by

Dr. Réka Albert

Distinguished Professor of Physics and Biology
152E Davey Laboratory
Pennsylvania State University

June 7, 2022 at 11:00 AM EST

Abstract

My group models cell types as attractors of a dynamic system of interacting (macro)molecules, and we aim to find the network patterns that determine these attractors. We collaborate with wet-bench biologists to develop and validate predictive dynamic models of specific systems. Over the years we found that network-based discrete dynamic modeling is very useful in synthesizing causal interaction information into a predictive, mechanistic model. We use the accumulated knowledge gained from specific models to draw general conclusions that connect a network’s structure and dynamics. An example of such a general connection is our identification of stable motifs, which are self-sustaining cyclic structures that determine points of no return in the dynamics of the system. We have shown that control of stable motifs can guide the system into a desired attractor. We have recently translated the concept of stable motif to a broad class of continuous models. Stable motif – based attractor control can form the foundation of therapeutic strategies on a wide application domain.

by

Benjamin M. Gyori

Research Fellow, Director of Machine-assisted Modeling & Analysis
Laboratory of Systems Pharmacology
Harvard Medical School

January 18, 2022 at 11:00 AM EST

Abstract

The rate at which biomedical knowledge is produced (both at the level of new publications and data sets) is accelerating, and there is an increasing need to monitor, extract and assemble this knowledge in an actionable form. Classic mechanistic models take substantial human effort to construct and rarely scale to the level of omics datasets, while statistical approaches often do not make use of prior knowledge about mechanisms. To address these challenges, we present INDRA, an automated knowledge assembly system which integrates multiple text mining tools that process the scientific literature, and structured sources (pathway databases, drug-target databases, etc.). INDRA standardizes knowledge extracted from these sources and corrects errors, resolves redundancies, fills in missing information, and calculates confidence to create a coherent knowledge base. From this knowledge, various executable model types (ODEs, Boolean networks, etc.) and causal networks can be generated automatically for further analysis. We discuss technology built on top of INDRA, including human-machine dialogue systems, and EMMAA, a framework which makes available a set of self-updating and self-analyzing models of specific diseases and pathways. We present applications of these tools to automatically construct explanations for experimental observations in multiple disease areas.