"Thus, beyond all questions of quantity there lie questions of pattern, which are essential for the understanding of Nature."
--Alfred North Whitehead (1934)
not follow where the path may lead.
we knew what we were doing,
-- Albert Einstein
Biological information comes in (at least) 2 flavors: spatial information (3-dimensional structure or topology of tissues, organs, and whole organisms) and temporal information (patterns within signals that occur as a function of time). Both of these kinds of information need to be detected, remembered, and processed by cells and tissues, and our lab uses a convergence of molecular biophysics and computational modeling to understand how this occurs at multiple levels of organization. The results of this effort will not only shed light on the fundamental nature of real living creatures but also will provide important clues to the capabilities of "life-as-it-could-be" in synthetic biology or hybrid cybernetic systems. The former ties our work to the biomedicine of birth defects, traumatic injury, and cancer. The latter has implications for the design of artificial life and the engineering of robust, adaptive devices. Altogether, we view this as a branch of information or computer science as much as it is biology.
"Computer science is no more about computers than astronomy is about telescopes." - Edsger Dijkstra
Our lab current mind-map can be schematized this way:
Most of the interesting questions in biology boil down to the control of shape. We all start life as a single cell – the egg, which somehow self-assembles into an incredibly complex organism (whether it be an oak tree, rabbit, or snail). The question of how it is able to achieve its intended pattern (or “morphology”) is the main issue of developmental biology. However, this problem is relevant throughout the life-span: as the body’s cells age and die, they are replaced so that the organism remains intact. Moreover, some organisms are good at repairing damage – salamanders re-grow limbs, hearts, eyes, and jaws if they are amputated. Thus, the body has to know when it is damaged, and decide precisely which growth programs to activate to get back to the original shape (and know when to stop growing). Even cancer is part of this puzzle, because tumors are, in an important sense, a disease of geometry – cancer results when cells stop attending to the normally tight patterning controls of the organism, and can be tamed by the strong patterning influence of regenerative or developmental processes. Thus, developmental, regeneration, and cancer biology all share a fundamental set of questions: how do cellular systems know what shape to build, and through what molecular mechanisms do they build that shape? We are interested in the information processing, communication, and computations that go on as cells perceive current patterning states of the host and change their behavior towards specific morphogenetic goals. The current state of the art focuses on gene regulation and biochemical signals, making network models out of those pathway data.
Biological strucrues have remarkable abilities to perceive patterns in the signals which impinge upon them. Athough traditionally this is studied by neurobiology and behavior science, it is not exclusively a property of neural networks. We are interested in signal processing and pattern inference by somatic tissues that detect and organize information during pattern formation and homeostatic physiology. This includes questions of memory storage outside the CNS, adaptive plasticity in the brain, and the mapping of cognitive programs on radically altered body structures.
Our lab is different in 3 ways:
1) We focus on information processing, treating morphogenetic systems as cognitive agents, and using techniques of artificial intelligence and cognitive science to find out what information cells have and how they process it to make decisions. Our focus on algorithmic (constructivist) computer models is an important component of explaining shape and its regulation, not only elucidating the details of what proteins talk to what other proteins.
2) We study bioelectrical signals that make up part of the language by which cell activities are orchestrated into the complex patterning needs of the host organism. These natural voltage gradients exist in all cells (not just neurons), and we have used a convergence of genetics, biophysics, and molecular physiology to develop new tools to track and manipulate these biophysical conversations between cells and tissues. The results have included novel findings about basic patterning as well as new strategies to induce regenerative repair.
3) We have projects in development, regeneration, and cancer, as well as in the plasticity of the brain and its connection to somatic tissues. These fields are treated as distinct by most labs, funding bodies, and educational units, but we span them because we are seeking the most fundamental aspects of biological regulation, and we believe that common rules of information processing are used throughout these aspects of biology. While our work will eventually give rise to practical applications in bioengineering or biomedicine, we are fundamentally interested in synthetic biology and artificial life – the understanding of living systems as cohesive, computational entities that process information about their shape.
Some new directions in our thinking can be found HERE.
Keywords: left-right asymmetry,
embryogenesis, biological information, gap junctions, ion channels, mathematical modeling, morphogenesis, pattern formation, vertebrate development, ion currents, voltage gradients, ion channels and pumps, regeneration, memory, learning, computational tissues, physiological networks