Computational Biology and Environmental Systems Lab (CBES)

Biological systems of every scale, from ecological to molecular, exhibit tremendous complexity. This complexity is manifest through a variety of dynamics in biological systems. Examples include:

• Environmental
  • Water Systems
  • Erosion
• Interspecies
  • Population Dynamics
  • Phylogenetics and Evolutionary Dynamics
  • Agriculture/Pestilence Cycles
  • Symbiosis
• Physiological
  • Infectious Diseases
  • Immunology
  • Toxicology
• Bioregulatory
  • Cardiac, Respiratory, and Circadian Rhythms
  • Protein-Protein Interactions
  • Molecular/Cellular Signaling and Dynamics

The CBES lab considers modeling, analysis, and control questions to provide a deeper understanding of these systems in spite of their complexity.

Understanding how water moves through natural hillslopes and catchments is still an active area of research. Numerous theories explain some phenomena, but no universal theory of hydrology that explains the broad range of hydrological behavior exhibited by catchments is currently accepted by the hydrological community. Instead, predictions of catchment behavior are often generated using distributed parameter models such as the SWAT model that require large amounts of geospatial data. We are developing a model based on simple hydrological theories that gives predictions of comparable accuracy to distributed parameter models with drastically fewer parameters based on emergent properties of catchment hydrology.

In 1945, Dr. Karl von Frisch published his discovery that the Western honeybee, Apis mellifera, communicates the polar coordinates of a located food source to other members of her colony through a cyclic waggle dance. Our current goal in the Bee Research Group is to use machine vision, machine learning, system identification, and control theory to build a real-time interpreter of the honeybee waggle dance that interprets the meaning of the dance for human viewers. The design of such a tool would be a step forward in providing fast quantitative measurements for agricultural and ecological health. A live stream of our beehive may be found here: https://bees.byu.edu.

Conservation of water resources is critical in many places of the world, including the American West. Current technologies enabling flow instrumentation and the remote control of automated gates and release mechanisms at dams facilitate our systematic modeling and control of complex river systems. Real-time flow data related to our work for the modeling and control of the Piute Reservoir and the Sevier River in Central Utah can be found at www.sevierriver.org.

Papers

• Modeling and Identification of the Sevier River System
• Model-based Autonomous Control of Piute Dam

Crop systems are a special class of production systems that depend on plant genetics, local ecology, soil chemistry, physical topography, weather, and management practices. Understanding these systems is particularly important to support an exploding global population without destroying natural resources. Moreover, new technologies bring new opportunities for sensing, actuating, and driving decisions with extensive data reserves. This project considers the modeling, identification and control of these complex systems.

Papers

• Farming as Feedback Control
• How Good is Bad Weather?

One of the key problems in analyzing complex systems is estimating a system’s network structure given only input/output data. This problem arises in a variety of applications, but especially in systems biology where scientists want to understand how various protein interactions and signaling “pathways” result in particular cellular dynamics. This project, in collaboration with Professor Jorge Goncalves and the Control Systems Group at Cambridge University, explores implications of the graphical structure of a decision processes on the achievable dynamics of the entire system.

Papers

• Robust Signal-Structure Reconstruction
• On the Necessity of Full-State Measurement for State-Space Network Reconstruction
• Dynamical Structure Function Identifiability Conditions Enabling Signal Structure Reconstruction
• Necessary and Sufficient Informativity Conditions for Robust Network Reconstruction Using Dynamical Structure Functions
• Analysis and Design Tools for Structured Feedback Systems
• Robust Dynamical Network Structure Reconstruction
• Validation of Dynamical Structure Functions for the Reconstruction of Biochemical Networks
• Mathematical Relationships Between Representations of Structure in Linear Interconnected Dynamical Systems
• Representing Structure in Linear Interconnected Dynamical Systems
• A Comparison of Network Reconstruction Methods for Chemical Reaction Networks
• Network Structure Preserving Model Reduction with Weak A Priori Structural Information
• Network Structure Preserving Model Reduction: Results of a Simulation Study
• Minimal Realisation of Dynamical Structure Functions and Its Application to Network Reconstruction from Data
• Necessary and Sufficient Conditions for Dynamical Structure Reconstruction of LTI Networks
• Dynamical Structure Analysis of Sparsity and Minimality Heuristics for Reconstruction of Biochemical Networks
• Application and Properties of Dynamical Structure Functions
• Dynamical Structure Functions for the Reverse Engineering of LTI Networks

Symbiosis refers to the long term, close association of two or more individuals of different species. We are interested in learning about the network structure of cooperation in competitive environments from symbiotic interactions. Specifically, we have studied how leafcutter ants and the mutualistic fungus they cultivate are affected by populations of mutualistic bacteria and parasitic fungi. One of the species of parasitic fungi attack the bacteria, causing it to produce an antibiotic in self-defense that also happens to kill the other parasitic fungi. Understanding how to model these complex relationships suggests new ways for thinking about the “structure” of complex systems.