressive intertwining of the two.

OBJECTIVES

To give an overview of early directions in AI in medicine and threads of some subsequent developments motivated by the very different goals of scientific inquiry for biomedical research, and for computational modeling of clinical reasoning and more general healthcare problem solving from the perspective of today's "AI-Deep Learning Boom". To show how, from the beginning, AI was central to Biomedical and Health Informatics (BMHI), as a field investigating how to understand intelligent thinking in dealing professionally with the practice for healthcare, developing mathematical models, technology, and software tools to aid human experts in biomedicine, despite many previous bouts of "exuberant optimism" about the methodologies deployed.

METHODS

An overview and commentary on some of the early research and publications in AI in biomedicine, emphasizing the different approaches to the modeling of problems involved in clinical practice in contrast to those of biomedical science. A concluding reflection of a few current challenges and pitfalls of AI in some biomedical applications.

CONCLUSION

While biomedical knowledge-based systems played a critical role in influencing AI in its early days, 50 years later they have taken a back seat behind "Deep Learning" which promises to discover knowledge structures for inference and prediction, both in science and for clinical decision-support. Early work on AI for medical consultation turned out to be more useful for explanation and teaching than for clinical practice, as had been originally intended. Today, despite the many reported successes of deep learning, fundamental scientific challenges arise in drawing on models of brain science, cognition, and language, if AI is to augment and complement rather than replace human judgment and expertise in biomedicine while also incorporating these advances for translational medicine. Understanding clinical phenotypes and how they relate to precision and personalization of care requires not only scientific inquiry, but also humanistic models of treatment that respond to patient and practitioner narrative exchanges, since it is the stories and insights of human experts which encourage what Norbert Weiner termed the ethical "human use of human beings", so central to adherence to the Hippocratic Oath.

An artificial-intelligence technique for qualitatively deriving enzyme kinetic mechanisms from initial-velocity measurements and its application to hexokinase

We have developed a computer method based on artificial-intelligence techniques for qualitatively analysing steady-state initial-velocity enzyme kinetic data. We have applied our system to experiments on hexokinase from a variety of sources: yeast, ascites and muscle. Our system accepts qualitative stylized descriptions of experimental data, infers constraints from the observed data behaviour and then compares the experimentally inferred constraints with corresponding theoretical model-based constraints. It is desirable to have large data sets which include the results of a variety of experiments. Human intervention is needed to interpret non-kinetic information, differences in conditions, etc. Different strategies were used by the several experimenters whose data was studied to formulate mechanisms for their enzyme preparations, including different methods (product inhibitors or alternate substrates), different experimental protocols (monitoring enzyme activity differently), or different experimental conditions (temperature, pH or ionic strength). The different ordered and rapid-equilibrium mechanisms proposed by these experimenters were generally consistent with their data. On comparing the constraints derived from the several experimental data sets, they are found to be in much less disagreement than the mechanisms published, and some of the disagreement can be ascribed to different experimental conditions (especially ionic strength).

Constraint matching as a means of designing biochemical experiments in multi-enzyme systems

A method of qualitative analysis by constraint matching, where expectations derived from theory or from data bases are systematically compared against experimental findings, is described. This was originally developed as an artificial intelligence technique to analyze enzyme kinetic mechanism determinations. It is shown to have been used (without computer involvement) in designing experiments involving a few enzymes, and is suggested as a useful experimental design tool. The experiments in question validate the behavior of insulinoma extracts as models for pancreatic islet glycolysis, which conform to the expectations from the relevant enzyme literature. Possible generalization to other areas of biological research is suggested.