[CSEE Talk] talk: Reverse Engineering of Dynamic Regulatory Networks from Morphological Data, 11am 4/7

[Tim Finin]

           Reverse Engineering of Dynamic Regulatory Networks
                  from Morphological Experimental Data

              Prof. Daniel Lobo, Biological Sciences, UMBC
        11:00am Thursday, 7 April 2016, ITE Building, Room 325b

Many crucial experiments in developmental, regenerative, and cancer
biology are based on manipulations and perturbations resulting in
morphological outcomes. For example, planarian worms can regenerate a
complete organism from almost any amputated piece, but knocking down
certain genes can result in the regeneration of double-head
phenotypes. However, the inherent complexity and non-linearity of
biological regulatory networks prevent us from manually discerning
testable comprehensive models from patterning and morphological
results, and existent bioinformatics tools are generally limited to
genomic or time-series concentration data. As a consequence, despite a
huge experimental dataset in the literature, we still lack mechanistic
explanations that can account for more than one or two morphological
results in many model organisms. To bridge this gulf separating
morphological data from an understanding of pattern and form
regulation, we developed a computational methodology to automate the
discovery of dynamic genetic networks directly from formalized
phenotypic experimental data. In this seminar, I will present novel
formal ontologies and databases of surgical, genetic, and
pharmacological experiments with their resultant morphological
phenotypes, together with artificial intelligence tools based on
evolutionary computation and in silico simulators that can directly
mine these data to reverse-engineer mechanistic dynamic genetic
models. We demonstrated this approach by automatically discovering the
first comprehensive model of planarian regeneration, which not only
explains at once all the key experiments available in the literature
(including surgical amputations, knock-down of specific genes, and
pharmacological treatments), but also predicts testable novel pathways
and genes. This approach is readily paving the way for understanding
the regulation (and dis-regulation) of complex patterns and shapes in
developmental, regenerative, and cancer biology.

Daniel Lobo is an Assistant Professor at the University of Maryland,
Baltimore County. His research aims to understand, control, and design
the dynamic regulatory mechanisms governing complex biological
processes. To this end, his group develops new computational methods,
ontologies, and high-performance in silico experiments to automate the
reverse-engineering of quantitative models from biological data and
the design of regulatory networks for specific functions. They seek to
discover the mechanisms of development and regeneration, find
therapies for cancer and other diseases, and streamline the
application of synthetic biology. His work has received widespread
media coverage including Wired, TechRadar, and Popular Mechanics.