The long-term research goal of Shukla group is to combine theory, computation, and experiments to develop quantitative models of biological phenomena relevant for health, energy and climate change. Under the broad umbrella of molecular engineering and sciences, our research program is focused on developing a platform for integrating novel evolution-based methods for understanding protein function, elucidating mechanistic insights to regulate plant growth and development in context of global climate change, and understanding complex manipulation of signaling networks in both plants and humans by small molecules. The answers to these questions would require integrating ideas from a wide range of disciplines tied together by a vision of “Dynamic” biology and its role in engineering products for human health, energy, and climate change. Click on the individual research topics below to learn more about these research directions in Shukla Group.

  • Development of novel computational methods

    Methods for efficient sampling, understanding and design of biological systems.

    We use distributed computing techniques and advanced Markov state models for obtaining mechanistic insights into bimolecular dynamics. We aim to further develop these methods and integrate them with stochastic dynamics models of cellular signaling networks. In the above projects, the central question that we are trying to answer is how protein function can be modulated to achieve a particular response such as closing of guard cells in plants and signaling in GPCRs. A unique perspective on these fundamental questions can be obtained using evolutionary biology, where the key aims are also (a) to understand how new protein functions emerge due to historical mutations and (b) how protein architecture shapes or was shaped by the evolutionary process. Ancestral gene resurrection involves identification of ancient proteins using computational phylogenetic methods and experimental characterization of ancestral proteins. A detailed understanding of the molecular evolution can help to explain the mechanistic basis of function in the modern-day proteins.

  • Molecular engineering of plants in response to the global climate change

    Computational modeling of stress and energy signaling enzymes in plants.

    With the rising population and the changes in the global climate, the biggest challenge facing humanity will be to meet the future food and energy demands. Photosynthetic plants are the principal source of food and biofuels on planet. Much as adrenaline coursing through our veins drives our body’s reactions to stress, the plant hormones are behind plants’ responses to stressful situations. Despite its fundamental importance, little is known about how plants adapt to environmental stresses such as water and nutrient shortage, fluctuations in temperature, light, CO2 concentration etc. Genetic and systems biology approaches have identified plant kinases and phosphatases as the key signaling enzymes involved in regulation of photosynthetic efficiency and response to external stresses. In humans, these enzymes are implicated in almost all forms of cancer and have been investigated extensively. However, the molecular understanding of these stress and energy signaling enzymes in plants remains elusive. Modern computational chemistry approaches could not only be used to obtain molecular description of these key processes in nature but also for the development of mutants and chemicals that can help plants survive the environmental stresses induced by global climate change.

  • Cellular signaling proteins involved in human diseases

    Understanding conformational changes in GPCRs and Kinases for drug design and development.

    G protein-coupled receptors (GPCRs) and kinases are key cellular signaling proteins and exceedingly prominent drug targets, responsible for more than 30% of all marketable drugs. These proteins regulate a large variety of physiological processes by sensing diverse signals and transmitting the signal to downstream effectors. The challenge is to construct computational models that account for the effects of ligands on the energetic landscape of protein conformational states to predict how these will translate into specific biological outcomes. The goal of the project is to not only obtain the conformational dynamics viewpoint of protein function but also to develop allosteric modulators of these proteins.

  • Multiscale modeling of plant growth and development

    Integrating molecular models of plant growth with cellular, species and ecosystem level models

    Specific spatial or temporal scale models of plant growth and development are limited in their ability to provide a holistic view of plant growth and its interaction with the ecosystem. However, this limitation can be addressed by integrative, multi-scale modeling, which will allow: i) identification and explanation of hidden/unknown parameters that can be targeted for experimental study; ii) simulation to design an ideal plant capable of withstanding future environmental challenges; iii) synergy among expertise at different levels of plant biology, engineering, math and computation.