Jayanth R. Banavar

Dr. Jayanth R. Banavar

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Academic Career Length

36Years (1991-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 May 1999 in Nature
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Journal Article
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JBJayanth R. Banavar
Jayanth R. Banavar
AMAmos Maritan
Amos Maritan
ARAndrea Rinaldo
Andrea Rinaldo
13 February 2001 in Proceedings of the National Academy of Sciences
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Journal Article
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NHNeal S. Holter
Neal S. Holter
AMAmos Maritan
Amos Maritan
MCMarek Cieplak
Marek Cieplak
NFNina V. Fedoroff
Nina V. Fedoroff
JBJayanth R. Banavar
Jayanth R. Banavar
We describe the time evolution of gene expression levels by using a time translational matrix to predict future expression levels of genes based on their expression levels at some initial time. We deduce the time translational matrix for previously published DNA microarray gene expression data sets by modeling them within a linear framework by using the characteristic modes obtained by singular value decomposition. The resulting time translation matrix provides a measure of the relationships among the modes and governs their time evolution. We show that a truncated matrix linking just a few modes is a good approximation of the full time translation matrix. This finding suggests that the number of essential connections among the genes is small.
18 August 2009 in Proceedings of the National Academy of Sciences
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Journal Article
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IVIgor Volkov
Igor Volkov
JBJayanth R. Banavar
Jayanth R. Banavar
SHStephen P. Hubbell
Stephen P. Hubbell
Amos MaritanAmos Maritan
Amos Maritan
We present 2 distinct and independent approaches to deduce the effective interaction strengths between species and apply it to the 20 most abundant species in the long-term 50-ha plot on Barro Colorado Island, Panama. The first approach employs the principle of maximum entropy, and the second uses a stochastic birth–death model. Both approaches yield very similar answers and show that the collective effects of the pairwise interspecific interaction strengths are weak compared with the intraspecific interactions. Our approaches can be applied to other ecological communities in steady state to evaluate the extent to which interactions need to be incorporated into theoretical explanations for their structure and dynamics.
30 December 2020 in Proceedings of the National Academy of Sciences
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MSMatteo Serafino
Matteo Serafino
GCGiulio Cimini
Giulio Cimini
AMAmos Maritan
Amos Maritan
ARAndrea Rinaldo
Andrea Rinaldo
SSSamir Suweis
Samir Suweis
Guido CaldarelliGuido Caldarelli
Guido Caldarelli
Significance: The generalized scale invariance of complex networks, whose trademark feature is the power law distributions of key structural properties like node degree, has recently been questioned on the basis of statistical testing of samples from model and real data. This has important implications on the dynamic origins of network self-organization and consequently, on the general interpretation of their function and resilience. However, a well-known mechanism of departure from scale invariance is the presence of finite size effects. Developed for critical phenomena, finite size scaling analysis assesses whether an underlying scale invariance is clouded by a sample limited in size. Our approach sorts out when we may reject the hypothesis that the inherent structure of networks is scale invariant.
1 June 2007 in Annual Review of Biophysics
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Journal Article
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JBJayanth R. Banavar
Jayanth R. Banavar
Amos MaritanAmos Maritan
Amos Maritan
Globular proteins are a key component of the network of life. Over many decades much experimental data on proteins have been gathered, yet theoretical progress has been somewhat limited. We show that the results accumulated over the years inexorably lead to a unified framework for understanding proteins. The framework predicts the existence of a fixed menu of folds determined by geometry, clarifies the role of the amino acid sequence in selecting the native-state structure from this menu, and explains the propensity for amyloid formation. The experimental data and the new approach reveal an astonishing simplicity underlying the protein problem.
26 May 2021 in Proceedings of the National Academy of Sciences
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LKLoren Kocillari
Loren Kocillari
MOMark E. Olson
Mark E. Olson
SSSamir Suweis
Samir Suweis
RRRodrigo P. Rocha
Rodrigo P. Rocha
ALAlberto Lovison
Alberto Lovison
AMAmos Maritan
Amos Maritan
Significance: For most of its path through plant bodies, water moves in conduits in the wood. Plant water conduction is crucial for Earth’s biogeochemical cycles, making it important to understand how natural selection shapes conduit diameters along the entire lengths of plant stems. Can mathematical modeling and global sampling explain how wood conduits ought to widen from the tip of a plant to its trunk base? This question is evolutionarily important because xylem conduits should widen in a way that keeps water supply constant to the leaves as a plant grows taller. Moreover, selection should act on economy of construction costs of the conducting system. This issue is ecologically important because it helps suggest why climate change alters vegetation height worldwide.