Perhaps the most active interdisciplinary research arena is the intersection of the life sciences with informatics. Understanding how the cell regulates collections of genes in evolutionary interaction with the environment, other organisms, drugs, and even society is increasingly done by merging biology with informatics and other areas of computational science. Techniques from information retrieval, text mining, knowledge discovery, machine learning, computational modeling, complexity theory and big data present us with the opportunity to make new discoveries in biology. In our CASCI group we are working on various projects which use data science and computational modeling to aid biomedical discovery in both health-related and theoretical questions.

Computational and Systems Biology



Biomedical Literature and Social Media Mining

Complex Adaptive Systems

Our approach to literature mining is based on network, data-driven or bio-inspired methods, which we have applied to text classification, relational inference and annotation of protein-protein and drug-drug interactions, pharmacokinetics numerical data, protein sequence family and structure prediction, functional annotation of transcription data, enzyme annotation, and so on. We have applied our methods to the published scientific literature, bioinformatics databases, and social media such as Instagram, Twitter, and ChaCha.

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Modeling of biochemical regulation dynamics

The paradigmatic example of a complex system is the web of biochemical interactions that make up life. We still know very little about the organization of life as a dynamical, interacting network of genes, proteins and biochemical reactions. We are focused on developing network and dynamical systems methodologies and informatics tools to study control, modularity, robustness and collective computation in automata networks used to model gene regulation and biochemical signaling. We are also using such methods to study the interplay between network structure and dynamics in the brain.

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Complex Adaptive Systems



Agent-Based Modeling of Evolutionary Systems

Agent-Based Modeling

We have been using the agent based modeling framework, to study evolutionary systems such as : models of RNA editing, artificial immune systems, evolving cellular automata, and experiments dealing with the interplay between self-organization and selection.

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Genomic Multivariate Analysis

We have worked in clustering methods for microarray analysis which allow multiple membership of genes in clusters. In particular, with various collaborators, we became very interested in using spectral analysis, such as Singular Value Decomposition (SVD), as an automated method for Functional Genomics.

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Genomic Multivariate Analysis



Biosemiotics: interplay between self-organization and selection.

Bio-semiotics

From my studies with Howard Pattee, I became preoccupied with the observation that while processes of a seemingly informational and indeed linguistic nature are fundamental to evolution in biology, computers which are based on the purely syntactic aspects of language were so non-adaptive. Therefore, I became interested in the linguistic/symbolic aspects of the living organization (the gene as a carrier of information, and DNA as memory) which play a large role in the seemingly open-ended evolution defined by natural selection. This lead me to study the interplay between self-organization and natural selection, introducing the concept of selected self-organization. I became particularly interested on the problem of how information, symbols, representations and the like can arise from a purely dynamical system of many components.

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