We are comprised of approximately 37 trillion tiny machines, each featuring a network for dynamic transfer and processing of biological information
|A 'bag of water' model||Reality|
Each of the ~37 trillion cells in our body have evolved to maintain homeostasis while adapting to the outside environment via a complex signaling network. Coordinated transfer and processing of information (i.e., signals) through that network is critical for executing cell’s essential functions, e.g., metabolism, gene expression, etc. and for making informed responses (live, die or multiply, etc.). Virtually all diseases are characterized by breakdowns in this biological network.
Until recently, it was thought that much of signals (information) in cells was initiated at the cell surface and that intracellular organelles are passive responders (the 'bag of water' model as shown here). However, the cell is anything but that. In reality (see right panel), a cell is comprised of several heterogeneous organelles. How these organelles communicate with each other and the outside to collectively and coherently impact cell behavior in response to a stimuli remains unknwon.
What does a cell's communication network look like? Are there rules that guide cell fate or behavior when perturbed?
Current networks lack architectural design, does not capture the cell's ability to iteratively sense, decide, act and adapt. It remains unknown how a large ensemble of dynamical systems that interact through a complex network topology can behave collectively. If we do not understand the network's structure, its architecture, its topology, or decode the principles or rules that are the basis of such a network, we cannot develop predictive models for cell behavior after perturbation.
Our Solution: The Intranet of Cells (IoC) Paradigm
Observations by others and us have shed light on the intricately organized information-transfer and processing events between/at diverse intracellular sites within what appears to be a self-organized network (SON) that is capable of sensing, deciding, acting and adapting to its exterior. These findings inspired the paradigm of intranet-of-cells (IoC), which makes a fundamental assumption--
like any complex dynamic self-organizing engineered entity, biological information transfer/processing in cells may be guided by a hitherto elusive set of rules. We seek to decode those rules.
We view the cell as a collection of dynamically interconnected heterogeneous agents (devices) that are that communicate via fundamental principles and rules of biological information transfer and process such information to decisively execute cellular processes (run applications)
|Agents that interact with each other and their environment according to pre-programmed rules|
Their interaction dictates the ability to
learn and adapt to the goals of the system.
Our Goals: What, Why, By when, and How...
What? Identify design principles of biological information transfer and processing. Because we cannot understand how something works without being able to build it, we will understand biological information transfer by building an
in silico eukaryotic cell.
Why? Understand how cells
sense perturbations (mutations, drugs, toxins, pathogens, etc.) and react to them, and how diseased cells
adapt to evolve over time and escape therapy. Success in building an
in silico cell will empower us to simulate and predict cell behavior, and usher a new era in network-based diagnostic tools and network-resetting therapeutics for major diseases. Findings will also provide insights into designing new kinds of SONs and provide a novel paradigm (beyond artificial neural networks) for developing Artificial Intelligence (AI).
By When? Using different experimental, theoretical, and computational approaches we will build:
in silico cell and homogeneous tissue -- 10-15 years
in silico heterogeneous tissues and organ -- 15-20 years
in silico patient -- 20-30 years
How? Our approach is understanding by building an in silico cell. Using a set of experimental, computational, systems, structural, and network biology tools, we will study the physicochemical basis for long-distance information transfer between the exterior (receptors) and the internal organelles and dissect the molecular basis for information processing that decisively executes cellular processes. In-depth analysis of a few key compartments will be queried for fundamental conserved rules of communication and tested in other cases before informing the in silico model. Our multi-pronged approach includes machine-learning methods to parse experimental data in an unbiased manner to identify recurrent motifs in signal transduction and the and identify their information-processing features and spatiotemporal dynamics within the cell.
To accomplish this ambitious goal, a multidisciplinary team across the UC system (mathematicians, material scientists, engineers, structural and cell biologists, pharmacologists, and translational researchers) have organized within the Center for Network Medicine.
- Network biology
- Network theory and analyses
- Structural basis of interaction interfaces
- Agent Based Modeling (ABM) for inter-organnellar communication
- Membrane mechanics and reaction-diffusion modeling
- Spatial modeling
- Cell and Molecular Biology
- Biophysical studies
- Omics (proteomics, phosphoproteomics, transcriptomics)
- Data science
- Machine learning and Articial Intelligence
- Compuper Science
- Information theory
- Control theory
- Tissue level modeling and cytomorphic engineering